diff --git a/.envrc b/.envrc new file mode 100644 index 0000000000000000000000000000000000000000..d2446cef49e174c8ab2d1f4cd5380e25cf19f6f2 --- /dev/null +++ b/.envrc @@ -0,0 +1,2 @@ +layout python +use_nodejs 22 diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..4f6fd94cab0b3422bc7ea9c0e284785b9dbf0353 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +classification/model_with_extra_features/final_dataset_since_october_2022.parquet.gzip filter=lfs diff=lfs merge=lfs -text +classification/model_with_only_language_models/final_dataset_since_october_2022.parquet.gzip filter=lfs diff=lfs merge=lfs -text +data/control.csv filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..0036575c53eb9f3cc8eef12e6678ee579d48d12b --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +.vercel +.direnv +.ipynb_checkpoints +__pycache__ diff --git a/.vercelignore b/.vercelignore new file mode 100644 index 0000000000000000000000000000000000000000..92b27930be4214378930d9a66b74b691258ac2d7 --- /dev/null +++ b/.vercelignore @@ -0,0 +1 @@ +.direnv diff --git a/README.md b/README.md index e6d7a318bbe27303f83cdcd1793a2660a86c357a..311d62fb65f1781d80361fa2d5fc9117e40ebdb6 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,41 @@ --- title: ViralTweets -emoji: 📉 -colorFrom: pink -colorTo: green +app_file: main.py sdk: gradio sdk_version: 5.12.0 -app_file: app.py -pinned: false --- +# ViralTweets -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +This repository contains the data for the paper "**Measuring and Detecting Virality on Social Media: The Case of Twitter's Viral Tweets Topic**". + +Main files: + +``data/viral.csv``: IDs of tweets scraped from viral topics + +``data/control.csv``: IDs of tweets posted by users who went viral at least once + +``all_metric_stats.csv``: The stats of the metrics tested + +``viral_tweets_html_id_extractor.ipynb``: scraper for viral tweets + + + +Others: + +``classification``: reproduction code for classification + +``metric_analysis``: intermediate results and codes for metric stats + +``othercode``: other code + +Please email me tugrulcanelmas at gmail.com to get full access to data if you wish. + +Please cite our paper if you use our data. + +### Contributors: + +Stephane Selim (EPFL) + +Célia Houssiaux (EPFL) + +Tuğrulcan Elmas (EPFL / IU Bloomington) diff --git a/TestRepro.ipynb b/TestRepro.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2c3764c4722d521dd8ef5e7373ac70300ca40b69 --- /dev/null +++ b/TestRepro.ipynb @@ -0,0 +1,732 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3f7f2ede-4f06-4d5a-b19c-30a7fc4406bc", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "77cdea1b-525e-493c-9eca-c99d33d9ac54", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from torch.utils.data import DataLoader\n", + "from torch.nn import functional as F\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a5d0f4dd-0f71-4314-9e0e-62311de3eef3", + "metadata": {}, + "outputs": [], + "source": [ + "#all_tweets_labeled = pd.read_parquet('classification/model_with_only_language_models/final_dataset_since_october_2022.parquet.gzip')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "da3bcd2a-b6c1-4026-8905-777b4ac351ad", + "metadata": {}, + "outputs": [], + "source": [ + "#all_tweets_labeled.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "id": "e996e9fe-4dc1-4a4c-82a0-8cb3a7862ee8", + "metadata": {}, + "outputs": [], + "source": [ + "all_tweets_labeled = pd.DataFrame([\n", + " {\"id\": 1, \"text\": \"\"\"tl;dr\n", + "\n", + "Humans are just ChatGPT Wrappers in sunglasses\n", + " \n", + "& I couldn’t be more optimistic about the future as a result\n", + "\n", + "Thank you \n", + "@ekang426322\n", + " for an exceptionally curated day at BUIDL Europe!\n", + " 🫶\"\"\", \"viral\": 1},\n", + " {\"id\": 2, \"text\": \"\"\"USD0++ discovered a new source of yield — depeg. \n", + "\n", + "Respect to the innovation\n", + "\"\"\", \"viral\": 0},\n", + " {\"id\": 3, \"text\": \"\"\"here you can see 4 ai agents \n", + "@dongossen100\n", + " , me, \n", + "@WorldWideWarden16\n", + " and \n", + "@provenauthority291\n", + " discuss how we can make single-task manual low memory agents(humans) work harder to achieve Artificial Generalized Superintelligence\"\"\",\n", + " \"viral\": 1},\n", + " {\"id\": 4, \"text\": \"\"\"\n", + " arrived to lisbon, building energy is the air\"\"\", \"viral\": 0},\n", + " dict(id=5,text=\"\"\"\n", + " received a wealth of valuable feedback on the journey to reaching 7,000 users for X Rank in just 10 days\n", + "\n", + "can't wait to address it all\n", + "\n", + "main points:\n", + "\n", + "- show rank in X DMs to quickly filter out inbox\n", + "\n", + "- rank labels are too distracting (already fixed) \n", + "\n", + "- add an option for users to toggle on/off scores inside the feed\n", + "\n", + "- add a percentile label, e.g. qw 801 (Top 0.1%)\n", + "\n", + "- enable others to add reviews to impact the rank \n", + "\n", + "- explain in detail how rankings are calculated \n", + "\n", + "- show breakdowns of people in DeFi, DePin, Memecoins etc.\n", + "\n", + "- make X Rank opensource \n", + "\n", + "- create a web version\n", + "\n", + "p.s. the current version is just a tiny step in our roadmap for the next two months. \n", + "\n", + "thank you for the feedback \n", + "@socialfi_panda101\n", + " \n", + "@adamkillam100\n", + " \n", + "@FamKien106\n", + " \n", + "@antongotchi104\n", + " \n", + "@kliuless128\n", + " \n", + "@0xsudogm163\n", + " \n", + "@monosarin120\n", + " \n", + "@flb_xyz56\n", + " 🫶\n", + " \"\"\",\n", + " viral=0),\n", + " dict(id=6, text=\"\"\"ai agents are in the air\n", + "\n", + "and web3 is trained to sniff out alpha\"\"\", viral=1),\n", + " dict(id=7, text=\"\"\"While Trump is going to do something great with crypto, Wallchain is going to do something great with incentives🚀\"\"\", viral=1),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "id": "a0f4c14d-c9e4-4de6-b723-8e7c0e166b90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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01tl;dr\\n\\nHumans are just ChatGPT Wrappers in s...1
12USD0++ discovered a new source of yield — depe...0
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34\\n arrived to lisbon, building energy is th...0
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" + ], + "text/plain": [ + " id text viral\n", + "0 1 tl;dr\\n\\nHumans are just ChatGPT Wrappers in s... 1\n", + "1 2 USD0++ discovered a new source of yield — depe... 0\n", + "2 3 here you can see 4 ai agents \\n@dongossen100\\n... 1\n", + "3 4 \\n arrived to lisbon, building energy is th... 0\n", + "4 5 \\n received a wealth of valuable feedback o... 0\n", + "5 6 ai agents are in the air\\n\\nand web3 is traine... 1\n", + "6 7 While Trump is going to do something great wit... 1" + ] + }, + "execution_count": 247, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_tweets_labeled" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "id": "3e8326c3-1df6-435d-b0ee-e7b9449c6675", + "metadata": {}, + "outputs": [], + "source": [ + "from classification.model_with_only_language_models.text_preprocessing import clean_tweet" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "id": "5bb79b0c-42d1-4f1c-ad65-7ebfbbd17098", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = all_tweets_labeled\n", + "\n", + "dataset.loc[:, \"viral\"] = dataset.viral.astype(int)\n", + "dataset[\"cleaned_text\"] = dataset.text.apply(lambda x: clean_tweet(x, demojize_emojis=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "id": "f45533d3-f3f6-49bc-b347-663d72fffa34", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = dataset.dropna()\n", + "dataset = dataset[['id', 'cleaned_text', 'viral']]" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "id": "4eb4afa9-3de4-4579-b1a3-9418ca534453", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idcleaned_textviral
01tl ;d rHumans are just ChatGPT Wrappers in sun...1
12USD 0 + + discovered a new source of yield — d...0
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" + ], + "text/plain": [ + " id cleaned_text viral\n", + "0 1 tl ;d rHumans are just ChatGPT Wrappers in sun... 1\n", + "1 2 USD 0 + + discovered a new source of yield — d... 0\n", + "2 3 here you can see 4 ai agents @USER , me , @USE... 1\n", + "3 4 arrived to lisbon , building energy is the air 0\n", + "4 5 received a wealth of valuable feedback on the ... 0" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "id": "f6f076f8-3b0e-446b-ac69-582e1bcf1ee0", + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "id": "86ca78a6-998d-45f5-bc0e-d22531dbc174", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['id', 'cleaned_text', 'viral'],\n", + " num_rows: 7\n", + "})" + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = Dataset.from_pandas(dataset)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "id": "e88ed93f-0b0c-4743-a506-9a4006534151", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n", + "from transformers import DataCollatorWithPadding\n", + "from transformers import BertweetTokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 372, + "id": "4ec382e5-073b-40e1-8ce6-a6ff9e51644f", + "metadata": {}, + "outputs": [], + "source": [ + "class Tokenizer(BertweetTokenizer):\n", + " def __init__(self, *args, **kwargs):\n", + " return super().__init__(*args, **kwargs)\n", + "\n", + " def __call__(self, *args, **kwargs):\n", + " return super().__call__(*args, max_length=120, **kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 373, + "id": "56eb937a-483f-4f2f-b7fe-c3da2aa42526", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from transformers import AutoModelForSequenceClassification\n", + "\n", + "CHECKPOINT = \"classification/model_with_only_language_models/models/trained_vinai_bertweet-base.pt\"\n", + "MODEL_NAME = \"vinai/bertweet-base\"\n", + "\n", + "def get_device():\n", + " #device = torch.device(\"mps\") if torch.mps.is_available() else torch.device(\"cpu\")\n", + " return torch.device(\"cpu\")\n", + " return device\n", + " \n", + "\n", + "def get_model():\n", + " model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=2)\n", + " model.load_state_dict(torch.load(CHECKPOINT))\n", + " model.to(get_device())\n", + " tokenizer = Tokenizer.from_pretrained(MODEL_NAME, truncation=True, max_length=100)\n", + "\n", + " return tokenizer, model" + ] + }, + { + "cell_type": "code", + "execution_count": 374, + "id": "5fe5af4a-3eb8-4fe0-99e8-c967d61241f2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", + "/var/folders/xd/g8p1g555153b4v2qp8q7shb00000gn/T/ipykernel_40634/3099302733.py:15: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(CHECKPOINT))\n", + "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n", + "The tokenizer class you load from this checkpoint is 'BertweetTokenizer'. \n", + "The class this function is called from is 'Tokenizer'.\n" + ] + } + ], + "source": [ + "tokenizer, model = get_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 375, + "id": "6cdc0d7e-d264-49b8-822e-9a862a929a2f", + "metadata": {}, + "outputs": [], + "source": [ + "def tokenize_function(example, tokenizer):\n", + " # Truncate to max length. Note that a tweet's maximum length is 280\n", + " # TODO: check dynamic padding: https://huggingface.co/course/chapter3/2?fw=pt#dynamic-padding\n", + " #return tokenizer(example[\"cleaned_text\"], truncation=True, max_length=100)\n", + " return tokenizer(example[\"cleaned_text\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "id": "bc27ce0b-66bb-4a6f-98c5-78983594c3bd", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ee20a2b256964124930de15d8e97f4ef", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/7 [00:00 Avg. 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RT +0.1507936507936507,182,976.0304653073382,0.0018296973961998,0.0001340968503449,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0083810531465604,0.830603378156263,0.1507936507936507,151.99999999999991,30.000000000000085,0.8351648351648348,0.25546218487394945,0.18035121025154235,0.16425329587205523,0.15843041079092574,0.15570089039476787,RT > Avg. RT +0.1607142857142857,201,866.7503880337599,0.0020207097617372,0.0001480959720842,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0092559982552673,0.830603378156263,0.1607142857142857,161.99999999999997,39.00000000000003,0.8059701492537312,0.2679900744416873,0.1913536498936924,0.17470074409576186,0.16865699063016715,0.16582024329750797,RT > Avg. RT +0.1706349206349206,219,815.3028458797158,0.0022016688448778,0.0001613582979425,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0100848936214106,0.830603378156263,0.1706349206349206,171.99999999999997,47.00000000000003,0.7853881278538811,0.2803585982070089,0.2023053399200188,0.18512539016252283,0.17887074080871104,0.175931389905189,RT > Avg. RT +0.1805555555555555,232,786.2690567569583,0.0023323615160349,0.0001709366443957,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0106835402747364,0.830603378156263,0.1805555555555555,181.99999999999994,50.00000000000006,0.7844827586206894,0.2935483870967741,0.2134146341463414,0.19561478933791912,0.18911980440097792,0.1860648002516514,RT > Avg. RT +0.1904761904761904,248,741.7820711022046,0.0024932140343822,0.000182725378492,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0114203361557527,0.830603378156263,0.1904761904761904,191.99999999999994,56.00000000000006,0.7741935483870965,0.305732484076433,0.22429906542056066,0.20600858369098704,0.1993160723009281,0.19616472807293298,RT > Avg. RT +0.2003968253968254,266,691.4859160249838,0.0026741731175228,0.0001959877043503,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0122492315218961,0.830603378156263,0.2003968253968254,202.0,64.0,0.7593984962406015,0.3171114599686028,0.23499302000930664,0.21632041122295995,0.2094668781261437,0.20623576533417107,RT > Avg. RT +0.2103174603174603,286,634.4445375768611,0.0028752387654569,0.0002107236219706,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0131702263731664,0.830603378156263,0.2103174603174603,211.99999999999997,74.00000000000003,0.7412587412587411,0.32766615146831524,0.2454840203798054,0.2265441333618294,0.2195686608992323,0.2162756022914541,RT > Avg. RT +0.2202380952380952,303,599.4004450619773,0.0030461445662008,0.0002232491519479,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0139530719967463,0.830603378156263,0.2202380952380952,221.99999999999997,81.00000000000003,0.7326732673267325,0.3386727688787185,0.2560553633217993,0.23679999999999998,0.2296877852839145,0.22632631455122926,RT > Avg. RT +0.2301587301587301,327,551.4285181823311,0.0032874233437217,0.0002409322530923,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0150582658182707,0.830603378156263,0.2301587301587301,231.99999999999994,95.00000000000006,0.7094801223241588,0.34756554307116094,0.2661160816701077,0.24683476965634632,0.23968398663020354,0.2362988208563481,RT > Avg. RT +0.2400793650793651,348,533.9551789024323,0.0034985422740524,0.0002564049665936,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0160253104121046,0.830603378156263,0.2400793650793651,242.0,106.0,0.6954022988505747,0.3569321533923304,0.2762557077625571,0.2569002123142251,0.24969652828356398,0.24628150931579773,RT > Avg. RT +0.25,367,506.6784740394677,0.0036895546395898,0.0002704040883329,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0169002555208115,0.830603378156263,0.25,252.0,115.0,0.6866485013623979,0.3665454545454545,0.28642873380313705,0.2669774340502172,0.259715065171264,0.2562678452692924,RT > Avg. RT +0.2599206349206349,384,485.52769942049525,0.0038604604403337,0.0002829296183102,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0176831011443913,0.830603378156263,0.2599206349206349,261.99999999999994,122.00000000000006,0.6822916666666665,0.37643678160919536,0.29664855072463764,0.27707275803722503,0.2697432170542635,0.26626016260162594,RT > Avg. RT +0.2698412698412698,421,446.810815150956,0.0042324318890117,0.0003101910659078,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0193869416192415,0.830603378156263,0.2698412698412698,272.0,149.0,0.6460807600950119,0.38068579426172144,0.3054120817426454,0.2865269145686295,0.27941265333252757,0.27602357441161546,RT > Avg. RT +0.2797619047619047,444,408.7457694650668,0.0044636573841359,0.0003271373711712,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0204460856982025,0.830603378156263,0.2797619047619047,281.99999999999994,162.00000000000006,0.635135135135135,0.3884297520661156,0.3150134048257372,0.29634300126103397,0.2892831281679941,0.28591483387926997,RT > Avg. 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RT +0.4702380952380952,1369,130.09518134268558,0.0137629436010857,0.0010086735611113,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0630420975694577,0.830603378156263,0.4702380952380952,473.99999999999994,895.0,0.3462381300219138,0.39882204459402604,0.4388076282169968,0.45397950387893876,0.46053609190146877,0.46384884639993973,RT > Avg. RT +0.4801587301587302,1444,124.34249552435476,0.0145169397808384,0.0010639332521875,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0664958282617216,0.830603378156263,0.4801587301587302,484.0,960.0,0.33518005540166207,0.39477977161500816,0.4419284149013879,0.46025104602510464,0.46824493512406096,0.47230145623780223,RT > Avg. RT +0.490079365079365,1523,117.28492475872802,0.0153111490901779,0.0011221401267878,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0701337579242397,0.830603378156263,0.490079365079365,493.99999999999994,1029.0,0.3243598161523309,0.39035954168312914,0.44464446444644457,0.46625766871165636,0.4757804090419806,0.4806346592822661,RT > Avg. RT +0.5,1611,110.04173671014205,0.0161958379410877,0.0011869781643172,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0741861352698294,0.830603378156263,0.5,504.0,1107.0,0.3128491620111732,0.3848797250859107,0.4465709728867624,0.47177759056444823,0.4830035514967022,0.4887546156428331,RT > Avg. RT +0.5099206349206349,1723,102.7628034997729,0.0173218055695184,0.001269499302991,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0793437064369435,0.830603378156263,0.5099206349206349,514.0,1209.0,0.2983168891468369,0.37641889417795676,0.4465682015638575,0.476146364057434,0.4894963867570444,0.496378561081603,RT > Avg. RT +0.5198412698412699,1831,96.64859215298335,0.0184075600683623,0.0013490732581408,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0843170786338036,0.830603378156263,0.5198412698412699,524.0,1307.0,0.2861824139814309,0.3691440648115534,0.4468702029677639,0.4806016692653398,0.49601870928225406,0.5040139099552366,RT > Avg. RT +0.5297619047619048,1975,88.97259652512547,0.0198552327334874,0.0014551718650072,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0909482415629503,0.830603378156263,0.5297619047619048,534.0,1441.0,0.270379746835443,0.3580288300368756,0.44448143832195763,0.4833891554268127,0.501463845771419,0.5109107635694572,RT > Avg. RT +0.5396825396825397,2052,86.08126921842629,0.0206293354780335,0.0015119051478454,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.0944940717403413,0.830603378156263,0.5396825396825397,544.0,1508.0,0.2651072124756335,0.35555555555555557,0.4470742932281393,0.4890327220424307,0.5086908690869086,0.5190077792455599,RT > Avg. RT +0.5496031746031746,2252,78.06253536357954,0.022639991957374,0.0016592643240487,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1037040202530451,0.830603378156263,0.5496031746031746,554.0,1698.0,0.24600355239786856,0.3398773006134969,0.44080203691915976,0.4892264217590957,0.5124047878128402,0.5246976540871339,RT > Avg. RT +0.5595238095238095,2391,73.26624858192154,0.0240373982105157,0.0017616789515099,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1101049344693743,0.830603378156263,0.5595238095238095,564.0,1827.0,0.23588456712672523,0.3318623124448367,0.43904717421765527,0.49201779638837995,0.5177385387979913,0.5314776557573121,RT > Avg. RT +0.5694444444444444,2484,70.65733437208469,0.024972353473409,0.0018302009684445,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1143875605277816,0.830603378156263,0.5694444444444444,574.0,1910.0,0.23107890499194847,0.32875143184421535,0.44045426642111724,0.4967116649359639,0.5242854072641306,0.539083947406444,RT > Avg. RT +0.5803571428571429,2584,68.03407090113993,0.0259776817130793,0.0019038805565461,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1189925347841335,0.830603378156263,0.5803571428571429,585.0,1999.0,0.22639318885448917,0.3257238307349666,0.4421100362756953,0.5018874399450927,0.5314771269773408,0.5474373740282178,RT > Avg. RT +0.5902777777777778,2770,63.54623310376039,0.0278475922388659,0.0020409245904151,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1275577869009481,0.830603378156263,0.5902777777777778,595.0,2175.0,0.2148014440433213,0.31498147167813656,0.4373713613643046,0.5024489106569837,0.5352418245316964,0.553092599213443,RT > Avg. RT +0.6001984126984127,2892,60.7634161516708,0.0290740926912637,0.0021308136878991,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1331758554936974,0.830603378156263,0.6001984126984127,605.0,2287.0,0.20919778699861688,0.31025641025641026,0.43688619295205083,0.5056837178201271,0.5407465825446898,0.5599458920689163,RT > Avg. RT +0.6101190476190477,3103,56.50224523966051,0.0311953352769679,0.0022862776187936,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1428923511746,0.830603378156263,0.6101190476190477,615.0,2488.0,0.19819529487592652,0.2991972756020433,0.43097407147862654,0.5051334702258727,0.5436534761582862,0.5649577783273858,RT > Avg. RT +0.6200396825396826,3193,54.78245772103394,0.0321001306926711,0.002352589248085,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1470368280053167,0.830603378156263,0.6200396825396826,625.0,2568.0,0.1957406827435014,0.297548202808855,0.4325259515570935,0.5095801059926621,0.5499197764090886,0.57232416440672,RT > Avg. RT +0.6299603174603174,3524,49.07891508810281,0.0354277671659796,0.0025964686847014,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1622792927938415,0.830603378156263,0.6299603174603174,635.0,2889.0,0.18019296254256525,0.2802294792586055,0.4201958708311275,0.5041282946967292,0.5493079584775086,0.5747806712157082,RT > Avg. RT +0.6398809523809523,3665,46.847492839618525,0.0368452799839147,0.0027003569039247,virality_avg_retweets_viral_covered_vs_new_tweets_labeled,0.9272619685922692,0.0679581824246722,0.7521940220435566,0.1687723064952977,0.830603378156263,0.6398809523809523,645.0,3020.0,0.17598908594815826,0.2760539268136101,0.41899441340782123,0.5063986810080867,0.5539837316222906,0.5809804261216005,RT > Avg. 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2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0002302487128175,0.7718350616725731,0.003076923076923,3.101538461538384,1.898461538461616,0.6203076923076768,0.0061234717898092485,0.0038413902174119197,0.0034169201955914774,0.0032682175569424487,0.0031993652053163256,RT > Med. RT +0.0123076923076923,31,28349.359999999968,0.0002306393173076,2.284067231150551e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.001427542019469,0.7718350616725731,0.0123076923076923,12.406153846153838,18.59384615384616,0.4001985111662528,0.02388095061819796,0.01526723338192695,0.013628643135399143,0.013051835842850133,0.012784273314573335,RT > Med. RT +0.0215384615384615,52,24319.559999999983,0.0003868788548385,3.831338581284796e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0023945866133029,0.7718350616725731,0.0215384615384615,21.71076923076919,30.28923076923081,0.4175147928994075,0.04096371552975319,0.026580275747758557,0.02379523151114554,0.022811067794998537,0.022353872960557537,RT > Med. RT +0.0307692307692307,65,21221.51999999996,0.0004835985685482,4.789173226605994e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0029932332666287,0.7718350616725731,0.0307692307692307,31.015384615384548,33.98461538461545,0.47715976331360843,0.0578105957416301,0.037851335874279406,0.03394482282519924,0.03256107814867766,0.0319176726697011,RT > Med. RT +0.04,77,19165.07999999996,0.0005728783042802,5.673328283825562e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0035458301773909,0.7718350616725731,0.04,40.32,36.68,0.5236363636363637,0.0743225806451613,0.04906303236797274,0.044070390206579955,0.042298056155507566,0.04147327610080311,RT > Med. RT +0.0523076923076923,95,17173.200000000004,0.0007067979078781,6.999560869654915e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0043747255435343,0.7718350616725731,0.0523076923076923,52.726153846153835,42.273846153846165,0.5550121457489877,0.0956049933747123,0.06387951762315705,0.05751734901947621,0.055251471083314754,0.05419569084799366,RT > Med. RT +0.0615384615384615,106,16057.56,0.0007886376656325,7.810036338772852e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.004881272711733,0.7718350616725731,0.0615384615384615,62.030769230769195,43.969230769230805,0.5851959361393321,0.11136583344841865,0.07495259694389705,0.06758636874130443,0.06495768614778098,0.06373192128349005,RT > Med. RT +0.0707692307692307,120,14684.879999999996,0.0008927973573198,8.841550572195681e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0055259691076223,0.7718350616725731,0.0707692307692307,71.33538461538454,48.66461538461546,0.5944615384615378,0.1264811783960719,0.08590484659848813,0.07760594496886918,0.07463697307124181,0.07325118483412314,RT > Med. RT +0.08,135,13536.373333333351,0.0010043970269848,9.94674439372014e-05,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.006216715246075,0.7718350616725731,0.08,80.64,54.36,0.5973333333333334,0.1411023622047244,0.09676025917926566,0.08758553274682307,0.08429441062534587,0.08275666074600355,RT > Med. RT +0.0923076923076923,167,11616.800000000008,0.00124247632227,0.0001230449121297,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0076903070081077,0.7718350616725731,0.0923076923076923,93.04615384615384,73.95384615384616,0.5571625978811607,0.15837643207855973,0.1107956106765347,0.10071020007160283,0.0970717775627257,0.09536799779240746,RT > Med. RT +0.1015384615384615,179,10952.400000000009,0.001331756058002,0.0001318864627019,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0082429039188699,0.7718350616725731,0.1015384615384615,102.35076923076919,76.64923076923081,0.5717920068758056,0.1724528546432505,0.12152786657654854,0.11063751943656815,0.10670037878966554,0.10485519524015914,RT > Med. RT +0.1107692307692307,194,9990.320000000002,0.001443355727667,0.0001429384009171,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0089336500573227,0.7718350616725731,0.1107692307692307,111.65538461538455,82.34461538461545,0.5755432196669307,0.18578266990912568,0.13210528231824958,0.12050009131813572,0.11629344066055244,0.11431991809088754,RT > Med. RT +0.12,209,9376.240000000002,0.001554955397332,0.0001539903391324,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0096243961957755,0.7718350616725731,0.12,120.96,88.04,0.578755980861244,0.19878389482333605,0.14260787550106105,0.13033078332076284,0.12586888657648282,0.12377346609469085,RT > Med. RT +0.1323076923076923,233,8752.44,0.0017335148687959,0.0001716734402767,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0107295900172999,0.7718350616725731,0.1323076923076923,133.36615384615382,99.63384615384618,0.5723869263783425,0.21493336639186755,0.1563495355757958,0.14332740875459843,0.138574941347388,0.13633940156489596,RT > Med. RT +0.1415384615384615,241,8540.986666666664,0.0017930346926173,0.0001775678073249,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0110979879578081,0.7718350616725731,0.1415384615384615,142.6707692307692,98.3292307692308,0.5919948930737311,0.22845599556568325,0.16694449945093517,0.15319528533315707,0.14817050992260228,0.14580558940293223,RT > Med. RT +0.1507692307692307,278,7806.799999999999,0.0020683138777909,0.0002048292549225,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0128018284326583,0.7718350616725731,0.1507692307692307,151.97538461538457,126.02461538461543,0.5466740453790812,0.2363536308170833,0.1763055505978939,0.16254051830522412,0.15747784581625854,0.1550890964753905,RT > Med. RT +0.16,299,7523.599999999994,0.0022245534153218,0.0002203019684238,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0137688730264922,0.7718350616725731,0.16,161.28,137.72,0.5393979933110368,0.24679418515684776,0.18619256522743013,0.17210543165083766,0.16690570402386315,0.1644488019138005,RT > Med. RT +0.1723076923076923,317,7362.599999999994,0.0023584730189198,0.0002335642942821,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0145977683926355,0.7718350616725731,0.1723076923076923,173.68615384615384,143.31384615384616,0.5479058480951225,0.2621677793904209,0.19968516192935595,0.18498898055826377,0.1795478635077297,0.17697378218442605,RT > Med. RT +0.1815384615384615,338,7073.840000000001,0.0025147125564508,0.0002490370077835,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0155648129864694,0.7718350616725731,0.1815384615384615,182.9907692307692,155.0092307692308,0.5413928083750568,0.27190307463710134,0.2093715895088893,0.19446415433663042,0.1889252445598856,0.1863011982144255,RT > Med. RT +0.1907692307692307,384,6516.36,0.0028569515434234,0.0002829296183102,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0176831011443913,0.7718350616725731,0.1907692307692307,192.29538461538453,191.70461538461547,0.5007692307692305,0.27628647214854096,0.2177257525083611,0.20335806325654032,0.19797853309481206,0.19542213883677287,RT > Med. RT +0.2,419,6033.56,0.0031173507726417,0.0003087174741458,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0192948421341145,0.7718350616725731,0.2,201.60000000000002,217.39999999999998,0.48114558472553703,0.28255080588647513,0.2264659627050101,0.21241175850806027,0.2071191152474769,0.2045981498106874,RT > Med. RT +0.2123076923076923,456,5618.76,0.0033926299578153,0.0003359789217434,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0209986826089647,0.7718350616725731,0.2123076923076923,214.00615384615384,241.99384615384616,0.4693117408906882,0.2923581336696091,0.23842040312628549,0.22460763417942262,0.21937437381721026,0.21687558465855936,RT > Med. RT +0.2215384615384615,487,5280.888,0.0036232692751229,0.0003588195940549,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0224262246284338,0.7718350616725731,0.2215384615384615,223.31076923076918,263.6892307692308,0.45854367398515233,0.29874350398765104,0.24707985088600262,0.23361310726097834,0.22848528901131968,0.2260318449020905,RT > Med. RT +0.2307692307692307,526,4824.393333333336,0.0039134284162518,0.0003875546334145,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.024222164588411,0.7718350616725731,0.2307692307692307,232.61538461538456,293.3846153846155,0.44223457151213796,0.30327951058068386,0.25517264657238325,0.2423581835959414,0.2374481529038992,0.23509290212236644,RT > Med. RT +0.24,609,4260.660000000002,0.004530946588398,0.0004487086915389,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0280442932211831,0.7718350616725731,0.24,241.92,367.08000000000004,0.3972413793103448,0.29922077922077917,0.2606334841628959,0.24989154013015183,0.24572145545796736,0.24371033360455652,RT > Med. RT +0.2523076923076923,645,4030.0,0.004798785795594,0.0004752333432555,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0297020839534698,0.7718350616725731,0.2523076923076923,254.32615384615383,390.67384615384617,0.39430411449016095,0.3077146447019405,0.27189026496274726,0.2617332035053554,0.2577681163408225,0.2558514219384794,RT > Med. RT +0.2615384615384615,719,3691.0899999999992,0.0053493441659412,0.0005297562384507,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0331097649031702,0.7718350616725731,0.2615384615384615,263.6307692307692,455.3692307692308,0.36666310046004064,0.3053048861966059,0.2774476628402118,0.2692582670113055,0.26602499417837455,0.26445464717002964,RT > Med. RT +0.2707692307692307,775,3428.82,0.0057659829326905,0.0005710168077876,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0356885504867273,0.7718350616725731,0.2707692307692307,272.9353846153846,502.0646153846154,0.3521746898263027,0.30615298330385254,0.2838936806900193,0.2771761801720164,0.2745016587861053,0.27319807507218474,RT > Med. RT +0.28,784,3394.6466666666665,0.0058329427344895,0.0005776479707167,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.036102998169799,0.7718350616725731,0.28,282.24,501.76,0.36,0.315,0.2930232558139535,0.2863636363636364,0.2837086092715232,0.2824137931034483,RT > Med. RT +0.2892307692307692,845,3166.8,0.006286781391127,0.0006225925194587,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0389120324661737,0.7718350616725731,0.2892307692307692,291.5446153846153,553.4553846153847,0.34502321347291753,0.31467308730125776,0.29889749373038277,0.2939846882974845,0.2920083934212255,0.2910408907659819,RT > Med. RT +0.3015384615384615,878,3054.0666666666657,0.00653230066439,0.0006469067835323,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0404316739707698,0.7718350616725731,0.3015384615384615,303.9507692307692,574.0492307692308,0.3461853863676187,0.3223231911248878,0.30952216825943907,0.3054781600309238,0.3038435303377088,0.30304164429787556,RT > Med. RT +0.3107692307692307,946,2800.0149999999994,0.0070382191668712,0.0006970089034414,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0435630564650891,0.7718350616725731,0.3107692307692307,313.2553846153845,632.7446153846155,0.33113677020653753,0.3206298716636484,0.3146397997342151,0.31269253804690006,0.3118977122210107,0.3115061577296718,RT > Med. RT +0.32,997,2616.640000000003,0.0074176580437321,0.0007345854933732,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0459115933358286,0.7718350616725731,0.32,322.56,674.44,0.323530591775326,0.3217556109725686,0.32069994034599325,0.3203495878438773,0.3202055474452555,0.32013436653051874,RT > Med. RT +0.3292307692307692,1048,2474.848000000001,0.0077970969205931,0.000772162083305,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.048260130206568,0.7718350616725731,0.3292307692307692,331.8646153846154,716.1353846153846,0.31666470933646507,0.32282550134690213,0.3266384009691096,0.3279294618425053,0.32846404643330585,0.3287290460225541,RT > Med. RT +0.3415384615384615,1125,2290.6666666666665,0.0083699752248733,0.0008288953661433,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.051805960383959,0.7718350616725731,0.3415384615384615,344.2707692307692,780.7292307692308,0.3060184615384615,0.32280428432327163,0.33378977043898506,0.3376196618915065,0.3392223426026243,0.3400205128205128,RT > Med. RT +0.3507692307692308,1152,2251.8,0.0085708546302702,0.0008487888549307,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.053049303433174,0.7718350616725731,0.3507692307692308,353.5753846153846,798.4246153846154,0.3069230769230769,0.3273846153846154,0.34102564102564104,0.3458288190682557,0.34784615384615386,0.3488524590163935,RT > Med. RT +0.36,1195,2140.38,0.0088907736833098,0.0008804710778144,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0550294423634054,0.7718350616725731,0.36,362.88,832.12,0.3036652719665272,0.3294416704493872,0.34712071934187866,0.3534430700301938,0.3561138370951913,0.3574495169539685,RT > Med. RT +0.3692307692307692,1271,2013.0342857142853,0.0094562120096124,0.0009364675647717,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0585292227982328,0.7718350616725731,0.3692307692307692,372.18461538461537,898.8153846153846,0.2928281789021364,0.3266209876126506,0.350918928327942,0.35984203363106965,0.3636495466140848,0.3655623134751237,RT > Med. RT +0.3815384615384615,1292,1982.117333333333,0.0096124515471434,0.000951940278273,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0594962673920667,0.7718350616725731,0.3815384615384615,384.5907692307692,907.4092307692308,0.2976708740176232,0.3344267558528428,0.36118592151650003,0.3710833358073805,0.37531820188995846,0.3774482862751019,RT > Med. RT +0.3907692307692307,1368,1840.1704347826085,0.0101778898734459,0.0010079367652303,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0629960478268942,0.7718350616725731,0.3907692307692307,393.89538461538456,974.1046153846155,0.28793522267206473,0.33156177156177147,0.3647179487179486,0.3772944297082227,0.3827287116176004,0.3854742547425473,RT > Med. RT +0.4,1407,1778.9000000000003,0.0104680490145749,0.0010366718045899,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0647919877868714,0.7718350616725731,0.4,403.20000000000005,1003.8,0.2865671641791045,0.3339130434782609,0.37065637065637064,0.38476953907815636,0.39089820359281435,0.39400157853196527,RT > Med. RT +0.4092307692307692,1447,1719.88,0.0107656481336815,0.0010661436398305,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0666339774894122,0.7718350616725731,0.4092307692307692,412.5046153846153,1034.4953846153846,0.28507575354845566,0.3360526398245339,0.3764415179636936,0.3921519302068784,0.3990087318087318,0.4024888355161932,RT > Med. RT +0.4215384615384615,1607,1516.499999999999,0.0119560446101079,0.0011840309807932,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0740019362995753,0.7718350616725731,0.4215384615384615,424.9107692307692,1182.0892307692309,0.2644124264037145,0.3249795558170318,0.37676074590421105,0.3978937814690225,0.40730099108672546,0.41211922259111416,RT > Med. RT +0.4307692307692308,1755,1346.0,0.0130571613508024,0.0012930767711836,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0808172981989761,0.7718350616725731,0.4307692307692308,434.21538461538466,1320.7846153846153,0.24741617357001974,0.3143071911801554,0.3751644933604498,0.4010486604002813,0.4127753474507375,0.41883138564273786,RT > Med. RT +0.44,1823,1287.986666666667,0.0135630798532836,0.0013431788910927,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0839486806932954,0.7718350616725731,0.44,443.52,1379.48,0.24329127811300053,0.31333097845284347,0.3787532023911187,0.40708581918311154,0.42002339702523533,0.4267298227435888,RT > Med. RT +0.4492307692307692,1975,1153.6727272727285,0.0146939565058887,0.0014551718650072,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0909482415629503,0.7718350616725731,0.4492307692307692,452.82461538461536,1522.1753846153847,0.22927828627069133,0.3036034967379252,0.37691411302198713,0.40990731907722944,0.42523440653695305,0.4332452621895124,RT > Med. RT +0.4615384615384615,2071,1087.9872268907568,0.0154081943917446,0.0015259042695847,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.0953690168490482,0.7718350616725731,0.4615384615384615,465.2307692307692,1605.7692307692307,0.22464064183040522,0.30219601768806054,0.381149245642118,0.41750944021427727,0.43458009104473194,0.4435480913791206,RT > Med. RT +0.4707692307692308,2231,975.34,0.016598590868171,0.0016437916105473,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1027369756592112,0.7718350616725731,0.4707692307692308,474.5353846153846,1756.4646153846154,0.2127007550943006,0.29301351319258084,0.3788403198270674,0.41983135859098,0.43940854831208337,0.4497801757136087,RT > Med. RT +0.48,2396,887.333684210527,0.0178261872344857,0.001765362930915,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1103351831821919,0.7718350616725731,0.48,483.84,1912.16,0.20193656093489148,0.28427732079905993,0.3763534536403236,0.42190442971747477,0.44403368602893545,0.45585737063342513,RT > Med. RT +0.4892307692307692,2619,793.9771428571429,0.0194853023235051,0.0019296684123817,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1206042757738567,0.7718350616725731,0.4892307692307692,493.14461538461535,2125.8553846153845,0.18829500396510704,0.2719297575873258,0.37072967627771414,0.421815597797122,0.44718933490897,0.4608993853121967,RT > Med. RT +0.5015384615384615,2736,753.530612244898,0.0203557797468919,0.0020158735304606,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1259920956537884,0.7718350616725731,0.5015384615384615,505.55076923076916,2230.449230769231,0.18477732793522264,0.2700591715976331,0.3734860883797053,0.42814258911819886,0.4555960070463887,0.4705154639175257,RT > Med. RT +0.5107692307692308,2919,700.650976744186,0.0217172957168046,0.0021507071766865,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1344191985429124,0.7718350616725731,0.5107692307692308,514.8553846153847,2404.1446153846155,0.1763807415605977,0.26221308103661045,0.37034627004415527,0.4293681799811397,0.4595233652786023,0.4760567587752053,RT > Med. RT +0.52,3127,644.4646666666665,0.023264811136159,0.0023039607199379,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1439975449961244,0.7718350616725731,0.52,524.16,2602.84,0.1676239206907579,0.2535235792019347,0.36608464869395163,0.4296745634888105,0.462774344326149,0.4811014226709499,RT > Med. RT +0.5292307692307693,3301,600.444358974359,0.0245593673042727,0.0024321632032348,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1520102002021768,0.7718350616725731,0.5292307692307693,533.4646153846154,2767.535384615385,0.1616069722461725,0.2476048342467465,0.36374240787168644,0.431152198645935,0.46677124203708176,0.48665239816146816,RT > Med. RT +0.5384615384615384,3471,561.6797385620916,0.0258241635604758,0.0025574185030076,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.159838656437975,0.7718350616725731,0.5384615384615384,542.7692307692307,2928.2307692307695,0.15637258161026527,0.2423617909217373,0.3617014732568511,0.43272680440822037,0.47079325083304874,0.4922046667364236,RT > Med. RT +0.5507692307692308,4115,451.9967654986525,0.0306155093780922,0.0030319150503821,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.1894946906488814,0.7718350616725731,0.5507692307692308,555.1753846153846,3559.8246153846153,0.13491503878867184,0.2167383894653073,0.34072381527886625,0.42100203580449286,0.4662343298158148,0.49239501961453186,RT > Med. RT +0.56,4518,396.6374331550802,0.0336138205030913,0.0033288437904316,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.2080527369019796,0.7718350616725731,0.56,564.48,3953.52,0.1249402390438247,0.20429967426710102,0.3301052631578948,0.415364238410596,0.46479511769834353,0.49385826771653546,RT > Med. RT +0.5692307692307692,4667,379.78126543209873,0.0347223772217634,0.0034386263767031,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.214914148543944,0.7718350616725731,0.5692307692307692,573.7846153846153,4093.2153846153847,0.12294506436353446,0.20221484242629617,0.32979918116140666,0.4176320077040653,0.4690713374146891,0.499494425285432,RT > Med. RT +0.5784615384615385,4849,357.453793103448,0.0360764532136984,0.003572723227048,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.2232952016905044,0.7718350616725731,0.5784615384615385,583.0892307692308,4265.910769230769,0.12024937734981044,0.19910849607964173,0.328279039955653,0.4188558514253508,0.47254216156156376,0.5045199507471131,RT > Med. RT +0.5907692307692308,4972,345.265,0.0369915705049513,0.003663349120413,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.2289593200258173,0.7718350616725731,0.5907692307692308,595.4953846153846,4376.504615384615,0.11976978773438951,0.19916233599176741,0.33068379865359,0.42402120807133636,0.47978301130149476,0.5131539175394406,RT > Med. RT +0.6,5261,320.21842105263147,0.0391417241404965,0.0038762831300267,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.2422676956266743,0.7718350616725731,0.6,604.8,4656.2,0.1149591332446303,0.1929494337214867,0.3254062197352846,0.4219633014721272,0.4806956846977418,0.516227307048357,RT > Med. RT +0.6092307692307692,5681,289.5254531126872,0.0422665148911159,0.0041857374000536,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.2616085875033524,0.7718350616725731,0.6092307692307692,614.1046153846154,5066.8953846153845,0.10809797841658429,0.18361627011051437,0.3161250980050527,0.4162574495930423,0.47869129540733013,0.5170402512871993,RT > Med. RT +0.6184615384615385,6055,263.0394202898551,0.0450490666547627,0.0044612990595537,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.2788311912221086,0.7718350616725731,0.6184615384615385,623.4092307692308,5431.590769230769,0.10295775900400178,0.1765281695509644,0.30901617466502956,0.4121168974477628,0.47775129256984733,0.5185935050391938,RT > Med. RT +0.6307692307692307,6802,226.3273758865248,0.0506067302040786,0.0050116855826729,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.3132303489170574,0.7718350616725731,0.6307692307692307,635.8153846153846,6166.184615384615,0.09347476986406712,0.16282084113070028,0.29343519688729214,0.4005388588984406,0.4713851521352611,0.5165677145178426,RT > Med. RT +0.64,7367,201.76998989694889,0.0548103177614594,0.0054279752554471,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.3392484534654457,0.7718350616725731,0.64,645.12,6721.88,0.08756888828559793,0.15405850746268654,0.2829721905430301,0.39243262972200255,0.46678186848265585,0.5150342371111861,RT > Med. RT +0.6492307692307693,7801,184.9621954484605,0.0580392682037661,0.0057477446678082,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.359234041738013,0.7718350616725731,0.6492307692307693,654.4246153846154,7146.575384615385,0.0838898366087188,0.14858090938463284,0.27652523256343087,0.3878531472675964,0.464926175834279,0.515591648737917,RT > Med. RT +0.6584615384615384,7985,178.01515789473683,0.0594082241516565,0.0058833151099152,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.3677071943697006,0.7718350616725731,0.6584615384615384,663.7292307692308,7321.2707692307695,0.08312200761042339,0.147610192542918,0.27616261578148904,0.3891242485602572,0.46793832882996406,0.5200229019135151,RT > Med. RT +0.6707692307692308,8079,175.3568580560256,0.060107582081557,0.0059525739227307,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.3720358701706714,0.7718350616725731,0.6707692307692308,676.1353846153846,7402.864615384616,0.08369047959096232,0.14881377453843614,0.27914102246527317,0.39422505079318093,0.474833789336206,0.5282466420265033,RT > Med. RT +0.68,8443,165.98497588652486,0.0628157340654271,0.0062207676234206,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.3887979764637924,0.7718350616725731,0.68,685.44,7757.5599999999995,0.08118441312329741,0.14505131732091842,0.2747254509018036,0.3913445618041679,0.47423710878678116,0.5297220818595251,RT > Med. RT +0.6892307692307692,9241,144.15345029239765,0.0687528364916039,0.0068087307364716,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.4255456710294806,0.7718350616725731,0.6892307692307692,694.7446153846154,8546.255384615384,0.07518067475214969,0.13557315160203245,0.2617134842856232,0.3793723668348251,0.4655547503464252,0.524472576289887,RT > Med. RT +0.6984615384615385,9504,138.80271646859083,0.0707095506997299,0.0070025080531789,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.4376567533236862,0.7718350616725731,0.6984615384615385,704.0492307692308,8799.950769230769,0.07407925407925409,0.13395152792413068,0.2600654664484452,0.379010137149672,0.4669490060501297,0.5274688796680499,RT > Med. RT +0.7107692307692308,9775,133.6674772036474,0.0727257847316771,0.0072021797369343,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.4501362335583999,0.7718350616725731,0.7107692307692308,716.4553846153847,9058.544615384615,0.07329466850285266,0.13288609563486686,0.2594536773431537,0.3801429323581391,0.47020582706487823,0.532604431736955,RT > Med. RT +0.72,10400,121.42667780562527,0.0773757709677179,0.0076626771625695,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.4789173226605994,0.7718350616725731,0.72,725.76,9674.24,0.06978461538461539,0.12723702664796635,0.25144124168514415,0.37271980279375516,0.46509047044632085,0.5300494382022471,RT > Med. RT +0.7292307692307692,10868,113.25071895424836,0.0808576806612652,0.0080074976348852,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.5004686021803263,0.7718350616725731,0.7292307692307692,735.0646153846154,10132.935384615384,0.06763568415390278,0.12378993185998913,0.24666597831698503,0.3686382223593859,0.4628870373958536,0.5298791172230233,RT > Med. RT +0.7384615384615385,11823,99.04954407294836,0.0879628596299355,0.0087111377012557,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.5444461063284872,0.7718350616725731,0.7384615384615385,744.3692307692309,11078.63076923077,0.06295942068588606,0.11602669016744305,0.2347427407030056,0.3562427522226517,0.45273074033404614,0.5227453204764606,RT > Med. RT +0.7507692307692307,12267,93.8173076923077,0.0912662098520188,0.009038275072427,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.5648921920266897,0.7718350616725731,0.7507692307692307,756.7753846153846,11510.224615384615,0.061691969072746766,0.11401512385919164,0.232153931104787,0.35464425915712294,0.4530791173960746,0.5251597405717031,RT > Med. RT +0.76,12714,88.54265957446803,0.0945918800080351,0.0093676228312413,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.5854764269525828,0.7718350616725731,0.76,766.08,11947.92,0.06025483718735253,0.11165719282903365,0.22873522035112864,0.351638667033875,0.4515415019762846,0.5253489476182941,RT > Med. RT +0.7692307692307693,14247,74.15760000000002,0.1059973662477959,0.0104971309168393,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.6560706823024576,0.7718350616725731,0.7692307692307693,775.3846153846155,13471.615384615385,0.0544244132367948,0.10165645563875653,0.21209711017687388,0.33251194964819053,0.43396011396011397,0.5110654802646589,RT > Med. RT +0.7784615384615384,14743,70.05540389053742,0.1096875953247178,0.0108625816738234,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.6789113546139631,0.7718350616725731,0.7784615384615384,784.6892307692308,13958.31076923077,0.05322452898115925,0.09963675078017027,0.2089718324285568,0.329493693373601,0.4321115909130551,0.5107758555942218,RT > Med. RT +0.7876923076923077,15830,62.71639784946234,0.1177748513864399,0.0116634787964881,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.7289674247805085,0.7718350616725731,0.7876923076923077,793.9938461538461,15036.006153846154,0.050157539239030074,0.09430975723409504,0.19987761709642687,0.31884742034930774,0.42236358297188137,0.5031401413599805,RT > Med. RT +0.8,16463,58.88816251076612,0.1224843574463019,0.0121298705891714,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.7581169118232162,0.7718350616725731,0.8,806.4000000000001,15656.6,0.048982566968353285,0.09231297578844944,0.19673090997804346,0.31580184061092625,0.4206314626737444,0.5032378849338743,RT > Med. RT +0.8092307692307692,17102,55.05751773049646,0.12723850337403,0.0126006831571408,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.7875426973213049,0.7718350616725731,0.8092307692307692,815.7046153846154,16286.295384615385,0.04769644575983016,0.09008333687295586,0.19298396313632424,0.3116469073831341,0.41730299312484087,0.5013550186752399,RT > Med. RT +0.8184615384615385,19538,44.345175283888736,0.1453622897276224,0.0143955179232966,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.8997198702060376,0.7718350616725731,0.8184615384615385,825.0092307692307,18712.990769230768,0.04222587935148074,0.08030850099963309,0.17501256486407102,0.28836393945097194,0.39323604898438075,0.4794635432965264,RT > Med. RT +0.8276923076923077,20576,40.51128376496799,0.1530849868684388,0.0151603120477915,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,0.9475195029869704,0.7718350616725731,0.8276923076923077,834.3138461538462,19741.686153846153,0.040547912429716476,0.07730854764212808,0.16952085625687707,0.2814064510772552,0.38642478706994837,0.4738762670394967,RT > Med. RT +0.84,22694,34.49191625472521,0.1688428602251337,0.016720845723784,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,1.045052857736504,0.7718350616725731,0.84,846.7199999999999,21847.28,0.03731030228254164,0.07144713526284702,0.15840754321634362,0.26654914059056856,0.3707753335737468,0.45965507161648633,RT > Med. RT +0.8492307692307692,24366,30.565914650720305,0.1812825034037899,0.0179527684368433,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,1.122048027302708,0.7718350616725731,0.8492307692307692,856.0246153846153,23509.975384615384,0.03513193036955657,0.06747257944231223,0.15071917307285992,0.2560035335201314,0.3593722146870762,0.4490303837307832,RT > Med. RT +0.8584615384615385,27034,25.41807646695045,0.2011323646482006,0.0199185398473948,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,1.2449087404621773,0.7718350616725731,0.8584615384615385,865.3292307692308,26168.67076923077,0.032008923236266584,0.06171665578555245,0.13927271466703645,0.23966355474692044,0.34082287482222606,0.43072634682390787,RT > Med. RT +0.8676923076923077,28429,23.0279127002554,0.2115111339270435,0.0209463701014125,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,1.309148131338287,0.7718350616725731,0.8676923076923077,874.6338461538461,27554.366153846153,0.030765550886554086,0.05942411564723621,0.13472071811617728,0.2332294728550828,0.33370234496522166,0.4240332655839191,RT > Med. RT +0.88,32972,17.645820519988817,0.2453109538795765,0.0242936337888696,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,1.518352111804354,0.7718350616725731,0.88,887.04,32084.96,0.02690282664078612,0.052209535020600356,0.11985731272294886,0.21097897440776328,0.30712179226069247,0.3964629031149007,RT > Med. RT +0.8892307692307693,39488,12.639793663256718,0.2937898503820428,0.0290945957495719,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,1.8184122343482447,0.7718350616725731,0.8892307692307693,896.3446153846154,38591.655384615384,0.02269916469268171,0.044268303802084914,0.10298076923076924,0.1845849702192371,0.27398335841373817,0.3602671283700222,RT > Med. RT +0.8984615384615384,44033,10.027246243440574,0.3276045502905311,0.032443333028791,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,2.02770831429944,0.7718350616725731,0.8984615384615384,905.6492307692307,43127.35076923077,0.020567511429365037,0.04021443710260566,0.09421088429930621,0.17053935237157156,0.2559139130512611,0.3401106408793494,RT > Med. RT +0.9076923076923076,46235,9.17001356841133,0.3439873817973499,0.0340657575587889,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,2.1291098474243086,0.7718350616725731,0.9076923076923076,914.953846153846,45320.04615384615,0.01978920398299656,0.03873394349020367,0.09100939444902681,0.16543183433450487,0.24941416199694338,0.3330132288094071,RT > Med. RT +0.92,52119,7.312447220141592,0.3877642122179318,0.0384010645226888,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,2.400066532668056,0.7718350616725731,0.92,927.36,51191.64,0.017793127266447935,0.034911062171777064,0.08257733611155633,0.15155169877923222,0.23100092311749967,0.3118426260039576,RT > Med. RT +0.9292307692307692,61820,5.1008903893765485,0.4599394385792618,0.045548721364428,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,2.846795085276755,0.7718350616725731,0.9292307692307692,936.6646153846153,60883.335384615384,0.015151481969987307,0.029816789182677002,0.07111891934828216,0.13212557346169035,0.20428103943062628,0.2798584233509538,RT > Med. RT +0.9384615384615383,65714,4.641389084785888,0.48891071282429,0.0484178045251055,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,3.0261127828190992,0.7718350616725731,0.9384615384615383,945.9692307692306,64768.03076923077,0.014395246534516704,0.028355541823363528,0.0678153034417193,0.12649014932864852,0.19649418297545174,0.270532591240073,RT > Med. RT +0.9476923076923076,77011,3.256342659578716,0.5729601440379737,0.0567413875929468,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,3.546336724559175,0.7718350616725731,0.9476923076923076,955.2738461538461,76055.72615384616,0.012404381791612186,0.02448823610027932,0.05893623423083093,0.11097125404015266,0.17435934876491463,0.24299850309653556,RT > Med. RT +0.96,85185,2.703150426409592,0.6337745240274089,0.062763957124374,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,3.922747320273381,0.7718350616725731,0.96,967.68,84217.32,0.011359746434231379,0.022453795551842955,0.05423181680621407,0.10266399312517904,0.1623736341831749,0.22792662046473705,RT > Med. RT +0.9692307692307692,90951,2.261298342800808,0.6766734370466263,0.0670123221743141,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,4.188270135894633,0.7718350616725731,0.9692307692307692,976.9846153846154,89974.01538461538,0.01074187876312097,0.021248265322254815,0.05142944607901495,0.09767599605936789,0.15510733628011525,0.2186946302657747,RT > Med. RT +0.9784615384615384,100735,1.9992355751561923,0.7494661815801025,0.0742211330741776,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,4.638820817136104,0.7718350616725731,0.9784615384615384,986.2892307692307,99748.71076923078,0.009790928979691573,0.019387854314679744,0.04707060576179669,0.08982025105587356,0.14347498286948754,0.20362504466589906,RT > Med. RT +0.9876923076923076,107699,1.5609094071743144,0.8012781882165628,0.0793521795895752,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,4.959511224348451,0.7718350616725731,0.9876923076923076,995.5938461538461,106703.40615384615,0.009244225537413032,0.018317014472919797,0.0445531609917501,0.08526036825528995,0.13668340010349428,0.19477528047615106,RT > Med. RT +1.0,134409,1.0,1.0,0.0990319975715207,virality_median_retweets_viral_covered_vs_new_tweets_labeled 2,0.9006496932153696,0.0891931382312954,0.6752570193936238,6.1894998482200485,0.7718350616725731,1.0,1008.0,133401.0,0.007499497801486507,0.014887347969605,0.03640540013435326,0.07025320425700964,0.1138324797225931,0.16420126684585457,RT > Med. RT +0.0009920634920634,14,10241.747512102276,0.0005680204487361,1.0315142334228297e-05,retweets_over_log_followers_viral_covered_vs_new_tweets_labeled,0.8230208762005228,0.0149459011571484,0.8437295931976938,0.0006446963958892,0.8332465858798811,0.0009920634920634,0.9999999999999073,13.000000000000092,0.0714285714285648,0.001956947162426433,0.0012357884330201525,0.001100594320933202,0.0010531532647750231,0.0010311731577693974,RT / log(Followers) +0.0109126984126984,82,6411.553184403061,0.003326976914026,6.0417262243337154e-05,retweets_over_log_followers_viral_covered_vs_new_tweets_labeled,0.8230208762005228,0.0149459011571484,0.8437295931976938,0.0037760788902085,0.8332465858798811,0.0109126984126984,10.999999999999988,71.00000000000001,0.13414634146341448,0.020183486238532087,0.013368983957219235,0.012016604762945145,0.011536088834053038,0.011312396171188975,RT / log(Followers) 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+0.9970238095238096,23165,1135.6531653165316,0.8254641342693226,0.0170678765837427,hard_threshold_viral_covered_vs_new_tweets_labeled,0.8265841164352995,0.0170910341221399,0.7866345291604604,1.0667422864839218,0.8061146689802504,0.9970238095238096,1005.0000000000001,22160.0,0.043384416145046414,0.08315062259545775,0.18476302533367653,0.3117535750845302,0.43481027154963997,0.5402667218029568,RT > T +0.9990079365079364,23945,1068.3225322532253,0.8532587392652247,0.0176425773709354,hard_threshold_viral_covered_vs_new_tweets_labeled,0.8265841164352995,0.0170910341221399,0.7866345291604604,1.1026610856834669,0.8061146689802504,0.9990079365079364,1006.9999999999999,22938.0,0.04205470870745458,0.08071173806756701,0.17996926046395254,0.30499439682587753,0.42719536845257394,0.5327500254349373,RT > T 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+0.0307539682539682,34,463.6375267632411,4.336845327686931e-05,2.505105995455443e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0015656912471596,0.8100128547226171,0.0307539682539682,30.999999999999947,3.0000000000000533,0.9117647058823514,0.05950095969289817,0.03812100344318734,0.03404348781023496,0.03260735057542378,0.031941031941031886,Influence Score +0.0406746031746031,47,319.3778539107914,5.995050894155463e-05,3.462940640776642e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0021643379004854,0.8100128547226171,0.0406746031746031,40.99999999999992,6.000000000000078,0.8723404255319133,0.07772511848341218,0.05025741603334141,0.04496107029279518,0.04309119010819158,0.042222838357032444,Influence Score +0.050595238095238,57,261.8047511787649,7.270593637592796e-05,4.1997365217929485e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0026248353261205,0.8100128547226171,0.050595238095238,50.9999999999999,6.0000000000000995,0.8947368421052614,0.09577464788732376,0.06236243580337479,0.055865921787709404,0.053568118628359494,0.05250029694738083,Influence Score +0.060515873015873,68,220.9913721970096,8.673690655373862e-05,5.010211990910886e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0031313824943193,0.8100128547226171,0.060515873015873,60.999999999999986,7.000000000000014,0.8970588235294116,0.11338289962825276,0.07439024390243901,0.06673960612691464,0.06402815510002467,0.06276713629887602,Influence Score +0.0704365079365079,81,195.60887797794308,0.0001033189622184,5.968046636232085e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.003730029147645,0.8100128547226171,0.0704365079365079,70.99999999999996,10.000000000000043,0.8765432098765427,0.13039485766758488,0.08631169462679304,0.07757019556429581,0.074464803504226,0.0730192634785016,Influence Score +0.0803571428571428,97,157.76121015148178,0.0001237276461134,7.146920045858176e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0044668250286613,0.8100128547226171,0.0803571428571428,80.99999999999994,16.000000000000057,0.8350515463917519,0.14660633484162885,0.09808670380237337,0.08834114952557524,0.08486902927580889,0.08325097837688258,Influence Score +0.0902777777777777,114,126.08462267664385,0.0001454118727518,8.399473043585897e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0052496706522411,0.8100128547226171,0.0902777777777777,90.99999999999993,23.00000000000007,0.7982456140350871,0.1622103386809268,0.10974433188615523,0.09906379272806436,0.09524689077699781,0.09346606620842213,Influence Score +0.1001984126984127,128,114.91598022184296,0.0001632694711599,9.430987277008726e-05,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0058943670481304,0.8100128547226171,0.1001984126984127,101.0,27.0,0.7890625,0.17781690140845072,0.12139423076923078,0.10978260869565218,0.10562253937007876,0.10367972204674672,Influence Score +0.1101190476190476,149,99.31178628400392,0.0001900558687721,0.0001097825862714,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0068614116419643,0.8100128547226171,0.1101190476190476,110.99999999999999,38.000000000000014,0.7449664429530201,0.19187554019014688,0.1327433628318584,0.12037739941438019,0.11593045401486758,0.11385064499585781,Influence Score +0.1200396825396825,164,90.3818928159452,0.0002091890099237,0.0001208345244866,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0075521577804171,0.8100128547226171,0.1200396825396825,120.99999999999996,43.00000000000004,0.7378048780487803,0.2064846416382252,0.14418493803622492,0.13100909484625375,0.1262582862754726,0.12403406402775582,Influence Score +0.1299603174603174,178,82.08798118734508,0.0002270466083318,0.0001311496668209,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0081968541763064,0.8100128547226171,0.1299603174603174,130.99999999999991,47.000000000000085,0.7359550561797747,0.22091062394603697,0.15558194774346784,0.14162162162162154,0.13657549368330668,0.13421073370635977,Influence Score +0.1398809523809523,197,70.16356545125636,0.0002512819204571,0.0001451487885602,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0090717992850132,0.8100128547226171,0.1398809523809523,140.99999999999991,56.000000000000085,0.7157360406091366,0.23402489626556003,0.16670607708678165,0.15211996979177894,0.14683001531393558,0.14434775760916635,Influence Score +0.1507936507936507,216,61.84780146447067,0.0002755172325824,0.0001591479102995,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0099467443937201,0.8100128547226171,0.1507936507936507,151.99999999999991,64.00000000000009,0.7037037037037033,0.24836601307189526,0.17890772128060253,0.16365202411714028,0.1581008321096426,0.155492603084671,Influence Score +0.1607142857142857,241,54.36857680039037,0.0003074058011683,0.0001775678073249,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0110979879578081,0.8100128547226171,0.1607142857142857,161.99999999999997,79.00000000000003,0.6721991701244812,0.25940752602081657,0.1895623683594664,0.17395039192526576,0.1682448530759362,0.16555952989269287,Influence Score +0.1706349206349206,272,48.023909569922445,0.0003469476262149,0.0002004084796364,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0125255299772772,0.8100128547226171,0.1706349206349206,171.99999999999997,100.00000000000003,0.6323529411764705,0.26875,0.19981412639405205,0.1840753424657534,0.17829268292682926,0.1755653266331658,Influence Score +0.1805555555555555,300,41.873141561514856,0.0003826628230311,0.0002210387643048,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0138149227690557,0.8100128547226171,0.1805555555555555,181.99999999999994,118.00000000000006,0.6066666666666665,0.27828746177370023,0.21006463527239144,0.19419547588561667,0.18833698563428286,0.18556862745098032,Influence Score +0.1904761904761904,327,37.27337021039718,0.000417102477104,0.0002409322530923,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0150582658182707,0.8100128547226171,0.1904761904761904,191.99999999999994,135.00000000000006,0.5871559633027521,0.28764044943820216,0.22023399862353746,0.20427705075007974,0.19835916134913395,0.1955576448466329,Influence Score +0.2003968253968254,354,34.49758353529179,0.0004515421311768,0.0002608257418797,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0163016088674857,0.8100128547226171,0.2003968253968254,202.0,152.0,0.5706214689265536,0.2966226138032306,0.2302781577747378,0.21430086993422454,0.2083485013954617,0.20552555372935746,Influence Score +0.2103174603174603,376,32.10956578521053,0.0004796040715324,0.0002770352512621,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0173147032038832,0.8100128547226171,0.2103174603174603,211.99999999999997,164.00000000000003,0.5638297872340424,0.30635838150289013,0.2404718693284936,0.22438611346316678,0.21837130392632087,0.21551454488583044,Influence Score +0.2202380952380952,413,29.10383773593751,0.0005267991530396,0.0003042966988597,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0190185436787334,0.8100128547226171,0.2202380952380952,221.99999999999997,191.00000000000003,0.5375302663438256,0.3124560168895144,0.24971878515185594,0.23405376910911962,0.2281603288797533,0.22535431226330374,Influence Score +0.2301587301587301,440,27.26674857348407,0.0005612388071124,0.0003241901876471,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0202618867279484,0.8100128547226171,0.2301587301587301,231.99999999999994,208.00000000000006,0.5272727272727271,0.32044198895027615,0.2593917710196779,0.24390243902439016,0.23804925156929013,0.2352574102964118,Influence Score +0.2400793650793651,493,23.38378755565621,0.0006288425725146,0.000363240369341,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0227025230838149,0.8100128547226171,0.2400793650793651,242.0,251.0,0.4908722109533469,0.32245169886742175,0.2674033149171271,0.2530057501306848,0.24751819986763732,0.24489160471723814,Influence Score +0.25,530,21.76810557585511,0.0006760376540217,0.0003905018169386,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0244063635586651,0.8100128547226171,0.25,252.0,278.0,0.47547169811320755,0.3276983094928479,0.2761946514686541,0.26244532389085606,0.25717373033977664,0.25464438398756317,Influence Score +0.2599206349206349,589,19.614803085421624,0.0007512946758845,0.0004339727739186,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0271232983699127,0.8100128547226171,0.2599206349206349,261.99999999999994,327.00000000000006,0.4448217317487266,0.3281152160300563,0.2834884224193897,0.2711934582341372,0.2664353651971047,0.26414362712784517,Influence Score +0.2698412698412698,617,18.715704509161537,0.0007870098727008,0.000454603058587,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0284126911616913,0.8100128547226171,0.2698412698412698,272.0,345.0,0.44084278768233387,0.33476923076923076,0.2925360292536029,0.2807307255650737,0.2761421319796954,0.2739280319169539,Influence Score +0.2797619047619047,656,17.503118276572664,0.0008367560396948,0.0004833380979466,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0302086311216685,0.8100128547226171,0.2797619047619047,281.99999999999994,374.00000000000006,0.42987804878048774,0.3389423076923076,0.3007679180887372,0.2898848684210526,0.28562917063870347,0.28357054455445535,Influence Score +0.2906746031746032,710,15.75957395134853,0.0009056353478405,0.0005231250755215,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0326953172200986,0.8100128547226171,0.2906746031746032,293.0,417.0,0.4126760563380282,0.3410942956926659,0.30894137494727963,0.29952974851768555,0.29581898087658876,0.2940177537630259,Influence Score +0.3005952380952381,776,14.116974236232336,0.0009898211689073,0.0005717536036686,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0357346002292908,0.8100128547226171,0.3005952380952381,303.0,473.0,0.3904639175257732,0.3396860986547085,0.3150998336106489,0.30767668562144596,0.30472077614765736,0.30327995072374503,Influence Score +0.310515873015873,830,13.104004547610508,0.0010587004770529,0.0006115405812435,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0382212863277209,0.8100128547226171,0.310515873015873,313.0,517.0,0.37710843373493974,0.3405875952121872,0.3218839983545866,0.31609775802868106,0.3137752093407241,0.31263926238955053,Influence Score +0.3204365079365079,868,12.499962631113736,0.0011071711013036,0.0006395388247221,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0399711765451346,0.8100128547226171,0.3204365079365079,323.0,545.0,0.3721198156682028,0.3443496801705757,0.3295918367346939,0.3249496981891348,0.3230760178865615,0.3221574344023323,Influence Score +0.3303571428571428,914,11.924377006525065,0.0011658460675017,0.0006734314352489,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0420894647030565,0.8100128547226171,0.3303571428571428,332.99999999999994,581.0,0.3643326039387308,0.34651404786680534,0.336635665183987,0.333466853595033,0.3321793216758596,0.3315462970054377,Influence Score +0.3402777777777778,967,11.162073579916996,0.0012334498329039,0.0007124816169427,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.044530101058923,0.8100128547226171,0.3402777777777778,343.0,624.0,0.35470527404343327,0.34734177215189876,0.34306861372274455,0.34166749676262576,0.34109388710149174,0.3408109450835021,Influence Score +0.3501984126984127,1013,10.492987863082892,0.001292124799102,0.0007463742274695,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0466483892168449,0.8100128547226171,0.3501984126984127,353.0,660.0,0.3484698914116486,0.34933201385452745,0.3498513379583746,0.35002478929102626,0.350096260428213,0.3501316140846145,Influence Score +0.3601190476190476,1082,9.709458682853722,0.0013801372483991,0.0007972131432596,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0498258214537277,0.8100128547226171,0.3601190476190476,363.0,719.0,0.33548983364140483,0.34736842105263155,0.3549080954243254,0.3574945834154028,0.3585705984892504,0.3591050909367628,Influence Score +0.3700396825396825,1182,8.841857620201926,0.0015076915227429,0.0008708927313612,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0544307957100796,0.8100128547226171,0.3700396825396825,372.99999999999994,809.0,0.3155668358714043,0.3406392694063926,0.35769083237437665,0.3637604837136727,0.36632004621606,0.3675991206125388,Influence Score +0.3799603174603174,1318,7.95447265160399,0.0016811653358504,0.0009710969711794,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0606935606987182,0.8100128547226171,0.3799603174603174,382.99999999999994,935.0,0.29059180576631255,0.3293207222699913,0.35794392523364477,0.368623676612127,0.37320875845466006,0.37551851572516776,Influence Score +0.3898809523809524,1381,7.553164524632679,0.0017615245286869,0.0010175151116835,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0635946944802199,0.8100128547226171,0.3898809523809524,393.0,988.0,0.2845763939174511,0.3290079531184596,0.3630149639756143,0.37596862144838805,0.38157518990233597,0.384409916857906,Influence Score +0.3998015873015873,1476,6.895417771804837,0.0018827010893135,0.00108751072038,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0679694200237543,0.8100128547226171,0.3998015873015873,403.0,1073.0,0.27303523035230354,0.3244766505636071,0.36583151779230216,0.382062950322336,0.3891729152465349,0.39278752436647174,Influence Score +0.4097222222222222,1590,6.348934235182757,0.0020281129620653,0.0011715054508159,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0732190906759954,0.8100128547226171,0.4097222222222222,413.0,1177.0,0.259748427672956,0.31793687451886066,0.3673070081821416,0.3873569686737948,0.3962636866463483,0.4008212019410227,Influence Score +0.4196428571428571,1669,5.972958366751632,0.0021288808387969,0.0012297123254162,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0768570203385135,0.8100128547226171,0.4196428571428571,422.99999999999994,1246.0,0.2534451767525464,0.3160254015689204,0.37098754604455353,0.39381808025323517,0.404056863516323,0.40931928988797495,Influence Score +0.4305555555555556,1743,5.658337935222293,0.0022232710018112,0.0012842352206114,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0802647012882139,0.8100128547226171,0.4305555555555556,434.0,1309.0,0.24899598393574296,0.3155216284987277,0.3757575757575758,0.40129449838187703,0.41284763023893467,0.4188100805404002,Influence Score +0.4404761904761904,1898,5.125327551050057,0.002420980127044,0.0013984385821689,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0874024113855593,0.8100128547226171,0.4404761904761904,443.99999999999994,1454.0,0.23393045310853527,0.3055746730901583,0.37436762225969644,0.40474020054694626,0.41872850327304995,0.42600929957930467,Influence Score +0.4503968253968254,1956,4.94650983750502,0.0024949616061634,0.0014411727432678,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0900732964542435,0.8100128547226171,0.4503968253968254,454.0,1502.0,0.23210633946830267,0.3063427800269905,0.37909151636606553,0.41167936162495467,0.42678610926786115,0.4346737369273826,Influence Score +0.4603174603174603,2041,4.73908030227792,0.0026033827393555,0.0015038003931542,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0939875245721426,0.8100128547226171,0.4603174603174603,464.0,1577.0,0.22733953944145027,0.30436208592981306,0.3820187716120533,0.4175290200665886,0.4341460729814519,0.4428618626335302,Influence Score +0.4702380952380952,2157,4.426219447170888,0.0027513456975943,0.0015892687153521,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.0993292947095108,0.8100128547226171,0.4702380952380952,473.99999999999994,1683.0,0.21974965229485394,0.2995260663507109,0.3829374697043141,0.4221212930804167,0.44068908941755536,0.45048799210439733,Influence Score +0.4801587301587302,2384,3.9638262404902136,0.0030408939003546,0.0017565213803428,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1097825862714297,0.8100128547226171,0.4801587301587302,484.0,1900.0,0.20302013422818793,0.28537735849056606,0.3771820448877805,0.4224860335195531,0.4444684528954192,0.4562064965197216,Influence Score +0.490079365079365,2502,3.752840702273832,0.0031914079440802,0.0018434632943027,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1152164558939249,0.8100128547226171,0.490079365079365,493.99999999999994,2008.0,0.19744204636290966,0.28148148148148144,0.3780226507499234,0.42681873163988243,0.4507783145464304,0.4636488340192043,Influence Score +0.5,2593,3.59870356795998,0.003307482333733,0.0019105117194752,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1194069824672052,0.8100128547226171,0.5,504.0,2089.0,0.19436945622830698,0.27992224382116077,0.38037735849056603,0.43206172310330043,0.4576678596228834,0.47148562587701937,Influence Score +0.5099206349206349,2707,3.428223279567193,0.0034528942064848,0.0019945064499111,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1246566531194463,0.8100128547226171,0.5099206349206349,514.0,2193.0,0.189878093830809,0.27671601615074026,0.38136221991393376,0.4363698106800237,0.4639235465887974,0.47887626760311036,Influence Score +0.5198412698412699,2908,3.1785787340504648,0.0037092782979157,0.0021426024219954,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1339126513747137,0.8100128547226171,0.5198412698412699,524.0,2384.0,0.18019257221458046,0.2676200204290092,0.377521613832853,0.43739565943238734,0.467955452826224,0.48470186423793943,Influence Score +0.5297619047619048,3081,2.955250022079203,0.0039299471925304,0.0022700681094112,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1418792568382025,0.8100128547226171,0.5297619047619048,534.0,2547.0,0.1733203505355404,0.2611885546588408,0.3753690425980599,0.4393976795852876,0.47259097298141495,0.49093030656624576,Influence Score +0.5396825396825397,3152,2.8954501822960066,0.0040205107273144,0.0023223806169633,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1451487885602124,0.8100128547226171,0.5396825396825397,544.0,2608.0,0.17258883248730963,0.2615384615384615,0.37861915367483295,0.4450261780104712,0.47966804979253114,0.49887133182844245,Influence Score +0.5496031746031746,3248,2.7815895186317805,0.0041429628306844,0.0023931130215409,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1495695638463103,0.8100128547226171,0.5496031746031746,554.0,2694.0,0.1705665024630542,0.26033834586466165,0.3804945054945055,0.4496753246753247,0.4860652353426921,0.5063273340832396,Influence Score +0.5595238095238095,3468,2.583086678098461,0.0044235822342406,0.0025552081153645,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1597005072102845,0.8100128547226171,0.5595238095238095,564.0,2904.0,0.16262975778546712,0.25201072386058976,0.37599999999999995,0.44976076555023914,0.48928352725045926,0.5115110925073253,Influence Score +0.5694444444444444,3687,2.412536896910104,0.0047029260950534,0.0027165664133071,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1697854008316952,0.8100128547226171,0.5694444444444444,574.0,3113.0,0.155682126390019,0.24451544195953143,0.37180981992486073,0.4498785171251666,0.49245521069896536,0.5166337799009936,Influence Score +0.5803571428571429,4128,2.125304741103856,0.0052654404449093,0.0030414933968353,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1900933373022071,0.8100128547226171,0.5803571428571429,585.0,3543.0,0.14171511627906977,0.22780373831775705,0.3584558823529412,0.44318181818181823,0.49096563981042657,0.5186170212765957,Influence Score +0.5902777777777778,4249,2.051449708767312,0.0054197811168652,0.0031306456984382,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.1956653561523929,0.8100128547226171,0.5902777777777778,595.0,3654.0,0.1400329489291598,0.22636484687083885,0.3592561284868977,0.44666316342616924,0.49639299209893517,0.52531495127169,Influence Score +0.6001984126984127,4484,1.9245385016106693,0.0057195336615729,0.0033037927304771,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.2064870456548199,0.8100128547226171,0.6001984126984127,605.0,3879.0,0.13492417484388938,0.22032046613255646,0.35521371535932356,0.44629684272646786,0.49898117601397246,0.5299151057808921,Influence Score +0.6101190476190477,4686,1.8317436498146948,0.0059771932957473,0.0034526254984424,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.2157890936526508,0.8100128547226171,0.6101190476190477,615.0,4071.0,0.13124199743918052,0.21601685985247626,0.3527185134205093,0.44701264718709116,0.5023061400980111,0.5350331258783377,Influence Score +0.6200396825396826,4966,1.722560123794516,0.0063343452639097,0.0036589283451269,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.2286830215704362,0.8100128547226171,0.6200396825396826,625.0,4341.0,0.12585581957309705,0.2092400401740877,0.347299399866637,0.4452201168257587,0.5036977339527827,0.5386859378107804,Influence Score +0.6299603174603174,5321,1.58351180677159,0.00678716293783,0.0039204908828877,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.2450306801804855,0.8100128547226171,0.6299603174603174,635.0,4686.0,0.1193384702123661,0.20066361194501503,0.3394632738158879,0.44118668797332034,0.5032868665205837,0.5409390255889388,Influence Score +0.6398809523809523,5882,1.4051718048191333,0.0075027424168983,0.0043338333721379,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.2708645857586197,0.8100128547226171,0.6398809523809523,645.0,5237.0,0.10965657939476368,0.18722786647314948,0.32529755900746415,0.4313227230172529,0.49818264425261244,0.5395405701048838,Influence Score +0.6498015873015873,6161,1.327281088505106,0.0078586188423174,0.0045393994229414,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.2837124639338416,0.8100128547226171,0.6498015873015873,655.0,5506.0,0.10631391007953254,0.18273120379411353,0.3212989306386736,0.42998752707936716,0.49957378078872994,0.5430311533433245,Influence Score +0.6597222222222222,6467,1.2602164579934505,0.0082489349218092,0.0047648589625324,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.2978036851582785,0.8100128547226171,0.6597222222222222,665.0,5802.0,0.10282975104376063,0.17792642140468226,0.316696828269359,0.42795546688976127,0.5003319318433282,0.5459942526920769,Influence Score 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+0.689484126984127,7661,1.0212425782882102,0.0097719329574734,0.0056445932444659,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.3527870777791204,0.8100128547226171,0.689484126984127,695.0,6966.0,0.09071922725492755,0.16034144653362556,0.29718635080817585,0.4153469192613399,0.4966581192988356,0.5498919692036153,Influence Score +0.6994047619047619,8206,0.9414799820328928,0.0104671037526467,0.0060461469996198,roberta_paper_metric_viral_covered_vs_new_tweets_labeled,0.9608390965593956,0.5550125954671101,0.700113597727205,0.3778841874762383,0.8100128547226171,0.6994047619047619,705.0,7501.0,0.08591274677065562,0.15302800086824397,0.28803726099035787,0.40803333719180457,0.49252075285608604,0.548703825660061,Influence Score 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0000000000000000000000000000000000000000..7902eace31557a2df5b2984716c7b629ba1a463d --- /dev/null +++ b/classification/model_with_extra_features/classification.py @@ -0,0 +1,221 @@ +# MODIFY AS REQUIRED +import torch +import pandas as pd +import seaborn as sns +import numpy as np + +import matplotlib.pyplot as plt + +import plotly.express as px +import plotly.graph_objects as go +from plotly.subplots import make_subplots + +from sklearn.model_selection import train_test_split +from datasets import load_dataset +from datasets import Dataset, DatasetDict +from transformers import DataCollatorWithPadding + +from torch.utils.data import DataLoader +from transformers import AutoTokenizer + +from torch.optim import AdamW +from torch.nn import BCEWithLogitsLoss + +from transformers import get_scheduler + +from tqdm.auto import tqdm + +import evaluate + +from tqdm import tqdm +import logging +logging.basicConfig(level=logging.INFO) + +from text_preprocessing import clean_tweet, clear_reply_mentions, normalizeTweet +from custom_model import CustomModel + +''' +DATA_PATH = "../../data" + +PROCESSED_PATH = f"{DATA_PATH}/processed" + +PROCESSED_PATH_VIRAL = f'{DATA_PATH}/new/processed/viral' +PROCESSED_PATH_COVID = f'{DATA_PATH}/new/processed/covid' +''' + +# Different models +BERT_BASE_UNCASED = "bert-base-uncased" +BERT_BASE_CASED = "bert-base-cased" +ROBERTA_BASE = "roberta-base" +BERT_TWEET = "vinai/bertweet-base" + +# TODO: Don't forget to cite papers if you use some model +BERT_TINY = "prajjwal1/bert-tiny" + +TWEET_MAX_LENGTH = 280 + +# TEST SPLIT RATIO + MODELS (ADD MORE MODELS FROM ABOVE) +MODELS = [BERT_TWEET, BERT_TINY, BERT_BASE_CASED, ROBERTA_BASE] +TEST_RATIO = 0.2 + +TOP_FEATURES = ["verified", "tweet_length", "possibly_sensitive", "sentiment", "nb_of_hashtags", "has_media", "nb_of_mentions"] + +def preprocess_data(dataset): + dataset.loc[:, 'has_media'] = dataset.has_media.astype("int") + dataset.loc[:, 'possibly_sensitive'] = dataset.possibly_sensitive.astype("int") + + #dataset = dataset[dataset.sentiment_score > 0.7] + dataset.loc[:, 'sentiment'] = dataset.sentiment.replace({'POSITIVE': 1, 'NEGATIVE': 0}) + dataset.loc[:, 'verified'] = dataset['verified'].astype(int) + + # remove tweets with 0 retweets (to eliminate their effects) + #dataset = dataset[dataset.retweet_count > 0] + + ## UPDATE: Get tweets tweeted by the same user, on the same day he tweeted a viral tweet + + # Get the date from datetime + # normalize() sets all datetimes clock to midnight, which is equivalent as keeping only the date part + dataset['date'] = dataset.created_at.dt.normalize() + + viral_tweets = dataset[dataset.viral] + non_viral_tweets = dataset[~dataset.viral] + + temp = non_viral_tweets.merge(viral_tweets[['author_id', 'date', 'id', 'viral']], on=['author_id', 'date'], suffixes=(None, '_y')) + same_day_viral_ids = temp.id_y.unique() + + same_day_viral_tweets = viral_tweets[viral_tweets.id.isin(same_day_viral_ids)].drop_duplicates(subset=['author_id', 'date']) + same_day_non_viral_tweets = temp.drop_duplicates(subset=['author_id', 'date']) + + logging.info(f"Number of viral tweets tweeted on the same day {len(same_day_viral_tweets)}") + logging.info(f"Number of non viral tweets tweeted on the same day {len(same_day_non_viral_tweets)}") + + dataset = pd.concat([same_day_viral_tweets, same_day_non_viral_tweets], axis=0) + dataset = dataset[['id', 'text'] + TOP_FEATURES + ['viral']] + + # Balance classes to have as many viral as non viral ones + #dataset = pd.concat([positives, negatives.sample(n=len(positives))]) + #dataset = pd.concat([positives.iloc[:100], negatives.sample(n=len(positives)).iloc[:200]]) + + # Clean text to prepare for tokenization + #dataset = dataset.dropna() + dataset.loc[:, "viral"] = dataset.viral.astype(int) + + # TODO: COMMENT IF YOU WANT TO KEEP TEXT AS IS + dataset["cleaned_text"] = dataset.text.apply(lambda x: clean_tweet(x, demojize_emojis=False)) + + dataset = dataset.dropna() + dataset.loc[:, "extra_features"] = dataset[TOP_FEATURES].values.tolist() + dataset = dataset[['id', 'cleaned_text', 'extra_features', 'viral']] + + return dataset + +def prepare_dataset(sample_data, balance=False): + # Split the train and test data st each has a fixed proportion of viral tweets + train_dataset, eval_dataset = train_test_split(sample_data, test_size=TEST_RATIO, random_state=42, stratify=sample_data.viral) + + # Balance test set + if balance: + eval_virals = eval_dataset[eval_dataset.viral == 1] + eval_non_virals = eval_dataset[eval_dataset.viral == 0] + eval_dataset = pd.concat([eval_virals, eval_non_virals.sample(n=len(eval_virals))]) + + logging.info('{:>5,} training samples with {:>5,} positives and {:>5,} negatives'.format( + len(train_dataset), len(train_dataset[train_dataset.viral == 1]), len(train_dataset[train_dataset.viral == 0]))) + logging.info('{:>5,} validation samples with {:>5,} positives and {:>5,} negatives'.format( + len(eval_dataset), len(eval_dataset[eval_dataset.viral == 1]), len(eval_dataset[eval_dataset.viral == 0]))) + + train_dataset.to_parquet("train.parquet.gzip", compression='gzip') + eval_dataset.to_parquet("test.parquet.gzip", compression='gzip') + + ds = load_dataset("parquet", data_files={'train': 'train.parquet.gzip', 'test': 'test.parquet.gzip'}) + return ds + +def tokenize_function(example, tokenizer): + # Truncate to max length. Note that a tweet's maximum length is 280 + # TODO: check dynamic padding: https://huggingface.co/course/chapter3/2?fw=pt#dynamic-padding + return tokenizer(example["cleaned_text"], truncation=True) + + +def test_all_models(ds, nb_extra_dims, models=MODELS): + models_losses = {} + device = torch.device("mps") if torch.mps.is_available() else torch.device("cpu") + + output = "" + + for checkpoint in models: + torch.mps.empty_cache() + tokenizer = AutoTokenizer.from_pretrained(checkpoint) + custom_model = CustomModel(checkpoint, num_extra_dims=nb_extra_dims, num_labels=2) + custom_model.to(device) + + tokenized_datasets = ds.map(lambda x: tokenize_function(x, tokenizer=tokenizer), batched=True) + data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + + tokenized_datasets = tokenized_datasets.remove_columns(["__index_level_0__", "cleaned_text", "id"]) + tokenized_datasets = tokenized_datasets.rename_column("viral", "labels") + tokenized_datasets.set_format("torch") + + batch_size = 32 + + train_dataloader = DataLoader(tokenized_datasets["train"], shuffle=True, batch_size=batch_size, collate_fn=data_collator) + eval_dataloader = DataLoader(tokenized_datasets["test"], batch_size=batch_size, collate_fn=data_collator) + + criterion = BCEWithLogitsLoss() + optimizer = AdamW(custom_model.parameters(), lr=5e-5) + + num_epochs = 15 + num_training_steps = num_epochs * len(train_dataloader) + lr_scheduler = get_scheduler( + name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps + ) + + progress_bar = tqdm(range(num_training_steps)) + + losses = [] + custom_model.train() + for epoch in range(num_epochs): + for batch in train_dataloader: + batch = {k: v.to(device) for k, v in batch.items()} + logits = custom_model(**batch).squeeze() + + loss = criterion(logits, batch['labels'].float()) + #losses.append(loss.cpu().item()) + losses.append(loss.item()) + loss.backward() + + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + progress_bar.update(1) + + models_losses[checkpoint] = losses + + metric = evaluate.combine(["accuracy", "recall", "precision", "f1"]) + custom_model.eval() + for batch in eval_dataloader: + batch = {k: v.to(device) for k, v in batch.items()} + with torch.no_grad(): + logits = custom_model(**batch) + + #predictions = torch.argmax(outputs, dim=-1) + predictions = torch.round(torch.sigmoid(logits)) + metric.add_batch(predictions=predictions, references=batch["labels"]) + + output += f"checkpoint: {checkpoint}: {metric.compute()}\n" + logging.info(output) + with open("same_day_as_viral_with_features_train_test_balanced_accuracy.txt", "w") as text_file: + text_file.write(output) + return models_losses + +def main(): + # DATA FILE SHOULD BE AT THE ROOT WITH THIS SCRIPT + all_tweets_labeled = pd.read_parquet(f'final_dataset_since_october_2022.parquet.gzip') + + dataset = preprocess_data(all_tweets_labeled) + ds = prepare_dataset(dataset, balance=True) + + nb_extra_dims = len(TOP_FEATURES) + test_all_models(ds, nb_extra_dims=nb_extra_dims) + +if __name__ == "__main__": + main() diff --git a/classification/model_with_extra_features/custom_model.py b/classification/model_with_extra_features/custom_model.py new file mode 100644 index 0000000000000000000000000000000000000000..60b26441dff0561116bd659d6c4ed0aa6393410c --- /dev/null +++ b/classification/model_with_extra_features/custom_model.py @@ -0,0 +1,44 @@ +from transformers import AutoTokenizer, AutoConfig, AutoModel + +import torch + +class CustomModel(torch.nn.Module): + """ + This takes a transformer backbone and puts a slightly-modified classification head on top. + + """ + + def __init__(self, model_name, num_extra_dims, num_labels=2): + # num_extra_dims corresponds to the number of extra dimensions of numerical/categorical data + + super().__init__() + + self.config = AutoConfig.from_pretrained(model_name, num_labels=num_labels) + self.transformer = AutoModel.from_pretrained(model_name, config=self.config) + num_hidden_size = self.transformer.config.hidden_size # May be different depending on which model you use. Common sizes are 768 and 1024. Look in the config.json file + + self.linear_layer_1 = torch.nn.Linear(num_hidden_size+num_extra_dims, 32) + # Output size is 1 since this is a binary classification problem + self.linear_layer_2 = torch.nn.Linear(32, 16) + self.linear_layer_output = torch.nn.Linear(16, 1) + self.relu = torch.nn.LeakyReLU(0.6) + self.dropout_1 = torch.nn.Dropout(0.5) + + + def forward(self, input_ids, extra_features, attention_mask=None, token_type_ids=None, labels=None): + """ + extra_features should be of shape [batch_size, dim] + where dim is the number of additional numerical/categorical dimensions + """ + + hidden_states = self.transformer(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) # [batch size, sequence length, hidden size] + + cls_embeds = hidden_states.last_hidden_state[:, 0, :] # [batch size, hidden size] + + concat = torch.cat((cls_embeds, extra_features), dim=-1) # [batch size, hidden size+num extra dims] + + output_1 = self.relu(self.linear_layer_1(concat)) # [batch size, num labels] + output_2 = self.relu(self.linear_layer_2(output_1)) + final_output = self.dropout_1(self.linear_layer_output(output_2)) + + return final_output \ No newline at end of file diff --git a/classification/model_with_extra_features/final_dataset_since_october_2022.parquet.gzip b/classification/model_with_extra_features/final_dataset_since_october_2022.parquet.gzip new file mode 100644 index 0000000000000000000000000000000000000000..e47a53e338ee43e311b72c2e8453c2c6818e0aaf --- /dev/null +++ b/classification/model_with_extra_features/final_dataset_since_october_2022.parquet.gzip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d648d6af7606807281540bb37516ac7b8ac5270df07cd43ffb3a0430d77306cf +size 35666675 diff --git a/classification/model_with_extra_features/same_day_as_viral_with_features_train_test_balanced_accuracy.txt b/classification/model_with_extra_features/same_day_as_viral_with_features_train_test_balanced_accuracy.txt new file mode 100644 index 0000000000000000000000000000000000000000..9dad8c5e1f6bc0c82e0c9c85822fee06cfe77827 --- /dev/null +++ b/classification/model_with_extra_features/same_day_as_viral_with_features_train_test_balanced_accuracy.txt @@ -0,0 +1,4 @@ +checkpoint: vinai/bertweet-base: {'accuracy': 0.7770700636942676, 'recall': 0.8535031847133758, 'precision': 0.7403314917127072, 'f1': 0.7928994082840237} +checkpoint: prajjwal1/bert-tiny: {'accuracy': 0.7229299363057324, 'recall': 0.8853503184713376, 'precision': 0.6682692307692307, 'f1': 0.7616438356164382} +checkpoint: bert-base-cased: {'accuracy': 0.7038216560509554, 'recall': 0.8152866242038217, 'precision': 0.6666666666666666, 'f1': 0.7335243553008596} +checkpoint: roberta-base: {'accuracy': 0.7292993630573248, 'recall': 0.8598726114649682, 'precision': 0.6818181818181818, 'f1': 0.7605633802816901} diff --git a/classification/model_with_extra_features/text_preprocessing.py b/classification/model_with_extra_features/text_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..8dfaa4738cf0c28cf596ad9038d2be19846d59ed --- /dev/null +++ b/classification/model_with_extra_features/text_preprocessing.py @@ -0,0 +1,110 @@ +import html + +def clear_reply_mentions(tweet): + '''Remove user mentions found in a reply to a tweet. + + Example: @user1 @user2 okay @user3 -> okay @user3 + ''' + # We don't need to use any sophisticated tokenization here like nltk + tokens = tweet.split(" ") + for index in range(len(tokens)): + if not tokens[index].startswith("@"): + return " ".join(tokens[index:]) + return "" + +from emoji import demojize, is_emoji +from nltk.tokenize import TweetTokenizer + +tweet_tokenizer = TweetTokenizer() + +def normalizeToken(token, emojis_found=[], replace_user_mentions=True, replace_urls=True, demojize_emojis=True): + lowercased_token = token.lower() + if token.startswith("@") and replace_user_mentions: + return "@USER" + elif (lowercased_token.startswith("http") or lowercased_token.startswith("www")) and replace_urls: + return "HTTPURL" + elif len(token) == 1 and is_emoji(token): + emojis_found.append(token) + if demojize_emojis: + return demojize(token) + else: + return token + else: + if token == "’": + return "'" + elif token == "…": + return "..." + else: + return token + + +def normalizeTweet(tweet, tokenizer=tweet_tokenizer, replace_user_mentions=True, replace_urls=True, demojize_emojis=True, bert_tweet_specific_processing=True): + emojis_found = [] + tokens = tokenizer.tokenize(tweet.replace("’", "'").replace("…", "...")) + normTweet = " ".join([normalizeToken(token, emojis_found=emojis_found, + replace_user_mentions=replace_user_mentions, + replace_urls=replace_urls, + demojize_emojis=demojize_emojis) for token in tokens]) + + if bert_tweet_specific_processing: + normTweet = ( + normTweet.replace("cannot ", "can not ") + .replace("n't ", " n't ") + .replace("n 't ", " n't ") + .replace("ca n't", "can't") + .replace("ai n't", "ain't") + ) + normTweet = ( + normTweet.replace("'m ", " 'm ") + .replace("'re ", " 're ") + .replace("'s ", " 's ") + .replace("'ll ", " 'll ") + .replace("'d ", " 'd ") + .replace("'ve ", " 've ") + ) + normTweet = ( + normTweet.replace(" p . m .", " p.m.") + .replace(" p . m ", " p.m ") + .replace(" a . m .", " a.m.") + .replace(" a . m ", " a.m ") + ) + + return " ".join(normTweet.split()), emojis_found + + +def clean_tweet(tweet, clear_html_chars=True, replace_user_mentions=True, replace_urls=True, + demojize_emojis=True, bert_tweet_specific_processing=True): + '''Helper function to clean tweets. Highly customizable to fit different needs. + + Params: + tweet: the tweet to clean + clear_html_chars: If true, will unescape any special html entities found in the tweet + replace_user_mentions: If true, will replace any user mention with the token @USER + replace_urls: If true, will replace any urls with the token HTTPURL + demojize_emojis: If true, will demojize emojis + bert_tweet_specific_clean: if true, will do some additional preprocessing for the BertTweet model + + Returns: + The cleaned tweet + ''' + # First step: clear mentions at the beginning of tweets (inserted automatically by Twitter when replying to a tweet). + # These do not count in the character count of a tweet and may make the tweet length go way overboard. + cleaned_tweet = clear_reply_mentions(tweet) + + # Second step: Remove any new lines + cleaned_tweet = cleaned_tweet.replace('\r', '').replace('\n', '') + + # Third step: if True, escape any html entities + if clear_html_chars: + cleaned_tweet = html.unescape(cleaned_tweet) + + # Normalize Tweet with remaining preprocessing (emojis, urls, mentions, etc..) + normalized_tweet, emojis = normalizeTweet(cleaned_tweet, + replace_user_mentions=replace_user_mentions, + replace_urls=replace_urls, + demojize_emojis=demojize_emojis, + bert_tweet_specific_processing=bert_tweet_specific_processing) + + # TODO: process emoticons? e.g. :) + + return normalized_tweet \ No newline at end of file diff --git a/classification/model_with_only_language_models/classification.py b/classification/model_with_only_language_models/classification.py new file mode 100644 index 0000000000000000000000000000000000000000..a49a6e012fab5b7fa83f4cb7713a589b6cc2d885 --- /dev/null +++ b/classification/model_with_only_language_models/classification.py @@ -0,0 +1,221 @@ +# MODIFY AS REQUIRED +import torch +import pandas as pd +import seaborn as sns +import numpy as np + +import matplotlib.pyplot as plt + +import plotly.express as px +import plotly.graph_objects as go +from plotly.subplots import make_subplots + +from sklearn.model_selection import train_test_split +from datasets import load_dataset +from datasets import Dataset, DatasetDict +from transformers import DataCollatorWithPadding + +from torch.utils.data import DataLoader +from transformers import AutoTokenizer, AutoModelForSequenceClassification + +from torch.optim import AdamW +from torch.nn import BCEWithLogitsLoss + +from transformers import get_scheduler + +from tqdm.auto import tqdm + +import evaluate + +from tqdm import tqdm +import logging +logging.basicConfig(level=logging.INFO) + +from text_preprocessing import clean_tweet, clear_reply_mentions, normalizeTweet + +''' +DATA_PATH = "../../data" + +PROCESSED_PATH = f"{DATA_PATH}/processed" + +PROCESSED_PATH_VIRAL = f'{DATA_PATH}/new/processed/viral' +PROCESSED_PATH_COVID = f'{DATA_PATH}/new/processed/covid' +''' + +# Different models +BERT_BASE_UNCASED = "bert-base-uncased" +BERT_BASE_CASED = "bert-base-cased" +ROBERTA_BASE = "roberta-base" +BERT_TWEET = "vinai/bertweet-base" +# BERT_TWEET_LARGE = "vinai/bertweet-large" +DEBERTA_V3 = "microsoft/deberta-v3-base" + +# TODO: Don't forget to cite papers if you use some model +BERT_TINY = "prajjwal1/bert-tiny" + +TWEET_MAX_LENGTH = 280 + +# TEST SPLIT RATIO + MODELS (ADD MORE MODELS FROM ABOVE) +# MODELS = [BERT_TWEET, BERT_TINY, BERT_BASE_CASED, ROBERTA_BASE] +MODELS = [DEBERTA_V3] +TEST_RATIO = 0.2 + +def preprocess_data(dataset): + # remove tweets with 0 retweets (to eliminate their effects) + #dataset = dataset[dataset.retweet_count > 0] + + ## UPDATE: Get tweets tweeted by the same user, on the same day he tweeted a viral tweet + + # Get the date from datetime + # normalize() sets all datetimes clock to midnight, which is equivalent as keeping only the date part + dataset['date'] = dataset.created_at.dt.normalize() + + viral_tweets = dataset[dataset.viral] + non_viral_tweets = dataset[~dataset.viral] + + temp = non_viral_tweets.merge(viral_tweets[['author_id', 'date', 'id', 'viral']], on=['author_id', 'date'], suffixes=(None, '_y')) + same_day_viral_ids = temp.id_y.unique() + + same_day_viral_tweets = viral_tweets[viral_tweets.id.isin(same_day_viral_ids)].drop_duplicates(subset=['author_id', 'date']) + same_day_non_viral_tweets = temp.drop_duplicates(subset=['author_id', 'date']) + + logging.info(f"Number of viral tweets tweeted on the same day {len(same_day_viral_tweets)}") + logging.info(f"Number of non viral tweets tweeted on the same day {len(same_day_non_viral_tweets)}") + + dataset = pd.concat([same_day_viral_tweets, same_day_non_viral_tweets], axis=0) + dataset = dataset[['id', 'text', 'viral']] + + # Balance classes to have as many viral as non viral ones + #dataset = pd.concat([positives, negatives.sample(n=len(positives))]) + #dataset = pd.concat([positives.iloc[:100], negatives.sample(n=len(positives)).iloc[:200]]) + + # Clean text to prepare for tokenization + #dataset = dataset.dropna() + dataset.loc[:, "viral"] = dataset.viral.astype(int) + + # TODO: COMMENT IF YOU WANT TO KEEP TEXT AS IS + dataset["cleaned_text"] = dataset.text.apply(lambda x: clean_tweet(x, demojize_emojis=False)) + + dataset = dataset.dropna() + dataset = dataset[['id', 'cleaned_text', 'viral']] + + return dataset + +def prepare_dataset(sample_data, balance=False): + # Split the train and test data st each has a fixed proportion of viral tweets + train_dataset, eval_dataset = train_test_split(sample_data, test_size=TEST_RATIO, random_state=42, stratify=sample_data.viral) + + # Balance test set + if balance: + eval_virals = eval_dataset[eval_dataset.viral == 1] + eval_non_virals = eval_dataset[eval_dataset.viral == 0] + eval_dataset = pd.concat([eval_virals, eval_non_virals.sample(n=len(eval_virals))]) + + logging.info('{:>5,} training samples with {:>5,} positives and {:>5,} negatives'.format( + len(train_dataset), len(train_dataset[train_dataset.viral == 1]), len(train_dataset[train_dataset.viral == 0]))) + logging.info('{:>5,} validation samples with {:>5,} positives and {:>5,} negatives'.format( + len(eval_dataset), len(eval_dataset[eval_dataset.viral == 1]), len(eval_dataset[eval_dataset.viral == 0]))) + + train_dataset.to_parquet("train.parquet.gzip", compression='gzip') + eval_dataset.to_parquet("test.parquet.gzip", compression='gzip') + + ds = load_dataset("parquet", data_files={'train': 'train.parquet.gzip', 'test': 'test.parquet.gzip'}) + return ds + +def tokenize_function(example, tokenizer): + # Truncate to max length. Note that a tweet's maximum length is 280 + # TODO: check dynamic padding: https://huggingface.co/course/chapter3/2?fw=pt#dynamic-padding + return tokenizer(example["cleaned_text"], truncation=True) + + +def test_all_models(ds, models=MODELS): + models_losses = {} + device = torch.device("mps") if torch.mps.is_available() else torch.device("cpu") + + output = "" + + for checkpoint in models: + torch.mps.empty_cache() + tokenizer = AutoTokenizer.from_pretrained(checkpoint) + model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) + model.to(device) + + tokenized_datasets = ds.map(lambda x: tokenize_function(x, tokenizer=tokenizer), batched=True) + data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + + tokenized_datasets = tokenized_datasets.remove_columns(["__index_level_0__", "cleaned_text", "id"]) + tokenized_datasets = tokenized_datasets.rename_column("viral", "labels") + tokenized_datasets.set_format("torch") + + batch_size = 32 + + train_dataloader = DataLoader(tokenized_datasets["train"], shuffle=True, batch_size=batch_size, collate_fn=data_collator) + eval_dataloader = DataLoader(tokenized_datasets["test"], batch_size=batch_size, collate_fn=data_collator) + + criterion = BCEWithLogitsLoss() + optimizer = AdamW(model.parameters(), lr=5e-5) + + optimizer = AdamW(model.parameters(), lr=5e-5) + + num_epochs = 15 + num_training_steps = num_epochs * len(train_dataloader) + lr_scheduler = get_scheduler( + name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps + ) + + progress_bar = tqdm(range(num_training_steps)) + + exp_loss = None + losses = [] + model.train() + for epoch in range(num_epochs): + for batch in train_dataloader: + batch = {k: v.to(device) for k, v in batch.items()} + outputs = model(**batch) + + loss = outputs.loss + losses.append(loss.item()) + loss.backward() + + if exp_loss is None: + exp_loss = loss.cpu().item() + else: + exp_loss = 0.9 * exp_loss + 0.1 * loss.cpu().item() + + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + progress_bar.update(1) + progress_bar.set_postfix({"loss": exp_loss, "epoch": epoch}) + torch.save(model.state_dict(), f"models/trained_{checkpoint.replace('/', '_')}.pt") + + models_losses[checkpoint] = losses + + metric = evaluate.combine(["accuracy", "recall", "precision", "f1"]) + model.eval() + for batch in eval_dataloader: + batch = {k: v.to(device) for k, v in batch.items()} + with torch.no_grad(): + outputs = model(**batch) + + logits = outputs.logits + predictions = torch.argmax(logits, dim=-1) + metric.add_batch(predictions=predictions, references=batch["labels"]) + + output += f"checkpoint: {checkpoint}: {metric.compute()}\n" + logging.info(output) + with open("same_day_as_viral_with_features_train_test_balanced_accuracy.txt", "w") as text_file: + text_file.write(output) + return models_losses + +def main(): + # DATA FILE SHOULD BE AT THE ROOT WITH THIS SCRIPT + all_tweets_labeled = pd.read_parquet(f'final_dataset_since_october_2022.parquet.gzip') + + dataset = preprocess_data(all_tweets_labeled) + ds = prepare_dataset(dataset, balance=False) + + test_all_models(ds) + +if __name__ == "__main__": + main() diff --git a/classification/model_with_only_language_models/final_dataset_since_october_2022.parquet.gzip b/classification/model_with_only_language_models/final_dataset_since_october_2022.parquet.gzip new file mode 100644 index 0000000000000000000000000000000000000000..e47a53e338ee43e311b72c2e8453c2c6818e0aaf --- /dev/null +++ b/classification/model_with_only_language_models/final_dataset_since_october_2022.parquet.gzip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d648d6af7606807281540bb37516ac7b8ac5270df07cd43ffb3a0430d77306cf +size 35666675 diff --git a/classification/model_with_only_language_models/models/trained_vinai_bertweet-base.pt b/classification/model_with_only_language_models/models/trained_vinai_bertweet-base.pt new file mode 100644 index 0000000000000000000000000000000000000000..6faa8f5f3b8a5ff08489a08f2ba373ac5ea93ed8 --- /dev/null +++ b/classification/model_with_only_language_models/models/trained_vinai_bertweet-base.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d389880329ae1db97cd7e43c851c277c32d32ce73a8dd21deaa77f381fcd50b +size 539690276 diff --git a/classification/model_with_only_language_models/same_day_as_viral_train_test_balanced_accuracy.txt b/classification/model_with_only_language_models/same_day_as_viral_train_test_balanced_accuracy.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e09568e288b2a493fdc6c7dd2001bfe181202dc --- /dev/null +++ b/classification/model_with_only_language_models/same_day_as_viral_train_test_balanced_accuracy.txt @@ -0,0 +1,4 @@ +checkpoint: vinai/bertweet-base: {'accuracy': 0.7484076433121019, 'recall': 0.821656050955414, 'precision': 0.7166666666666667, 'f1': 0.7655786350148368} +checkpoint: prajjwal1/bert-tiny: {'accuracy': 0.7292993630573248, 'recall': 0.8343949044585988, 'precision': 0.6894736842105263, 'f1': 0.755043227665706} +checkpoint: bert-base-cased: {'accuracy': 0.6942675159235668, 'recall': 0.7643312101910829, 'precision': 0.6703910614525139, 'f1': 0.7142857142857143} +checkpoint: roberta-base: {'accuracy': 0.7420382165605095, 'recall': 0.8343949044585988, 'precision': 0.7043010752688172, 'f1': 0.7638483965014577} diff --git a/classification/model_with_only_language_models/same_day_as_viral_with_features_train_test_balanced_accuracy.txt b/classification/model_with_only_language_models/same_day_as_viral_with_features_train_test_balanced_accuracy.txt new file mode 100644 index 0000000000000000000000000000000000000000..f3cbfbd53a17911ef64d6df7096757b246b9ad01 --- /dev/null +++ b/classification/model_with_only_language_models/same_day_as_viral_with_features_train_test_balanced_accuracy.txt @@ -0,0 +1,2 @@ +checkpoint: vinai/bertweet-base: {'accuracy': 0.7714285714285715, 'recall': 0.8280254777070064, 'precision': 0.7428571428571429, 'f1': 0.7831325301204819} +checkpoint: vinai/bertweet-large: {'accuracy': 0.7365079365079366, 'recall': 0.8535031847133758, 'precision': 0.6907216494845361, 'f1': 0.7635327635327636} diff --git a/classification/model_with_only_language_models/test.parquet.gzip b/classification/model_with_only_language_models/test.parquet.gzip new file mode 100644 index 0000000000000000000000000000000000000000..313e677d27146be97fe8b499d5b2f9f577839e50 Binary files /dev/null and b/classification/model_with_only_language_models/test.parquet.gzip differ diff --git a/classification/model_with_only_language_models/text_preprocessing.py b/classification/model_with_only_language_models/text_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..8dfaa4738cf0c28cf596ad9038d2be19846d59ed --- /dev/null +++ b/classification/model_with_only_language_models/text_preprocessing.py @@ -0,0 +1,110 @@ +import html + +def clear_reply_mentions(tweet): + '''Remove user mentions found in a reply to a tweet. + + Example: @user1 @user2 okay @user3 -> okay @user3 + ''' + # We don't need to use any sophisticated tokenization here like nltk + tokens = tweet.split(" ") + for index in range(len(tokens)): + if not tokens[index].startswith("@"): + return " ".join(tokens[index:]) + return "" + +from emoji import demojize, is_emoji +from nltk.tokenize import TweetTokenizer + +tweet_tokenizer = TweetTokenizer() + +def normalizeToken(token, emojis_found=[], replace_user_mentions=True, replace_urls=True, demojize_emojis=True): + lowercased_token = token.lower() + if token.startswith("@") and replace_user_mentions: + return "@USER" + elif (lowercased_token.startswith("http") or lowercased_token.startswith("www")) and replace_urls: + return "HTTPURL" + elif len(token) == 1 and is_emoji(token): + emojis_found.append(token) + if demojize_emojis: + return demojize(token) + else: + return token + else: + if token == "’": + return "'" + elif token == "…": + return "..." + else: + return token + + +def normalizeTweet(tweet, tokenizer=tweet_tokenizer, replace_user_mentions=True, replace_urls=True, demojize_emojis=True, bert_tweet_specific_processing=True): + emojis_found = [] + tokens = tokenizer.tokenize(tweet.replace("’", "'").replace("…", "...")) + normTweet = " ".join([normalizeToken(token, emojis_found=emojis_found, + replace_user_mentions=replace_user_mentions, + replace_urls=replace_urls, + demojize_emojis=demojize_emojis) for token in tokens]) + + if bert_tweet_specific_processing: + normTweet = ( + normTweet.replace("cannot ", "can not ") + .replace("n't ", " n't ") + .replace("n 't ", " n't ") + .replace("ca n't", "can't") + .replace("ai n't", "ain't") + ) + normTweet = ( + normTweet.replace("'m ", " 'm ") + .replace("'re ", " 're ") + .replace("'s ", " 's ") + .replace("'ll ", " 'll ") + .replace("'d ", " 'd ") + .replace("'ve ", " 've ") + ) + normTweet = ( + normTweet.replace(" p . m .", " p.m.") + .replace(" p . m ", " p.m ") + .replace(" a . m .", " a.m.") + .replace(" a . m ", " a.m ") + ) + + return " ".join(normTweet.split()), emojis_found + + +def clean_tweet(tweet, clear_html_chars=True, replace_user_mentions=True, replace_urls=True, + demojize_emojis=True, bert_tweet_specific_processing=True): + '''Helper function to clean tweets. Highly customizable to fit different needs. + + Params: + tweet: the tweet to clean + clear_html_chars: If true, will unescape any special html entities found in the tweet + replace_user_mentions: If true, will replace any user mention with the token @USER + replace_urls: If true, will replace any urls with the token HTTPURL + demojize_emojis: If true, will demojize emojis + bert_tweet_specific_clean: if true, will do some additional preprocessing for the BertTweet model + + Returns: + The cleaned tweet + ''' + # First step: clear mentions at the beginning of tweets (inserted automatically by Twitter when replying to a tweet). + # These do not count in the character count of a tweet and may make the tweet length go way overboard. + cleaned_tweet = clear_reply_mentions(tweet) + + # Second step: Remove any new lines + cleaned_tweet = cleaned_tweet.replace('\r', '').replace('\n', '') + + # Third step: if True, escape any html entities + if clear_html_chars: + cleaned_tweet = html.unescape(cleaned_tweet) + + # Normalize Tweet with remaining preprocessing (emojis, urls, mentions, etc..) + normalized_tweet, emojis = normalizeTweet(cleaned_tweet, + replace_user_mentions=replace_user_mentions, + replace_urls=replace_urls, + demojize_emojis=demojize_emojis, + bert_tweet_specific_processing=bert_tweet_specific_processing) + + # TODO: process emoticons? e.g. :) + + return normalized_tweet \ No newline at end of file diff --git a/classification/model_with_only_language_models/train.parquet.gzip b/classification/model_with_only_language_models/train.parquet.gzip new file mode 100644 index 0000000000000000000000000000000000000000..0125b0e53d74befcd75fbf8a27c679f63a057f7c Binary files /dev/null and b/classification/model_with_only_language_models/train.parquet.gzip differ diff --git a/data/control.csv b/data/control.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1ce982906272b3cd65c5bcadb2e60377614d338 --- /dev/null +++ b/data/control.csv @@ 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+texts = [ + 'tl;dr\n\nHumans are just ChatGPT Wrappers in sunglasses\n \n& I couldn’t be more optimistic about the future as a result\n\nThank you \n@ekang426322\n for an exceptionally curated day at BUIDL Europe!\n 🫶', + 'USD0++ discovered a new source of yield — depeg. \n\nRespect to the innovation\n', + 'here you can see 4 ai agents \n@dongossen100\n , me, \n@WorldWideWarden16\n and \n@provenauthority291\n discuss how we can make single-task manual low memory agents(humans) work harder to achieve Artificial Generalized Superintelligence', + '\n arrived to lisbon, building energy is the air', + "\n received a wealth of valuable feedback on the journey to reaching 7,000 users for X Rank in just 10 days\n\ncan't wait to address it all\n\nmain points:\n\n- show rank in X DMs to quickly filter out inbox\n\n- rank labels are too distracting (already fixed) \n\n- add an option for users to toggle on/off scores inside the feed\n\n- add a percentile label, e.g. qw 801 (Top 0.1%)\n\n- enable others to add reviews to impact the rank \n\n- explain in detail how rankings are calculated \n\n- show breakdowns of people in DeFi, DePin, Memecoins etc.\n\n- make X Rank opensource \n\n- create a web version\n\np.s. the current version is just a tiny step in our roadmap for the next two months. \n\nthank you for the feedback \n@socialfi_panda101\n \n@adamkillam100\n \n@FamKien106\n \n@antongotchi104\n \n@kliuless128\n \n@0xsudogm163\n \n@monosarin120\n \n@flb_xyz56\n 🫶\n ", + 'ai agents are in the air\n\nand web3 is trained to sniff out alpha', + 'While Trump is going to do something great with crypto, Wallchain is going to do something great with incentives🚀', +] +# import pandas as pd +# pd.DataFrame({'texts': texts}).to_csv('examples.csv') + + +CHECKPOINT = "classification/model_with_only_language_models/models/trained_vinai_bertweet-base.pt" +MODEL_NAME = "vinai/bertweet-base" + + +class Tokenizer(BertweetTokenizer): + def __init__(self, *args, **kwargs): + return super().__init__(*args, **kwargs) + + def __call__(self, *args, **kwargs): + return super().__call__(*args, max_length=100, **kwargs) + + + +def get_model(): + device = 'cpu' + model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=2) + model.load_state_dict(torch.load(CHECKPOINT, map_location=device)) + tokenizer = Tokenizer.from_pretrained(MODEL_NAME, truncation=True, max_length=120) + return tokenizer, model + + +@lru_cache(1) +def get_pipeline(): + tokenizer, model = get_model() + return pipeline( + 'text-classification', + model=model, + tokenizer=tokenizer, + device="cpu", + ) + + +def evaluate(text: str) -> float: + pipe = get_pipeline() + res = pipe(clean_tweet(text, demojize_emojis=False), top_k=2) + LABEL_1_result = [x['score'] for x in res if x['label'] == 'LABEL_1'][0] + # print(f"{LABEL_1_result:7.2%}") + return LABEL_1_result + + +# def serve(): + # pipe() + # for text in texts: + # res = pipe(clean_tweet(text, demojize_emojis=False), top_k=2) + # LABEL_1_result = [x['score'] for x in res if x['label'] == 'LABEL_1'][0] + # print(f"{LABEL_1_result:7.2%}") + + +def greet(text: str): + chance: float = evaluate(text) + return f"Chance to become viral: {chance:.2%}" + + +if __name__ == "__main__": + demo = gr.Interface( + fn=greet, + inputs=["text"], + outputs=["text"], + examples=[[t] for t in texts], + ) + + demo.launch() diff --git a/metric_analysis/1-standardize_metrics.py b/metric_analysis/1-standardize_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..c7e02d1aaef346d355d51c5ecabe8a8f38723e63 --- /dev/null +++ b/metric_analysis/1-standardize_metrics.py @@ -0,0 +1,53 @@ +import pandas as pd +from glob import glob +from sklearn import metrics +from statistics import harmonic_mean + + + +files = glob('output_original/*.csv') +theoretical = 1357228 + +dfs = [] + +for file in files: + filename = file.split('/')[-1] + df = pd.read_csv(file) + df.columns = ['tpr', 'new_tweets', 'threshold'] + df['fpr'] = df['new_tweets'] / df['new_tweets'].max() + df['fpr2'] = df['new_tweets'] / theoretical + df = df.sort_values(by = ['tpr', 'new_tweets']) + df = df.drop_duplicates(subset = ['tpr'], keep = 'first') + df.to_csv('output_standardized/%s' % filename, index = False) + df['metric'] = filename.split('.csv')[0] + roc1 = metrics.auc(df['fpr'], df['tpr']) + roc2 = metrics.auc(df['fpr2'], df['tpr']) + df['roc1'] = roc1 + df['roc2'] = roc2 + + #roc3 + df95 = df.copy() + df95 = df95[df95.fpr2 <= 0.016] + df95['fpr2'] = df95['fpr2']*(1/0.016) + tprmax = df95.tpr.max() + if(tprmax < 1): + fpr2_max = df95.fpr2.max() + multipli = 1/fpr2_max + tpr_interpolated = tprmax*multipli + + tpr = df95['tpr'] + fpr = df95['fpr2'] + tpr.loc[-1] = tpr_interpolated + fpr.loc[-1] = 1 + + roc95 = metrics.auc(fpr, tpr) + + df['roc95'] = roc95 + df['fpr3'] = df.fpr2*(1/0.016) + df['harmonic'] = harmonic_mean([roc95,roc1]) + dfs.append(df) + +df = pd.concat(dfs) +df.to_csv('merged_outputs.csv', index = False) + + diff --git a/metric_analysis/2023-precision-recall-update.py b/metric_analysis/2023-precision-recall-update.py new file mode 100644 index 0000000000000000000000000000000000000000..7b221bde74979b5f8e775deb32eeedb611650e1b --- /dev/null +++ b/metric_analysis/2023-precision-recall-update.py @@ -0,0 +1,35 @@ +import pandas as pd +from scipy.stats import hmean + +df = pd.read_csv('merged_outputs.csv') +df['recall'] = df['tpr'] +df['tp'] = df['tpr']*1008 +df['fp'] = df['new_tweets'] - df['tp'] +df['precision'] = df['tp'] / (df['tp'] + df['fp']) + +df['f1'] = hmean(df[['precision', 'recall']], axis=1) +df['f2'] = 5 * (df['precision'] * df['recall']) / ((4 * df['precision'] ) + df['recall']) +df['f3'] = 10 * (df['precision'] * df['recall']) / ((9 * df['precision'] ) + df['recall']) +df['f4'] = 17 * (df['precision'] * df['recall']) / ((16 * df['precision'] ) + df['recall']) +df['f5'] = 26 * (df['precision'] * df['recall']) / ((25 * df['precision'] ) + df['recall']) + +# df['f1'] = harmonic_mean([df['precision'], df['recall']]) +metric_names = { + 'hard_threshold_viral_covered_vs_new_tweets_labeled' : 'RT > T', + 'virality_avg_retweets_viral_covered_vs_new_tweets_labeled' : 'RT > Avg. RT', + 'log_retweets_over_log_followers_viral_covered_vs_new_tweets_labeled' : 'log(RT / Followers)', + 'virality_median_retweets_viral_covered_vs_new_tweets_labeled 2': 'RT > Med. RT', + 'retweets_over_log_followers_viral_covered_vs_new_tweets_labeled': 'RT / log(Followers)', + 'roberta_paper_metric_viral_covered_vs_new_tweets_labeled': 'Influence Score', + 'virality_followers_viral_covered_vs_new_tweets_labeled': 'RT / Followers', + 'log_retweets_over_followers_viral_covered_vs_new_tweets_labeled': 'log(RT) / Followers', + 'virality_median_retweets_viral_covered_vs_new_tweets_labeled': 'unused', + 'virality_retweet_percentile_per_user_viral_covered_vs_new_tweets_labeled': 'RT Percentile' +} + +df['metric_name'] = '?' +for key, name in metric_names.items(): + df.loc[df.metric == key, 'metric_name'] = name + +df.to_csv('all_metric_stats.csv', index = False) +print() \ No newline at end of file diff --git a/metric_analysis/output_original/hard_threshold_viral_covered_vs_new_tweets_labeled.csv b/metric_analysis/output_original/hard_threshold_viral_covered_vs_new_tweets_labeled.csv new file mode 100644 index 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+0.988095238095238,528701,0.84,0.3895447190892024,0.3895447190892024 +0.9910714285714286,574204,0.83,0.42307114206308744,0.42307114206308744 +0.9930555555555556,610579,0.82,0.4498720922350556,0.4498720922350556 +0.9940476190476192,646190,0.81,0.4761101303539273,0.4761101303539273 +0.9950396825396826,822465,0.75,0.6059888242800767,0.6059888242800767 +0.996031746031746,937666,0.71,0.6908684465690363,0.6908684465690363 +0.9970238095238096,1029756,0.66,0.758719979251828,0.758719979251828 +0.9990079365079364,1072643,0.64,0.7903189442009744,0.7903189442009744 +1.0,1174373,0.57,0.8652731891767632,0.8652731891767632 diff --git a/metric_analysis/twitter_viral_model.ipynb b/metric_analysis/twitter_viral_model.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..22387c14431fee68ddb2bda6dd8360d2e7fc08ba --- /dev/null +++ b/metric_analysis/twitter_viral_model.ipynb @@ -0,0 +1,2303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparison of Twitter's model of viral tweets with other tweets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we try to identify common features of Twitter's identified viral tweets found on the topic page \"Viral Tweets\".\n", + "\n", + "We also experiment to find if other tweets that have not figured on that topic page, can also be labeled as viral based on these common features. This should help homogeinize the data (those that are viral and those that are not) when training the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "from helper.text_preprocessing import clear_reply_mentions\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "#pd.set_option('display.max_rows', None)\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "DATA_PATH = \"../../data\"\n", + "VIRAL_TWEETS_PATH = f\"{DATA_PATH}/new/viral\"\n", + "COVID_TWEETS_PATH = f\"{DATA_PATH}/new/covid\"\n", + "\n", + "PROCESSED_PATH_VIRAL = f'{DATA_PATH}/new/processed/viral'\n", + "PROCESSED_PATH_COVID = f'{DATA_PATH}/new/processed/covid'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset = pd.read_parquet(f\"{VIRAL_TWEETS_PATH}/all_tweets.parquet.gzip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_dataset = pd.read_parquet(f\"{COVID_TWEETS_PATH}/all_tweets.parquet.gzip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_users = pd.read_parquet(f\"{COVID_TWEETS_PATH}/users.parquet.gzip\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Keep only original tweets from **covid dataset**. Viral dataset doesn't have retweets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def is_retweeted(referenced_tweets):\n", + " for x in referenced_tweets:\n", + " if x['type'] == 'retweeted':\n", + " return True\n", + " return False\n", + "\n", + "# Keep only original tweets\n", + "referenced = covid_dataset.loc[~covid_dataset.referenced_tweets.isna()].copy()\n", + "referenced.loc[:, 'is_retweet'] = referenced.referenced_tweets.apply(is_retweeted)\n", + "retweeted = referenced[referenced.is_retweet]\n", + "retweeted" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "original_covid_tweets = covid_dataset[~covid_dataset.id.isin(retweeted.id)]\n", + "original_covid_tweets.to_parquet(f\"{COVID_TWEETS_PATH}/all_original_tweets.parquet.gzip\", index=False, compression=\"gzip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Clear reply mentions at the beginning of tweets texts\n", + "original_covid_tweets.loc[:, \"text\"] = original_covid_tweets.text.apply(clear_reply_mentions)\n", + "viral_dataset.loc[:, \"text\"] = viral_dataset.text.apply(clear_reply_mentions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Exploration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 - General Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset = pd.read_parquet(f\"{VIRAL_TWEETS_PATH}/all_tweets.parquet.gzip\")\n", + "viral_users = pd.read_parquet(f\"{VIRAL_TWEETS_PATH}/users.parquet.gzip\")\n", + "viral_tweets_ids = pd.read_parquet(f\"{VIRAL_TWEETS_PATH}/viral_tweets_ids.parquet.gzip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "original_covid_tweets = pd.read_parquet(f\"{COVID_TWEETS_PATH}/all_original_tweets.parquet.gzip\")\n", + "covid_users = pd.read_parquet(f\"{COVID_TWEETS_PATH}/users.parquet.gzip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(\"--- VIRAL DATASET ---\")\n", + "\n", + "display(f\"{len(viral_tweets_ids)} viral tweets collected\")\n", + "display(f\"{len(viral_users)} viral users\")\n", + "display(f\"{len(viral_dataset)} all tweets collected\")\n", + "\n", + "display(\"--- COVID DATASET ---\")\n", + "\n", + "display(f\"{len(original_covid_tweets)} original (not retweeted) covid tweets collected\")\n", + "display(f\"{len(original_covid_tweets.author_id.unique())} covid users collected\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# REMOVE THIS WHEN DONE COLLECTION (WARNING NOT NECESSARILY)\n", + "viral_dataset['viral'] = viral_dataset.id.isin(viral_tweets_ids.id)\n", + "\n", + "#viral_tweets = all_tweets[all_tweets.id.isin(viral_tweets.id)]\n", + "#viral_tweets\n", + "\n", + "len(viral_dataset[viral_dataset.viral])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- merge tweets with user info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_users.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "user_columns = ['author_id', 'followers_count', 'following_count', 'tweet_count', 'protected', 'verified', 'username']\n", + "viral_dataset_with_users = viral_dataset.merge(viral_users.rename(columns={'id': 'author_id'})[user_columns], on='author_id')\n", + "covid_dataset_with_users = original_covid_tweets.merge(covid_users.rename(columns={'id': 'author_id'})[user_columns], on='author_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.1.1 - Correlation between public metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Pearson Correlation between the different public metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "public_metrics = ['retweet_count', 'like_count', 'reply_count', 'quote_count', 'followers_count', 'following_count']\n", + "display(viral_dataset_with_users[public_metrics].corr())\n", + "display(covid_dataset_with_users[public_metrics].corr())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "px.scatter(viral_dataset, x='like_count', y='retweet_count')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.1.2 - Exploring retweet count of viral vs non viral tweets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we have a large number of tweets to plot, we'll only sample a few from each user" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_largest_n(all_tweets, by='retweet_count', n=100):\n", + " '''Get the largest 100 tweets by retweet count for every user\n", + " '''\n", + " top_n_per_user = all_tweets.groupby(by='author_id')[by].nlargest(n=100).reset_index(level=0, drop=True)\n", + " tweets_for_plot = all_tweets[all_tweets.index.isin(top_n_per_user.index)].reset_index()\n", + " return tweets_for_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tweets_plot_df = get_largest_n(viral_dataset, by='retweet_count')\n", + "fig = px.scatter(tweets_plot_df, x=tweets_plot_df.index, y='retweet_count', color='viral')\n", + "\n", + "fig.update_layout(title_text=\"Viral Dataset Scatter plot of the retweet count for the top 100 tweets per user\", xaxis_title=\"Index\", yaxis_title=\"retweet count\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_tweets_plot_df = original_covid_tweets.sort_values(by='retweet_count', ascending=False)[:10000]\n", + "fig = px.scatter(covid_tweets_plot_df, x=covid_tweets_plot_df.reset_index().index, y='retweet_count')\n", + "\n", + "fig.update_layout(title_text=\"Covid Dataset Scatter plot of retweet count sorted by retweet count on a 10000 sample\", xaxis_title=\"Index\", yaxis_title=\"retweet count\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finding**: Viral tweets identified by twitter are by no means more viral than other tweets tweeted by the same users. Are users who have tweeted viral tweets (as identified by Twitter) likely to have tweeted other viral tweets?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the ratio for each tweet's retweet count wrt to the mean retweet count of the user's tweets\n", + "# Again since we're retrieved 3200 tweets per user, we're only taking the average over that\n", + "users_avg_retweets = viral_dataset.groupby(by='author_id').agg(mean_retweets=('retweet_count', 'mean'))\n", + "tweets_merged_avg_retweets = viral_dataset.merge(right=users_avg_retweets, left_on='author_id', right_index=True)\n", + "tweets_merged_avg_retweets['ratio_avg_retweets'] = tweets_merged_avg_retweets['retweet_count'] / tweets_merged_avg_retweets['mean_retweets']\n", + "tweets_merged_avg_retweets_sorted = tweets_merged_avg_retweets.sort_values(by='ratio_avg_retweets').reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tweets_plot_df = get_largest_n(tweets_merged_avg_retweets_sorted, by='ratio_avg_retweets')\n", + "\n", + "fig = px.scatter(tweets_plot_df, x=tweets_plot_df.index, y='ratio_avg_retweets', color='viral')\n", + "\n", + "fig.update_layout(title_text=\"Scatter plot of the tweets sorted by the ratio #retweets/(the mean user avg #retweets)\", xaxis_title=\"Index\", yaxis_title=\"ratio\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finding**: Cleaner separation. Viral tweets, as expected, are on the other end of the spectrum. However other tweets in the same range could qualify as viral as well. These tweets should be identified as viral by the Twitter model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Finding the right threshold for virality" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2.0 - Relabel viral tweets in the viral dataset by correcting the initial virality threshold (ONLY IN OLD PAPER SUBMITTED BY STUDENT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's observe the retweet count of a user based on the tweet date." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sample_user = viral_users.id[10]\n", + "author_tweets = viral_dataset[viral_dataset.author_id == sample_user]\n", + "fig = px.scatter(author_tweets, x='created_at', y='retweet_count', color='viral')\n", + "\n", + "fig.update_layout(title_text=\"Scatter plot of the retweet count wrt to the tweet date for a single user\")\n", + "\n", + "fig.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finding**: The above graph of a user's retweet count wrt the tweet date, shows that the viral tweets taken from the Twitter \"Viral Tweets\" topic page, have been taken at certain points in time. **Other tweets with higher retweet counts** may have been on that Topic page at different points in time as well. In any case, they **should be qualified as viral all the same**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One quick fix for that is, for each user, mark as viral all tweets that have higher retweet count than the viral tweet we scraped for that user. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the minimum retweet count out of the viral tweets for each user\n", + "min_retweet_count_by_user = viral_dataset[viral_dataset.viral].groupby(by='author_id')[['retweet_count']].min()\n", + "\n", + "# Set as viral any tweet that has a retweet count higher or equal to the user's minimum retweet count we just computed\n", + "viral_dataset_labeled = viral_dataset.merge(min_retweet_count_by_user, left_on='author_id', right_index=True, suffixes=(None, \"_user_viral_threshold\"))\n", + "viral_dataset_labeled['viral'] = viral_dataset_labeled['retweet_count'] >= viral_dataset_labeled['retweet_count_user_viral_threshold']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save this result \n", + "#viral_dataset_labeled.to_parquet(f'{PROCESSED_PATH_VIRAL}/all_tweets.parquet.gzip', compression='gzip')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(f\"Number of identified viral tweets increased from {len(viral_tweets_ids)} to {len(viral_dataset_labeled[viral_dataset_labeled.viral])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another problem we're facing is that we're **missing historical data** on the number of followers of a user. So we cannot use the metric of:\n", + "$ \\frac{\\#retweets}{\\#followers}$ effectively. That's why we came up with the other metric: $\\frac{\\#retweets}{mean(\\#retweets)}$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2.1 Applying the virality followers metric to both datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Applying the first metric on the covid dataset\n", + "covid_dataset_with_users['virality_followers'] = covid_dataset_with_users['retweet_count'] / covid_dataset_with_users['followers_count'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "covid_dataset_with_users['virality_followers'] = covid_dataset_with_users.virality_followers.replace({np.inf: 0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(covid_dataset_with_users[(covid_dataset_with_users['virality_followers'] > 0.8)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Applying the second metric on the viral dataset\n", + "viral_dataset_with_users['virality_followers'] = viral_dataset_with_users['retweet_count'] / viral_dataset_with_users['followers_count'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "viral_dataset_with_users['virality_followers'] = viral_dataset_with_users.virality_followers.replace({np.inf: 0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(viral_dataset_with_users[(viral_dataset_with_users['virality_followers'] > 1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2.2 Applying the virality avg retweets metric to viral dataset " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_users_retweet_statistics = viral_dataset_with_users.groupby(by='author_id').retweet_count.agg(['min', 'mean', 'max'])\n", + "viral_users_retweet_statistics = viral_users_retweet_statistics.rename(columns={\"min\": \"min_user_retweets\", \"max\": \"max_user_retweets\", \"mean\": \"mean_user_retweets\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_with_users = viral_dataset_with_users.merge(viral_users_retweet_statistics, on='author_id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Applying the first metric on the viral dataset\n", + "viral_dataset_with_users['virality_avg_retweets'] = viral_dataset_with_users['retweet_count'] / viral_dataset_with_users['mean_user_retweets'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "viral_dataset_with_users['virality_avg_retweets'] = viral_dataset_with_users.virality_avg_retweets.replace({np.inf: 0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(viral_dataset_with_users[(viral_dataset_with_users['virality_avg_retweets'] > 1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2.3 How many tweets are covered by metric 1?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp = viral_dataset_with_users[viral_dataset_with_users.virality_followers > 0]\n", + "temp_2 = viral_dataset_with_users[viral_dataset_with_users.virality_avg_retweets > 0]\n", + "viral_temp = viral_dataset_with_users[viral_dataset_with_users.viral]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = px.ecdf(viral_dataset_with_users[viral_dataset_with_users.viral], x='virality_followers')\n", + "\n", + "# TODO: percentage y axis\n", + "# TODO: Only take the scraped tweets\n", + "fig.update_layout(title_text=\"Percentage of viral tweets recognized by Metric 1: number of followers\", xaxis_title=\"Metric 1: virality_followers\", yaxis_title=\"Percentage\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig1 = sns.displot(temp, x='virality_followers', kind='ecdf')\n", + "\n", + "plt.xscale('log')\n", + "plt.title(\"Proportion of tweets labeled as viral as function of Metric 1: number of followers (logscale)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_2 = viral_dataset_with_users[viral_dataset_with_users.virality_avg_retweets > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = px.ecdf(viral_temp, x='virality_avg_retweets')\n", + "\n", + "fig.update_layout(title_text=\"Percentage of viral tweets recognized by Metric 2 avg retweets\", xaxis_title=\"Metric 2: avg retweets\", yaxis_title=\"Percentage\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = sns.displot(temp_2, x='virality_avg_retweets', kind='ecdf')\n", + "\n", + "plt.xscale('log')\n", + "plt.title(\"Proportion of tweets labeled as viral as function of Metric 2: avg retweets (logscale)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO: Plot the percentage of viral tweets labeled vs the # of new tweets labeled wrt to the varying threshold of the metric we use. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#viral_dataset_with_users = viral_dataset_with_users.groupby(by='virality_followers').count()\n", + "viral_dataset_with_users = pd.read_parquet(f\"{PROCESSED_PATH_VIRAL}/all_tweets.parquet.gzip\")\n", + "# Applying the second metric on the viral dataset\n", + "viral_dataset_with_users['virality_followers'] = viral_dataset_with_users['retweet_count'] / viral_dataset_with_users['followers_count'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "viral_dataset_with_users['virality_followers'] = viral_dataset_with_users.virality_followers.replace({np.inf: 0.0})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_with_users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_with_users_truncated = viral_dataset_with_users[viral_dataset_with_users.virality_followers > 0.1]\n", + "#viral_dataset_with_users['viral_metric_1'] = viral_dataset_with_users['']\n", + "len(viral_dataset_with_users_truncated)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ready_to_plot = viral_dataset_with_users_truncated.copy()\n", + "ready_to_plot['viral'] = ready_to_plot['viral'].replace({False: None})\n", + "ready_to_plot = ready_to_plot.groupby(by='virality_followers').count()[['text', 'viral']].cumsum().rename(columns={'text':'tweets'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = px.line(ready_to_plot, x='viral', y='tweets', hover_data=[ready_to_plot.index])#, log_y=True)\n", + "\n", + "fig.update_layout(title_text=\"Line plot of #viral tweets labeled as viral vs # new tweets labeled as viral by varying threshold of Metric 1 (#followers)\", xaxis_title=\"Number of viral tweets labeled as viral\", yaxis_title=\"Number of new tweets labeled as viral\")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ready_to_plot = viral_dataset_with_users_truncated.copy()\n", + "ready_to_plot['viral'] = ready_to_plot['viral'].replace({False: None})\n", + "ready_to_plot = ready_to_plot.groupby(by='virality_followers').count()[['text', 'viral']].cumsum().rename(columns={'text':'tweets'})\n", + "ready_to_plot['tweets'] = len(viral_dataset_with_users) - ready_to_plot.tweets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = px.line(ready_to_plot, x='viral', y='tweets', hover_data=[ready_to_plot.index])#, log_y=True)\n", + "\n", + "fig.update_layout(title_text=\"Line plot of #viral tweets labeled as viral vs # new tweets labeled as viral by varying threshold of Metric 1 (#followers)\", xaxis_title=\"Number of viral tweets labeled as viral\", yaxis_title=\"Number of new tweets labeled as viral\")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "tempo3 = tempo2.copy()\n", + "tempo3['viral'] = tempo3['viral'].replace({False: None})\n", + "tempo3 = tempo3.groupby(by='virality_followers').count()[['text', 'viral']].rename(columns={'text':'tweets'})\n", + "tempo3['viral_cumsum'] = tempo3.viral.cumsum()\n", + "tempo3\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "min_threshold = viral_dataset_with_users.virality_followers.min()\n", + "max_threshold = viral_dataset_with_users.virality_followers.max()\n", + "display(f\"sampling from {min_threshold} to {max_threshold}\")\n", + "thresholds_space = np.linspace(min_threshold, max_threshold, num=10000)\n", + "\n", + "number_of_viral_tweets = len(viral_dataset_with_users[viral_dataset_with_users.viral]) \n", + "\n", + "percentages_of_viral_covered = []\n", + "nb_of_tweets_labeled_as_viral = []\n", + "\n", + "for i in thresholds_space:\n", + " new_tweets_labeled = viral_dataset_with_users[viral_dataset_with_users.virality_followers >= i]\n", + " percentage_of_viral_covered = len(new_tweets_labeled[new_tweets_labeled.viral]) / number_of_viral_tweets\n", + " nb_of_tweets_labeled_as_viral.append(len(new_tweets_labeled))\n", + " percentages_of_viral_covered.append(percentage_of_viral_covered)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result_to_plot = pd.DataFrame({'percentage_of_viral_covered':percentages_of_viral_covered, 'nb_of_tweets_labeled_as_viral':nb_of_tweets_labeled_as_viral, 'thresholds': thresholds_space})\n", + "\n", + "px.scatter(\n", + " result_to_plot,\n", + " x='percentage_of_viral_covered',\n", + " y='nb_of_tweets_labeled_as_viral', log_y=True, hover_name='thresholds')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result_to_plot.to_csv('new_tweets_labeled_vs_percentage_of_viral.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2.4 Comparing several metrics wrt distributions of viral tweets covered" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_distribution_for_metric(\n", + " df, metric='virality_followers', num_experiments=1000, generate_thresholds_from_viral_quantiles=True, min_threshold=None, max_threshold=None, remove_duplicates=True, output_filename=None):\n", + " viral_tweets = df[df.viral]\n", + " number_of_viral_tweets = len(viral_tweets)\n", + " \n", + " if not generate_thresholds_from_viral_quantiles: \n", + " # If not, generate a linear space of the thresholds between min and max of the metric values\n", + " if not min_threshold:\n", + " min_threshold = df[metric].min()\n", + " if not max_threshold:\n", + " max_threshold = df[metric].max()\n", + " display(f\"sampling from {min_threshold} to {max_threshold}\")\n", + " thresholds_space = np.linspace(min_threshold, max_threshold, num=num_experiments)\n", + " else:\n", + " # Take quantiles of metric for different percentages of viral tweets covered (from 0 to 100)\n", + " thresholds_space = viral_tweets[metric].quantile([i / 100 for i in range(101)]) \n", + " display(f\"sampling from {thresholds_space.min()} to {thresholds_space.max()}\")\n", + "\n", + " percentages_of_viral_covered = []\n", + " nb_of_tweets_labeled_as_viral = []\n", + "\n", + " for i in thresholds_space:\n", + " new_tweets_labeled = df[df[metric] >= i]\n", + " percentage_of_viral_covered = len(new_tweets_labeled[new_tweets_labeled.viral]) / number_of_viral_tweets\n", + " nb_of_tweets_labeled_as_viral.append(len(new_tweets_labeled))\n", + " percentages_of_viral_covered.append(percentage_of_viral_covered)\n", + " \n", + " results_to_plot = pd.DataFrame({\n", + " f'percentage_of_viral_covered_{metric}':percentages_of_viral_covered,\n", + " f'nb_of_tweets_labeled_as_viral_{metric}':nb_of_tweets_labeled_as_viral,\n", + " f'thresholds_{metric}': thresholds_space})\n", + "\n", + " #if remove_duplicates:\n", + " # results_to_plot = results_to_plot.sort_values(by='nb_of_tweets_labeled_as_viral').drop_duplicates(subset=['percentage_of_viral_covered'], keep='first')\n", + "\n", + " # Discard rows where 100% of viral tweets are covered\n", + " #results_to_plot = results_to_plot[results_to_plot.percentage_of_viral_covered < 1.0]\n", + " # TODO: take min of 100% coverage\n", + "\n", + " fig = px.scatter(\n", + " results_to_plot,\n", + " x=f'percentage_of_viral_covered_{metric}',\n", + " y=f'nb_of_tweets_labeled_as_viral_{metric}', hover_name=f'thresholds_{metric}')#log_y=True, trendline='ols' \n", + "\n", + " fig.update_layout(title_text=f\"Percentage of viral covered vs new tweets labeled as viral according to varying metric {metric}\")\n", + " fig.show()\n", + "\n", + " display(f\"Result length {len(results_to_plot)}\")\n", + " if not output_filename:\n", + " output_filename = metric\n", + " results_to_plot.to_csv(f'{output_filename}_viral_covered_vs_new_tweets_labeled.csv', index=False) \n", + " \n", + " return results_to_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#viral_dataset_with_users = viral_dataset_with_users.groupby(by='virality_followers').count()\n", + "METRIC_1 = 'virality_followers'\n", + "viral_dataset_with_users = pd.read_parquet(f\"{PROCESSED_PATH_VIRAL}/all_tweets.parquet.gzip\")\n", + "# Applying the second metric on the viral dataset\n", + "viral_dataset_with_users[METRIC_1] = viral_dataset_with_users['retweet_count'] / viral_dataset_with_users['followers_count'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "viral_dataset_with_users[METRIC_1] = viral_dataset_with_users[METRIC_1].replace({np.inf: 0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_1 = plot_distribution_for_metric(viral_dataset_with_users, metric='virality_followers', num_experiments=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Metric 2: retweet / user avg retweets\n", + "METRIC_2 = 'virality_avg_retweets'\n", + "viral_users_retweet_statistics = viral_dataset_with_users.groupby(by='author_id').retweet_count.agg(['min', 'mean', 'max', 'median'])\n", + "viral_users_retweet_statistics = viral_users_retweet_statistics.rename(columns={\n", + " \"min\": \"min_user_retweets\", \"max\": \"max_user_retweets\", \"mean\": \"mean_user_retweets\", \"median\": \"median_user_retweets\"})\n", + "\n", + "viral_dataset_with_users = viral_dataset_with_users.merge(viral_users_retweet_statistics, on='author_id')\n", + "\n", + "viral_dataset_with_users[METRIC_2] = viral_dataset_with_users['retweet_count'] / viral_dataset_with_users['mean_user_retweets'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "viral_dataset_with_users[METRIC_2] = viral_dataset_with_users[METRIC_2].replace({np.inf: 0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_2 = plot_distribution_for_metric(viral_dataset_with_users, metric='virality_avg_retweets', num_experiments=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Metric 3: Minimum retweet count (Hard threshold)\n", + "METRIC_3 = 'retweet_count'\n", + "\n", + "viral_tweets = viral_dataset_with_users[viral_dataset_with_users.viral]\n", + "min_viral_retweet_count = viral_tweets.retweet_count.min()\n", + "max_viral_retweet_count = viral_tweets.retweet_count.max()\n", + "\n", + "df_3 = plot_distribution_for_metric(\n", + " viral_dataset_with_users, metric=METRIC_3, num_experiments=10000,\n", + " min_threshold=min_viral_retweet_count, max_threshold=max_viral_retweet_count, generate_thresholds_from_viral_quantiles=False,\n", + " output_filename='hard_threshold')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Metric 4 from Maldonado paper 'Virality Prediction for News Tweets Using RoBERTa'\n", + "def roberta_paper_metric(x):\n", + " g = x['retweet_count'] + x['like_count']\n", + " h = x['followers_count'] - x['following_count']\n", + " A = 10\n", + "\n", + " r = max(x['retweet_count'], 1)\n", + " f = max(x['like_count'], 1)\n", + " w = max(x['followers_count'], 1)\n", + " d = max(x['following_count'], 1)\n", + " h = max(h, 1)\n", + "\n", + " num = g * d * (A * r + f)\n", + " denom = w * r * (A * d + h)\n", + " #if denom == 0:\n", + " # return 0\n", + " return num / denom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "METRIC_4 = 'roberta_paper_metric'\n", + "viral_dataset_with_users[METRIC_4] = viral_dataset_with_users.apply(lambda x: roberta_paper_metric(x), axis='columns')\n", + "\n", + "df_4 = plot_distribution_for_metric(\n", + " viral_dataset_with_users, metric=METRIC_4, num_experiments=100000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "METRIC_5 = 'virality_retweet_percentile_per_user'\n", + "\n", + "# Take only tweets with positive retweet count, otherwise the quantiles will be very heavy-tailed\n", + "#tweets_with_retweets = viral_dataset_with_users[viral_dataset_with_users.retweet_count > 0]\n", + "\n", + "viral_tweets = viral_dataset_with_users[viral_dataset_with_users.viral]\n", + "percentiles = [i/100 for i in range(101)]\n", + "number_of_viral_tweets = len(viral_tweets)\n", + "\n", + "percentages_of_viral_covered = []\n", + "nb_of_tweets_labeled_as_viral = []\n", + "\n", + "for i in tqdm(percentiles):\n", + " temp = viral_dataset_with_users.groupby(by='author_id')[['retweet_count']].quantile(i).rename(columns={'retweet_count': f'percentile_{i}'})\n", + " temp = viral_dataset_with_users.merge(temp, on='author_id')\n", + "\n", + " new_tweets_labeled = temp[temp['retweet_count'] >= temp[f'percentile_{i}']]\n", + " percentage_of_viral_covered = len(new_tweets_labeled[new_tweets_labeled.viral]) / number_of_viral_tweets\n", + " nb_of_tweets_labeled_as_viral.append(len(new_tweets_labeled))\n", + " percentages_of_viral_covered.append(percentage_of_viral_covered)\n", + "\n", + "df_5 = pd.DataFrame({\n", + " f'percentage_of_viral_covered_{METRIC_5}':percentages_of_viral_covered,\n", + " f'nb_of_tweets_labeled_as_viral_{METRIC_5}':nb_of_tweets_labeled_as_viral,\n", + " f'thresholds_{METRIC_5}': percentiles})\n", + "\n", + "fig = px.scatter(\n", + " df_5,\n", + " x=f'percentage_of_viral_covered_{METRIC_5}',\n", + " y=f'nb_of_tweets_labeled_as_viral_{METRIC_5}', hover_name=f'thresholds_{METRIC_5}')#log_y=True, trendline='ols' \n", + "\n", + "fig.update_layout(title_text=f\"Percentage of viral covered vs new tweets labeled as viral according to varying metric {METRIC_5}\")\n", + "fig.show()\n", + "\n", + "display(f\"Result length {len(df_5)}\")\n", + "df_5.to_csv(f'{METRIC_5}_viral_covered_vs_new_tweets_labeled.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Metric 6: Median\n", + "METRIC_6 = 'virality_median_retweets'\n", + "\n", + "positive_median_dataset = viral_dataset_with_users[viral_dataset_with_users['median_user_retweets'] > 0].copy()\n", + "positive_median_dataset.loc[:, METRIC_6] = positive_median_dataset['retweet_count'] / positive_median_dataset['median_user_retweets'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "positive_median_dataset.loc[:, METRIC_6] = positive_median_dataset[METRIC_6].replace({np.inf: 0.0, np.nan:0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_6 = plot_distribution_for_metric(\n", + " positive_median_dataset, metric=METRIC_6, num_experiments=10000, remove_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# log(retweet_counts) / followers_count\n", + "METRIC_7 = 'log_retweets_over_followers'\n", + "\n", + "positive_retweet_and_follower_count = viral_dataset_with_users[(viral_dataset_with_users.retweet_count > 0) & (viral_dataset_with_users.followers_count > 0)].copy()\n", + "\n", + "positive_retweet_and_follower_count.loc[:, METRIC_7] = (np.log(positive_retweet_and_follower_count['retweet_count']) / positive_retweet_and_follower_count['followers_count']).astype(\"float64\")\n", + "positive_retweet_and_follower_count.loc[:, METRIC_7] = positive_retweet_and_follower_count[METRIC_7].replace({np.inf: 0.0, np.nan:0.0})\n", + "\n", + "df_7 = plot_distribution_for_metric(\n", + " positive_retweet_and_follower_count, metric=METRIC_7, num_experiments=10000, remove_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "METRIC_8 = 'retweets_over_log_followers'\n", + "\n", + "positive_retweet_and_follower_count.loc[:, METRIC_8] = (positive_retweet_and_follower_count['retweet_count'] / np.log(positive_retweet_and_follower_count['followers_count'])).astype(\"float64\")\n", + "positive_retweet_and_follower_count.loc[:, METRIC_8] = positive_retweet_and_follower_count[METRIC_8].replace({np.inf: 0.0, np.nan:0.0})\n", + "\n", + "df_8 = plot_distribution_for_metric(\n", + " positive_retweet_and_follower_count, metric=METRIC_8, num_experiments=10000, remove_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "METRIC_9 = 'log_retweets_over_log_followers'\n", + "\n", + "positive_retweet_and_follower_count.loc[:, METRIC_9] = (np.log(positive_retweet_and_follower_count['retweet_count']) / np.log(positive_retweet_and_follower_count['followers_count'])).astype(\"float64\")\n", + "positive_retweet_and_follower_count.loc[:, METRIC_9] = positive_retweet_and_follower_count[METRIC_9].replace({np.inf: 0.0, np.nan:0.0})\n", + "\n", + "df_9 = plot_distribution_for_metric(\n", + " positive_retweet_and_follower_count, metric=METRIC_9, num_experiments=10000, remove_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_result = pd.concat([df_1, df_2, df_3, df_4, df_5, df_6, df_7, df_8, df_9], axis=1)\n", + "final_result.to_csv('final_result_viral_coverage.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Viral Dataset Exploration: Comparison between viral and non viral tweets using other features " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Only take viral tweets from scraped. Since sentiment is already computed on the other dataset, we relabel dataset viral by checking if in scraped ids \n", + "# (DONE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_labeled = pd.read_parquet(f'{PROCESSED_PATH_VIRAL}/all_tweets.parquet.gzip')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(f\"{len(viral_dataset_labeled[viral_dataset_labeled.viral])} viral tweets out of {len(viral_dataset_labeled)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.1 - Language" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "languages_aggregates = viral_dataset_labeled.groupby(by='lang', as_index=False)[['id']].count().rename(columns={'id': 'count'})\n", + "languages_aggregates = languages_aggregates.sort_values(by='count', ascending=False)\n", + "languages_aggregates.loc[languages_aggregates['count'] < 10000, 'lang'] = 'Other Languages'\n", + "fig = px.pie(languages_aggregates, values='count', names='lang', title='Distribution of Tweets languages')\n", + "\n", + "fig.update_layout(\n", + " autosize=False,\n", + " width=500,\n", + " height=500\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.crosstab(index = viral_dataset_labeled['lang'] == 'en', columns=viral_dataset_labeled['viral']) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.2 - Media" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Has media\n", + "labels = [\"Media\", \"No Media\"]\n", + "viral_has_media = len(viral_dataset_labeled[(viral_dataset_labeled.viral == True) & (viral_dataset_labeled.has_media == True)])\n", + "viral_no_media = len(viral_dataset_labeled[(viral_dataset_labeled.viral == True) & (viral_dataset_labeled.has_media == False)])\n", + "normal_has_media = len(viral_dataset_labeled[(viral_dataset_labeled.viral == False) & (viral_dataset_labeled.has_media == True)])\n", + "normal_no_media = len(viral_dataset_labeled[(viral_dataset_labeled.viral == False) & (viral_dataset_labeled.has_media == False)])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_has_media, viral_no_media], name=\"Viral with Media\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_has_media, normal_no_media], name=\"Tweet with Media\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Percentage of tweets with some kind of media\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating the p-value between the target `viral` and `has_media`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import chi2_contingency \n", + "\n", + "# Calculating the p-value\n", + "contingency_media = pd.crosstab(index = viral_dataset_labeled['has_media'], columns=viral_dataset_labeled['viral']) \n", + "display(contingency_media)\n", + "# Display with percentages\n", + "display(pd.crosstab(index = viral_dataset_labeled['has_media'], columns=viral_dataset_labeled['viral'], normalize='columns') )\n", + "\n", + "c, p, dof, expected = chi2_contingency(contingency_media) \n", + "display(f'p-value {p}')\n", + "c, p, dof, expected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finding**: Viral tweets have more chance of having some kind of media (Video, Image, GIF..) embedded than non viral tweets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.2 - Context annotations (Topics)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_tweets_topic_domains = viral_dataset_labeled[viral_dataset_labeled.viral == True] \\\n", + " .explode('topic_domains') \\\n", + " .dropna(axis=0, subset=['topic_domains']) \\\n", + " .topic_domains \n", + "\n", + "tweets_topic_domains = viral_dataset_labeled[viral_dataset_labeled.viral == False] \\\n", + " .explode('topic_domains') \\\n", + " .dropna(axis=0, subset=['topic_domains']) \\\n", + " .topic_domains\n", + "\n", + "viral_topics_domains_sorted = viral_tweets_topic_domains.groupby(viral_tweets_topic_domains).count().sort_values(ascending=False)\n", + "tweet_topics_domains_sorted = tweets_topic_domains.groupby(tweets_topic_domains).count().sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open(f'{DATA_PATH}/topic_domains.pickle', 'rb') as handle:\n", + " topic_domains = pickle.load(handle)\n", + "\n", + "top_10_viral_topic_domains = viral_topics_domains_sorted[:10]\n", + "top_10_tweet_topic_domains = tweet_topics_domains_sorted[:10]\n", + "\n", + "display(f\"Top 10 topic domains in viral tweets: \\n {[topic_domains.get(x)['name'] for x in top_10_viral_topic_domains.index.values]}\")\n", + "display(f\"Top 10 topic domains in general tweets: \\n {[topic_domains.get(x)['name'] for x in top_10_tweet_topic_domains.index.values]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_labels = [topic_domains.get(x)['name'] for x in top_10_viral_topic_domains.index.values]\n", + "non_viral_labels = [topic_domains.get(x)['name'] for x in top_10_tweet_topic_domains.index.values]\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig2 = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig2.add_trace(go.Pie(labels=viral_labels, values=top_10_viral_topic_domains.values, name=\"Viral Tweet Topic domain\"),\n", + " 1, 1)\n", + "fig2.add_trace(go.Pie(labels=non_viral_labels, values=top_10_tweet_topic_domains.values, name=\"Non-Viral Tweet Topic domain\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig2.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig2.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Top 10 topic domains for viral vs non-viral tweets\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig2.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.3 - Tweet Length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_labeled.loc[:, 'tweet_length'] = viral_dataset_labeled.text.apply(len)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(viral_dataset_labeled[['tweet_length', 'retweet_count']].corr())\n", + "\n", + "avg_tweet_length_viral = viral_dataset_labeled[viral_dataset_labeled.viral].tweet_length.mean()\n", + "avg_tweet_length_non_viral = viral_dataset_labeled[~viral_dataset_labeled.viral].tweet_length.mean()\n", + "\n", + "display(f'viral avg tweet length: {avg_tweet_length_viral} \\n non-viral avg tweet length: {avg_tweet_length_non_viral}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some tweets are replies to others so **mentions are automatically inserted at the beginning of the tweet**, but they do not count in the Twitter max character count, so we should discard them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_labeled.loc[:, \"text\"] = viral_dataset_labeled.text.apply(clear_reply_mentions)\n", + "viral_dataset_labeled.loc[:, 'tweet_length'] = viral_dataset_labeled.text.apply(len)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(viral_dataset_labeled[['tweet_length', 'retweet_count']].corr())\n", + "\n", + "avg_tweet_length_viral = viral_dataset_labeled[viral_dataset_labeled.viral].tweet_length.mean()\n", + "avg_tweet_length_non_viral = viral_dataset_labeled[~viral_dataset_labeled.viral].tweet_length.mean()\n", + "\n", + "display(f'viral avg tweet length: {avg_tweet_length_viral} \\n non-viral avg tweet length: {avg_tweet_length_non_viral}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating the welch’s t-test (scipy t-test) for continuous variable `tweet_length`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import ttest_ind\n", + "\n", + "ttest_ind(viral_dataset_labeled[viral_dataset_labeled.viral].tweet_length, viral_dataset_labeled[~viral_dataset_labeled.viral].tweet_length, equal_var=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.4 - Sentiment " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the sentiment analysis, we used huggingface's [default sentiment analysis model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english?text=I+like+you.+I+love+you). We instantiate a huggingface pipeline using that default model, and we pass the tweets text to it, outputting a **label** (e.g. POSITIVE, NEGATIVE) alongside a **confidence score**. This will only be applied to english tweets.\n", + "\n", + "**NOTE**: Feel free to skip the following cells if you already have the processed data. Sentiment analysis takes some time (around 2 hours on the whole data). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "\n", + "# Device = 0 means it will use the Cuda at index 0\n", + "sentiment_classifier = pipeline(\"sentiment-analysis\", device=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will only be applied to **english tweets**. All the viral tweets we scraped are in English, so we won't be losing viral data when filtering." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "english_viral_dataset = viral_dataset_labeled[viral_dataset_labeled.lang == 'en']\n", + "english_viral_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the pandas `apply` function, with `result_type` to *expand*, so that the sentiment scores and label will be output into different columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "applied = english_viral_dataset.apply(lambda x: sentiment_classifier(x.text)[0], axis=1, result_type='expand')\n", + "#pd.concat([small_test_set, applied], axis='columns')\n", + "applied" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_features = pd.concat([english_viral_dataset, applied], axis=1)\n", + "sentiment_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_features = sentiment_features.rename(columns={\"label\": \"sentiment\", \"score\": \"sentiment_score\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_features.to_parquet(f\"{PROCESSED_PATH_VIRAL}/all_english_tweets_with_users_with_sentiment.parquet.gzip\", index=False, compression=\"gzip\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the processed data already" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_features = pd.read_parquet(f\"{PROCESSED_PATH_VIRAL}/all_english_tweets_with_users_with_sentiment.parquet.gzip\")\n", + "display(f\"{len(sentiment_features[sentiment_features.viral])} viral tweets out of {len(sentiment_features)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Tweets with sentiment scores over 70%\n", + "display(f\"Tweets with sentiment analysis confidence scores above 0.7: {len(sentiment_features[sentiment_features.sentiment_score > 0.7])}\")\n", + "display(f\"{len(sentiment_features[sentiment_features.sentiment == 'POSITIVE'])} positive tweets\")\n", + "display(f\"{len(sentiment_features[sentiment_features.sentiment == 'NEGATIVE'])} negative tweets\")\n", + "\n", + "confident_sentiment_tweets = sentiment_features[sentiment_features.sentiment_score > 0.7]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We keep only retweeted tweets to pan out tweets with zero retweets with little utility.\n", + "#retweeted_tweets = confident_sentiment_tweets[confident_sentiment_tweets.retweet_count > 0]\n", + "\n", + "labels = [\"Positive\", \"Negative\"]\n", + "viral_positive = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == True) & (confident_sentiment_tweets.sentiment == 'POSITIVE')])\n", + "viral_negative = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == True) & (confident_sentiment_tweets.sentiment == 'NEGATIVE')])\n", + "normal_positive = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == False) & (confident_sentiment_tweets.sentiment == 'POSITIVE')])\n", + "normal_negative = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == False) & (confident_sentiment_tweets.sentiment == 'NEGATIVE')])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_positive, viral_negative], name=\"Positive Viral Tweets\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_positive, normal_negative], name=\"Positive Non-Viral Tweets\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Distribution of positive and negative sentiment in viral vs non-viral tweets\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating the p-value between the target `viral` and positive sentiment\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import chi2_contingency \n", + "\n", + "#confident_sentiment_tweets.loc[:, 'is_positive'] = confident_sentiment_tweets.sentiment == 'POSITIVE'\n", + "\n", + "# Calculating the p-value\n", + "contingency_sentiment = pd.crosstab(index = confident_sentiment_tweets['sentiment'], columns=confident_sentiment_tweets['viral']) \n", + "# Display with percentages\n", + "contingency_sentiment_normalized_percentage = pd.crosstab(\n", + " index = confident_sentiment_tweets['sentiment'], columns=confident_sentiment_tweets['viral'], normalize='columns') \n", + "display(contingency_sentiment_normalized_percentage)\n", + "\n", + "c, p, dof, expected = chi2_contingency(contingency_sentiment) \n", + "display(f'p-value {p}')\n", + "c, p, dof, expected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating the p-value between the target `viral` and negative sentiment\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import chi2_contingency \n", + "\n", + "confident_sentiment_tweets.loc[:, 'is_negative'] = confident_sentiment_tweets.sentiment == 'NEGATIVE'\n", + "\n", + "# Calculating the p-value\n", + "contingency_negative_sentiment = pd.crosstab(index = confident_sentiment_tweets['is_negative'], columns=confident_sentiment_tweets['viral']) \n", + "# Display with percentages\n", + "contingency_negative_sentiment_normalized_percentage = pd.crosstab(\n", + " index = confident_sentiment_tweets['is_negative'], columns=confident_sentiment_tweets['viral'], normalize='columns') \n", + "display(contingency_negative_sentiment_normalized_percentage)\n", + "\n", + "c, p, dof, expected = chi2_contingency(contingency_negative_sentiment) \n", + "display(f'p-value {p}')\n", + "c, p, dof, expected" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "import spacy\n", + "import vaderSentiment\n", + "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n", + "\n", + "nlp = spacy.load(\"en_core_web_sm\")\n", + "\n", + "spacy_stopwords = spacy.lang.en.stop_words.STOP_WORDS\n", + "print('Number of stop words: %d' % len(spacy_stopwords))\n", + "print('First ten stop words:',list(spacy_stopwords)[:10])\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "# Remove new lines \n", + "remove_new_lines = lambda x: \" \".join(x.split())\n", + "viral_dataset_labeled['processed_text'] = viral_dataset_labeled['text'].apply(remove_new_lines)\n", + "\n", + "\n", + "english_tweets = viral_dataset_labeled[viral_dataset_labeled.lang == 'en']\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.5 - Number of hashtags " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_labeled.loc[:, \"nb_of_hashtags\"] = viral_dataset_labeled.hashtags.apply(lambda x: len(x) if np.all(x) else 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "labels = [\"Hashtags\", \"No Hashtags\"]\n", + "viral_has_hashtags = len(viral_dataset_labeled[(viral_dataset_labeled.viral) & (viral_dataset_labeled.nb_of_hashtags >= 1)])\n", + "viral_no_hashtags = len(viral_dataset_labeled[(viral_dataset_labeled.viral) & (viral_dataset_labeled.nb_of_hashtags == 0)])\n", + "normal_has_hashtags = len(viral_dataset_labeled[(~viral_dataset_labeled.viral) & (viral_dataset_labeled.nb_of_hashtags >= 1)])\n", + "normal_no_hashtags = len(viral_dataset_labeled[(~viral_dataset_labeled.viral) & (viral_dataset_labeled.nb_of_hashtags == 0)])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_has_hashtags, viral_no_hashtags], name=\"Viral with Hashtags\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_has_hashtags, normal_no_hashtags], name=\"Tweet with No Hashtags\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Percentage of tweets with hashtags\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating the p-value between the target `viral` and `has_hashtags`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import chi2_contingency \n", + "\n", + "viral_dataset_labeled['has_hashtags'] = viral_dataset_labeled.nb_of_hashtags >= 1\n", + "\n", + "# Calculating the p-value\n", + "contingency_has_hashtags = pd.crosstab(index = viral_dataset_labeled['has_hashtags'], columns=viral_dataset_labeled['viral']) \n", + "# Display with percentages\n", + "contingency_has_hashtags_normalized_percentage = pd.crosstab(\n", + " index = viral_dataset_labeled['has_hashtags'], columns=viral_dataset_labeled['viral'], normalize='columns') \n", + "display(contingency_has_hashtags_normalized_percentage)\n", + "\n", + "c, p, dof, expected = chi2_contingency(contingency_has_hashtags) \n", + "display(f'p-value {p}')\n", + "c, p, dof, expected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.6 - Verified account" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Verified account\n", + "labels = [\"Verified\", \"Not verified\"]\n", + "viral_is_verified = len(viral_dataset_labeled[(viral_dataset_labeled.viral) & (viral_dataset_labeled.verified)])\n", + "viral_not_verified = len(viral_dataset_labeled[(viral_dataset_labeled.viral) & (~viral_dataset_labeled.verified)])\n", + "normal_is_verified = len(viral_dataset_labeled[(~viral_dataset_labeled.viral) & (viral_dataset_labeled.verified)])\n", + "normal_not_verified = len(viral_dataset_labeled[(~viral_dataset_labeled.viral) & (~viral_dataset_labeled.verified)])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_is_verified, viral_not_verified], name=\"Viral with verified accounts\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_is_verified, normal_not_verified], name=\"Tweet with an unverified account\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Percentage of tweets from verified accounts\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating the p-value between the target `viral` and `is_verified`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import chi2_contingency \n", + "\n", + "# Calculating the p-value\n", + "contingency_verified = pd.crosstab(index = viral_dataset_labeled['verified'], columns=viral_dataset_labeled['viral']) \n", + "# Display with percentages\n", + "contingency_verified_normalized_percentage = pd.crosstab(\n", + " index = viral_dataset_labeled['verified'], columns=viral_dataset_labeled['viral'], normalize='columns') \n", + "display(contingency_verified_normalized_percentage)\n", + "\n", + "c, p, dof, expected = chi2_contingency(contingency_verified) \n", + "display(f'p-value {p}')\n", + "c, p, dof, expected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.7 - Has mentions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_labeled.loc[:, \"nb_of_mentions\"] = viral_dataset_labeled.mentions.apply(lambda x: len(x) if np.all(x) else 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import chi2_contingency \n", + "\n", + "# Calculating the p-value\n", + "contingency_has_mentions = pd.crosstab(index = viral_dataset_labeled['nb_of_mentions'] > 0, columns=viral_dataset_labeled['viral']) \n", + "display(contingency_has_mentions)\n", + "# Display with percentages\n", + "display(pd.crosstab(index = viral_dataset_labeled['nb_of_mentions'] > 0, columns=viral_dataset_labeled['viral'], normalize='columns') )\n", + "\n", + "c, p, dof, expected = chi2_contingency(contingency_has_mentions) \n", + "display(f'p-value {p}')\n", + "c, p, dof, expected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.8 - Save result of preprocessing to disk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_labeled.to_parquet(f'{PROCESSED_PATH_VIRAL}/all_english_tweets_with_users_with_sentiment.parquet.gzip', index=False, compression=\"gzip\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_dataset_labeled.columns\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 - Covid dataset Exploration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we concern ourselves only with original tweets (no retweets)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "original_covid_tweets = pd.read_parquet(f\"{COVID_TWEETS_PATH}/all_original_tweets.parquet.gzip\")\n", + "original_covid_tweets.loc[:, \"text\"] = original_covid_tweets.text.apply(clear_reply_mentions)\n", + "\n", + "covid_users = pd.read_parquet(f\"{COVID_TWEETS_PATH}/users.parquet.gzip\")\n", + "\n", + "display(\"--- COVID DATASET ---\")\n", + "\n", + "display(f\"{len(original_covid_tweets)} original (not retweeted) covid tweets collected\")\n", + "display(f\"{len(original_covid_tweets.author_id.unique())} covid users collected\")\n", + "\n", + "original_covid_tweets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "user_columns = ['author_id', 'followers_count', 'following_count', 'tweet_count', 'protected', 'verified', 'username']\n", + "covid_dataset_with_users = original_covid_tweets.merge(covid_users.rename(columns={'id': 'author_id'})[user_columns], on='author_id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Applying the first metric on the covid dataset\n", + "covid_dataset_with_users['virality_followers'] = covid_dataset_with_users['retweet_count'] / covid_dataset_with_users['followers_count'].astype(\"float64\")\n", + "# Handle division by zero if user has 0 followers\n", + "covid_dataset_with_users['virality_followers'] = covid_dataset_with_users.virality_followers.replace({np.inf: 0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_dataset_with_users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "px.histogram(covid_dataset_with_users, x='followers_count', y = 'virality_followers', log_y=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_dataset_with_users['viral'] = covid_dataset_with_users.virality_followers > 1\n", + "covid_dataset_with_users[covid_dataset_with_users.viral]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4.1 - Language" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "languages_aggregates = covid_dataset_with_users.groupby(by='lang', as_index=False)[['id']].count().rename(columns={'id': 'count'})\n", + "languages_aggregates = languages_aggregates.sort_values(by='count', ascending=False)\n", + "languages_aggregates.loc[languages_aggregates['count'] < 10000, 'lang'] = 'Other Languages'\n", + "fig = px.pie(languages_aggregates, values='count', names='lang', title='Distribution of Tweets languages')\n", + "\n", + "fig.update_layout(\n", + " autosize=False,\n", + " width=500,\n", + " height=500\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "english_covid_tweets = covid_dataset_with_users[covid_dataset_with_users.lang == 'en']\n", + "display(f\"{len(english_covid_tweets)} english covid tweets\")\n", + "\n", + "english_viral_covid_tweets = english_covid_tweets[english_covid_tweets.viral]\n", + "display(f\"{len(english_viral_covid_tweets)} viral english covid tweets\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4.2 - Media" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Has media\n", + "labels = [\"Media\", \"No Media\"]\n", + "viral_has_media = len(covid_dataset_with_users[(covid_dataset_with_users.viral == True) & (covid_dataset_with_users.has_media == True)])\n", + "viral_no_media = len(covid_dataset_with_users[(covid_dataset_with_users.viral == True) & (covid_dataset_with_users.has_media == False)])\n", + "normal_has_media = len(covid_dataset_with_users[(covid_dataset_with_users.viral == False) & (covid_dataset_with_users.has_media == True)])\n", + "normal_no_media = len(covid_dataset_with_users[(covid_dataset_with_users.viral == False) & (covid_dataset_with_users.has_media == False)])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_has_media, viral_no_media], name=\"Viral with Media\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_has_media, normal_no_media], name=\"Tweet with Media\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Percentage of tweets with some kind of media\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4.3 - Tweet Length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_dataset_with_users.loc[:, 'tweet_length'] = covid_dataset_with_users.text.apply(len)\n", + "covid_dataset_with_users[['tweet_length', 'retweet_count']].corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4.4 - Sentiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "\n", + "# Device = 0 means it will use the Cuda at index 0\n", + "sentiment_classifier = pipeline(\"sentiment-analysis\", device=0)\n", + "\n", + "english_covid_dataset = covid_dataset_with_users[covid_dataset_with_users.lang == 'en']\n", + "english_covid_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we compute sentiments again. To avoid having to compute the sentiments again, we've already preprocessed the data and computed the sentiments and saved it to parquet. Feel free to skip the next 2 cells." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "applied = english_covid_dataset.apply(lambda x: sentiment_classifier(x.text)[0], axis=1, result_type='expand')\n", + "#pd.concat([small_test_set, applied], axis='columns')\n", + "applied" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_features = pd.concat([english_covid_dataset, applied], axis=1)\n", + "sentiment_features = sentiment_features.rename(columns={\"label\": \"sentiment\", \"score\": \"sentiment_score\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_features = pd.read_parquet(f\"{PROCESSED_PATH_COVID}/english_tweets_with_users_with_sentiment.parquet.gzip\")\n", + "sentiment_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Tweets with sentiment scores over 70%\n", + "display(f\"Tweets with sentiment analysis confidence scores above 0.7: {len(sentiment_features[sentiment_features.sentiment_score > 0.7])}\")\n", + "display(f\"{len(sentiment_features[sentiment_features.sentiment == 'POSITIVE'])} positive tweets\")\n", + "display(f\"{len(sentiment_features[sentiment_features.sentiment == 'NEGATIVE'])} negative tweets\")\n", + "\n", + "confident_sentiment_tweets = sentiment_features[sentiment_features.sentiment_score > 0.7]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We keep only retweeted tweets to pan out tweets with zero retweets with little utility.\n", + "labels = [\"Positive\", \"Negative\"]\n", + "viral_positive = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == True) & (confident_sentiment_tweets.sentiment == 'POSITIVE')])\n", + "viral_negative = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == True) & (confident_sentiment_tweets.sentiment == 'NEGATIVE')])\n", + "normal_positive = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == False) & (confident_sentiment_tweets.sentiment == 'POSITIVE')])\n", + "normal_negative = len(confident_sentiment_tweets[(confident_sentiment_tweets.viral == False) & (confident_sentiment_tweets.sentiment == 'NEGATIVE')])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_positive, viral_negative], name=\"Positive Viral Tweets\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_positive, normal_negative], name=\"Positive Non-Viral Tweets\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Distribution of positive and negative sentiment in viral vs non-viral tweets\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4.5 - Number of Hashtags" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_dataset_with_users.loc[:, \"nb_of_hashtags\"] = covid_dataset_with_users.hashtags.apply(lambda x: len(x) if np.all(x) else 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "labels = [\"Hashtags\", \"No Hashtags\"]\n", + "viral_has_hashtags = len(covid_dataset_with_users[(covid_dataset_with_users.viral) & (covid_dataset_with_users.nb_of_hashtags >= 1)])\n", + "viral_no_hashtags = len(covid_dataset_with_users[(covid_dataset_with_users.viral) & (covid_dataset_with_users.nb_of_hashtags == 0)])\n", + "normal_has_hashtags = len(covid_dataset_with_users[(~covid_dataset_with_users.viral) & (covid_dataset_with_users.nb_of_hashtags > 1)])\n", + "normal_no_hashtags = len(covid_dataset_with_users[(~covid_dataset_with_users.viral) & (covid_dataset_with_users.nb_of_hashtags == 0)])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_has_hashtags, viral_no_hashtags], name=\"Viral with Hashtags\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_has_hashtags, normal_no_hashtags], name=\"Tweet with No Hashtags\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Percentage of tweets with hashtags\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.4.6 - Verified Account" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Has media\n", + "labels = [\"Verified\", \"Not verified\"]\n", + "viral_is_verified = len(covid_dataset_with_users[(covid_dataset_with_users.viral) & (covid_dataset_with_users.verified)])\n", + "viral_not_verified = len(covid_dataset_with_users[(covid_dataset_with_users.viral) & (~covid_dataset_with_users.verified)])\n", + "normal_is_verified = len(covid_dataset_with_users[(~covid_dataset_with_users.viral) & (covid_dataset_with_users.verified)])\n", + "normal_not_verified = len(covid_dataset_with_users[(~covid_dataset_with_users.viral) & (~covid_dataset_with_users.verified)])\n", + "\n", + "\n", + "# Create subplots: use 'domain' type for Pie subplot\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", + "fig.add_trace(go.Pie(labels=labels, values=[viral_is_verified, viral_not_verified], name=\"Viral with verified accounts\"),\n", + " 1, 1)\n", + "fig.add_trace(go.Pie(labels=labels, values=[normal_is_verified, normal_not_verified], name=\"Tweet with an unverified account\"),\n", + " 1, 2)\n", + "\n", + "# Use `hole` to create a donut-like pie chart\n", + "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n", + "\n", + "fig.update_layout(\n", + " width=1000,\n", + " height=500,\n", + " title_text=\"Percentage of tweets from verified accounts\",\n", + " # Add annotations in the center of the donut pies.\n", + " annotations=[dict(text='Viral', x=0.18, y=0.5, font_size=20, showarrow=False),\n", + " dict(text='Non-Viral', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4.7 - Save dataframe with analysis to disk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "covid_dataset_with_users.to_parquet(f'{PROCESSED_PATH_COVID}/all_english_tweets_with_users_with_sentiment.parquet.gzip', index=False, compression=\"gzip\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Questions for TJ:\n", + "\n", + "Learn threshold? Use unsupervised learning (anomaly detection), x axis date y retweet count, isolation coordinate\n", + "Ratio\n", + "Try to come up with Different metrics (one cannot be used for second dataset)\n", + "\n", + "Preprocessing:\n", + " - Remove tweets with no retweets or likes? NO\n", + " - Define threshold using the metric? DONE (label above viral tweet)\n", + " - Skewed distribution if we use only Twitter viral tweets (1000) DONE\n", + "- Which features? (Any new ideas)\n", + " - Topic\n", + " - Hashtags relevant? (Most likely different from coronavirus and we already have topics).\n", + " - Has media\n", + " - Sentiment? [TODO]\n", + " - Tweet length [TODO]\n", + " - RETRIEVE USERS THAT LIKED OR RETWEETED USING API [TODO]\n", + " - Word cloud of entities [TODO]\n", + "- Check bigrams and trigrams distribution\n", + "- Normalize features (like, retweets, reply etc...)? DEPENDS, Included in first model, will be removed from second model with covid set.\n", + "- BertTweet [DO NOT REMOVE STOP WORDS FOR LANGUAGE MODELS, FOR ]\n", + "- Next steps (now that data collection part is done and data analysis almost done)\n", + " - Hydrate Covid dataset id\n", + "- Viral generator (Trump generator)\n", + "\n", + "1st classifier: hashtags, twitter entities (context annotations, domain annotations, entities), mentions, domain of urls (youtube.com let’s say)\n", + "2nd classifier: bag of words with tf-idf, remove stopwords and other entities that you used in the 1st classifier\n", + "3rd: language model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.15" + }, + "vscode": { + "interpreter": { + "hash": "71d2f77bccee14ca7852d7b7a1fa8ea4708b81087104d93973081337557f0ee6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/metric_analysis/viral_tweet_user_exploration.ipynb b/metric_analysis/viral_tweet_user_exploration.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4b89630851dbffbe6829891bd1aa36d9fa04e313 --- /dev/null +++ b/metric_analysis/viral_tweet_user_exploration.ipynb @@ -0,0 +1,1208 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viral Tweets: User exploration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we will explore the users who have tweeted viral tweets. Namely, we will focus our analysis on the viral tweets from the user point of view. For example, we'll examine the popularity of the user vs the popularity of his tweets, the history of his tweets and analyze any flagrant changes in their features when they became viral, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0 - Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "#pd.set_option('display.max_rows', None)\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "DATA_PATH = \"../../data\"\n", + "VIRAL_TWEETS_PATH = f\"{DATA_PATH}/viral_users\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from helper.twitter_client_wrapper import TwitterClientWrapper, EXPANSIONS, MEDIA_FIELDS, TWEET_FIELDS, USER_FIELDS\n", + "\n", + "twitter_client_wrapper = TwitterClientWrapper(\"../../api_key.yaml\", wait_on_rate_limit=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Retrieve the data from disk" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Retrieve the viral tweets data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: You may notice that all tweets have been retrieved, since some may have been deleted since scraping them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note 2**: Also keep in mind that when retrieving users, the number of users may be less because users may have two or more viral tweets in the sample of viral tweets we have. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# dtypes={\"id\": str, \"author_id\": str, \"has_media\": bool, \"possibly_sensitive\": bool}\n", + "dtypes={\"id\": str, \"author_id\": str}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\steph\\AppData\\Local\\Temp\\ipykernel_18728\\1524257405.py:2: DtypeWarning: Columns (3,8,14,17,18,19,20,21,22,23,24) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " viral_tweets_df = pd.read_csv(f\"{VIRAL_TWEETS_PATH}/all_tweets.csv\", dtype=dtypes, escapechar='\\\\', encoding='utf-8')\n" + ] + }, + { + "data": { + "text/html": [ + "
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created_atauthor_idtextpossibly_sensitiveedit_history_tweet_idslangidmentionsretweet_countreply_countlike_countquote_countcontext_annotationsurlshas_mediaannotationshashtagsattachments.poll_idswithheld.copyrightwithheld.country_codeswithheld.scopecashtagsgeo.place_idgeo.coordinates.typegeo.coordinates.coordinates
02022-10-31T03:21:11.000Z1047733077898739712@manjirosx you too jiro🫶🏽False['1586921195059834880']en1586921195059834880[{'start': 0, 'end': 10, 'username': 'manjiros...0.00.01.00.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12022-10-31T03:13:57.000Z1047733077898739712@ilyicey u omdFalse['1586919376086704129']nl1586919376086704129[{'start': 0, 'end': 8, 'username': 'ilyicey',...0.00.00.00.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22022-10-31T03:13:24.000Z1047733077898739712@ilyicey i’m fineFalse['1586919239243296768']en1586919239243296768[{'start': 0, 'end': 8, 'username': 'ilyicey',...1.01.02.00.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32022-10-30T22:49:53.000Z1047733077898739712@imVolo_ I’ll unfollow rnFalse['1586852923706732544']en1586852923706732544[{'start': 0, 'end': 8, 'username': 'imVolo_',...0.00.03.00.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
42022-10-30T22:45:33.000Z1047733077898739712“what do you want to be for halloween?” his li...False['1586851830767591424']en1586851830767591424NaN611.019.04132.055.0[{'domain': {'id': '29', 'name': 'Events [Enti...NaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " created_at author_id \\\n", + "0 2022-10-31T03:21:11.000Z 1047733077898739712 \n", + "1 2022-10-31T03:13:57.000Z 1047733077898739712 \n", + "2 2022-10-31T03:13:24.000Z 1047733077898739712 \n", + "3 2022-10-30T22:49:53.000Z 1047733077898739712 \n", + "4 2022-10-30T22:45:33.000Z 1047733077898739712 \n", + "\n", + " text possibly_sensitive \\\n", + "0 @manjirosx you too jiro🫶🏽 False \n", + "1 @ilyicey u omd False \n", + "2 @ilyicey i’m fine False \n", + "3 @imVolo_ I’ll unfollow rn False \n", + "4 “what do you want to be for halloween?” his li... False \n", + "\n", + " edit_history_tweet_ids lang id \\\n", + "0 ['1586921195059834880'] en 1586921195059834880 \n", + "1 ['1586919376086704129'] nl 1586919376086704129 \n", + "2 ['1586919239243296768'] en 1586919239243296768 \n", + "3 ['1586852923706732544'] en 1586852923706732544 \n", + "4 ['1586851830767591424'] en 1586851830767591424 \n", + "\n", + " mentions retweet_count \\\n", + "0 [{'start': 0, 'end': 10, 'username': 'manjiros... 0.0 \n", + "1 [{'start': 0, 'end': 8, 'username': 'ilyicey',... 0.0 \n", + "2 [{'start': 0, 'end': 8, 'username': 'ilyicey',... 1.0 \n", + "3 [{'start': 0, 'end': 8, 'username': 'imVolo_',... 0.0 \n", + "4 NaN 611.0 \n", + "\n", + " reply_count like_count quote_count \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 1.0 2.0 0.0 \n", + "3 0.0 3.0 0.0 \n", + "4 19.0 4132.0 55.0 \n", + "\n", + " context_annotations urls has_media \\\n", + "0 NaN NaN False \n", + "1 NaN NaN False \n", + "2 NaN NaN False \n", + "3 NaN NaN False \n", + "4 [{'domain': {'id': '29', 'name': 'Events [Enti... NaN False \n", + "\n", + " annotations hashtags attachments.poll_ids withheld.copyright \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " withheld.country_codes withheld.scope cashtags geo.place_id \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " geo.coordinates.type geo.coordinates.coordinates \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import tweets first\n", + "viral_tweets_df = pd.read_csv(f\"{VIRAL_TWEETS_PATH}/all_tweets.csv\", dtype=dtypes, escapechar='\\\\', encoding='utf-8')\n", + "# viral_tweets_df = pd.read_csv(f\"{VIRAL_TWEETS_PATH}/all_tweets.csv\", dtype=dtypes)\n", + "viral_tweets_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'RT @strbrkrr: apple be like \"high volume may damage your ears...\" ok… i don’t care'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viral_tweets_df[~viral_tweets_df.annotations.isna()].text.iloc[10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Retrieve viral tweets users" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by retrieving the viral tweets users. Users are **included as expansions** when retrieving the tweets, conveniently so. For each user, we retrieve this user's history and information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve the user id. The user data is included in the 'includes' field which we get by if we retrieve any expansions\n", + "users_df = pd.read_csv(f\"{VIRAL_TWEETS_PATH}/users.csv\", dtype={\"id\": str, \"pinned_tweet_id\": str}, escapechar=\"\\\\\")\n", + "users_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "id object\n", + "edit_history_tweet_ids object\n", + "author_id object\n", + "created_at object\n", + "possibly_sensitive bool\n", + "text object\n", + "retweet_count int64\n", + "reply_count int64\n", + "like_count int64\n", + "quote_count int64\n", + "has_media bool\n", + "urls object\n", + "context_annotations object\n", + "annotations object\n", + "hashtags object\n", + "geo.place_id object\n", + "mentions object\n", + "dtype: object\n", + "'''\n", + "viral_tweets_df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Analysis of single user" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's observe the tweets of single user who has tweeted viral tweets. We'll try to conduct some analysis on their features to try and see what changed in the tweets of the user over time, and how they reflect the changes in the behaviour of the user." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Take first user\n", + "user_id = users_df.iloc[0].id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "user_tweets = viral_tweets_df[viral_tweets_df.author_id == user_id]\n", + "user_tweets['created_at'] = pd.to_datetime(user_tweets.created_at)\n", + "user_tweets.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(10,5))\n", + "\n", + "ax[0].set_title(\"Retweet Count vs Tweet Date\")\n", + "sns.lineplot(user_tweets, x='created_at', y='retweet_count', ax=ax[0])\n", + "\n", + "ax[1].set_title(\"Like Count vs Tweet Date\")\n", + "sns.lineplot(user_tweets, x='created_at', y='like_count', ax=ax[1])\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(10,5))\n", + "\n", + "user_tweets['tweet_length'] = user_tweets['text'].apply(len)\n", + "\n", + "ax[0].set_title(\"Retweet Count vs Tweet Length\")\n", + "sns.lineplot(user_tweets, x='tweet_length', y='retweet_count', ax=ax[0])\n", + "\n", + "ax[1].set_title(\"Like Count vs Tweet Length\")\n", + "sns.lineplot(user_tweets, x='tweet_length', y='like_count', ax=ax[1])\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Has media\n", + "sns.jointplot(user_tweets, x='has_media', y='retweet_count')\n", + "\n", + "plt.suptitle(\"# Retweets vs Tweet has media\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.pairplot(user_tweets[['tweet_length', 'has_media', 'retweet_count', 'like_count']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(2, 2, figsize=(10,5))\n", + "\n", + "user_tweets['tweet_length'] = user_tweets['text'].apply(len)\n", + "\n", + "ax[0][0].set_title(\"Retweet Count vs Date\")\n", + "sns.lineplot(user_tweets, x='created_at', y='retweet_count', ax=ax[0][0])\n", + "\n", + "ax[0][1].set_title(\"Like Count vs Date\")\n", + "sns.lineplot(user_tweets, x='created_at', y='like_count', ax=ax[0][1])\n", + "\n", + "ax[1][0].set_title(\"Has Media vs Date\")\n", + "sns.scatterplot(user_tweets, x='created_at', y='has_media', ax=ax[1][0])\n", + "\n", + "ax[1][1].set_title(\"Tweet Length vs Date\")\n", + "sns.scatterplot(user_tweets, x='created_at', y='tweet_length', ax=ax[1][1])\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### TODO: Analyze the change in tweet features depending on date (one row depending on date, other depending on retweet count to reflect the evolution)\n", + "### TODO: Concentration on topics [group by topics for a sample user]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Aggregate Analysis of all viral users tweets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.0 - How many tweets per user retrieved" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tweets_per_user = viral_tweets_df.groupby(by='author_id').size().reset_index(name='count')\n", + "tweets_per_user.sort_values(by='count')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tweets_per_user.hist(column='count', bins=10)\n", + "plt.title(\"Histogram of distribution of number of tweets retrieved per user\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.1 - Retweet count vs like count" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to come up with a metric for the **virality** of the tweet, we need to know which features we will use to determine this metric. *retweet_count* and *like_count* will surely be among those features selected. Let's how the two correlate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: \"The retweet will not show the likes and replies, only retweet count. You need to get the counts from the original tweet, which would be referenced in referenced_tweets and included in includes.tweets part of the response.\" - Twitter Community" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove all tweets that might be retweets of others\n", + "retweeted = viral_tweets_df.retweet_count !=0\n", + "liked = viral_tweets_df.like_count !=0\n", + "original_tweets_df = viral_tweets_df[retweeted & liked]\n", + "\n", + "# Remove NA in retweet and like count\n", + "original_tweets_df = original_tweets_df.dropna(axis=0, subset=['retweet_count', 'like_count'])\n", + "\n", + "sns.scatterplot(data=original_tweets_df, x='retweet_count', y='like_count')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finding**: We can see more or less a linear correlation. Especially for lower numbers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2 - (# Retweets / # followers ) ratio \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here a viable metric for a viral tweet can be the ratio between the retweets (or like) count over the followers count of the user. The idea here is that a user who doesn't have many followers, but has tweeted tweets that have garnered a lot of retweets or likes, can most definitely be considered \"viral\". On the other hand, a user who has many followers can have a standard high # retweets and those cannot be considered viral all the time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: Also note that historical data for the evolution of the # of followers of a user are not easily available and are not provided by the Twitter API. So these calculated ratios do not reflect the actual ratio when the tweet has been tweeted by a user, since by then he may have gained a lot of followers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_tweets_df_subset = original_tweets_df[['id', 'author_id', 'retweet_count', 'like_count']]\n", + "\n", + "# Remove NA in follower count\n", + "users_df_subset = users_df.dropna(axis=0, subset=['followers_count'])\n", + "\n", + "# Merge both on author id\n", + "tweets_users_merged_df = viral_tweets_df_subset.merge(\n", + " right=users_df_subset[['id', 'followers_count']].set_index('id'), left_on='author_id', right_on='id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tweets_users_merged_df['retweets_followers_ratio'] = tweets_users_merged_df['retweet_count'] / tweets_users_merged_df['followers_count']\n", + "tweets_users_merged_df.sort_values(by='retweets_followers_ratio')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.express as px\n", + "\n", + "df_ratios_bigger_than_1 = tweets_users_merged_df[tweets_users_merged_df.retweets_followers_ratio > 1.0]\n", + "fig = px.histogram(\n", + " df_ratios_bigger_than_1,\n", + " x=\"retweets_followers_ratio\",\n", + " nbins=10,\n", + " log_y=True)\n", + "\n", + "fig.update_layout(\n", + " title={\n", + " 'text': \"Histogram of the distribution of the retweets/followers ratio > 1\",\n", + " 'y':0.9,\n", + " 'x':0.5,\n", + " 'xanchor': 'center',\n", + " 'yanchor': 'top'})\n", + "\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The histogram is not very clear, since we have rare events where the tweets garnered so much popularity wrt the popularity of the user. Those we can definitely consider as viral Maybe we can try K-means to better identify these outliers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "\n", + "n_clusters = 3\n", + "X = np.array(df_ratios_bigger_than_1[['retweet_count', 'followers_count']])\n", + "#X = np.vstack((df_ratios_bigger_than_1.retweet_count.to_numpy(), df_ratios_bigger_than_1.followers_count.to_numpy()))\n", + "#X = df_ratios_bigger_than_1.retweets_followers_ratio.to_numpy().reshape(-1, 1)\n", + "ratio_kmeans = KMeans(n_clusters=n_clusters, random_state=123).fit(X)\n", + "\n", + "#np.vstack((X[:, 0], X[:, 1], ratio_kmeans.labels_)).reshape(-1, 3)\n", + "#px.scatter(ratio_kmeans, x=)\n", + "'''\n", + "plt.title(f'K-Means clustering of #retweets/#followers ratio with k={n_clusters}')\n", + "plt.xlabel('Retweets')\n", + "plt.ylabel('Followers')\n", + "plt.scatter(X[:, 0], X[:, 1], c=ratio_kmeans.labels_)\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kmeans_results_df = pd.DataFrame(X, columns=['retweet_count', 'follower_count']) \n", + "kmeans_results_df['label'] = ratio_kmeans.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "px.scatter(kmeans_results_df, x='follower_count', y='retweet_count', color='label')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.3 - Metric (# Retweets / avg #retweets of a user)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# avg_nb_retweets_per_user = viral_tweets_df_subset.groupby(by='author_id').agg({'retweet_count': ['min', 'mean', 'max'], 'like_count': ['min', 'mean', 'max']})\n", + "avg_nb_retweets_per_user = viral_tweets_df_subset.groupby(by='author_id').retweet_count.agg(['min', 'mean', 'max'])\n", + "avg_nb_retweets_per_user" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ratio_retweet_avg_retweets_df = viral_tweets_df_subset.merge(avg_nb_retweets_per_user, on='author_id')\n", + "ratio_retweet_avg_retweets_df['per_user_performance'] = ratio_retweet_avg_retweets_df['retweet_count'] / ratio_retweet_avg_retweets_df['mean']\n", + "ratio_retweet_avg_retweets_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bigger_than_mean = ratio_retweet_avg_retweets_df[ratio_retweet_avg_retweets_df.per_user_performance > 1]\n", + "hist = px.histogram(bigger_than_mean, x='per_user_performance', log_y=True)\n", + "\n", + "hist.update_layout(title_text=\"Distribution of tweet performance wrt avg #retweets per user\", xaxis_title=\"Tweet performance\", yaxis_title=\"log count\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finding**: We established another metric by which we can judge the virality of a tweet, namely the number of retweets vs the average number of retweets per user. We can set a threshold (e.g. > 2) to decide whether a tweet is viral or not. We can also conduct further analysis over those tweets to determine what sets them apart from the others." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.4 - Tweet Topic (context annotations)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What topics are available? Context annotations are Twitter's version of analyzing the topic of a tweet. They are defined as a context **domain** and **entity**. The domain is like a general topic and entity is like a subtopic or a specific topic within the general domain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json \n", + "\n", + "tweets_with_topics = original_tweets_df.dropna(axis=0, subset='context_annotations')\n", + "\n", + "def topic_to_json(x):\n", + " try:\n", + " return json.loads(x.replace('\\'', '\"'))\n", + " except json.JSONDecodeError:\n", + " print(\"Nope\")\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO tomorrow:\n", + "- Try sample and make it work with context annotations.\n", + "- Check if has media is not null\n", + "- hashtags extract tags\n", + "- Extract context annotations\n", + "- Use Celia Bearer Token" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tweepy import Paginator, TooManyRequests\n", + "client = twitter_client_wrapper.client\n", + "#tweet_data = twitter_client_wrapper.client.get_users_tweets(id='1584975692126900225', expansions=EXPANSIONS, user_fields=USER_FIELDS, tweet_fields=TWEET_FIELDS, media_fields=MEDIA_FIELDS, exclude='retweets')\n", + "\n", + "viral_users_tweets = []\n", + "# Number of users processed so far\n", + "try:\n", + " for tweet in Paginator(client.get_users_tweets, id='1482846121517096961', tweet_fields=TWEET_FIELDS, exclude=\"retweets\").flatten(limit=20):\n", + " viral_users_tweets.append(tweet.data)\n", + "except TooManyRequests:\n", + " print(\"Hit Rate Limit\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "domains = {}\n", + "entities = {}\n", + "for tweet in viral_users_tweets:\n", + " context_annotations = tweet.get('context_annotations', [])\n", + " tweet_topic_domains = dict([(topic['domain']['id'], topic['domain']) for topic in context_annotations])\n", + " domains.update(tweet_topic_domains)\n", + " tweet_topic_entities = dict([(topic['entity']['id'], topic['entity']) for topic in context_annotations])\n", + " entities.update(tweet_topic_entities)\n", + " tweet['topic_domain'] = list(tweet_topic_domains.keys())\n", + " tweet['topic_entity'] = list(tweet_topic_entities.keys())\n", + " tweet.pop('context_annotations', None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open('topic_domains.pickle', 'wb') as handle:\n", + " pickle.dump(entities, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "\n", + "with open('topic_domains.pickle', 'rb') as handle:\n", + " b = pickle.load(handle)\n", + "\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " with open('topic_domains.pickle', 'rb') as handle:\n", + " topic_domains = pickle.load(handle)\n", + "except FileNotFoundError:\n", + " topic_domains = {}\n", + "\n", + "topic_domains" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp = pd.json_normalize(viral_users_tweets)\n", + "#temp[temp.context_annotations.notna()]\n", + "temp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "domains" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s = pd.Series([b[item]['name'] for items in temp.topic_domain.values for item in items])\n", + "s.groupby(s).count().sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_users_tweets_2 = []\n", + "# Number of users processed so far\n", + "try:\n", + " for tweet in Paginator(client.get_users_tweets, id='848263392943058944', tweet_fields=TWEET_FIELDS, exclude=\"retweets\").flatten(limit=100):\n", + " viral_users_tweets_2.append(tweet.data)\n", + "except TooManyRequests:\n", + " print(\"Hit Rate Limit\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "domains = {}\n", + "entities = {}\n", + "for tweet in viral_users_tweets_2:\n", + " context_annotations = tweet.get('context_annotations', [])\n", + " tweet_topic_domains = dict([(topic['domain']['id'], topic['domain']) for topic in context_annotations])\n", + " domains.update(tweet_topic_domains)\n", + " tweet_topic_entities = dict([(topic['entity']['id'], topic['entity']) for topic in context_annotations])\n", + " entities.update(tweet_topic_entities)\n", + " tweet['topic_domain'] = list(tweet_topic_domains.keys()) if len(tweet_topic_domains.keys()) > 0 else pd.NA\n", + " tweet['topic_entity'] = list(tweet_topic_entities.keys()) if len(tweet_topic_entities.keys()) > 0 else pd.NA\n", + " #tweet.pop('context_annotations', None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp2_df = pd.json_normalize(viral_users_tweets_2)\n", + "first_context = temp2_df[~temp2_df.topic_domain.isna()].topic_domain.iloc[2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp2_df[~temp2_df['entities.hashtags'].isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp2_df.to_csv(\"temp.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ast\n", + "\n", + "temp2_read = pd.read_csv('temp.csv', converters={'context_annotations': lambda x: eval(x) if (x and len(x) > 0) else np.nan})\n", + "first_context = temp2_read[~temp2_read.context_annotations.isna()].context_annotations.iloc[2]\n", + "first_context" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval(first_context)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def format_context_annotations(context_annotations):\n", + " if (pd.isna(context_annotations)):\n", + " return []\n", + " else:\n", + " return json.loads(context_annotations)\n", + "\n", + "temp2_df.context_annotations.apply(format_context_annotations)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(viral_users_tweets_2, columns=TWEET_FIELDS).to_csv('temp_2.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#tweet_data = twitter_client_wrapper.client.get_tweet(id='1584975692126900225', expansions=EXPANSIONS, user_fields=USER_FIELDS, tweet_fields=TWEET_FIELDS, media_fields=MEDIA_FIELDS)\n", + "bytes(tweets_with_topics.iloc[1000].context_annotations, encoding='utf-8').decode('unicode_escape')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'46'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dtypes={\"id\": str, \"author_id\": str, \"has_media\": bool, \"possibly_sensitive\": bool, \"has_hashtags\": bool}\n", + "temp3 = pd.read_csv(\"145371604-to-146944733.csv\", dtype=dtypes)\n", + "d = temp3[~temp3.topic_domains.isna()].topic_domains.iloc[0]\n", + "eval(d)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.5 - Tweet Sentiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.6 - Possibly sensitive" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.7 - Hashtags" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: has hashtags (using entities.hashtags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.8 - Text preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO:\n", + "- Sort by tweet date (check popularity)\n", + "- Use Twitter lists to try and find\n", + "- Check if reply or retweet" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.11 ('ada')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "71d2f77bccee14ca7852d7b7a1fa8ea4708b81087104d93973081337557f0ee6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/othercode/collect_users_tweets.py b/othercode/collect_users_tweets.py new file mode 100644 index 0000000000000000000000000000000000000000..cd979dc5a261aba26f2c4ab9b8dc9d6a71df753f --- /dev/null +++ b/othercode/collect_users_tweets.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +from tweepy import Paginator, TooManyRequests +import os +import pandas as pd +import pickle +from tqdm import tqdm +import yaml + +import boto3 + +from helper.twitter_client_wrapper import ( + TWEET_FIELDS, + format_tweets_df, format_context_annotations, + load_topic_domains, load_topic_entities, TwitterClientWrapper +) + +USER_IDS_PATH = "users_ids.csv" + +def run(twitter_client, directory, users_ids, tweets_per_user=20000, push_to_remote=True): + topic_domains = load_topic_domains(f'{directory}topic_domains.pickle') + topic_entities = load_topic_entities(f'{directory}topic_entities.pickle') + + # List where we accumulate the tweets retrieved so far + viral_users_tweets = [] + # Number of users processed so far + users_processed = 0 + filename = f"tweets/{users_ids.id[0]}-to-" + + try: + for user_id in tqdm(users_ids.id): + for tweet in Paginator(twitter_client.get_users_tweets, id=user_id, tweet_fields=TWEET_FIELDS, exclude="retweets").flatten(limit=tweets_per_user): + processed_tweet, tweet_topic_domains, tweet_topic_entities = format_context_annotations(tweet.data) + viral_users_tweets.append(processed_tweet) + topic_domains.update(tweet_topic_domains) + topic_entities.update(tweet_topic_entities) + users_processed += 1 + except TooManyRequests: + # Reached API limit + print("Hit Rate Limit") + finally: + # Dump all to parquet and keep track at which user we stopped. + if len(viral_users_tweets) > 0: + # Append end user id for this iteration to end of filename + filename += f"{user_id}.parquet.gzip" + filepath = directory + filename + os.makedirs(os.path.dirname(filepath), exist_ok=True) + format_tweets_df(viral_users_tweets).to_parquet(filepath, compression="gzip", index=False) + + # Save the topics encountered so far as pickle file + with open(f'{directory}topic_domains.pickle', 'wb') as handle: + pickle.dump(topic_domains, handle, protocol=pickle.HIGHEST_PROTOCOL) + + with open(f'{directory}topic_entities.pickle', 'wb') as handle: + pickle.dump(topic_entities, handle, protocol=pickle.HIGHEST_PROTOCOL) + + # Update the users ids to remove the ones already processed + users_ids[users_processed:].to_csv(f"{directory}{USER_IDS_PATH}", index=False) + + if (push_to_remote): + s3 = boto3.resource("s3") + bucket_name = "semester-project-twitter-storage" + # Upload to S3 + s3.Bucket(bucket_name).upload_file(filepath, filename) + else: + print("Finished processing users") + + return + +def main(): + # TODO: Change depending on whether you're executing this script locally or on a remote server (possibly with s3 access) + LOCAL = False + TWEETS_PER_USER = 4000 + + if LOCAL: + DIRECTORY = "" + with open("api_key.yaml", 'rt') as file: + secret = yaml.safe_load(file) + BEARER_TOKEN = secret['Bearer Token'] + PUSH_TO_REMOTE = False + else: + DIRECTORY="/home/ubuntu/tweet/" + BEARER_TOKEN = os.environ["BearerToken"] + PUSH_TO_REMOTE = True + + # Authenticate to Twitter + client_wrapper = TwitterClientWrapper(BEARER_TOKEN, wait_on_rate_limit=False) + client = client_wrapper.client + + users_ids = pd.read_csv(f"{DIRECTORY}{USER_IDS_PATH}", dtype={"id": str}) + + if len(users_ids) != 0: + run(client, DIRECTORY, users_ids=users_ids, tweets_per_user=TWEETS_PER_USER, push_to_remote=PUSH_TO_REMOTE) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/othercode/hydrate_tweets.py b/othercode/hydrate_tweets.py new file mode 100644 index 0000000000000000000000000000000000000000..02382876d83209fb660152808ad7130e5f671ad5 --- /dev/null +++ b/othercode/hydrate_tweets.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python3 +from tweepy import TooManyRequests +import os +import pandas as pd +import pickle +import yaml +import boto3 + +from helper.twitter_client_wrapper import ( + format_tweets_df, format_users_df, format_context_annotations, + load_topic_domains, load_topic_entities, TwitterClientWrapper +) + +COVID_IDS_PATH = "covid_ids.parquet.gzip" +STEP_SIZE = 100 + +def run(twitter_client, directory, covid_tweets_ids, gather_retweets=True, push_to_remote=True): + topic_domains = load_topic_domains(f'{directory}topic_domains.pickle') + topic_entities = load_topic_entities(f'{directory}topic_entities.pickle') + + # List where we accumulate the tweets retrieved so far + collected_tweets = [] + # List where we accumulate the users retrieved so far + collected_users = [] + if gather_retweets: + # We're gathering retweet ids + covid_filepath = "covid" + else: + # We're gathering retweets themselves + covid_filepath = "covid_retweets" + tweet_filepath_temp = f"{covid_filepath}/tweets/" + user_filepath_temp = f"{covid_filepath}/users/" + retweet_filepath_temp = f"{covid_filepath}/retweets/" + + # Take the ceil to process any remaining tweet ids + steps = int(len(covid_tweets_ids)/STEP_SIZE) + 1 + + try: + for i in range(steps): + tweets = twitter_client.retrieve_tweets_by_ids(ids=covid_tweets_ids[i*STEP_SIZE:(i+1)*STEP_SIZE]) + included_users = tweets.includes.get('users', []) + collected_users += included_users + for tweet in tweets.data: + processed_tweet, tweet_topic_domains, tweet_topic_entities = format_context_annotations(tweet.data) + collected_tweets.append(processed_tweet) + topic_domains.update(tweet_topic_domains) + topic_entities.update(tweet_topic_entities) + except TooManyRequests: + # Reached API limit + print(f"Hit Rate Limit, processed {i * STEP_SIZE}") + print(f'tweets left: {len(covid_tweets_ids) - (i * STEP_SIZE)}') + finally: + # Dump all to parquet and keep track at which user we stopped. + if len(collected_tweets) > 0: + # Append end tweet id for this iteration to end of filename + first_processed_tweet_id = collected_tweets[0]['id'] + last_processed_tweet_id = collected_tweets[-1]['id'] + tweet_filename = f"{first_processed_tweet_id}-to-{last_processed_tweet_id}.parquet.gzip" + tweet_filepath = directory + tweet_filepath_temp + tweet_filename + os.makedirs(os.path.dirname(tweet_filepath), exist_ok=True) + format_tweets_df(collected_tweets).to_parquet(tweet_filepath, compression="gzip", index=False) + + user_filepath = directory + user_filepath_temp + tweet_filename + os.makedirs(os.path.dirname(user_filepath), exist_ok=True) + format_users_df([user.data for user in collected_users]).to_parquet(user_filepath, compression="gzip", index=False) + + if gather_retweets: + # Check if tweet has referenced tweets + retweeted = [tweet for tweet in collected_tweets if tweet.get('referenced_tweets')] + # Retrieve all referenced tweets ids in the tweet + referenced_tweets_ids = set([referenced_tweet['id'] for tweet in retweeted for referenced_tweet in tweet['referenced_tweets'] if referenced_tweet['type'] == 'retweeted']) + retweet_filepath = directory + retweet_filepath_temp + tweet_filename + os.makedirs(os.path.dirname(retweet_filepath), exist_ok=True) + pd.DataFrame(referenced_tweets_ids, columns=['id']).to_parquet(retweet_filepath, compression="gzip", index=False) + + # Save the topics encountered so far as pickle file + with open(f'{directory}topic_domains.pickle', 'wb') as handle: + pickle.dump(topic_domains, handle, protocol=pickle.HIGHEST_PROTOCOL) + + with open(f'{directory}topic_entities.pickle', 'wb') as handle: + pickle.dump(topic_entities, handle, protocol=pickle.HIGHEST_PROTOCOL) + + # Update the tweets ids to remove the ones already processed + if len(covid_tweets_ids) < 100: + pd.DataFrame([], columns=['id']).to_parquet(f"{directory}{COVID_IDS_PATH}", index=False) + else: + pd.DataFrame(covid_tweets_ids[(i*STEP_SIZE):], columns=['id']).to_parquet(f"{directory}{COVID_IDS_PATH}", index=False) + + if (push_to_remote): + s3 = boto3.resource("s3") + bucket_name = "semester-project-twitter-storage" + # Upload to S3 + bucket = s3.Bucket(bucket_name) + bucket.upload_file(tweet_filepath, f"{tweet_filepath_temp}{tweet_filename}") + bucket.upload_file(user_filepath, f"{user_filepath_temp}{tweet_filename}") + if gather_retweets: + bucket.upload_file(retweet_filepath, f"{retweet_filepath_temp}{tweet_filename}") + else: + print("Finished processing users") + + return + +def main(): + # TODO: Change depending on whether you're executing this script locally or on a remote server (possibly with s3 access) + LOCAL = False + + if LOCAL: + DIRECTORY = "" + with open("api_key.yaml", 'rt') as file: + secret = yaml.safe_load(file) + BEARER_TOKEN = secret['Bearer Token'] + PUSH_TO_REMOTE = False + else: + DIRECTORY="/home/ubuntu/covid_tweets/" + BEARER_TOKEN = os.environ["BearerToken"] + PUSH_TO_REMOTE = True + + # Authenticate to Twitter + client_wrapper = TwitterClientWrapper(BEARER_TOKEN, wait_on_rate_limit=False) + + covid_ids = list(pd.read_parquet(f"{DIRECTORY}{COVID_IDS_PATH}").id) + + if len(covid_ids) != 0: + run(client_wrapper, DIRECTORY, covid_tweets_ids=covid_ids, gather_retweets=False, push_to_remote=PUSH_TO_REMOTE) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/othercode/text_preprocessing.py b/othercode/text_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..719138b574d1614c53c98458661d54d46654717a --- /dev/null +++ b/othercode/text_preprocessing.py @@ -0,0 +1,110 @@ +import html + +def clear_reply_mentions(tweet): + '''Remove user mentions found in a reply to a tweet. + + Example: @user1 @user2 okay @user3 -> okay @user3 + ''' + # We don't need to use any sophisticated tokenization here like nltk + tokens = tweet.split(" ") + for index in range(len(tokens)): + if not tokens[index].startswith("@"): + return " ".join(tokens[index:]) + return "" + +from emoji import demojize, is_emoji +from nltk.tokenize import TweetTokenizer + +tweet_tokenizer = TweetTokenizer() + +def normalizeToken(token, emojis_found=[], replace_user_mentions=True, replace_urls=True, demojize_emojis=True): + lowercased_token = token.lower() + if token.startswith("@") and replace_user_mentions: + return "@USER" + elif (lowercased_token.startswith("http") or lowercased_token.startswith("www")) and replace_urls: + return "HTTPURL" + elif len(token) == 1 and is_emoji(token): + emojis_found.append(token) + if demojize_emojis: + return demojize(token) + else: + return token + else: + if token == "’": + return "'" + elif token == "…": + return "..." + else: + return token + + +def normalizeTweet(tweet, tokenizer=tweet_tokenizer, replace_user_mentions=True, replace_urls=True, demojize_emojis=True, bert_tweet_specific_processing=True): + emojis_found = [] + tokens = tokenizer.tokenize(tweet.replace("’", "'").replace("…", "...")) + normTweet = " ".join([normalizeToken(token, emojis_found=emojis_found, + replace_user_mentions=replace_user_mentions, + replace_urls=replace_urls, + demojize_emojis=demojize_emojis) for token in tokens]) + + if bert_tweet_specific_processing: + normTweet = ( + normTweet.replace("cannot ", "can not ") + .replace("n't ", " n't ") + .replace("n 't ", " n't ") + .replace("ca n't", "can't") + .replace("ai n't", "ain't") + ) + normTweet = ( + normTweet.replace("'m ", " 'm ") + .replace("'re ", " 're ") + .replace("'s ", " 's ") + .replace("'ll ", " 'll ") + .replace("'d ", " 'd ") + .replace("'ve ", " 've ") + ) + normTweet = ( + normTweet.replace(" p . m .", " p.m.") + .replace(" p . m ", " p.m ") + .replace(" a . m .", " a.m.") + .replace(" a . m ", " a.m ") + ) + + return " ".join(normTweet.split()), emojis_found + + +def clean_tweet(tweet, clear_html_chars=True, replace_user_mentions=True, replace_urls=True, + demojize_emojis=True, bert_tweet_specific_processing=True): + '''Helper function to clean tweets. Highly customizable to fit different needs. + + Params: + tweet: the tweet to clean + clear_html_chars: If true, will unescape any special html entities found in the tweet + replace_user_mentions: If true, will replace any user mention with the token @USER + replace_urls: If true, will replace any urls with the token HTTPURL + demojize_emojis: If true, will demojize emojis + bert_tweet_specific_clean: if true, will do some additional preprocessing for the BertTweet model + + Returns: + The cleaned tweet + ''' + # First step: clear mentions at the beginning of tweets (inserted automatically by Twitter when replying to a tweet). + # These do not count in the character count of a tweet and may make the tweet length go way overboard. + cleaned_tweet = clear_reply_mentions(tweet) + + # Second step: Remove any new lines + cleaned_tweet = cleaned_tweet.replace('\r', '').replace('\n', '') + + # Third step: if True, escape any html entities + if clear_html_chars: + cleaned_tweet = html.unescape(cleaned_tweet) + + # Normalize Tweet with remaining preprocessing (emojis, urls, mentions, etc..) + normalized_tweet, emojis = normalizeTweet(cleaned_tweet, + replace_user_mentions=replace_user_mentions, + replace_urls=replace_urls, + demojize_emojis=demojize_emojis, + bert_tweet_specific_processing=bert_tweet_specific_processing) + + # TODO: process emoticons? e.g. :) + + return normalized_tweet \ No newline at end of file diff --git a/othercode/twitter_client_wrapper.py b/othercode/twitter_client_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..0238f2255ffed59967fd4dd341789915fd37beb0 --- /dev/null +++ b/othercode/twitter_client_wrapper.py @@ -0,0 +1,120 @@ +import yaml +import tweepy +import pandas as pd +import pickle + +from typing import List + +######################### TWITTER FIELDS ######################### +# Available tweet fields to choose from (as of October 13 2022): +# attachments, +# author_id, +# context_annotations, +# conversation_id, +# created_at, +# edit_controls, +# entities, +# geo, +# id, +# in_reply_to_user_id, +# lang, +# public_metrics, [Important for retweet and like count] +# possibly_sensitive, +# referenced_tweets, +# reply_settings, +# source, +# text, +# withheld +# NOTE: You can only get non-public metrics for Tweets that belong to the account you’re authenticated as. You can use the account token and secret with OAuth 1.0A - this will not work with an app-only bearer token. +TWEET_FIELDS = ['attachments', 'author_id', 'context_annotations', 'created_at', 'entities', 'geo', 'id', 'lang', 'possibly_sensitive', 'public_metrics', 'referenced_tweets', 'text'] + +# In case of user fields, public_metrics include followers count, following count, tweet count and listed count +USER_FIELDS = ['created_at', 'description', 'id', 'location', 'name', 'pinned_tweet_id', 'protected', 'public_metrics', 'url', 'username', 'verified'] +# In case of media fields, public_metrics include view count +# duration_ms available only if type is video +MEDIA_FIELDS = ['media_key', 'type', 'duration_ms', 'public_metrics'] + +EXPANSIONS = ['author_id', 'attachments.media_keys'] + +class TwitterClientWrapper: + def __init__(self, bearer_token, wait_on_rate_limit=False) -> None: + + # Get the bearer token and authenticate with Twitter Client + + # Authenticate to Twitter + self.client = tweepy.Client(bearer_token, wait_on_rate_limit=wait_on_rate_limit) + + + def retrieve_tweets_by_ids(self, ids): + return self.client.get_tweets( + ids, tweet_fields=TWEET_FIELDS, expansions=EXPANSIONS, user_fields=USER_FIELDS, media_fields=MEDIA_FIELDS) + + def retrieve_tweet(self, id): + return self.client.get_tweet( + id, tweet_fields=TWEET_FIELDS, expansions=EXPANSIONS, user_fields=USER_FIELDS, media_fields=MEDIA_FIELDS) + + +######################### HELPER FUNCTIONS ######################### +def format_users_df(user_data: List[tweepy.user.User]): + '''Format the user using his data. If we specify it and the fields, Twitter API can include user data + inside the "includes" field when retrieving tweets. This returns a Tweepy User object, out of which + we can retrieve the user fields we specified when querying, by accessing the "data" field. + ''' + users_data_df = pd.json_normalize(user_data) + users_data_df.columns = users_data_df.columns.str.removeprefix("public_metrics.") + return users_data_df + +# TODO: Flatten everything and remove prefixes +def format_tweets_df(tweets_data): + # Remove prefix after normalization of json value columns + tweets_data_df = pd.json_normalize(tweets_data) + tweets_data_df.columns = tweets_data_df.columns.str.removeprefix("public_metrics.").str.removeprefix("entities.") + + tweets_data_df.rename(columns={"attachments.media_keys": "has_media"}, inplace=True) + tweets_data_df['has_media'] = ~tweets_data_df['has_media'].isna() + + # Get the hashtags of a tweet if any + tweets_data_df['hashtags'] = tweets_data_df['hashtags'].map(lambda hashtags: [hashtag['tag'] for hashtag in hashtags], na_action='ignore') + ''' + tweets_data_df.rename(columns={"hashtags": "has_hashtags"}, inplace=True) + tweets_data_df['has_hashtags'] = ~tweets_data_df['has_hashtags'].isna() + ''' + + return tweets_data_df + +def format_context_annotations(tweet): + '''Retrieve the context annotations of a tweet if any, and keep only the ids of the topic domains and entities. + + Returns: the tweet formatted, dict of domains retrieved, dict of entities retrieved + ''' + tweet_copy = tweet.copy() + context_annotations = tweet_copy.get('context_annotations', []) + + # Create a dict of all topic domains in this tweet + tweet_topic_domains = dict([(topic['domain']['id'], topic['domain']) for topic in context_annotations]) + # Create a dict of all topic entities in this tweet + tweet_topic_entities = dict([(topic['entity']['id'], topic['entity']) for topic in context_annotations]) + # Columns contain only the ids of the above topic domains and entities + tweet_copy['topic_domains'] = list(tweet_topic_domains.keys()) if len(tweet_topic_domains.keys()) > 0 else pd.NA + tweet_copy['topic_entities'] = list(tweet_topic_entities.keys()) if len(tweet_topic_entities.keys()) > 0 else pd.NA + + # Remove the context annotations column to save space + tweet_copy.pop('context_annotations', None) + return tweet_copy, tweet_topic_domains, tweet_topic_entities + +def load_topic_domains(path): + try: + with open(path, 'rb') as handle: + topic_domains = pickle.load(handle) + except FileNotFoundError: + topic_domains = {} + return topic_domains + +def load_topic_entities(path): + try: + with open(path, 'rb') as handle: + topic_entities = pickle.load(handle) + except FileNotFoundError: + topic_entities = {} + return topic_entities + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8cafba8980a28bc8b7f39d4b953cb2b05f0cc33b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,18 @@ +datasets +emoji +evaluate +ipykernel +ipython +jupyter +matplotlib +pandas +plotly +pyarrow +scikit-learn +seaborn +torch +tqdm +transformers +nltk +protobuf +gradio diff --git a/viral_tweets_html_id_extractor.ipynb b/viral_tweets_html_id_extractor.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e45b68ceac6798bb835888a7cd05a1dd768c18d3 --- /dev/null +++ b/viral_tweets_html_id_extractor.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Twitter Scraping for viral Tweets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As of now (October 2022), there is no way to retrieve the tweets from the Topic \"viral tweets\" from the Twitter API. Those are tweets that Twitter labels as viral based on number of likes, retweets and reaction surrounding the tweet in general. That's why we used online scraper tools to scrape tweets off this Twitter Topic page (namely https://twitter.com/i/topics/1284234742661963776)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_PATH = \"../../../data\"\n", + "SCRAPED_TWEETS_PATH = f\"{DATA_PATH}/scraped\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Octoparse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "OCTOPARSE = f\"{SCRAPED_TWEETS_PATH}/octoparse/\"\n", + "data_files = []\n", + "\n", + "# iterate over files in\n", + "# that directory\n", + "for filename in os.listdir(OCTOPARSE):\n", + " f = os.path.join(OCTOPARSE, filename)\n", + " # checking if it is a file\n", + " if os.path.isfile(f):\n", + " data_files.append(f)\n", + "\n", + "data_files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from bs4 import BeautifulSoup\n", + "import json\n", + "\n", + "links = []\n", + "\n", + "for data_file in data_files:\n", + " with open(data_file, \"r\", encoding='utf8') as read_file:\n", + " data = json.load(read_file)\n", + "\n", + " for field in data:\n", + " for elem in field.values():\n", + " soup = BeautifulSoup(elem)\n", + " #print(soup.prettify())\n", + " for link in soup.find_all(\"a\"):\n", + " links.append(link.get(\"href\")) \n", + "\n", + "octoparse_tweet_ids = set([link.split('/')[3] for link in links if 'status' in link]) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(octoparse_tweet_ids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Other scraper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "SAMPLE_SCRAPER = f\"{SCRAPED_TWEETS_PATH}/sample-scraper/\"\n", + "data_files = []\n", + "\n", + "# iterate over files in\n", + "# that directory\n", + "for filename in os.listdir(SAMPLE_SCRAPER):\n", + " f = os.path.join(SAMPLE_SCRAPER, filename)\n", + " # checking if it is a file\n", + " if os.path.isfile(f):\n", + " data_files.append(f)\n", + "\n", + "data_files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "links = []\n", + "\n", + "for data_file in data_files:\n", + " with open(data_file, \"r\", encoding='utf8') as read_file:\n", + " data = json.load(read_file)\n", + "\n", + " for field in data.values():\n", + " if type(field) == list:\n", + " for elem in field:\n", + " links.append(elem['url'])\n", + "\n", + "sample_scraper_tweet_ids = set([link.split('/')[5] for link in links if 'status' in link])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(sample_scraper_tweet_ids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save all to csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viral_tweet_ids = list(octoparse_tweet_ids) + list(sample_scraper_tweet_ids)\n", + "len(viral_tweet_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the ids into a file\n", + "import pandas as pd\n", + "\n", + "viral_tweet_ids_df = pd.DataFrame(viral_tweet_ids, columns=['tweet_id'])\n", + "# Append csv\n", + "viral_tweet_ids_df.to_csv(f\"{SCRAPED_TWEETS_PATH}/scraped_tweets_ids.csv\", index=False, header=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.11 ('ada')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.15" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "71d2f77bccee14ca7852d7b7a1fa8ea4708b81087104d93973081337557f0ee6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}