scrape tweets advanced search python github
https://github.com/taspinar/twitterscraper
GitHub - taspinar/twitterscraper: Scrape Twitter for Tweets
2020-07-28
Taspinar
<div><div>
 <article><p><a href="https://pepy.tech/project/twitterscraper"></a> <a href="https://pepy.tech/project/twitterscraper/month"></a> <a href="https://badge.fury.io/py/twitterscraper">
</a> <a href="https://github.com/taspinar/twitterscraper/graphs/contributors">
</a></p>
<a></a>
<h2>Backers</h2>
<p>Thank you to all our backers! 🙏 [<a href="https://opencollective.com/twitterscraper#backer">Become a backer</a>]</p>
<a></a>
<h2>Sponsors</h2>
<p>Support this project by becoming a sponsor. Your logo will show up here
with a link to your website. [<a href="https://opencollective.com/twitterscraper#sponsor">Become a sponsor</a>]</p>
<a></a>
<h2>Synopsis</h2>
<p>A simple script to scrape Tweets using the Python package <code>requests</code>
to retrieve the content and <code>Beautifulsoup4</code> to parse the retrieved
content.</p>
<a></a>
<h2>1. Motivation</h2>
<p>Twitter has provided <a href="https://dev.twitter.com/rest/public">REST
API's</a> which can be used by
developers to access and read Twitter data. They have also provided a
<a href="https://dev.twitter.com/streaming/overview">Streaming API</a> which can
be used to access Twitter Data in real-time.</p>
<p>Most of the software written to access Twitter data provide a library
which functions as a wrapper around Twitter's Search and Streaming API's
and are therefore constrained by the limitations of the API's.</p>
<p>With Twitter's Search API you can only send 180 Requests every 15
minutes. With a maximum number of 100 tweets per Request, you
can mine 72 tweets per hour (4 x 180 x 100 =72) . By using
TwitterScraper you are not limited by this number but by your internet
speed/bandwith and the number of instances of TwitterScraper you are
willing to start.</p>
<p>One of the bigger disadvantages of the Search API is that you can only
access Tweets written in the <strong>past 7 days</strong>. This is a major bottleneck
for anyone looking for older data. With TwitterScraper there is no such
limitation.</p>
<dl>
<dt>Per Tweet it scrapes the following information:</dt>
<dd><ul>
<li>Tweet-id</li>
<li>Tweet-url</li>
<li>Tweet text</li>
<li>Tweet html</li>
<li>Links inside Tweet</li>
<li>Hashtags inside Tweet</li>
<li>Image URLS inside Tweet</li>
<li>Video URL inside Tweet</li>
<li>Tweet timestamp</li>
<li>Tweet Epoch timestamp</li>
<li>Tweet No. of likes</li>
<li>Tweet No. of replies</li>
<li>Tweet No. of retweets</li>
<li>Username</li>
<li>User Full Name / Screen Name</li>
<li>User ID</li>
<li>Tweet is an reply to</li>
<li>Tweet is replied to</li>
<li>List of users Tweet is an reply to</li>
<li>Tweet ID of parent tweet</li>
</ul>
</dd>
<dt>In addition it can scrape for the following user information:</dt>
<dd><ul>
<li>Date user joined</li>
<li>User location (if filled in)</li>
<li>User blog (if filled in)</li>
<li>User No. of tweets</li>
<li>User No. of following</li>
<li>User No. of followers</li>
<li>User No. of likes</li>
<li>User No. of lists</li>
<li>User is verified</li>
</ul>
</dd>
</dl>
<a></a>
<h2>2. Installation and Usage</h2>
<p>To install <strong>twitterscraper</strong>:</p>
<div><pre>(sudo) pip install twitterscraper</pre></div>
<p>or you can clone the repository and in the folder containing setup.py</p>
<p>If you prefer more isolation you can build a docker image</p>
<div><pre>docker build -t twitterscraper:build .</pre></div>
<p>and run your container with:</p>
<div><pre>docker run --rm -it -v/&lt;PATH_TO_SOME_SHARED_FOLDER_FOR_RESULTS&gt;:/app/data twitterscraper:build &lt;YOUR_QUERY&gt;</pre></div>
<a></a>
<h3>2.2 The CLI</h3>
<p>You can use the command line application to get your tweets stored to
JSON right away. Twitterscraper takes several arguments:</p>
<ul>
<li><code>-h</code> or <code>--help</code> Print out the help message and exits.</li>
<li><code>-l</code> or <code>--limit</code> TwitterScraper stops scraping when <em>at least</em>
the number of tweets indicated with <code>--limit</code> is scraped. Since
tweets are retrieved in batches of 20, this will always be a multiple
of 20. Omit the limit to retrieve all tweets. You can at any time abort the
scraping by pressing Ctrl+C, the scraped tweets will be stored safely
in your JSON file.</li>
<li><code>--lang</code> Retrieves tweets written in a specific language. Currently
30+ languages are supported. For a full list of the languages print
out the help message.</li>
<li><code>-bd</code> or <code>--begindate</code> Set the date from which TwitterScraper
should start scraping for your query. Format is YYYY-MM-DD. The
default value is set to 2006-03-21. This does not work in combination with <code>--user</code>.</li>
<li><code>-ed</code> or <code>--enddate</code> Set the enddate which TwitterScraper should
use to stop scraping for your query. Format is YYYY-MM-DD. The
default value is set to today. This does not work in combination with <code>--user</code>.</li>
<li><code>-u</code> or <code>--user</code> Scrapes the tweets from that users' profile page.
This also includes all retweets by that user. See section 2.2.4 in the examples below
for more information.</li>
<li><code>--profiles</code> : Twitterscraper will in addition to the tweets, also scrape for the profile
information of the users who have written these tweets. The results will be saved in the
file userprofiles_&lt;filename&gt;.</li>
<li><code>-p</code> or <code>--poolsize</code> Set the number of parallel processes
TwitterScraper should initiate while scraping for your query. Default
value is set to 20. Depending on the computational power you have,
you can increase this number. It is advised to keep this number below
the number of days you are scraping. For example, if you are
scraping from 2017-01-10 to 2017-01-20, you can set this number to a
maximum of 10. If you are scraping from 2016-01-01 to 2016-12-31, you
can increase this number to a maximum of 150, if you have the
computational resources. Does not work in combination with <code>--user</code>.</li>
<li><code>-o</code> or <code>--output</code> Gives the name of the output file. If no
output filename is given, the default filename 'tweets.json' or 'tweets.csv'
will be used.</li>
<li><code>-c</code> or <code>--csv</code> Write the result to a CSV file instead of a JSON file.</li>
<li><code>-d</code> or <code>--dump</code>: With this argument, the scraped tweets will be
printed to the screen instead of an outputfile. If you are using this
argument, the <code>--output</code> argument doe not need to be used.</li>
<li><code>-ow</code> or <code>--overwrite</code>: With this argument, if the output file already exists
it will be overwritten. If this argument is not set (default) twitterscraper will
exit with the warning that the output file already exists.</li>
<li><code>-dp</code> or <code>--disableproxy</code>: With this argument, proxy servers are not used when scrapping tweets or user profiles from twitter.</li>
</ul>
<a></a>
<h4>2.2.1 Examples of simple queries</h4>
<p>Below is an example of how twitterscraper can be used:</p>
<p><code>twitterscraper Trump --limit 1000 --output=tweets.json</code></p>
<p><code>twitterscraper Trump -l 1000 -o tweets.json</code></p>
<p><code>twitterscraper Trump -l 1000 -bd 2017-01-01 -ed 2017-06-01 -o tweets.json</code></p>
<a></a>
<h4>2.2.2 Examples of advanced queries</h4>
<p>You can use any advanced query Twitter supports. An advanced query
should be placed within quotes, so that twitterscraper can recognize it
as one single query.</p>
<p>Here are some examples:</p>
<ul>
<li>search for the occurence of 'Bitcoin' or 'BTC':
<code>twitterscraper "Bitcoin OR BTC" -o bitcoin_tweets.json -l 1000</code></li>
<li>search for the occurence of 'Bitcoin' and 'BTC':
<code>twitterscraper "Bitcoin AND BTC" -o bitcoin_tweets.json -l 1000</code></li>
<li>search for tweets from a specific user:
<code>twitterscraper "Blockchain from:VitalikButerin" -o blockchain_tweets.json -l 1000</code></li>
<li>search for tweets to a specific user:
<code>twitterscraper "Blockchain to:VitalikButerin" -o blockchain_tweets.json -l 1000</code></li>
<li>search for tweets written from a location:
<code>twitterscraper "Blockchain near:Seattle within:15mi" -o blockchain_tweets.json -l 1000</code></li>
</ul>
<p>You can construct an advanced query on <a href="https://twitter.com/search-advanced?lang=en">Twitter Advanced Search</a> or use one of the operators shown on <a href="https://lifehacker.com/search-twitter-more-efficiently-with-these-search-opera-1598165519">this page</a>.
Also see <a href="https://developer.twitter.com/en/docs/tweets/search/guides/standard-operators.html">Twitter's Standard operators</a></p>
<a></a>
<h4>2.2.3 Examples of scraping user pages</h4>
<p>You can also scraped all tweets written or retweeted by a specific user.
This can be done by adding the boolean argument <code>-u / --user</code> argument.
If this argument is used, the search term should be equal to the username.</p>
<p>Here is an example of scraping a specific user:</p>
<p><code>twitterscraper realDonaldTrump --user -o tweets_username.json</code></p>
<p>This does not work in combination with <code>-p</code>, <code>-bd</code>, or <code>-ed</code>.</p>
<p>The main difference with the example "search for tweets from a specific user" in section 2.2.2 is that this method really scrapes
all tweets from a profile page (including retweets).
The example in 2.2.2 scrapes the results from the search page (excluding retweets).</p>
<a></a>
<h3>2.3 From within Python</h3>
<p>You can easily use TwitterScraper from within python:</p>
<pre>from twitterscraper import query_tweets
if __name__ == '__main__':
 list_of_tweets = query_tweets("Trump OR Clinton", 10)
 #print the retrieved tweets to the screen:
 for tweet in query_tweets("Trump OR Clinton", 10):
 print(tweet)
 #Or save the retrieved tweets to file:
 file = open(“output.txt”,”w”)
 for tweet in query_tweets("Trump OR Clinton", 10):
 file.write(str(tweet.text.encode('utf-8')))
 file.close()
</pre>
<a></a>
<h3>2.3.1 Examples of Python Queries</h3>
<blockquote>
<ul>
<li><dl>
<dt>Query tweets from a given URL:</dt>
<dd><dl>
<dt>Parameters:</dt>
<dd><ul>
<li>query: The query search parameter of url</li>
<li>lang: Language of queried url</li>
<li>pos: Parameter passed for where to start looking in url</li>
<li>retry: Number of times to retry if error</li>
</ul>
</dd>
</dl>
<div><pre>query_single_page(query, lang, pos, retry=50, from_user=False, timeout=60)</pre></div>
</dd>
</dl>
</li>
<li><dl>
<dt>Query all tweets that match qeury:</dt>
<dd><dl>
<dt>Parameters:</dt>
<dd><ul>
<li>query: The query search parameter</li>
<li>limit: Number of tweets returned</li>
<li>begindate: Start date of query</li>
<li>enddate: End date of query</li>
<li>poolsize: Tweets per poolsize</li>
<li>lang: Language of query</li>
</ul>
</dd>
</dl>
<div><pre>query_tweets('query', limit=None, begindate=dt.date.today(), enddate=dt.date.today(), poolsize=20, lang='')</pre></div>
</dd>
</dl>
</li>
<li><dl>
<dt>Query tweets from a specific user:</dt>
<dd><dl>
<dt>Parameters:</dt>
<dd><ul>
<li>user: Twitter username</li>
<li>limit: Number of tweets returned</li>
</ul>
</dd>
</dl>
<div><pre>query_tweets(user, limit=None)</pre></div>
</dd>
</dl>
</li>
</ul>
</blockquote>
<a></a>
<h3>2.4 Scraping for retweets</h3>
<p>A regular search within Twitter will not show you any retweets.
Twitterscraper therefore does not contain any retweets in the output.</p>
<p>To give an example: If user1 has written a tweet containing <code>#trump2020</code> and user2 has retweetet this tweet,
a search for <code>#trump2020</code> will only show the original tweet.</p>
<p>The only way you can scrape for retweets is if you scrape for all tweets of a specific user with the <code>-u / --user</code> argument.</p>
<a></a>
<h3>2.5 Scraping for User Profile information</h3>
<p>By adding the argument <code>--profiles</code> twitterscraper will in addition to the tweets, also scrape for the profile information of the users who have written these tweets.
The results will be saved in the file "userprofiles_&lt;filename&gt;".</p>
<p>Try not to use this argument too much. If you have already scraped profile information for a set of users, there is no need to do it again :)
It is also possible to scrape for profile information without scraping for tweets.
Examples of this can be found in the examples folder.</p>
<a></a>
<h2>3. Output</h2>
<p>All of the retrieved Tweets are stored in the indicated output file. The
contents of the output file will look like:</p>
<pre>[{"fullname": "Rupert Meehl", "id": "892397793071050752", "likes": "1", "replies": "0", "retweets": "0", "text": "Latest: Trump now at lowest Approval and highest Disapproval ratings yet. Oh, we're winning bigly here ...\n\nhttps://projects.fivethirtyeight.com/trump-approval-ratings/?ex_cid=rrpromo\u00a0\u2026", "timestamp": "2017-08-01T14:53:08", "user": "Rupert_Meehl"}, {"fullname": "Barry Shapiro", "id": "892397794375327744", "likes": "0", "replies": "0", "retweets": "0", "text": "A former GOP Rep quoted this line, which pretty much sums up Donald Trump. https://twitter.com/davidfrum/status/863017301595107329\u00a0\u2026", "timestamp": "2017-08-01T14:53:08", "user": "barryshap"}, (...)
]
</pre>
<a></a>
<h3>3.1 Opening the output file</h3>
<p>In order to correctly handle all possible characters in the tweets
(think of Japanese or Arabic characters), the output is saved as utf-8
encoded bytes. That is why you could see text like
"u30b1 u30f3 u3055 u307e u30fe ..." in the output file.</p>
<p>What you should do is open the file with the proper encoding:</p>
<div>
<p><a href="https://user-images.githubusercontent.com/4409108/30702318-f05bc196-9eec-11e7-8234-a07aabec294f.PNG"></a></p><p>Example of output with Japanese characters</p>
</div>
<a></a>
<h3>3.1.2 Opening into a pandas dataframe</h3>
<p>After the file has been opened, it can easily be converted into a <code>`pandas`</code> DataFrame</p>
<pre>import pandas as pd
df = pd.read_json('tweets.json', encoding='utf-8')
</pre>
</article>
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 Scrape Twitter for Tweets

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 View code
 Backers Sponsors Synopsis 1. Motivation 2. Installation and Usage 2.2 The CLI 2.2.1 Examples of simple queries 2.2.2 Examples of advanced queries 2.2.3 Examples of scraping user pages 2.3 From within Python 2.3.1 Examples of Python Queries 2.4 Scraping for retweets 2.5 Scraping for User Profile information 3. Output 3.1 Opening the output file 3.1.2 Opening into a pandas dataframe

 README.rst

 Backers

 Thank you to all our backers! 🙏 [Become a backer]

 Sponsors

 Support this project by becoming a sponsor. Your logo will show up here with a link to your website. [Become a sponsor]

 Synopsis

 A simple script to scrape Tweets using the Python package requests to retrieve the content and Beautifulsoup4 to parse the retrieved content.

 1. Motivation

 Twitter has provided REST API's which can be used by developers to access and read Twitter data. They have also provided a Streaming API which can be used to access Twitter Data in real-time.

 Most of the software written to access Twitter data provide a library which functions as a wrapper around Twitter's Search and Streaming API's and are therefore constrained by the limitations of the API's.

 With Twitter's Search API you can only send 180 Requests every 15 minutes. With a maximum number of 100 tweets per Request, you can mine 72 tweets per hour (4 x 180 x 100 =72) . By using TwitterScraper you are not limited by this number but by your internet speed/bandwith and the number of instances of TwitterScraper you are willing to start.

 One of the bigger disadvantages of the Search API is that you can only access Tweets written in the past 7 days. This is a major bottleneck for anyone looking for older data. With TwitterScraper there is no such limitation.

 Per Tweet it scrapes the following information:
 * Tweet-id
 * Tweet-url
 * Tweet text
 * Tweet html
 * Links inside Tweet
 * Hashtags inside Tweet
 * Image URLS inside Tweet
 * Video URL inside Tweet
 * Tweet timestamp
 * Tweet Epoch timestamp
 * Tweet No. of likes
 * Tweet No. of replies
 * Tweet No. of retweets
 * Username
 * User Full Name / Screen Name
 * User ID
 * Tweet is an reply to
 * Tweet is replied to
 * List of users Tweet is an reply to
 * Tweet ID of parent tweet
 In addition it can scrape for the following user information:
 * Date user joined
 * User location (if filled in)
 * User blog (if filled in)
 * User No. of tweets
 * User No. of following
 * User No. of followers
 * User No. of likes
 * User No. of lists
 * User is verified

 2. Installation and Usage

 To install twitterscraper:

 (sudo) pip install twitterscraper

 or you can clone the repository and in the folder containing setup.py

 python setup.py install

 If you prefer more isolation you can build a docker image

 docker build -t twitterscraper:build .

 and run your container with:

 docker run --rm -it -v/<PATH_TO_SOME_SHARED_FOLDER_FOR_RESULTS>:/app/data twitterscraper:build <YOUR_QUERY>

 2.2 The CLI

 You can use the command line application to get your tweets stored to JSON right away. Twitterscraper takes several arguments:

 * -h or --help Print out the help message and exits.
 * -l or --limit TwitterScraper stops scraping when at least the number of tweets indicated with --limit is scraped. Since tweets are retrieved in batches of 20, this will always be a multiple of 20. Omit the limit to retrieve all tweets. You can at any time abort the scraping by pressing Ctrl+C, the scraped tweets will be stored safely in your JSON file.
 * --lang Retrieves tweets written in a specific language. Currently 30+ languages are supported. For a full list of the languages print out the help message.
 * -bd or --begindate Set the date from which TwitterScraper should start scraping for your query. Format is YYYY-MM-DD. The default value is set to 2006-03-21. This does not work in combination with --user.
 * -ed or --enddate Set the enddate which TwitterScraper should use to stop scraping for your query. Format is YYYY-MM-DD. The default value is set to today. This does not work in combination with --user.
 * -u or --user Scrapes the tweets from that users' profile page. This also includes all retweets by that user. See section 2.2.4 in the examples below for more information.
 * --profiles : Twitterscraper will in addition to the tweets, also scrape for the profile information of the users who have written these tweets. The results will be saved in the file userprofiles_<filename>.
 * -p or --poolsize Set the number of parallel processes TwitterScraper should initiate while scraping for your query. Default value is set to 20. Depending on the computational power you have, you can increase this number. It is advised to keep this number below the number of days you are scraping. For example, if you are scraping from 2017-01-10 to 2017-01-20, you can set this number to a maximum of 10. If you are scraping from 2016-01-01 to 2016-12-31, you can increase this number to a maximum of 150, if you have the computational resources. Does not work in combination with --user.
 * -o or --output Gives the name of the output file. If no output filename is given, the default filename 'tweets.json' or 'tweets.csv' will be used.
 * -c or --csv Write the result to a CSV file instead of a JSON file.
 * -d or --dump: With this argument, the scraped tweets will be printed to the screen instead of an outputfile. If you are using this argument, the --output argument doe not need to be used.
 * -ow or --overwrite: With this argument, if the output file already exists it will be overwritten. If this argument is not set (default) twitterscraper will exit with the warning that the output file already exists.
 * -dp or --disableproxy: With this argument, proxy servers are not used when scrapping tweets or user profiles from twitter.

 2.2.1 Examples of simple queries

 Below is an example of how twitterscraper can be used:

 twitterscraper Trump --limit 1000 --output=tweets.json

 twitterscraper Trump -l 1000 -o tweets.json

 twitterscraper Trump -l 1000 -bd 2017-01-01 -ed 2017-06-01 -o tweets.json

 2.2.2 Examples of advanced queries

 You can use any advanced query Twitter supports. An advanced query should be placed within quotes, so that twitterscraper can recognize it as one single query.

 Here are some examples:

 * search for the occurence of 'Bitcoin' or 'BTC': twitterscraper "Bitcoin OR BTC" -o bitcoin_tweets.json -l 1000
 * search for the occurence of 'Bitcoin' and 'BTC': twitterscraper "Bitcoin AND BTC" -o bitcoin_tweets.json -l 1000
 * search for tweets from a specific user: twitterscraper "Blockchain from:VitalikButerin" -o blockchain_tweets.json -l 1000
 * search for tweets to a specific user: twitterscraper "Blockchain to:VitalikButerin" -o blockchain_tweets.json -l 1000
 * search for tweets written from a location: twitterscraper "Blockchain near:Seattle within:15mi" -o blockchain_tweets.json -l 1000

 You can construct an advanced query on Twitter Advanced Search or use one of the operators shown on this page. Also see Twitter's Standard operators

 2.2.3 Examples of scraping user pages

 You can also scraped all tweets written or retweeted by a specific user. This can be done by adding the boolean argument -u / --user argument. If this argument is used, the search term should be equal to the username.

 Here is an example of scraping a specific user:

 twitterscraper realDonaldTrump --user -o tweets_username.json

 This does not work in combination with -p, -bd, or -ed.

 The main difference with the example "search for tweets from a specific user" in section 2.2.2 is that this method really scrapes all tweets from a profile page (including retweets). The example in 2.2.2 scrapes the results from the search page (excluding retweets).

 2.3 From within Python

 You can easily use TwitterScraper from within python:

 from twitterscraper import query_tweets
 
 if __name__ == '__main__':
 list_of_tweets = query_tweets("Trump OR Clinton", 10)
 
 #print the retrieved tweets to the screen:
 for tweet in query_tweets("Trump OR Clinton", 10):
 print(tweet)
 
 #Or save the retrieved tweets to file:
 file = open(“output.txt”,”w”)
 for tweet in query_tweets("Trump OR Clinton", 10):
 file.write(str(tweet.text.encode('utf-8')))
 file.close()
 

 2.3.1 Examples of Python Queries

 * Query tweets from a given URL: Parameters:
 + query: The query search parameter of url
 + lang: Language of queried url
 + pos: Parameter passed for where to start looking in url
 + retry: Number of times to retry if error
 query_single_page(query, lang, pos, retry=50, from_user=False, timeout=60)
 * Query all tweets that match qeury: Parameters:
 + query: The query search parameter
 + limit: Number of tweets returned
 + begindate: Start date of query
 + enddate: End date of query
 + poolsize: Tweets per poolsize
 + lang: Language of query
 query_tweets('query', limit=None, begindate=dt.date.today(), enddate=dt.date.today(), poolsize=20, lang='')
 * Query tweets from a specific user: Parameters:
 + user: Twitter username
 + limit: Number of tweets returned
 query_tweets(user, limit=None)

 2.4 Scraping for retweets

 A regular search within Twitter will not show you any retweets. Twitterscraper therefore does not contain any retweets in the output.

 To give an example: If user1 has written a tweet containing #trump2020 and user2 has retweetet this tweet, a search for #trump2020 will only show the original tweet.

 The only way you can scrape for retweets is if you scrape for all tweets of a specific user with the -u / --user argument.

 2.5 Scraping for User Profile information

 By adding the argument --profiles twitterscraper will in addition to the tweets, also scrape for the profile information of the users who have written these tweets. The results will be saved in the file "userprofiles_<filename>".

 Try not to use this argument too much. If you have already scraped profile information for a set of users, there is no need to do it again :) It is also possible to scrape for profile information without scraping for tweets. Examples of this can be found in the examples folder.

 3. Output

 All of the retrieved Tweets are stored in the indicated output file. The contents of the output file will look like:

 [{"fullname": "Rupert Meehl", "id": "892397793071050752", "likes": "1", "replies": "0", "retweets": "0", "text": "Latest: Trump now at lowest Approval and highest Disapproval ratings yet. Oh, we're winning bigly here ...\n\nhttps://projects.fivethirtyeight.com/trump-approval-ratings/?ex_cid=rrpromo\u00a0\u2026", "timestamp": "2017-08-01T14:53:08", "user": "Rupert_Meehl"}, {"fullname": "Barry Shapiro", "id": "892397794375327744", "likes": "0", "replies": "0", "retweets": "0", "text": "A former GOP Rep quoted this line, which pretty much sums up Donald Trump. https://twitter.com/davidfrum/status/863017301595107329\u00a0\u2026", "timestamp": "2017-08-01T14:53:08", "user": "barryshap"}, (...)
 ]
 

 3.1 Opening the output file

 In order to correctly handle all possible characters in the tweets (think of Japanese or Arabic characters), the output is saved as utf-8 encoded bytes. That is why you could see text like "u30b1 u30f3 u3055 u307e u30fe ..." in the output file.

 What you should do is open the file with the proper encoding:

 Example of output with Japanese characters

 3.1.2 Opening into a pandas dataframe

 After the file has been opened, it can easily be converted into a `pandas` DataFrame

 import pandas as pd
 df = pd.read_json('tweets.json', encoding='utf-8')
 

 About

 Scrape Twitter for Tweets

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Synopsis
A simple script to scrape Tweets using the Python package requests
to retrieve the content and Beautifulsoup4 to parse the retrieved
content.
 
1. Motivation
Twitter has provided REST
API's which can be used by
developers to access and read Twitter data. They have also provided a
Streaming API which can
be used to access Twitter Data in real-time.
Most of the software written to access Twitter data provide a library
which functions as a wrapper around Twitter's Search and Streaming API's
and are therefore constrained by the limitations of the API's.
With Twitter's Search API you can only send 180 Requests every 15
minutes. With a maximum number of 100 tweets per Request, you
can mine 72 tweets per hour (4 x 180 x 100 =72) . By using
TwitterScraper you are not limited by this number but by your internet
speed/bandwith and the number of instances of TwitterScraper you are
willing to start.
One of the bigger disadvantages of the Search API is that you can only
access Tweets written in the past 7 days. This is a major bottleneck
for anyone looking for older data. With TwitterScraper there is no such
limitation.
Per Tweet it scrapes the following information:
 
Tweet-id
Tweet-url
Tweet text
Tweet html
Links inside Tweet
Hashtags inside Tweet
Image URLS inside Tweet
Video URL inside Tweet
Tweet timestamp
Tweet Epoch timestamp
Tweet No. of likes
Tweet No. of replies
Tweet No. of retweets
Username
User Full Name / Screen Name
User ID
Tweet is an reply to
Tweet is replied to
List of users Tweet is an reply to
Tweet ID of parent tweet
In addition it can scrape for the following user information:
 
Date user joined
User location (if filled in)
User blog (if filled in)
User No. of tweets
User No. of following
User No. of followers
User No. of likes
User No. of lists
User is verified
 
2. Installation and Usage
To install twitterscraper:
 (sudo) pip install twitterscraper 
or you can clone the repository and in the folder containing setup.py
If you prefer more isolation you can build a docker image
 docker build -t twitterscraper:build . 
and run your container with:
 docker run --rm -it -v/&lt;PATH_TO_SOME_SHARED_FOLDER_FOR_RESULTS&gt;:/app/data twitterscraper:build &lt;YOUR_QUERY&gt; 
 
2.2 The CLI
You can use the command line application to get your tweets stored to
JSON right away. Twitterscraper takes several arguments:
 -h or --help Print out the help message and exits.
 -l or --limit TwitterScraper stops scraping when at least
the number of tweets indicated with --limit is scraped. Since
tweets are retrieved in batches of 20, this will always be a multiple
of 20. Omit the limit to retrieve all tweets. You can at any time abort the
scraping by pressing Ctrl+C, the scraped tweets will be stored safely
in your JSON file.
 --lang Retrieves tweets written in a specific language. Currently
30+ languages are supported. For a full list of the languages print
out the help message.
 -bd or --begindate Set the date from which TwitterScraper
should start scraping for your query. Format is YYYY-MM-DD. The
default value is set to 2006-03-21. This does not work in combination with --user.
 -ed or --enddate Set the enddate which TwitterScraper should
use to stop scraping for your query. Format is YYYY-MM-DD. The
default value is set to today. This does not work in combination with --user.
 -u or --user Scrapes the tweets from that users' profile page.
This also includes all retweets by that user. See section 2.2.4 in the examples below
for more information.
 --profiles : Twitterscraper will in addition to the tweets, also scrape for the profile
information of the users who have written these tweets. The results will be saved in the
file userprofiles_&lt;filename&gt;.
 -p or --poolsize Set the number of parallel processes
TwitterScraper should initiate while scraping for your query. Default
value is set to 20. Depending on the computational power you have,
you can increase this number. It is advised to keep this number below
the number of days you are scraping. For example, if you are
scraping from 2017-01-10 to 2017-01-20, you can set this number to a
maximum of 10. If you are scraping from 2016-01-01 to 2016-12-31, you
can increase this number to a maximum of 150, if you have the
computational resources. Does not work in combination with --user.
 -o or --output Gives the name of the output file. If no
output filename is given, the default filename 'tweets.json' or 'tweets.csv'
will be used.
 -c or --csv Write the result to a CSV file instead of a JSON file.
 -d or --dump: With this argument, the scraped tweets will be
printed to the screen instead of an outputfile. If you are using this
argument, the --output argument doe not need to be used.
 -ow or --overwrite: With this argument, if the output file already exists
it will be overwritten. If this argument is not set (default) twitterscraper will
exit with the warning that the output file already exists.
 -dp or --disableproxy: With this argument, proxy servers are not used when scrapping tweets or user profiles from twitter.
 
2.2.1 Examples of simple queries
Below is an example of how twitterscraper can be used:
 twitterscraper Trump --limit 1000 --output=tweets.json 
 twitterscraper Trump -l 1000 -o tweets.json 
 twitterscraper Trump -l 1000 -bd 2017-01-01 -ed 2017-06-01 -o tweets.json 
 
2.2.2 Examples of advanced queries
You can use any advanced query Twitter supports. An advanced query
should be placed within quotes, so that twitterscraper can recognize it
as one single query.
Here are some examples:
search for the occurence of 'Bitcoin' or 'BTC':
twitterscraper "Bitcoin OR BTC" -o bitcoin_tweets.json -l 1000 
search for the occurence of 'Bitcoin' and 'BTC':
twitterscraper "Bitcoin AND BTC" -o bitcoin_tweets.json -l 1000 
search for tweets from a specific user:
twitterscraper "Blockchain from:VitalikButerin" -o blockchain_tweets.json -l 1000 
search for tweets to a specific user:
twitterscraper "Blockchain to:VitalikButerin" -o blockchain_tweets.json -l 1000 
search for tweets written from a location:
twitterscraper "Blockchain near:Seattle within:15mi" -o blockchain_tweets.json -l 1000 
You can construct an advanced query on Twitter Advanced Search or use one of the operators shown on this page.
Also see Twitter's Standard operators 
 
2.2.3 Examples of scraping user pages
You can also scraped all tweets written or retweeted by a specific user.
This can be done by adding the boolean argument -u / --user argument.
If this argument is used, the search term should be equal to the username.
Here is an example of scraping a specific user:
 twitterscraper realDonaldTrump --user -o tweets_username.json 
This does not work in combination with -p, -bd, or -ed.
The main difference with the example "search for tweets from a specific user" in section 2.2.2 is that this method really scrapes
all tweets from a profile page (including retweets).
The example in 2.2.2 scrapes the results from the search page (excluding retweets).
 
2.3 From within Python
You can easily use TwitterScraper from within python:
from twitterscraper import query_tweets
if __name__ == '__main__':
 list_of_tweets = query_tweets("Trump OR Clinton", 10)
 #print the retrieved tweets to the screen:
 for tweet in query_tweets("Trump OR Clinton", 10):
 print(tweet)
 #Or save the retrieved tweets to file:
 file = open(“output.txt”,”w”)
 for tweet in query_tweets("Trump OR Clinton", 10):
 file.write(str(tweet.text.encode('utf-8')))
 file.close()
 
2.3.1 Examples of Python Queries
 
Query tweets from a given URL:
 
Parameters:
 
query: The query search parameter of url
lang: Language of queried url
pos: Parameter passed for where to start looking in url
retry: Number of times to retry if error
 query_single_page(query, lang, pos, retry=50, from_user=False, timeout=60) 
 
Query all tweets that match qeury:
 
Parameters:
 
query: The query search parameter
limit: Number of tweets returned
begindate: Start date of query
enddate: End date of query
poolsize: Tweets per poolsize
lang: Language of query
 query_tweets('query', limit=None, begindate=dt.date.today(), enddate=dt.date.today(), poolsize=20, lang='') 
 
Query tweets from a specific user:
 
Parameters:
 
user: Twitter username
limit: Number of tweets returned
 query_tweets(user, limit=None) 
 
2.4 Scraping for retweets
A regular search within Twitter will not show you any retweets.
Twitterscraper therefore does not contain any retweets in the output.
To give an example: If user1 has written a tweet containing #trump2020 and user2 has retweetet this tweet,
a search for #trump2020 will only show the original tweet.
The only way you can scrape for retweets is if you scrape for all tweets of a specific user with the -u / --user argument.
 
2.5 Scraping for User Profile information
By adding the argument --profiles twitterscraper will in addition to the tweets, also scrape for the profile information of the users who have written these tweets.
The results will be saved in the file "userprofiles_&lt;filename&gt;".
Try not to use this argument too much. If you have already scraped profile information for a set of users, there is no need to do it again :)
It is also possible to scrape for profile information without scraping for tweets.
Examples of this can be found in the examples folder.
 
3. Output
All of the retrieved Tweets are stored in the indicated output file. The
contents of the output file will look like:
[{"fullname": "Rupert Meehl", "id": "892397793071050752", "likes": "1", "replies": "0", "retweets": "0", "text": "Latest: Trump now at lowest Approval and highest Disapproval ratings yet. Oh, we're winning bigly here ...\n\nhttps://projects.fivethirtyeight.com/trump-approval-ratings/?ex_cid=rrpromo\u00a0\u2026", "timestamp": "2017-08-01T14:53:08", "user": "Rupert_Meehl"}, {"fullname": "Barry Shapiro", "id": "892397794375327744", "likes": "0", "replies": "0", "retweets": "0", "text": "A former GOP Rep quoted this line, which pretty much sums up Donald Trump. https://twitter.com/davidfrum/status/863017301595107329\u00a0\u2026", "timestamp": "2017-08-01T14:53:08", "user": "barryshap"}, (...)
]
 
3.1 Opening the output file
In order to correctly handle all possible characters in the tweets
(think of Japanese or Arabic characters), the output is saved as utf-8
encoded bytes. That is why you could see text like
"u30b1 u30f3 u3055 u307e u30fe ..." in the output file.
What you should do is open the file with the proper encoding:
    Example of output with Japanese characters
 
3.1.2 Opening into a pandas dataframe
After the file has been opened, it can easily be converted into a `pandas` DataFrame
import pandas as pd
df = pd.read_json('tweets.json', encoding='utf-8')
GitHub - taspinar/twitterscraper: Scrape Twitter for Tweets
https://github.com/taspinar/twitterscraper
https://github.com/Akash-Sharma-1/Advanced-Twitter-Scraper
GitHub - Akash-Sharma-1/Advanced-Twitter-Scraper: An advanced Twitter Scrapper which bypasses Twitter's official API Limit on tweet data retrieval.
2023-01-01
Akash-Sharma-1
<div><div>
 <article><h2>Twitter Scraper (Bypassing the Twitter's Official API Limit)</h2>
<p>The Twitter API restricts the developer from getting all of a user's tweets (assuming they have more than 3200) by placing an API retrieval limit over the tweets (as well as the information) which is quite understandable given that they have to manage unprecedented amounts of scraping activities over their platform.
My approach in the implementation of the Scraper and its ideation is to develop a workaround to go over this problem using Python, Selenium, and Tweepy. However, to admit it might be slower than any other scrapper which directly uses the Twitter API but it is a tradeoff between speed and size. You can go for this approach if you are looking for scraping a big data source from twitter then this will definitely be helpful.</p>
<p>Essentially, we will use Selenium to open up a browser and automatically visit Twitter's search page, searching for a single user's tweets on a single day. If we want all tweets from 2015, we will check all 365 days / pages. This would be a nightmare to do manually, so the <code>scrape.py</code> script does it all for you - all you have to do is input a date range and a twitter user handle, and wait for it to finish.</p>
<p>The <code>scrape.py</code> script collects tweet ids. If you know a tweet's id number, you can get all the information available about that tweet using Tweepy - text, timestamp, number of retweets / replies / favorites, geolocation, etc. Tweepy uses Twitter's API, so you will need to get API keys. Once you have them, you can run the <code>get_metadata.py</code> script.</p>
<h2>Pre- Requisites</h2>
<ul>
<li>Basic knowledge on how to use a terminal/cmd</li>
<li><strong>WEBDRIVERS</strong>
<ul>
<li>If using Chrome, install ChromeDriver's latest version on the Chrome.</li>
<li>If using Safari, version 10+ with 'Allow Remote Automation' option enabled in Safari's Develop menu to control Safari via WebDriver.</li>
</ul>
</li>
<li>python3
<ul>
<li>to check, in your terminal, enter <code>python3</code></li>
<li>if you don't have it, check YouTube for installation instructions</li>
</ul>
</li>
<li>pip or pip3
<ul>
<li>to check, in your terminal, enter <code>pip</code> or <code>pip3</code></li>
<li>if you don't have it, again, check YouTube for installation instructions</li>
</ul>
</li>
<li>selenium (3.0.1)
<ul>
<li><code>pip3 install selenium</code></li>
</ul>
</li>
<li>tweepy (3.5.0)
<ul>
<li><code>pip3 install tweepy</code></li>
</ul>
</li>
</ul>
<h2>Running the scraper</h2>
<ul>
<li>open up <code>scrape.py</code> and edit the user, start, and end variables (and save the file)</li>
<li>
<div><pre> # edit these three variables
 user = '____USERNAME HERE____'
 start = datetime.datetime(2018, 12, 19) # year, month, day
 end = datetime.datetime(2018, 12, 22) # year, month, day</pre></div>
</li>
<li>run <code>python3 scrape.py</code></li>
<li>you'll see a browser pop up and output in the terminal</li>
<li>do some fun other task until it finishes</li>
<li>once it's done, it outputs all the tweet ids it found into <code>all_ids.json</code></li>
<li>every time you run the scraper with different dates, it will add the new ids to the same file
<ul>
<li>it automatically removes duplicates so don't worry about small date overlaps</li>
</ul>
</li>
</ul>
<h2>Troubleshooting the scraper</h2>
<ul>
<li>do you get a <code>no such file</code> error? you need to cd to the directory of <code>scrape.py</code></li>
<li>do you get a driver error when you try and run the script?
<ul>
<li>open <code>scrape.py</code> and change the driver to use Chrome() or Firefox()
<ul>
<li>if neither work, google the error (you probably need to install a new driver)</li>
</ul>
</li>
</ul>
</li>
<li>does it seem like it's not collecting tweets for days that have tweets?
<ul>
<li>open <code>scrape.py</code> and change the delay variable to 2 or 3</li>
</ul>
</li>
</ul>
<h2>Getting the metadata</h2>
<ul>
<li>first you'll need to get twitter API keys
<ul>
<li>sign up for a developer account here <a href="https://dev.twitter.com/">https://dev.twitter.com/</a></li>
<li>get your keys here: <a href="https://apps.twitter.com/">https://apps.twitter.com/</a></li>
<li>enable Read and Write operation for the access tokens and keys (Optional)</li>
</ul>
</li>
<li>put your keys into the <code>api_keys.json</code> file</li>
<li>open up <code>get_metadata.py</code> and edit the user variable (and save the file)
<div><pre>user = '____USERNAME HERE____'</pre></div>
</li>
<li>run <code>python3 get_metadata.py</code></li>
<li>this will get metadata for every tweet id in <code>all_ids.json</code></li>
<li>it will create 4 files
<ul>
<li><code>username.json</code> (master file with all metadata)</li>
<li><code>username.zip</code> (a zipped file of the master file with all metadata)</li>
<li><code>username_short.json</code> (smaller master file with relevant metadata fields - with less number of fields)</li>
<li><code>username.csv</code> (csv version of the smaller master file)</li>
</ul>
</li>
</ul>
<h2>Warning</h2>
<table>
<thead>
<tr>
<th>Be careful while running the selenium scraping script repeatedly on Twitter might temporarily ban your account !</th>
</tr>
</thead>
</table>
<h2>Author</h2>
<ul>
<li><strong>Akash Sharma</strong></li>
</ul>
<h2>License</h2>
<p>This project is licensed under the MIT License - see the <a href="/Akash-Sharma-1/Advanced-Twitter-Scraper/blob/master/LICENSE">LICENSE</a> file for details</p>
</article>
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 An advanced Twitter Scrapper which bypasses Twitter's official API Limit on tweet data retrieval.

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 Twitter Scraper (Bypassing the Twitter's Official API Limit) Pre- Requisites Running the scraper Troubleshooting the scraper Getting the metadata Warning Author License

 README.md

 Twitter Scraper (Bypassing the Twitter's Official API Limit)

 The Twitter API restricts the developer from getting all of a user's tweets (assuming they have more than 3200) by placing an API retrieval limit over the tweets (as well as the information) which is quite understandable given that they have to manage unprecedented amounts of scraping activities over their platform. My approach in the implementation of the Scraper and its ideation is to develop a workaround to go over this problem using Python, Selenium, and Tweepy. However, to admit it might be slower than any other scrapper which directly uses the Twitter API but it is a tradeoff between speed and size. You can go for this approach if you are looking for scraping a big data source from twitter then this will definitely be helpful.

 Essentially, we will use Selenium to open up a browser and automatically visit Twitter's search page, searching for a single user's tweets on a single day. If we want all tweets from 2015, we will check all 365 days / pages. This would be a nightmare to do manually, so the scrape.py script does it all for you - all you have to do is input a date range and a twitter user handle, and wait for it to finish.

 The scrape.py script collects tweet ids. If you know a tweet's id number, you can get all the information available about that tweet using Tweepy - text, timestamp, number of retweets / replies / favorites, geolocation, etc. Tweepy uses Twitter's API, so you will need to get API keys. Once you have them, you can run the get_metadata.py script.

 Pre- Requisites

 * Basic knowledge on how to use a terminal/cmd
 * WEBDRIVERS
 + If using Chrome, install ChromeDriver's latest version on the Chrome.
 + If using Safari, version 10+ with 'Allow Remote Automation' option enabled in Safari's Develop menu to control Safari via WebDriver.
 * python3
 + to check, in your terminal, enter python3
 + if you don't have it, check YouTube for installation instructions
 * pip or pip3
 + to check, in your terminal, enter pip or pip3
 + if you don't have it, again, check YouTube for installation instructions
 * selenium (3.0.1)
 + pip3 install selenium
 * tweepy (3.5.0)
 + pip3 install tweepy

 Running the scraper

 * open up scrape.py and edit the user, start, and end variables (and save the file)
 * # edit these three variables
 user = '____USERNAME HERE____'
 start = datetime.datetime(2018, 12, 19) # year, month, day
 end = datetime.datetime(2018, 12, 22) # year, month, day
 * run python3 scrape.py
 * you'll see a browser pop up and output in the terminal
 * do some fun other task until it finishes
 * once it's done, it outputs all the tweet ids it found into all_ids.json
 * every time you run the scraper with different dates, it will add the new ids to the same file
 + it automatically removes duplicates so don't worry about small date overlaps

 Troubleshooting the scraper

 * do you get a no such file error? you need to cd to the directory of scrape.py
 * do you get a driver error when you try and run the script?
 + open scrape.py and change the driver to use Chrome() or Firefox()
 o if neither work, google the error (you probably need to install a new driver)
 * does it seem like it's not collecting tweets for days that have tweets?
 + open scrape.py and change the delay variable to 2 or 3

 Getting the metadata

 * first you'll need to get twitter API keys
 + sign up for a developer account here https://dev.twitter.com/
 + get your keys here: https://apps.twitter.com/
 + enable Read and Write operation for the access tokens and keys (Optional)
 * put your keys into the api_keys.json file
 * open up get_metadata.py and edit the user variable (and save the file)
 user = '____USERNAME HERE____'
 * run python3 get_metadata.py
 * this will get metadata for every tweet id in all_ids.json
 * it will create 4 files
 + username.json (master file with all metadata)
 + username.zip (a zipped file of the master file with all metadata)
 + username_short.json (smaller master file with relevant metadata fields - with less number of fields)
 + username.csv (csv version of the smaller master file)

 Warning

 Be careful while running the selenium scraping script repeatedly on Twitter might temporarily ban your account !
 

 Author

 * Akash Sharma

 License

 This project is licensed under the MIT License - see the LICENSE file for details

 About

 An advanced Twitter Scrapper which bypasses Twitter's official API Limit on tweet data retrieval.

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 scraper twitter twitter-api selenium tweepy

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Twitter Scraper (Bypassing the Twitter's Official API Limit)
The Twitter API restricts the developer from getting all of a user's tweets (assuming they have more than 3200) by placing an API retrieval limit over the tweets (as well as the information) which is quite understandable given that they have to manage unprecedented amounts of scraping activities over their platform.
My approach in the implementation of the Scraper and its ideation is to develop a workaround to go over this problem using Python, Selenium, and Tweepy. However, to admit it might be slower than any other scrapper which directly uses the Twitter API but it is a tradeoff between speed and size. You can go for this approach if you are looking for scraping a big data source from twitter then this will definitely be helpful.
Essentially, we will use Selenium to open up a browser and automatically visit Twitter's search page, searching for a single user's tweets on a single day. If we want all tweets from 2015, we will check all 365 days / pages. This would be a nightmare to do manually, so the scrape.py script does it all for you - all you have to do is input a date range and a twitter user handle, and wait for it to finish.
The scrape.py script collects tweet ids. If you know a tweet's id number, you can get all the information available about that tweet using Tweepy - text, timestamp, number of retweets / replies / favorites, geolocation, etc. Tweepy uses Twitter's API, so you will need to get API keys. Once you have them, you can run the get_metadata.py script.
Pre- Requisites
Basic knowledge on how to use a terminal/cmd
 WEBDRIVERS
If using Chrome, install ChromeDriver's latest version on the Chrome.
If using Safari, version 10+ with 'Allow Remote Automation' option enabled in Safari's Develop menu to control Safari via WebDriver.
python3
to check, in your terminal, enter python3 
if you don't have it, check YouTube for installation instructions
pip or pip3
to check, in your terminal, enter pip or pip3 
if you don't have it, again, check YouTube for installation instructions
selenium (3.0.1)
 pip3 install selenium 
tweepy (3.5.0)
 pip3 install tweepy 
Running the scraper
open up scrape.py and edit the user, start, and end variables (and save the file)
  # edit these three variables
 user = '____USERNAME HERE____'
 start = datetime.datetime(2018, 12, 19) # year, month, day
 end = datetime.datetime(2018, 12, 22) # year, month, day 
run python3 scrape.py 
you'll see a browser pop up and output in the terminal
do some fun other task until it finishes
once it's done, it outputs all the tweet ids it found into all_ids.json 
every time you run the scraper with different dates, it will add the new ids to the same file
it automatically removes duplicates so don't worry about small date overlaps
Troubleshooting the scraper
do you get a no such file error? you need to cd to the directory of scrape.py 
do you get a driver error when you try and run the script?
open scrape.py and change the driver to use Chrome() or Firefox()
if neither work, google the error (you probably need to install a new driver)
does it seem like it's not collecting tweets for days that have tweets?
open scrape.py and change the delay variable to 2 or 3
Getting the metadata
first you'll need to get twitter API keys
sign up for a developer account here https://dev.twitter.com/ 
get your keys here: https://apps.twitter.com/ 
enable Read and Write operation for the access tokens and keys (Optional)
put your keys into the api_keys.json file
open up get_metadata.py and edit the user variable (and save the file)
 user = '____USERNAME HERE____' 
run python3 get_metadata.py 
this will get metadata for every tweet id in all_ids.json 
it will create 4 files
 username.json (master file with all metadata)
 username.zip (a zipped file of the master file with all metadata)
 username_short.json (smaller master file with relevant metadata fields - with less number of fields)
 username.csv (csv version of the smaller master file)
Warning
Be careful while running the selenium scraping script repeatedly on Twitter might temporarily ban your account !
Author
 Akash Sharma 
License
This project is licensed under the MIT License - see the LICENSE file for details
GitHub - Akash-Sharma-1/Advanced-Twitter-Scraper: An advanced Twitter Scrapper which bypasses Twitter's official API Limit on tweet data retrieval.
https://github.com/Akash-Sharma-1/Advanced-Twitter-Scraper
https://github.com/twintproject/twint
GitHub - twintproject/twint: An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.
2021-03-02
Twintproject
<div><div>
 <article><h2>TWINT - Twitter Intelligence Tool</h2>
<p><a href="https://camo.githubusercontent.com/e07e699c55c6e7f0c45a924cbe4387e0c53cd8766333a8e9929feff1419008fe/68747470733a2f2f692e696d6775722e636f6d2f6961483373377a2e706e67"></a>
<a href="https://camo.githubusercontent.com/60a6d4694ac33c17356bc4f09b1814ea72c43098bbee764f800fe97a580445e5/68747470733a2f2f692e696d6775722e636f6d2f6856654372714c2e706e67"></a></p>
<p><a href="https://pypi.org/project/twint/"></a> <a href="https://travis-ci.org/twintproject/twint"></a> <a href="https://www.python.org/download/releases/3.0/"></a> <a href="https://github.com/haccer/tweep/blob/master/LICENSE"></a> <a href="https://pepy.tech/project/twint"></a> <a href="https://pepy.tech/project/twint/week"></a> <a href="https://www.patreon.com/twintproject"></a> <a href="https://camo.githubusercontent.com/4631b2d0052055b89b395ff0bd9d2a74bb427c74ea5b0a50b5890c387b3a8c79/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f6e6f6e65707269766163792e7376673f6c6162656c3d466f6c6c6f77267374796c653d736f6369616c"></a></p>
<blockquote>
<p>No authentication. No API. No limits.</p>
</blockquote>
<p>Twint is an advanced Twitter scraping tool written in Python that allows for scraping Tweets from Twitter profiles <strong>without</strong> using Twitter's API.</p>
<p>Twint utilizes Twitter's search operators to let you scrape Tweets from specific users, scrape Tweets relating to certain topics, hashtags &amp; trends, or sort out <em>sensitive</em> information from Tweets like e-mail and phone numbers. I find this very useful, and you can get really creative with it too.</p>
<p>Twint also makes special queries to Twitter allowing you to also scrape a Twitter user's followers, Tweets a user has liked, and who they follow <strong>without</strong> any authentication, API, Selenium, or browser emulation.</p>
<h2>tl;dr Benefits</h2>
<p>Some of the benefits of using Twint vs Twitter API:</p>
<ul>
<li>Can fetch almost <strong>all</strong> Tweets (Twitter API limits to last 3200 Tweets only);</li>
<li>Fast initial setup;</li>
<li>Can be used anonymously and without Twitter sign up;</li>
<li><strong>No rate limitations</strong>.</li>
</ul>
<h2>Limits imposed by Twitter</h2>
<p>Twitter limits scrolls while browsing the user timeline. This means that with <code>.Profile</code> or with <code>.Favorites</code> you will be able to get ~3200 tweets.</p>
<h2>Requirements</h2>
<ul>
<li>Python 3.6;</li>
<li>aiohttp;</li>
<li>aiodns;</li>
<li>beautifulsoup4;</li>
<li>cchardet;</li>
<li>dataclasses</li>
<li>elasticsearch;</li>
<li>pysocks;</li>
<li>pandas (&gt;=0.23.0);</li>
<li>aiohttp_socks;</li>
<li>schedule;</li>
<li>geopy;</li>
<li>fake-useragent;</li>
<li>py-googletransx.</li>
</ul>
<h2>Installing</h2>
<p><strong>Git:</strong></p>
<div><pre>git clone --depth=1 https://github.com/twintproject/twint.git
cd twint
pip3 install . -r requirements.txt</pre></div>
<p><strong>Pip:</strong></p>
<p>or</p>
<div><pre>pip3 install --user --upgrade git+https://github.com/twintproject/twint.git@origin/master#egg=twint</pre></div>
<p><strong>Pipenv</strong>:</p>
<div><pre>pipenv install git+https://github.com/twintproject/twint.git#egg=twint</pre></div>
<h3>March 2, 2021 Update</h3>
<p><strong>Added</strong>: Dockerfile</p>
<p>Noticed a lot of people are having issues installing (including me). Please use the Dockerfile temporarily while I look into them.</p>
<h2>CLI Basic Examples and Combos</h2>
<p>A few simple examples to help you understand the basics:</p>
<ul>
<li><code>twint -u username</code> - Scrape all the Tweets of a <em>user</em> (doesn't include <strong>retweets</strong> but includes <strong>replies</strong>).</li>
<li><code>twint -u username -s pineapple</code> - Scrape all Tweets from the <em>user</em>'s timeline containing <em>pineapple</em>.</li>
<li><code>twint -s pineapple</code> - Collect every Tweet containing <em>pineapple</em> from everyone's Tweets.</li>
<li><code>twint -u username --year 2014</code> - Collect Tweets that were tweeted <strong>before</strong> 2014.</li>
<li><code>twint -u username --since "2015-12-20 20:30:15"</code> - Collect Tweets that were tweeted since 2015-12-20 20:30:15.</li>
<li><code>twint -u username --since 2015-12-20</code> - Collect Tweets that were tweeted since 2015-12-20 00:00:00.</li>
<li><code>twint -u username -o file.txt</code> - Scrape Tweets and save to file.txt.</li>
<li><code>twint -u username -o file.csv --csv</code> - Scrape Tweets and save as a csv file.</li>
<li><code>twint -u username --email --phone</code> - Show Tweets that might have phone numbers or email addresses.</li>
<li><code>twint -s "Donald Trump" --verified</code> - Display Tweets by verified users that Tweeted about Donald Trump.</li>
<li><code>twint -g="48.880048,2.385939,1km" -o file.csv --csv</code> - Scrape Tweets from a radius of 1km around a place in Paris and export them to a csv file.</li>
<li><code>twint -u username -es localhost:9200</code> - Output Tweets to Elasticsearch</li>
<li><code>twint -u username -o file.json --json</code> - Scrape Tweets and save as a json file.</li>
<li><code>twint -u username --database tweets.db</code> - Save Tweets to a SQLite database.</li>
<li><code>twint -u username --followers</code> - Scrape a Twitter user's followers.</li>
<li><code>twint -u username --following</code> - Scrape who a Twitter user follows.</li>
<li><code>twint -u username --favorites</code> - Collect all the Tweets a user has favorited (gathers ~3200 tweet).</li>
<li><code>twint -u username --following --user-full</code> - Collect full user information a person follows</li>
<li><code>twint -u username --timeline</code> - Use an effective method to gather Tweets from a user's profile (Gathers ~3200 Tweets, including <strong>retweets</strong> &amp; <strong>replies</strong>).</li>
<li><code>twint -u username --retweets</code> - Use a quick method to gather the last 900 Tweets (that includes retweets) from a user's profile.</li>
<li><code>twint -u username --resume resume_file.txt</code> - Resume a search starting from the last saved scroll-id.</li>
</ul>
<p>More detail about the commands and options are located in the <a href="https://github.com/twintproject/twint/wiki/Commands">wiki</a></p>
<h2>Module Example</h2>
<p>Twint can now be used as a module and supports custom formatting. <strong>More details are located in the <a href="https://github.com/twintproject/twint/wiki/Module">wiki</a></strong></p>
<div><pre>import twint
# Configure
c = twint.Config()
c.Username = "realDonaldTrump"
c.Search = "great"
# Run
twint.run.Search(c)</pre></div>
<blockquote>
<p>Output</p>
</blockquote>
<p><code>955511208597184512 2018-01-22 18:43:19 GMT &lt;now&gt; pineapples are the best fruit</code></p>
<div><pre>import twint
c = twint.Config()
c.Username = "noneprivacy"
c.Custom["tweet"] = ["id"]
c.Custom["user"] = ["bio"]
c.Limit = 10
c.Store_csv = True
c.Output = "none"
twint.run.Search(c)</pre></div>
<h2>Storing Options</h2>
<ul>
<li>Write to file;</li>
<li>CSV;</li>
<li>JSON;</li>
<li>SQLite;</li>
<li>Elasticsearch.</li>
</ul>
<h2>Elasticsearch Setup</h2>
<p>Details on setting up Elasticsearch with Twint is located in the <a href="https://github.com/twintproject/twint/wiki/Elasticsearch">wiki</a>.</p>
<h2>Graph Visualization</h2>
<p><a href="https://camo.githubusercontent.com/eddbe0074e20e53b32e42aed1527a416f4adc7e1d64a897105b4b3d4f079beef/68747470733a2f2f692e696d6775722e636f6d2f45454a7142386e2e706e67"></a></p>
<p><a href="https://github.com/twintproject/twint/wiki/Graph">Graph</a> details are also located in the <a href="https://github.com/twintproject/twint/wiki/Graph">wiki</a>.</p>
<p>We are developing a Twint Desktop App.</p>
<p><a href="https://camo.githubusercontent.com/df0444dbc905eb677dbc47aef762b188f5fcc0759d2b4e525d77a8cf69061466/68747470733a2f2f692e696d6775722e636f6d2f447a636649674c2e706e67"></a></p>
<h2>FAQ</h2>
<blockquote>
<p>I tried scraping tweets from a user, I know that they exist but I'm not getting them</p>
</blockquote>
<p>Twitter can shadow-ban accounts, which means that their tweets will not be available via search. To solve this, pass <code>--profile-full</code> if you are using Twint via CLI or, if are using Twint as module, add <code>config.Profile_full = True</code>. Please note that this process will be quite slow.</p>
<h2>More Examples</h2>
<h4>Followers/Following</h4>
<blockquote>
<p>To get only follower usernames/following usernames</p>
</blockquote>
<p><code>twint -u username --followers</code></p>
<p><code>twint -u username --following</code></p>
<blockquote>
<p>To get user info of followers/following users</p>
</blockquote>
<p><code>twint -u username --followers --user-full</code></p>
<p><code>twint -u username --following --user-full</code></p>
<h4>userlist</h4>
<blockquote>
<p>To get only user info of user</p>
</blockquote>
<p><code>twint -u username --user-full</code></p>
<blockquote>
<p>To get user info of users from a userlist</p>
</blockquote>
<p><code>twint --userlist inputlist --user-full</code></p>
<h4>tweet translation (experimental)</h4>
<blockquote>
<p>To get 100 english tweets and translate them to italian</p>
</blockquote>
<p><code>twint -u noneprivacy --csv --output none.csv --lang en --translate --translate-dest it --limit 100</code></p>
<p>or</p>
<div><pre>import twint
c = twint.Config()
c.Username = "noneprivacy"
c.Limit = 100
c.Store_csv = True
c.Output = "none.csv"
c.Lang = "en"
c.Translate = True
c.TranslateDest = "it"
twint.run.Search(c)</pre></div>
<p>Notes:</p>
<ul>
<li><a href="https://cloud.google.com/translate/quotas">Google translate has some quotas</a></li>
</ul>
<h2>Featured Blog Posts:</h2>
<ul>
<li><a href="https://pielco11.ovh/posts/twint-osint/">How to use Twint as an OSINT tool</a></li>
<li><a href="https://null-byte.wonderhowto.com/how-to/mine-twitter-for-targeted-information-with-twint-0193853/">Basic tutorial made by Null Byte</a></li>
<li><a href="https://towardsdatascience.com/analyzing-tweets-with-nlp-in-minutes-with-spark-optimus-and-twint-a0c96084995f">Analyzing Tweets with NLP in minutes with Spark, Optimus and Twint</a></li>
<li><a href="https://markhneedham.com/blog/2019/05/29/loading-tweets-twint-kafka-neo4j/">Loading tweets into Kafka and Neo4j</a></li>
</ul>
<h2>Contact</h2>
<p>If you have any question, want to join in discussions, or need extra help, you are welcome to join our Twint focused channel at <a href="https://osint.team/">OSINT team</a></p>
</article>
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 An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.

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 View code
 TWINT - Twitter Intelligence Tool tl;dr Benefits Limits imposed by Twitter Requirements Installing March 2, 2021 Update CLI Basic Examples and Combos Module Example Storing Options Elasticsearch Setup Graph Visualization FAQ More Examples Followers/Following userlist tweet translation (experimental) Featured Blog Posts: Contact

 README.md

 TWINT - Twitter Intelligence Tool

 No authentication. No API. No limits.

 Twint is an advanced Twitter scraping tool written in Python that allows for scraping Tweets from Twitter profiles without using Twitter's API.

 Twint utilizes Twitter's search operators to let you scrape Tweets from specific users, scrape Tweets relating to certain topics, hashtags & trends, or sort out sensitive information from Tweets like e-mail and phone numbers. I find this very useful, and you can get really creative with it too.

 Twint also makes special queries to Twitter allowing you to also scrape a Twitter user's followers, Tweets a user has liked, and who they follow without any authentication, API, Selenium, or browser emulation.

 tl;dr Benefits

 Some of the benefits of using Twint vs Twitter API:

 * Can fetch almost all Tweets (Twitter API limits to last 3200 Tweets only);
 * Fast initial setup;
 * Can be used anonymously and without Twitter sign up;
 * No rate limitations.

 Limits imposed by Twitter

 Twitter limits scrolls while browsing the user timeline. This means that with .Profile or with .Favorites you will be able to get ~3200 tweets.

 Requirements

 * Python 3.6;
 * aiohttp;
 * aiodns;
 * beautifulsoup4;
 * cchardet;
 * dataclasses
 * elasticsearch;
 * pysocks;
 * pandas (>=0.23.0);
 * aiohttp_socks;
 * schedule;
 * geopy;
 * fake-useragent;
 * py-googletransx.

 Installing

 Git:

 git clone --depth=1 https://github.com/twintproject/twint.git
 cd twint
 pip3 install . -r requirements.txt

 Pip:

 pip3 install twint

 or

 pip3 install --user --upgrade git+https://github.com/twintproject/twint.git@origin/master#egg=twint

 Pipenv:

 pipenv install git+https://github.com/twintproject/twint.git#egg=twint

 March 2, 2021 Update

 Added: Dockerfile

 Noticed a lot of people are having issues installing (including me). Please use the Dockerfile temporarily while I look into them.

 CLI Basic Examples and Combos

 A few simple examples to help you understand the basics:

 * twint -u username - Scrape all the Tweets of a user (doesn't include retweets but includes replies).
 * twint -u username -s pineapple - Scrape all Tweets from the user's timeline containing pineapple.
 * twint -s pineapple - Collect every Tweet containing pineapple from everyone's Tweets.
 * twint -u username --year 2014 - Collect Tweets that were tweeted before 2014.
 * twint -u username --since "2015-12-20 20:30:15" - Collect Tweets that were tweeted since 2015-12-20 20:30:15.
 * twint -u username --since 2015-12-20 - Collect Tweets that were tweeted since 2015-12-20 00:00:00.
 * twint -u username -o file.txt - Scrape Tweets and save to file.txt.
 * twint -u username -o file.csv --csv - Scrape Tweets and save as a csv file.
 * twint -u username --email --phone - Show Tweets that might have phone numbers or email addresses.
 * twint -s "Donald Trump" --verified - Display Tweets by verified users that Tweeted about Donald Trump.
 * twint -g="48.880048,2.385939,1km" -o file.csv --csv - Scrape Tweets from a radius of 1km around a place in Paris and export them to a csv file.
 * twint -u username -es localhost:9200 - Output Tweets to Elasticsearch
 * twint -u username -o file.json --json - Scrape Tweets and save as a json file.
 * twint -u username --database tweets.db - Save Tweets to a SQLite database.
 * twint -u username --followers - Scrape a Twitter user's followers.
 * twint -u username --following - Scrape who a Twitter user follows.
 * twint -u username --favorites - Collect all the Tweets a user has favorited (gathers ~3200 tweet).
 * twint -u username --following --user-full - Collect full user information a person follows
 * twint -u username --timeline - Use an effective method to gather Tweets from a user's profile (Gathers ~3200 Tweets, including retweets & replies).
 * twint -u username --retweets - Use a quick method to gather the last 900 Tweets (that includes retweets) from a user's profile.
 * twint -u username --resume resume_file.txt - Resume a search starting from the last saved scroll-id.

 More detail about the commands and options are located in the wiki

 Module Example

 Twint can now be used as a module and supports custom formatting. More details are located in the wiki

 import twint
 
 # Configure
 c = twint.Config()
 c.Username = "realDonaldTrump"
 c.Search = "great"
 
 # Run
 twint.run.Search(c)

 Output

 955511208597184512 2018-01-22 18:43:19 GMT <now> pineapples are the best fruit

 import twint
 
 c = twint.Config()
 
 c.Username = "noneprivacy"
 c.Custom["tweet"] = ["id"]
 c.Custom["user"] = ["bio"]
 c.Limit = 10
 c.Store_csv = True
 c.Output = "none"
 
 twint.run.Search(c)

 Storing Options

 * Write to file;
 * CSV;
 * JSON;
 * SQLite;
 * Elasticsearch.

 Elasticsearch Setup

 Details on setting up Elasticsearch with Twint is located in the wiki.

 Graph Visualization

 Graph details are also located in the wiki.

 We are developing a Twint Desktop App.

 FAQ

 I tried scraping tweets from a user, I know that they exist but I'm not getting them

 Twitter can shadow-ban accounts, which means that their tweets will not be available via search. To solve this, pass --profile-full if you are using Twint via CLI or, if are using Twint as module, add config.Profile_full = True. Please note that this process will be quite slow.

 More Examples

 Followers/Following

 To get only follower usernames/following usernames

 twint -u username --followers

 twint -u username --following

 To get user info of followers/following users

 twint -u username --followers --user-full

 twint -u username --following --user-full

 userlist

 To get only user info of user

 twint -u username --user-full

 To get user info of users from a userlist

 twint --userlist inputlist --user-full

 tweet translation (experimental)

 To get 100 english tweets and translate them to italian

 twint -u noneprivacy --csv --output none.csv --lang en --translate --translate-dest it --limit 100

 or

 import twint
 
 c = twint.Config()
 c.Username = "noneprivacy"
 c.Limit = 100
 c.Store_csv = True
 c.Output = "none.csv"
 c.Lang = "en"
 c.Translate = True
 c.TranslateDest = "it"
 twint.run.Search(c)

 Notes:

 * Google translate has some quotas

 Featured Blog Posts:

 * How to use Twint as an OSINT tool
 * Basic tutorial made by Null Byte
 * Analyzing Tweets with NLP in minutes with Spark, Optimus and Twint
 * Loading tweets into Kafka and Neo4j

 Contact

 If you have any question, want to join in discussions, or need extra help, you are welcome to join our Twint focused channel at OSINT team

 About

 An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.

 Topics

 python elasticsearch kibana osint twitter tweets scrape twint tweep scrape-followers scrape-likes scrape-following

 Resources

 Readme

 License

 MIT license

 Stars

 14.4k stars

 Watchers

 312 watching

 Forks

 2.5k forks

 Releases 9

 Fixes and little improvements. Changed Elasticsearch Schema Latest
 Oct 19, 2019
 + 8 releases

 Sponsor this project

 * patreon.com/ twintproject
 * paypal.me/noneprivacy

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TWINT - Twitter Intelligence Tool
  
  
                 
No authentication. No API. No limits.
Twint is an advanced Twitter scraping tool written in Python that allows for scraping Tweets from Twitter profiles without using Twitter's API.
Twint utilizes Twitter's search operators to let you scrape Tweets from specific users, scrape Tweets relating to certain topics, hashtags &amp; trends, or sort out sensitive information from Tweets like e-mail and phone numbers. I find this very useful, and you can get really creative with it too.
Twint also makes special queries to Twitter allowing you to also scrape a Twitter user's followers, Tweets a user has liked, and who they follow without any authentication, API, Selenium, or browser emulation.
tl;dr Benefits
Some of the benefits of using Twint vs Twitter API:
Can fetch almost all Tweets (Twitter API limits to last 3200 Tweets only);
Fast initial setup;
Can be used anonymously and without Twitter sign up;
 No rate limitations.
Limits imposed by Twitter
Twitter limits scrolls while browsing the user timeline. This means that with .Profile or with .Favorites you will be able to get ~3200 tweets.
Requirements
Python 3.6;
aiohttp;
aiodns;
beautifulsoup4;
cchardet;
dataclasses
elasticsearch;
pysocks;
pandas (&gt;=0.23.0);
aiohttp_socks;
schedule;
geopy;
fake-useragent;
py-googletransx.
Installing
 Git: 
 git clone --depth=1 https://github.com/twintproject/twint.git
cd twint
pip3 install . -r requirements.txt 
 Pip: 
or
 pip3 install --user --upgrade git+https://github.com/twintproject/twint.git@origin/master#egg=twint 
 Pipenv:
 pipenv install git+https://github.com/twintproject/twint.git#egg=twint 
March 2, 2021 Update
 Added: Dockerfile
Noticed a lot of people are having issues installing (including me). Please use the Dockerfile temporarily while I look into them.
CLI Basic Examples and Combos
A few simple examples to help you understand the basics:
 twint -u username - Scrape all the Tweets of a user (doesn't include retweets but includes replies).
 twint -u username -s pineapple - Scrape all Tweets from the user's timeline containing pineapple.
 twint -s pineapple - Collect every Tweet containing pineapple from everyone's Tweets.
 twint -u username --year 2014 - Collect Tweets that were tweeted before 2014.
 twint -u username --since "2015-12-20 20:30:15" - Collect Tweets that were tweeted since 2015-12-20 20:30:15.
 twint -u username --since 2015-12-20 - Collect Tweets that were tweeted since 2015-12-20 00:00:00.
 twint -u username -o file.txt - Scrape Tweets and save to file.txt.
 twint -u username -o file.csv --csv - Scrape Tweets and save as a csv file.
 twint -u username --email --phone - Show Tweets that might have phone numbers or email addresses.
 twint -s "Donald Trump" --verified - Display Tweets by verified users that Tweeted about Donald Trump.
 twint -g="48.880048,2.385939,1km" -o file.csv --csv - Scrape Tweets from a radius of 1km around a place in Paris and export them to a csv file.
 twint -u username -es localhost:9200 - Output Tweets to Elasticsearch
 twint -u username -o file.json --json - Scrape Tweets and save as a json file.
 twint -u username --database tweets.db - Save Tweets to a SQLite database.
 twint -u username --followers - Scrape a Twitter user's followers.
 twint -u username --following - Scrape who a Twitter user follows.
 twint -u username --favorites - Collect all the Tweets a user has favorited (gathers ~3200 tweet).
 twint -u username --following --user-full - Collect full user information a person follows
 twint -u username --timeline - Use an effective method to gather Tweets from a user's profile (Gathers ~3200 Tweets, including retweets &amp; replies).
 twint -u username --retweets - Use a quick method to gather the last 900 Tweets (that includes retweets) from a user's profile.
 twint -u username --resume resume_file.txt - Resume a search starting from the last saved scroll-id.
More detail about the commands and options are located in the wiki 
Module Example
Twint can now be used as a module and supports custom formatting. More details are located in the wiki  
 import twint
# Configure
c = twint.Config()
c.Username = "realDonaldTrump"
c.Search = "great"
# Run
twint.run.Search(c) 
Output
 955511208597184512 2018-01-22 18:43:19 GMT &lt;now&gt; pineapples are the best fruit 
 import twint
c = twint.Config()
c.Username = "noneprivacy"
c.Custom["tweet"] = ["id"]
c.Custom["user"] = ["bio"]
c.Limit = 10
c.Store_csv = True
c.Output = "none"
twint.run.Search(c) 
Storing Options
Write to file;
CSV;
JSON;
SQLite;
Elasticsearch.
Elasticsearch Setup
Details on setting up Elasticsearch with Twint is located in the wiki.
Graph Visualization
   
 Graph details are also located in the wiki.
We are developing a Twint Desktop App.
   
FAQ
I tried scraping tweets from a user, I know that they exist but I'm not getting them
Twitter can shadow-ban accounts, which means that their tweets will not be available via search. To solve this, pass --profile-full if you are using Twint via CLI or, if are using Twint as module, add config.Profile_full = True. Please note that this process will be quite slow.
More Examples
Followers/Following
To get only follower usernames/following usernames
 twint -u username --followers 
 twint -u username --following 
To get user info of followers/following users
 twint -u username --followers --user-full 
 twint -u username --following --user-full 
userlist
To get only user info of user
 twint -u username --user-full 
To get user info of users from a userlist
 twint --userlist inputlist --user-full 
tweet translation (experimental)
To get 100 english tweets and translate them to italian
 twint -u noneprivacy --csv --output none.csv --lang en --translate --translate-dest it --limit 100 
or
 import twint
c = twint.Config()
c.Username = "noneprivacy"
c.Limit = 100
c.Store_csv = True
c.Output = "none.csv"
c.Lang = "en"
c.Translate = True
c.TranslateDest = "it"
twint.run.Search(c) 
Notes:
 Google translate has some quotas 
Featured Blog Posts:
 How to use Twint as an OSINT tool 
 Basic tutorial made by Null Byte 
 Analyzing Tweets with NLP in minutes with Spark, Optimus and Twint 
 Loading tweets into Kafka and Neo4j 
Contact
If you have any question, want to join in discussions, or need extra help, you are welcome to join our Twint focused channel at OSINT team
GitHub - twintproject/twint: An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.
https://github.com/twintproject/twint
https://hackernoon.com/scraping-tweet-replies-with-python-and-tweepy-twitter-api-a-step-by-step-guide-z11x3yr8
Scraping Tweet Replies with Python and Tweepy Twitter API [A Step-by-Step Guide] | HackerNoon
2020-06-21
Nicholas Resendez
<div><div><h2>A Quick Method To Extract Tweets and Replies For Free </h2><p>Twitter is beginning to take over the social media realm. As more communities move to Twitter, we begin to see how valuable data is to advertisers, researchers, and even consumers. </p><p>Data is now the next gold rush as we begin to understand how data<strong> </strong>needs to be extracted, transformed, loaded, and for full benefit, turned into Information. In theory, like gold, data is a commodity. </p><p>In this article, I plan to explain how easy it is to scrape Tweets from Twitter in Python3 using Tweepy’s Twitter API. This data can be accessed by Twitter’s API and Tweepy which ended up being the most successful method. I plan to focus on scraping specific user Tweet replies, since I have not discovered any tutorials which specifically highlight how to extract Tweet replies.</p><p><em>If you want to jump straight into the code then you can find the full code on my </em><a href="http://github.com/nirholas?ref=hackernoon.com"><em>Github</em></a><em>.The Python code requires your Twitter API and consumer keys, as well as the Twitter username you plan to extract replies, and the Tweet ID.</em></p><h2>Setting up Twitter Development Account &amp; Tweepy</h2><p>Make sure you have Python installed on your machine. If you do not, I suggest using <a href="https://www.anaconda.com/products/individual?ref=hackernoon.com">Anaconda</a>, otherwise read the official <a href="https://www.python.org/downloads/?ref=hackernoon.com">Python documentation</a> to find additional resources.</p><p>To perform Twitter operations from your machine, I suggest using <a href="https://tweepy.readthedocs.io/en/v3.5.0/?ref=hackernoon.com">Tweepy</a>. To install Tweepy, navigate to your environment and run:</p><p>Python3:</p><p>If you’re using Anaconda for Python:</p><div><pre><code>conda install -c conda-forge tweepy</code></pre></div><h2>Developers for Twitter</h2><p>If you would like to interact with Twitter from a computer or machine, you will need to <a href="https://developer.twitter.com/en/apps?ref=hackernoon.com">apply for Twitter Developers</a>. The application is straight forward, be honest with your intentions within the application, and you will become approved if you are deemed trustworthy from Twitter. Once approved, you will be able to create an app on the platform which provides you with credentials to authorize from Tweepy or your Python Twitter library. </p><p>Twitter for Developers provides access to the Twitter API in order to Publish and analyze Tweets, optimize ads, and create unique customer experiences. Check out the <a href="https://developer.twitter.com/en/application/use-case?ref=hackernoon.com">Twitter API documentation here</a>. </p><p>Before you are able to use the Twitter API endpoints, create a developer account and generate your API keys. <a href="https://developer.twitter.com/en.html?ref=hackernoon.com">You can apply for a developer account directly here</a>. You must answer questions on how you plan to use the API and accept the Twitter Developer Agreement, and then you will be granted access to the Developer Dashboard.</p><p>Once you are approved access to the Developers for Twitter, log in to the <a href="https://developer.twitter.com/?ref=hackernoon.com">developer site</a> and create your App. This step will automatically generate your consumer API keys and access tokens, remember, you should keep them secret:</p><p><div><p></p></div></p><p>The developer account should be linked to the Twitter account where you want to have the bot active. From the Twitter Development platform, you are able to edit the app permissions. In my example, I have granted my app permission to read, write and send direct messages.</p><h2>Twitter Authentication with Python</h2><p>We must import Tweepy then OAuth interface to collect data as well as csv, and ssl.</p><div><pre><code>import csv
import tweepy
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# Oauth keys
consumer_key = "YOUR_CONSUMER_KEY"
consumer_secret = "YOUR_CONSUMER_SECRET"
access_token = "YOUR_ACCESS_TOKEN"
access_token_secret = "YOUR_ACCESS_SECRET"
# Authentication with Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)</code></pre></div><p>To collect tweet replies for a specific user and tweet, we must list the username of the user being scraped, as well as the Tweet ID which can be found by copying from the URL.</p><div><pre><code># update these for whatever tweet you want to process replies to
name = 'LunarCRUSH'
tweet_id = '1270923526690664448'
replies=[]
for tweet in tweepy.Cursor(api.search,q='to:'+name, result_type='recent', timeout=999999).items(1000):
 if hasattr(tweet, 'in_reply_to_status_id_str'):
 if (tweet.in_reply_to_status_id_str==tweet_id):
 replies.append(tweet)</code></pre></div><p>Since I was hoping to analyze the responses, I decided to export all replies to a .csv file format which can be opened in Microsoft Excel or Google Sheets.</p><p>Here’s a brief glimpse of the returned csv:</p><div><pre><code>user,text
CryptoEntuziast,@LunarCRUSH @zilliqa ofcourse 🚀🚀🚀😎😎😎
ecossexrp1,@LunarCRUSH $VET $XRP 👌🏻
crypto19711,@LunarCRUSH @DAPScoin the best privacy coin in this world! https://t.co/xFHs3cYFmK
lacryptohero,@LunarCRUSH @Theta_Network
Greenmi74576867,@LunarCRUSH https://t.co/vwmznwu77V
SplendidMite,@LunarCRUSH #VeChain $VET
DAPS_CLimaDelta,"@LunarCRUSH Because I am judging a project for the best technology, transparency, reliable team and dedicated commu… https://t.co/6xS9vdx1oY"
DigiBur,@LunarCRUSH #digibyte
M_SRHI,@LunarCRUSH $ICX 💎 $ELA 💎❤️ $NOIA💎
SURAJ_041,@LunarCRUSH @electroneum #ETN .
GbhoyDownUnder,@LunarCRUSH @maticnetwork
jodibreeze86,@LunarCRUSH Zilliqa and Vechain
ghurabar1,@LunarCRUSH $EWT
SamManzi,@LunarCRUSH @NoiaNetwork @NoiaFr $NOIA
IamDavidGC,@LunarCRUSH Proud of DigiByte community and technology. $dgb
holder2017,@LunarCRUSH @Falcon_SBS #FNT token traded on #exchanges. #Anonymous coin #FNC is not traded anywhere. connected b… https://t.co/0mz7bmaG1k
Lilt8888,@LunarCRUSH It would have to be $ICX
Creeptwo_guy13,@LunarCRUSH That question is way too easy. Absolutely its $ICX #ICON.
BitStreetSheep,@LunarCRUSH #VeChain without question
jms3333333,@LunarCRUSH LInk UBT KNC EWT SOLVE
einnorka,@LunarCRUSH Digibyte
HamishDonalds0n,@LunarCRUSH $icx $vet $zil $ada $eth $link
amity3013,@LunarCRUSH $zil you know it
elianhuesca,"@LunarCRUSH @decredproject by far: hybrid PoW/PoS blockchain, formal governance in place, Treasury with 10% of bloc… https://t.co/oRnMc4UD5P"
AaronMilo,@LunarCRUSH #digibyte https://t.co/000HoTfLqB
majjjubu,@LunarCRUSH Chz
Benjy25680913,@LunarCRUSH $LUNARCRUSH
ItchyTommi,@LunarCRUSH https://t.co/y8l2WwP3qK Stakenet. The one and only
siggebaskero,@LunarCRUSH #PIVX thanks to @PIVX_Community who's doing a great job 💜 Engaging with a growing community like… https://t.co/CBlhJm7gZj
DanXrp,@LunarCRUSH $VET no doubt
crypto1618,@LunarCRUSH #icx
thelionshire,@LunarCRUSH ICON $icx
ChillMa27939777,@LunarCRUSH #Zilliqa #ZIL ✌😎
BeholdTheBeard,@LunarCRUSH Tezos $XTZ Theta $THETA
lennyshots,@LunarCRUSH #DigiByte
Shatochzi,@LunarCRUSH $CHZ #chiliz
RonDalton01,@LunarCRUSH #VET
Realmikeleonard,@LunarCRUSH #XMR no doubt about it
Incognitor00st1,@LunarCRUSH $DGB 🔥
Cryptowhale10,@LunarCRUSH $ICX https://t.co/WQTbyPkpEB
XxVegetta,@LunarCRUSH We are DAPS soliders I have been dedicated to our project for 2 years and I think for many years to co… https://t.co/QLk7kKJkhk
CaliCryptoCo,@LunarCRUSH $ICX man
MoonShotCaller,@LunarCRUSH #VeChain 💙 $VET
Dominic_LTC_DGB,@LunarCRUSH @DigiByteCoin
GrowlerGregg,@LunarCRUSH $LINK
adflondon,@LunarCRUSH We all know its $ICX
SajawalOnTech,@LunarCRUSH To many projects but I guess $Wan $link $Zil $Icx
IconPilipinas,@LunarCRUSH $ICX
jonade,@LunarCRUSH $ZIL
twills2,@LunarCRUSH Do we really have to say it...... $zil 🚀</code></pre></div><h2><a href="https://github.com/nirholas/Get-Tweet-Replies-With-Python-Tweepy?ref=hackernoon.com">Full Python Code </a></h2><p>You can view all of the code to get this working by visiting <a href="https://github.com/nirholas/Get-Tweet-Replies-With-Python-Tweepy?ref=hackernoon.com">this link</a>. </p><div><pre><code>import csv
import tweepy
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# Oauth keys
consumer_key = "XXX"
consumer_secret = "XXX"
access_token = "XXX"
access_token_secret = "XXX"
# Authentication with Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
# update these for the tweet you want to process replies to 'name' = the account username and you can find the tweet id within the tweet URL
name = 'LunarCRUSH'
tweet_id = '1270923526690664448'
replies=[]
for tweet in tweepy.Cursor(api.search,q='to:'+name, result_type='recent', timeout=999999).items(1000):
 if hasattr(tweet, 'in_reply_to_status_id_str'):
 if (tweet.in_reply_to_status_id_str==tweet_id):
 replies.append(tweet)
with open('replies_clean.csv', 'w') as f:
 csv_writer = csv.DictWriter(f, fieldnames=('user', 'text'))
 csv_writer.writeheader()
 for tweet in replies:
 row = {'user': tweet.user.screen_name, 'text': tweet.text.replace('\n', ' ')}
 csv_writer.writerow(row)</code></pre></div><h2>Final Thoughts</h2><p>Within a few lines of code, your configurable Twitter reply scraper now pulls data from Twitter and automatically saved Tweet replies to your machine. </p><p>There are a few things that can be done to improve the code, such as mapping multiple replies, or getting the responses from those that replied to the original post. Please let me know in the comments if you have any questions or suggestions.</p><p><strong>Knowledge is Power! </strong>Share your knowledge, open source your projects , participate in a community (any community!), and maybe just maybe publish a blog post about it.</p><p><strong>Thank you For Reading</strong></p><p>Constructive criticism and feedback is welcomed. Nicholas Resendez can be reached on Instagram <a href="http://instagram.com/nirholas?ref=hackernoon.com">@nirholas</a>, on <a href="http://linkedin.com/in/nixholas?ref=hackernoon.com">LinkedIn</a><strong> , </strong>and Twitter <a href="http://twitter.com/bothersome?ref=hackernoon.com"></a><a href="https://twitter.com/nickresendez?ref=hackernoon.com">@nickresendez</a> for updates on new articles.</p></div>||||I|||| Start Writing
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 Scraping Tweet Replies with Python and Tweepy Twitter API [A Step-by-Step Guide] by@nich
 14,032 reads

 Scraping Tweet Replies with Python and Tweepy Twitter API [A Step-by-Step Guide]


 June 21st 2020 5 min by @nich

 Nicholas Resendez

 @nich

 instagram.com/nirholas | lnk.to/nich

 14,032 reads


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 Scraping Tweet Replies with Python and Tweepy Twitter API [A Step-by-Step Guide] The Python code requires your Twitter API and consumer keys, as well as the Twitter username you plan to extract replies, and the Tweet ID. I plan to focus on scraping specific user Tweet replies, since I have not discovered any tutorials which specifically highlight how to extract Tweet replies. The full code can be found on my Github page, here is how to use the Python code.

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 Nicholas Resendez

 @nich

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 #python#tweepy#twitter#data-science#data-scraping#web-scraping#intelligence#hackernoon-top-story
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 Read by Dr. One (en-US)
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 @nich

 Nicholas Resendez

 instagram.com/nirholas | lnk.to/nich

 About @nich
 LEARN MORE ABOUT @NICH'S EXPERTISE AND PLACE ON THE INTERNET.

 A Quick Method To Extract Tweets and Replies For Free

 Twitter is beginning to take over the social media realm. As more communities move to Twitter, we begin to see how valuable data is to advertisers, researchers, and even consumers.

 Data is now the next gold rush as we begin to understand how data needs to be extracted, transformed, loaded, and for full benefit, turned into Information. In theory, like gold, data is a commodity.

 In this article, I plan to explain how easy it is to scrape Tweets from Twitter in Python3 using Tweepy’s Twitter API. This data can be accessed by Twitter’s API and Tweepy which ended up being the most successful method. I plan to focus on scraping specific user Tweet replies, since I have not discovered any tutorials which specifically highlight how to extract Tweet replies.

 If you want to jump straight into the code then you can find the full code on my Github.The Python code requires your Twitter API and consumer keys, as well as the Twitter username you plan to extract replies, and the Tweet ID.

 Setting up Twitter Development Account & Tweepy

 Make sure you have Python installed on your machine. If you do not, I suggest using Anaconda, otherwise read the official Python documentation to find additional resources.

 To perform Twitter operations from your machine, I suggest using Tweepy. To install Tweepy, navigate to your environment and run:

 Python3:

 pip install tweepy
 

 If you’re using Anaconda for Python:

 conda install -c conda-forge tweepy

 Developers for Twitter

 If you would like to interact with Twitter from a computer or machine, you will need to apply for Twitter Developers. The application is straight forward, be honest with your intentions within the application, and you will become approved if you are deemed trustworthy from Twitter. Once approved, you will be able to create an app on the platform which provides you with credentials to authorize from Tweepy or your Python Twitter library.

 Twitter for Developers provides access to the Twitter API in order to Publish and analyze Tweets, optimize ads, and create unique customer experiences. Check out the Twitter API documentation here.

 Before you are able to use the Twitter API endpoints, create a developer account and generate your API keys. You can apply for a developer account directly here. You must answer questions on how you plan to use the API and accept the Twitter Developer Agreement, and then you will be granted access to the Developer Dashboard.

 Once you are approved access to the Developers for Twitter, log in to the developer site and create your App. This step will automatically generate your consumer API keys and access tokens, remember, you should keep them secret:

 The developer account should be linked to the Twitter account where you want to have the bot active. From the Twitter Development platform, you are able to edit the app permissions. In my example, I have granted my app permission to read, write and send direct messages.

 Twitter Authentication with Python

 We must import Tweepy then OAuth interface to collect data as well as csv, and ssl.

 import csv
 import tweepy
 import ssl
 ssl._create_default_https_context = ssl._create_unverified_context
 # Oauth keys
 consumer_key = "YOUR_CONSUMER_KEY"
 consumer_secret = "YOUR_CONSUMER_SECRET"
 access_token = "YOUR_ACCESS_TOKEN"
 access_token_secret = "YOUR_ACCESS_SECRET"
 # Authentication with Twitter
 auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
 auth.set_access_token(access_token, access_token_secret)
 api = tweepy.API(auth)

 To collect tweet replies for a specific user and tweet, we must list the username of the user being scraped, as well as the Tweet ID which can be found by copying from the URL.

 # update these for whatever tweet you want to process replies to
 name = 'LunarCRUSH'
 tweet_id = '1270923526690664448'
 replies=[]
 for tweet in tweepy.Cursor(api.search,q='to:'+name, result_type='recent', timeout=999999).items(1000):
 if hasattr(tweet, 'in_reply_to_status_id_str'):
 if (tweet.in_reply_to_status_id_str==tweet_id):
 replies.append(tweet)

 Since I was hoping to analyze the responses, I decided to export all replies to a .csv file format which can be opened in Microsoft Excel or Google Sheets.

 Here’s a brief glimpse of the returned csv:

 user,text
 CryptoEntuziast,@LunarCRUSH @zilliqa ofcourse 🚀🚀🚀😎😎😎
 ecossexrp1,@LunarCRUSH $VET $XRP 👌🏻
 crypto19711,@LunarCRUSH @DAPScoin the best privacy coin in this world! https://t.co/xFHs3cYFmK
 lacryptohero,@LunarCRUSH @Theta_Network
 Greenmi74576867,@LunarCRUSH https://t.co/vwmznwu77V
 SplendidMite,@LunarCRUSH #VeChain $VET
 DAPS_CLimaDelta,"@LunarCRUSH Because I am judging a project for the best technology, transparency, reliable team and dedicated commu… https://t.co/6xS9vdx1oY"
 DigiBur,@LunarCRUSH #digibyte
 M_SRHI,@LunarCRUSH $ICX 💎 $ELA 💎❤️ $NOIA💎
 SURAJ_041,@LunarCRUSH @electroneum #ETN .
 GbhoyDownUnder,@LunarCRUSH @maticnetwork
 jodibreeze86,@LunarCRUSH Zilliqa and Vechain
 ghurabar1,@LunarCRUSH $EWT
 SamManzi,@LunarCRUSH @NoiaNetwork @NoiaFr $NOIA
 IamDavidGC,@LunarCRUSH Proud of DigiByte community and technology. $dgb
 holder2017,@LunarCRUSH @Falcon_SBS #FNT token traded on #exchanges. #Anonymous coin #FNC is not traded anywhere. connected b… https://t.co/0mz7bmaG1k
 Lilt8888,@LunarCRUSH It would have to be $ICX
 Creeptwo_guy13,@LunarCRUSH That question is way too easy. Absolutely its $ICX #ICON.
 BitStreetSheep,@LunarCRUSH #VeChain without question
 jms3333333,@LunarCRUSH LInk UBT KNC EWT SOLVE
 einnorka,@LunarCRUSH Digibyte
 HamishDonalds0n,@LunarCRUSH $icx $vet $zil $ada $eth $link
 amity3013,@LunarCRUSH $zil you know it
 elianhuesca,"@LunarCRUSH @decredproject by far: hybrid PoW/PoS blockchain, formal governance in place, Treasury with 10% of bloc… https://t.co/oRnMc4UD5P"
 AaronMilo,@LunarCRUSH #digibyte https://t.co/000HoTfLqB
 majjjubu,@LunarCRUSH Chz
 Benjy25680913,@LunarCRUSH $LUNARCRUSH
 ItchyTommi,@LunarCRUSH https://t.co/y8l2WwP3qK Stakenet. The one and only
 siggebaskero,@LunarCRUSH #PIVX thanks to @PIVX_Community who's doing a great job 💜 Engaging with a growing community like… https://t.co/CBlhJm7gZj
 DanXrp,@LunarCRUSH $VET no doubt
 crypto1618,@LunarCRUSH #icx
 thelionshire,@LunarCRUSH ICON $icx
 ChillMa27939777,@LunarCRUSH #Zilliqa #ZIL ✌😎
 BeholdTheBeard,@LunarCRUSH Tezos $XTZ Theta $THETA
 lennyshots,@LunarCRUSH #DigiByte
 Shatochzi,@LunarCRUSH $CHZ #chiliz
 RonDalton01,@LunarCRUSH #VET
 Realmikeleonard,@LunarCRUSH #XMR no doubt about it
 Incognitor00st1,@LunarCRUSH $DGB 🔥
 Cryptowhale10,@LunarCRUSH $ICX https://t.co/WQTbyPkpEB
 XxVegetta,@LunarCRUSH We are DAPS soliders I have been dedicated to our project for 2 years and I think for many years to co… https://t.co/QLk7kKJkhk
 CaliCryptoCo,@LunarCRUSH $ICX man
 MoonShotCaller,@LunarCRUSH #VeChain 💙 $VET
 Dominic_LTC_DGB,@LunarCRUSH @DigiByteCoin
 GrowlerGregg,@LunarCRUSH $LINK
 adflondon,@LunarCRUSH We all know its $ICX
 SajawalOnTech,@LunarCRUSH To many projects but I guess $Wan $link $Zil $Icx
 IconPilipinas,@LunarCRUSH $ICX
 jonade,@LunarCRUSH $ZIL
 twills2,@LunarCRUSH Do we really have to say it...... $zil 🚀

 Full Python Code

 You can view all of the code to get this working by visiting this link.

 import csv
 import tweepy
 import ssl
 
 ssl._create_default_https_context = ssl._create_unverified_context
 
 # Oauth keys
 consumer_key = "XXX"
 consumer_secret = "XXX"
 access_token = "XXX"
 access_token_secret = "XXX"
 
 # Authentication with Twitter
 auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
 auth.set_access_token(access_token, access_token_secret)
 api = tweepy.API(auth)
 
 # update these for the tweet you want to process replies to 'name' = the account username and you can find the tweet id within the tweet URL
 name = 'LunarCRUSH'
 tweet_id = '1270923526690664448'
 
 replies=[]
 for tweet in tweepy.Cursor(api.search,q='to:'+name, result_type='recent', timeout=999999).items(1000):
 if hasattr(tweet, 'in_reply_to_status_id_str'):
 if (tweet.in_reply_to_status_id_str==tweet_id):
 replies.append(tweet)
 
 with open('replies_clean.csv', 'w') as f:
 csv_writer = csv.DictWriter(f, fieldnames=('user', 'text'))
 csv_writer.writeheader()
 for tweet in replies:
 row = {'user': tweet.user.screen_name, 'text': tweet.text.replace('\n', ' ')}
 csv_writer.writerow(row)

 Final Thoughts

 Within a few lines of code, your configurable Twitter reply scraper now pulls data from Twitter and automatically saved Tweet replies to your machine.

 There are a few things that can be done to improve the code, such as mapping multiple replies, or getting the responses from those that replied to the original post. Please let me know in the comments if you have any questions or suggestions.

 Knowledge is Power! Share your knowledge, open source your projects , participate in a community (any community!), and maybe just maybe publish a blog post about it.

 Thank you For Reading

 Constructive criticism and feedback is welcomed. Nicholas Resendez can be reached on Instagram @nirholas, on LinkedIn , and Twitter @nickresendez for updates on new articles.

 by Nicholas Resendez @nich . instagram.com/nirholas | lnk.to/nich
 ToTheMoon ┗(°0°)┛

 Get started with this writing template

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A Quick Method To Extract Tweets and Replies For Free  Twitter is beginning to take over the social media realm. As more communities move to Twitter, we begin to see how valuable data is to advertisers, researchers, and even consumers.  Data is now the next gold rush as we begin to understand how data needs to be extracted, transformed, loaded, and for full benefit, turned into Information. In theory, like gold, data is a commodity.  In this article, I plan to explain how easy it is to scrape Tweets from Twitter in Python3 using Tweepy’s Twitter API. This data can be accessed by Twitter’s API and Tweepy which ended up being the most successful method. I plan to focus on scraping specific user Tweet replies, since I have not discovered any tutorials which specifically highlight how to extract Tweet replies.  If you want to jump straight into the code then you can find the full code on my   Github  .The Python code requires your Twitter API and consumer keys, as well as the Twitter username you plan to extract replies, and the Tweet ID.  Setting up Twitter Development Account &amp; Tweepy Make sure you have Python installed on your machine. If you do not, I suggest using Anaconda, otherwise read the official Python documentation to find additional resources. To perform Twitter operations from your machine, I suggest using Tweepy. To install Tweepy, navigate to your environment and run: Python3: If you’re using Anaconda for Python:   conda install -c conda-forge tweepy   Developers for Twitter If you would like to interact with Twitter from a computer or machine, you will need to apply for Twitter Developers. The application is straight forward, be honest with your intentions within the application, and you will become approved if you are deemed trustworthy from Twitter. Once approved, you will be able to create an app on the platform which provides you with credentials to authorize from Tweepy or your Python Twitter library.  Twitter for Developers provides access to the Twitter API in order to Publish and analyze Tweets, optimize ads, and create unique customer experiences. Check out the Twitter API documentation here.  Before you are able to use the Twitter API endpoints, create a developer account and generate your API keys. You can apply for a developer account directly here. You must answer questions on how you plan to use the API and accept the Twitter Developer Agreement, and then you will be granted access to the Developer Dashboard. Once you are approved access to the Developers for Twitter, log in to the developer site and create your App. This step will automatically generate your consumer API keys and access tokens, remember, you should keep them secret:       The developer account should be linked to the Twitter account where you want to have the bot active. From the Twitter Development platform, you are able to edit the app permissions. In my example, I have granted my app permission to read, write and send direct messages. Twitter Authentication with Python We must import Tweepy then OAuth interface to collect data as well as csv, and ssl.   import csv
import tweepy
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# Oauth keys
consumer_key = "YOUR_CONSUMER_KEY"
consumer_secret = "YOUR_CONSUMER_SECRET"
access_token = "YOUR_ACCESS_TOKEN"
access_token_secret = "YOUR_ACCESS_SECRET"
# Authentication with Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)   To collect tweet replies for a specific user and tweet, we must list the username of the user being scraped, as well as the Tweet ID which can be found by copying from the URL.   # update these for whatever tweet you want to process replies to
name = 'LunarCRUSH'
tweet_id = '1270923526690664448'
replies=[]
for tweet in tweepy.Cursor(api.search,q='to:'+name, result_type='recent', timeout=999999).items(1000):
 if hasattr(tweet, 'in_reply_to_status_id_str'):
 if (tweet.in_reply_to_status_id_str==tweet_id):
 replies.append(tweet)   Since I was hoping to analyze the responses, I decided to export all replies to a .csv file format which can be opened in Microsoft Excel or Google Sheets. Here’s a brief glimpse of the returned csv:   user,text
CryptoEntuziast,@LunarCRUSH @zilliqa ofcourse 🚀🚀🚀😎😎😎
ecossexrp1,@LunarCRUSH $VET $XRP 👌🏻
crypto19711,@LunarCRUSH @DAPScoin the best privacy coin in this world! https://t.co/xFHs3cYFmK
lacryptohero,@LunarCRUSH @Theta_Network
Greenmi74576867,@LunarCRUSH https://t.co/vwmznwu77V
SplendidMite,@LunarCRUSH #VeChain $VET
DAPS_CLimaDelta,"@LunarCRUSH Because I am judging a project for the best technology, transparency, reliable team and dedicated commu… https://t.co/6xS9vdx1oY"
DigiBur,@LunarCRUSH #digibyte
M_SRHI,@LunarCRUSH $ICX 💎 $ELA 💎❤️ $NOIA💎
SURAJ_041,@LunarCRUSH @electroneum #ETN .
GbhoyDownUnder,@LunarCRUSH @maticnetwork
jodibreeze86,@LunarCRUSH Zilliqa and Vechain
ghurabar1,@LunarCRUSH $EWT
SamManzi,@LunarCRUSH @NoiaNetwork @NoiaFr $NOIA
IamDavidGC,@LunarCRUSH Proud of DigiByte community and technology. $dgb
holder2017,@LunarCRUSH @Falcon_SBS #FNT token traded on #exchanges. #Anonymous coin #FNC is not traded anywhere. connected b… https://t.co/0mz7bmaG1k
Lilt8888,@LunarCRUSH It would have to be $ICX
Creeptwo_guy13,@LunarCRUSH That question is way too easy. Absolutely its $ICX #ICON.
BitStreetSheep,@LunarCRUSH #VeChain without question
jms3333333,@LunarCRUSH LInk UBT KNC EWT SOLVE
einnorka,@LunarCRUSH Digibyte
HamishDonalds0n,@LunarCRUSH $icx $vet $zil $ada $eth $link
amity3013,@LunarCRUSH $zil you know it
elianhuesca,"@LunarCRUSH @decredproject by far: hybrid PoW/PoS blockchain, formal governance in place, Treasury with 10% of bloc… https://t.co/oRnMc4UD5P"
AaronMilo,@LunarCRUSH #digibyte https://t.co/000HoTfLqB
majjjubu,@LunarCRUSH Chz
Benjy25680913,@LunarCRUSH $LUNARCRUSH
ItchyTommi,@LunarCRUSH https://t.co/y8l2WwP3qK Stakenet. The one and only
siggebaskero,@LunarCRUSH #PIVX thanks to @PIVX_Community who's doing a great job 💜 Engaging with a growing community like… https://t.co/CBlhJm7gZj
DanXrp,@LunarCRUSH $VET no doubt
crypto1618,@LunarCRUSH #icx
thelionshire,@LunarCRUSH ICON $icx
ChillMa27939777,@LunarCRUSH #Zilliqa #ZIL ✌😎
BeholdTheBeard,@LunarCRUSH Tezos $XTZ Theta $THETA
lennyshots,@LunarCRUSH #DigiByte
Shatochzi,@LunarCRUSH $CHZ #chiliz
RonDalton01,@LunarCRUSH #VET
Realmikeleonard,@LunarCRUSH #XMR no doubt about it
Incognitor00st1,@LunarCRUSH $DGB 🔥
Cryptowhale10,@LunarCRUSH $ICX https://t.co/WQTbyPkpEB
XxVegetta,@LunarCRUSH We are DAPS soliders I have been dedicated to our project for 2 years and I think for many years to co… https://t.co/QLk7kKJkhk
CaliCryptoCo,@LunarCRUSH $ICX man
MoonShotCaller,@LunarCRUSH #VeChain 💙 $VET
Dominic_LTC_DGB,@LunarCRUSH @DigiByteCoin
GrowlerGregg,@LunarCRUSH $LINK
adflondon,@LunarCRUSH We all know its $ICX
SajawalOnTech,@LunarCRUSH To many projects but I guess $Wan $link $Zil $Icx
IconPilipinas,@LunarCRUSH $ICX
jonade,@LunarCRUSH $ZIL
twills2,@LunarCRUSH Do we really have to say it...... $zil 🚀    Full Python Code   You can view all of the code to get this working by visiting this link.    import csv
import tweepy
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# Oauth keys
consumer_key = "XXX"
consumer_secret = "XXX"
access_token = "XXX"
access_token_secret = "XXX"
# Authentication with Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
# update these for the tweet you want to process replies to 'name' = the account username and you can find the tweet id within the tweet URL
name = 'LunarCRUSH'
tweet_id = '1270923526690664448'
replies=[]
for tweet in tweepy.Cursor(api.search,q='to:'+name, result_type='recent', timeout=999999).items(1000):
 if hasattr(tweet, 'in_reply_to_status_id_str'):
 if (tweet.in_reply_to_status_id_str==tweet_id):
 replies.append(tweet)
with open('replies_clean.csv', 'w') as f:
 csv_writer = csv.DictWriter(f, fieldnames=('user', 'text'))
 csv_writer.writeheader()
 for tweet in replies:
 row = {'user': tweet.user.screen_name, 'text': tweet.text.replace('\n', ' ')}
 csv_writer.writerow(row)   Final Thoughts Within a few lines of code, your configurable Twitter reply scraper now pulls data from Twitter and automatically saved Tweet replies to your machine.  There are a few things that can be done to improve the code, such as mapping multiple replies, or getting the responses from those that replied to the original post. Please let me know in the comments if you have any questions or suggestions.  Knowledge is Power! Share your knowledge, open source your projects , participate in a community (any community!), and maybe just maybe publish a blog post about it.  Thank you For Reading  Constructive criticism and feedback is welcomed. Nicholas Resendez can be reached on Instagram @nirholas, on LinkedIn  , and Twitter   @nickresendez for updates on new articles.
Scraping Tweet Replies with Python and Tweepy Twitter API [A Step-by-Step Guide] | HackerNoon
https://hackernoon.com/scraping-tweet-replies-with-python-and-tweepy-twitter-api-a-step-by-step-guide-z11x3yr8
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 Ed Summers May 18, 2023
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 Inside GitHub: Working with the LLMs behind GitHub Copilot

 Developers behind GitHub Copilot discuss what it was like to work with OpenAI’s large language model and how it informed the development of Copilot as we know it today.

 Sara Verdi May 17, 2023
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 How GitHub Copilot is getting better at understanding your code

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 Addressing GitHub’s recent availability issues

 GitHub recently experienced several availability incidents, both long running and shorter duration. We have since mitigated these incidents and all systems are now operating normally. Read on for more details about what caused these incidents and what we’re doing to mitigate in the future.

 Mike Hanley May 16, 2023
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 This month on The ReadME Podcast: Balancing openness and control

 Open vs. control: the paradox of open source. We take a look at the expectations of open source, how the definition has evolved, and when ‘closed to contributions’ is the right move. Tune in to the latest episode of The ReadME Podcast for more.

 Martin Woodward & Neha Batra May 12, 2023

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 How I used GitHub Copilot to build a browser extension

 Here’s how, in seven steps, I built my first browser extension with GitHub Copilot—and my three major takeaways about learning and pair programming in the age of AI.

 Rizel Scarlett May 12, 2023
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 Navigation and search improvements for accessibility

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 Benjamin Allen May 11, 2023
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 Unlocking inclusive design: how Primer's color system is making GitHub.com more inclusive

 How Primer’s updated light and dark theme color contrast strategy resolved hundreds of color-contrast-related accessibility issues over one thousand use cases.

 Lukas Oppermann & Natalya Shelburne May 11, 2023
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 Revolutionize your open source workflows: the top 3 reasons why GitHub Codespaces is a must-have for maintainers

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 GitHub celebrates developers with disabilities on Global Accessibility Awareness Day

 GitHub is the home for all developers and on this Global Accessibility Awareness Day we are thrilled to celebrate the achievements of disabled developers and recent ships that help them build on GitHub.

 Ed Summers May 18, 2023

 Inside GitHub: Working with the LLMs behind GitHub Copilot

 Developers behind GitHub Copilot discuss what it was like to work with OpenAI’s large language model and how it informed the development of Copilot as we know it today.

 Sara Verdi

 How GitHub Copilot is getting better at understanding your code

 With a new Fill-in-the-Middle paradigm, GitHub engineers improved the way GitHub Copilot contextualizes your code. By continuing to develop and test advanced retrieval algorithms, they’re working on making our AI tool even more advanced.

 Johan Rosenkilde

 Addressing GitHub’s recent availability issues

 GitHub recently experienced several availability incidents, both long running and shorter duration. We have since mitigated these incidents and all systems are now operating normally. Read on for more details about what caused these incidents and what we’re doing to mitigate in the future.

 Mike Hanley

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 GitHub celebrates developers with disabilities on Global Accessibility Awareness Day

 GitHub is the home for all developers and on this Global Accessibility Awareness Day we are thrilled to celebrate the achievements of disabled developers and recent ships that help them build on GitHub.

 Ed Summers May 18, 2023

 This month on The ReadME Podcast: Balancing openness and control

 Open vs. control: the paradox of open source. We take a look at the expectations of open source, how the definition has evolved, and when ‘closed to contributions’ is the right move. Tune in to the latest episode of The ReadME Podcast for more.

 Martin Woodward & Neha Batra

 Revolutionize your open source workflows: the top 3 reasons why GitHub Codespaces is a must-have for maintainers

 GitHub Codespaces is reliable, accessible, and always-ready. Try it out during Maintainer Month and take your projects to new heights!

 Andrea Griffiths

 Game Bytes · May 2023

 Game Bytes is our monthly series taking a peek at the world of gamedev on GitHub—featuring game engine updates, game jam details, open source games, mods, maps, and more. Game on!

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 Addressing GitHub’s recent availability issues

 GitHub recently experienced several availability incidents, both long running and shorter duration. We have since mitigated these incidents and all systems are now operating normally. Read on for more details about what caused these incidents and what we’re doing to mitigate in the future.

 Mike Hanley
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 How GitHub Copilot is getting better at understanding your code

 With a new Fill-in-the-Middle paradigm, GitHub engineers improved the way GitHub Copilot contextualizes your code. By continuing to develop and test advanced retrieval algorithms, they’re working on making our AI tool even more advanced.

 Johan Rosenkilde
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 Inside GitHub: Working with the LLMs behind GitHub Copilot

 Developers behind GitHub Copilot discuss what it was like to work with OpenAI’s large language model and how it informed the development of Copilot as we know it today.

 Sara Verdi
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 We updated our RSA SSH host key

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 How I used GitHub Copilot to build a browser extension

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 GitHub celebrates developers with disabilities on Global Accessibility Awareness Day

 GitHub is the home for all developers and on this Global Accessibility Awareness Day we are thrilled to celebrate the achievements of disabled developers and recent ships that help them build on GitHub.

 Ed Summers May 18, 2023

 Inside GitHub: Working with the LLMs behind GitHub Copilot

 Developers behind GitHub Copilot discuss what it was like to work with OpenAI’s large language model and how it informed the development of Copilot as we know it today.

 Sara Verdi

 How GitHub Copilot is getting better at understanding your code

 With a new Fill-in-the-Middle paradigm, GitHub engineers improved the way GitHub Copilot contextualizes your code. By continuing to develop and test advanced retrieval algorithms, they’re working on making our AI tool even more advanced.

 Johan Rosenkilde

 How I used GitHub Copilot to build a browser extension

 Here’s how, in seven steps, I built my first browser extension with GitHub Copilot—and my three major takeaways about learning and pair programming in the age of AI.

 Rizel Scarlett

 Navigation and search improvements for accessibility

 Discover the accessibility features within our new navigation and code search which make it easier to use for many more people.

 Benjamin Allen

 Unlocking inclusive design: how Primer's color system is making GitHub.com more inclusive

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 Push protection is generally available, and free for all public repositories

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 Martin Woodward & Neha Batra May 12, 2023

 Revolutionize your open source workflows: the top 3 reasons why GitHub Codespaces is a must-have for maintainers

 GitHub Codespaces is reliable, accessible, and always-ready. Try it out during Maintainer Month and take your projects to new heights!

 Andrea Griffiths May 10, 2023

 Game Bytes · May 2023

 Game Bytes is our monthly series taking a peek at the world of gamedev on GitHub—featuring game engine updates, game jam details, open source games, mods, maps, and more. Game on!

 Lee Reilly May 10, 2023

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 Kevin Backhouse May 9, 2023

 Enterprise

 Addressing GitHub’s recent availability issues

 GitHub recently experienced several availability incidents, both long running and shorter duration. We have since mitigated these incidents and all systems are now operating normally. Read on for more details about what caused these incidents and what we’re doing to mitigate in the future.

 Mike Hanley

 How companies are boosting productivity with generative AI

 Explore how generative AI coding tools are changing the way developers and companies build software.

 Chris Reddington

 Building a culture of innovation in your business with GitHub

 Consider the typical software development practices in an organization. Projects are commonly closed, and causes friction across engineering teams. But open source communities work asynchronously, openly, remotely and at global-scale. What if our internal teams could reuse those same practices?

 Chris Reddington

 Education

 How I used GitHub Copilot to build a browser extension

 Here’s how, in seven steps, I built my first browser extension with GitHub Copilot—and my three major takeaways about learning and pair programming in the age of AI.

 Rizel Scarlett

 Building a more inclusive GitHub Global Campus

 Every student and teacher deserves the same access to GitHub Education offerings. We’ve enlisted GitHub’s Accessibility team to help identify areas for improving inclusivity.

 Arelia Jones

 Build a secure code mindset with the GitHub Secure Code Game

 Writing secure code is as much of an art as writing functional code, and it is the only way to write quality code. Learn how our Secure Code Game can provide you with hands-on training to spot and fix security issues in your code so that you can build a secure code mindset.

 Joseph Katsioloudes

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 GitHub joins industry commitment to curb cyber mercenaries

 GitHub is proud to join 40 companies endorsing the Cybersecurity Tech Accord principles limiting offensive operations in cyberspace.

 Mike Linksvayer

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 We’re looking forward to working with policymakers to improve cybersecurity and support developers.

 Mike Linksvayer

 Don’t leave developers behind in the Section 230 debate

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 Shelley McKinley

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 GitHub celebrates developers with disabilities on Global Accessibility Awareness Day

 GitHub is the home for all developers and on this Global Accessibility Awareness Day we are thrilled to celebrate the achievements of disabled developers and recent ships that help them build on GitHub.

 Ed Summers

 Design's journey towards accessibility

 Design can have a significant impact on delivering accessible experiences to our users. It takes a cultural shift, dedicated experts, and permission to make progress over perfection in order to build momentum. We’ve got a long way to go, but we’re starting to see a real shift in our journey to make GitHub a true home for all developers.

 Diana Mounter

 Addressing GitHub’s recent availability issues

 GitHub recently experienced several availability incidents, both long running and shorter duration. We have since mitigated these incidents and all systems are now operating normally. Read on for more details about what caused these incidents and what we’re doing to mitigate in the future.

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Design's journey towards accessibility  Design can have a significant impact on delivering accessible experiences to our users. It takes a cultural shift, dedicated experts, and permission to make progress over perfection in order to build momentum. We’ve got a long way to go, but we’re starting to see a real shift in our journey to make GitHub a true home for all developers.  	
	
	   
	 Addressing GitHub’s recent availability issues  GitHub recently experienced several availability incidents, both long running and shorter duration. We have since mitigated these incidents and all systems are now operating normally. Read on for more details about what caused these incidents and what we’re doing to mitigate in the future.      
	 This month on The ReadME Podcast: Balancing openness and control  Open vs. control: the paradox of open source. We take a look at the expectations of open source, how the definition has evolved, and when ‘closed to contributions’ is the right move. Tune in to the latest episode of The ReadME Podcast for more.  
	
	Latest posts
	
	Engineering
	    Inside GitHub: Working with the LLMs behind GitHub Copilot  Developers behind GitHub Copilot discuss what it was like to work with OpenAI’s large language model and how it informed the development of Copilot as we know it today.      How GitHub Copilot is getting better at understanding your code  With a new Fill-in-the-Middle paradigm, GitHub engineers improved the way GitHub Copilot contextualizes your code. By continuing to develop and test advanced retrieval algorithms, they’re working on making our AI tool even more advanced.      Addressing GitHub’s recent availability issues  GitHub recently experienced several availability incidents, both long running and shorter duration. We have since mitigated these incidents and all systems are now operating normally. Read on for more details about what caused these incidents and what we’re doing to mitigate in the future.   
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https://medium.com/swlh/how-to-scrape-tweets-by-location-in-python-using-snscrape-8c870fa6ec25
Scraping Tweets by Location in Python using snscrape
2020-12-23
Scott Tomlins
<div><article><div><div><div><a href="https://medium.com/@stomlins?source=post_page-----8c870fa6ec25--------------------------------"><div><p></p></div></a><a href="https://medium.com/swlh?source=post_page-----8c870fa6ec25--------------------------------"><div><p></p></div></a></div><p>A deep dive into the snscrape Python wrapper, how to use it to search for tweets by location, and why it sometimes doesn’t work as expected.</p><figure><figcaption>Photo by <a href="https://unsplash.com/@purzlbaum?utm_source=medium&amp;utm_medium=referral">🇨🇭 Claudio Schwarz | @purzlbaum</a> on <a href="https://unsplash.com/?utm_source=medium&amp;utm_medium=referral">Unsplash</a></figcaption></figure><p>snscrape is a library that allows anyone to scrape tweets without requiring personal API keys. It can return thousands of tweets in seconds, and has powerful search tools that allows for highly customisable searches. Currently it is lacking in documentation, especially surrounding scraping tweets by location, so I hope to provide a thorough introduction to the topic.</p></div><div><h2>Introduction to snscrape in Python</h2><p>Throughout this article I’ll be using the development version of snscrape, which can be installed using</p><p><code>pip install git+https://github.com/JustAnotherArchivist/snscrape.git</code></p><p><em>Note: this requires Python 3.8 or higher</em></p><p>Some knowledge of the <code>Pandas</code> module is also necessary.</p></div><div><div><p>Only three packages are required, shown below</p><figure></figure><p>To find the first (i.e. most recent) 100 tweets that contains the phrase <em>data science</em> we can use the code:</p><figure></figure><p>Which can be shortened to the following line:</p><figure></figure><p>Outputting the first five results we can start to see the information this line gives us:</p></div><div><p>but that isn’t it! In total it returns 21 columns of data, these are:</p><figure></figure><p>I’d recommend having a play around to see what each of these are, or <a href="https://medium.com/better-programming/how-to-scrape-tweets-with-snscrape-90124ed006af">have a browse of this article to find out more</a>. I’ll go a bit deeper into the <em>user</em> field later on.</p><h2>Advanced Search Features</h2><p>The <code>TwitterSearchScraper</code> uses Twitter’s advanced search; for an in depth run down of all its capabilities <a href="https://github.com/igorbrigadir/twitter-advanced-search">check out this table</a> — I cannot recommend it enough!</p></div></div><div><h2>Scraping by Location</h2><p>When filtering by location there are two options: you can use the <code>near:city</code> tag alongside <code>within:radius</code> or <code>geocode:lat,long,radius</code> . After extensive research I can confirm they yield identical results when used correctly (or should I say, as Twitter interprets them).</p><p>As an example, say you wanted to find all tweets about <em>pizza</em> in Los Angeles. This can be achieved using the following code:</p><figure></figure><p>Instead of using the city’s name you can use its coordinates:</p><figure></figure><p>To compare the results we can use an inner merge on the two DataFrames:</p><figure></figure><p>Which returns <code>50</code> , i.e. they both contain the exact same rows.</p><h2>What exactly is this location?</h2><p>There are two ways to get a location from Twitter; a geo-tag from a specific tweet, or a user’s location as part of their profile. <a href="https://developer.twitter.com/en/docs/tutorials/tweet-geo-metadata">According to Twitter, only 1–2% of tweets are geo-tagged</a> hence it isn’t a great metric to be using; on the other hand a significant amount of users have a location in their profile, but they can enter whatever they want. Some are nice to people like us and will write ‘London, England’ or similar, while others are less useful, putting things like ‘My Parents Basement’.</p><p>All the documentation I could find indicated using a location as part of the search would only find geo-tagged tweets, however this isn’t the case. From my investigations I have discovered Twitter have some algorithms working as part of their advanced search that can interpret where a user’s location is from their profile, and assumes all of their tweets come from there. This means when you search for tweets using coordinates or a city name, <strong>the search returns tweets that are geo-tagged from that location, or that were tweeted by users with that location (or somewhere close-by) in their profile.</strong></p><figure><figcaption>Photo by <a href="https://unsplash.com/@lucas_davies?utm_source=medium&amp;utm_medium=referral">Lucas Davies</a> on <a href="https://unsplash.com/?utm_source=medium&amp;utm_medium=referral">Unsplash</a></figcaption></figure><p>As an example, when I searched for tweets <code>near:"London"</code> I managed to find examples of both:</p><p>The first tweet <strong>is geo-tagged</strong>, and the user <strong>does not</strong> have a location as part of the profile, i.e. the tweet was found because of its geo-tag. The second tweet <strong>is not</strong> <strong>geo-tagged</strong>, and was found because the user has their location in their profile.</p><figure><figcaption>A geo-tagged tweet by a user without a location</figcaption></figure><figure><figcaption>A tweet with no geo-tag from a user with a location</figcaption></figure></div><div><div><h2>Getting location from a scraped tweet</h2><p>If you want to get the user’s location having scraped the tweet, this is also possible using snscrape. In the example below I scrape 50 tweets from within 10km of LA, store it as a DataFrame and then create a new column for the user’s location.</p><figure></figure><p>Looking at the first 5 rows, we can see that although not all locations are formatted as the same, they can all be interpreted as Los Angeles.</p></div><div><figure><figcaption>Five results for tweets from LA about pizza, with a user location column</figcaption></figure></div></div><div><h2>When it doesn’t work as expected</h2><p>The way Twitter uses this search tag isn’t obvious, and when iterating through <a href="https://data.gov.uk/dataset/cd97a8df-e2fe-4f3d-a60f-1f871a317d31/counties-and-unitary-authorities-december-2016-full-extent-boundaries-in-england-and-wales">Counties and Unitary Authorities</a> in England I found the results to be inconsistent. For example, when searching for tweets <code>near:Lewisham</code>, all the tweets appear to be geo-tagged and come from Hobart, Australia (see below); that’s more than 17,000 km away! I found that <strong>using city names worked as expected</strong> but towns, villages and even countries returned suspicious results.</p><figure><figcaption>Tweet supposedly from Lewisham which is in fact from Hobart, Tasmania</figcaption></figure><p>When using snscrape to scrape tweets by location <strong>I’d always recommend using the </strong><code><strong>geocode</strong></code><strong> tag with longitude and latitude coordinates</strong>, with a radius to bound the search area. This will provide the <strong>most accurate results </strong>possible given the data available and features at hand.</p></div></div></article></div>||||I|||| Open in app

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 Scraping Tweets by Location in Python using snscrape

 Scott Tomlins

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 Dec 23, 2020

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 A deep dive into the snscrape Python wrapper, how to use it to search for tweets by location, and why it sometimes doesn’t work as expected.

 Photo by 🇨🇭 Claudio Schwarz | @purzlbaum on Unsplash

 snscrape is a library that allows anyone to scrape tweets without requiring personal API keys. It can return thousands of tweets in seconds, and has powerful search tools that allows for highly customisable searches. Currently it is lacking in documentation, especially surrounding scraping tweets by location, so I hope to provide a thorough introduction to the topic.

 Introduction to snscrape in Python

 Throughout this article I’ll be using the development version of snscrape, which can be installed using

 pip install git+https://github.com/JustAnotherArchivist/snscrape.git

 Note: this requires Python 3.8 or higher

 Some knowledge of the Pandas module is also necessary.

 Only three packages are required, shown below

 To find the first (i.e. most recent) 100 tweets that contains the phrase data science we can use the code:

 Which can be shortened to the following line:

 Outputting the first five results we can start to see the information this line gives us:

 but that isn’t it! In total it returns 21 columns of data, these are:

 I’d recommend having a play around to see what each of these are, or have a browse of this article to find out more. I’ll go a bit deeper into the user field later on.

 Advanced Search Features

 The TwitterSearchScraper uses Twitter’s advanced search; for an in depth run down of all its capabilities check out this table — I cannot recommend it enough!

 Scraping by Location

 When filtering by location there are two options: you can use the near:city tag alongside within:radius or geocode:lat,long,radius . After extensive research I can confirm they yield identical results when used correctly (or should I say, as Twitter interprets them).

 As an example, say you wanted to find all tweets about pizza in Los Angeles. This can be achieved using the following code:

 Instead of using the city’s name you can use its coordinates:

 To compare the results we can use an inner merge on the two DataFrames:

 Which returns 50 , i.e. they both contain the exact same rows.

 What exactly is this location?

 There are two ways to get a location from Twitter; a geo-tag from a specific tweet, or a user’s location as part of their profile. According to Twitter, only 1–2% of tweets are geo-tagged hence it isn’t a great metric to be using; on the other hand a significant amount of users have a location in their profile, but they can enter whatever they want. Some are nice to people like us and will write ‘London, England’ or similar, while others are less useful, putting things like ‘My Parents Basement’.

 All the documentation I could find indicated using a location as part of the search would only find geo-tagged tweets, however this isn’t the case. From my investigations I have discovered Twitter have some algorithms working as part of their advanced search that can interpret where a user’s location is from their profile, and assumes all of their tweets come from there. This means when you search for tweets using coordinates or a city name, the search returns tweets that are geo-tagged from that location, or that were tweeted by users with that location (or somewhere close-by) in their profile.

 Photo by Lucas Davies on Unsplash

 As an example, when I searched for tweets near:"London" I managed to find examples of both:

 The first tweet is geo-tagged, and the user does not have a location as part of the profile, i.e. the tweet was found because of its geo-tag. The second tweet is not geo-tagged, and was found because the user has their location in their profile.

 A geo-tagged tweet by a user without a location

 A tweet with no geo-tag from a user with a location

 Getting location from a scraped tweet

 If you want to get the user’s location having scraped the tweet, this is also possible using snscrape. In the example below I scrape 50 tweets from within 10km of LA, store it as a DataFrame and then create a new column for the user’s location.

 Looking at the first 5 rows, we can see that although not all locations are formatted as the same, they can all be interpreted as Los Angeles.

 Five results for tweets from LA about pizza, with a user location column

 When it doesn’t work as expected

 The way Twitter uses this search tag isn’t obvious, and when iterating through Counties and Unitary Authorities in England I found the results to be inconsistent. For example, when searching for tweets near:Lewisham, all the tweets appear to be geo-tagged and come from Hobart, Australia (see below); that’s more than 17,000 km away! I found that using city names worked as expected but towns, villages and even countries returned suspicious results.

 Tweet supposedly from Lewisham which is in fact from Hobart, Tasmania

 When using snscrape to scrape tweets by location I’d always recommend using the geocode tag with longitude and latitude coordinates, with a radius to bound the search area. This will provide the most accurate results possible given the data available and features at hand.

 Conclusion

 This simple yet incredibly powerful Python module allows for some very specific searches. Again, I’d recommend checking this table out for a clear run down of its full capabilities. Twitter have done the hard work in converting user input locations into real places, allowing us to find them by name or coordinates. Tweets provide a great source of information, and when used in conjunction with a tool as powerful as snscrape allows for wide range interesting projects to be completed without much experience of Data Science, or knowledge of the subject! Happy scraping :)

 All the code mentioned in the article can be found here: https://github.com/satomlins/snscrape-by-location

 Find me on LinkedIn

 References

 snscrape GitHub: https://github.com/JustAnotherArchivist/snscrape

 Twitter’s advanced search operators: https://github.com/igorbrigadir/twitter-advanced-search

 Reference article on how to use snscrape Python wrapper: https://medium.com/better-programming/how-to-scrape-tweets-with-snscrape-90124ed006af

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A deep dive into the snscrape Python wrapper, how to use it to search for tweets by location, and why it sometimes doesn’t work as expected.  Photo by 🇨🇭 Claudio Schwarz | @purzlbaum on Unsplash   snscrape is a library that allows anyone to scrape tweets without requiring personal API keys. It can return thousands of tweets in seconds, and has powerful search tools that allows for highly customisable searches. Currently it is lacking in documentation, especially surrounding scraping tweets by location, so I hope to provide a thorough introduction to the topic.   Introduction to snscrape in Python Throughout this article I’ll be using the development version of snscrape, which can be installed using  pip install git+https://github.com/JustAnotherArchivist/snscrape.git   Note: this requires Python 3.8 or higher  Some knowledge of the Pandas module is also necessary.    Only three packages are required, shown below   To find the first (i.e. most recent) 100 tweets that contains the phrase data science we can use the code:   Which can be shortened to the following line:   Outputting the first five results we can start to see the information this line gives us:   but that isn’t it! In total it returns 21 columns of data, these are:   I’d recommend having a play around to see what each of these are, or have a browse of this article to find out more. I’ll go a bit deeper into the user field later on. Advanced Search Features The TwitterSearchScraper uses Twitter’s advanced search; for an in depth run down of all its capabilities check out this table — I cannot recommend it enough!    Scraping by Location When filtering by location there are two options: you can use the near:city tag alongside within:radius or geocode:lat,long,radius . After extensive research I can confirm they yield identical results when used correctly (or should I say, as Twitter interprets them). As an example, say you wanted to find all tweets about pizza in Los Angeles. This can be achieved using the following code:   Instead of using the city’s name you can use its coordinates:   To compare the results we can use an inner merge on the two DataFrames:   Which returns 50 , i.e. they both contain the exact same rows. What exactly is this location? There are two ways to get a location from Twitter; a geo-tag from a specific tweet, or a user’s location as part of their profile. According to Twitter, only 1–2% of tweets are geo-tagged hence it isn’t a great metric to be using; on the other hand a significant amount of users have a location in their profile, but they can enter whatever they want. Some are nice to people like us and will write ‘London, England’ or similar, while others are less useful, putting things like ‘My Parents Basement’. All the documentation I could find indicated using a location as part of the search would only find geo-tagged tweets, however this isn’t the case. From my investigations I have discovered Twitter have some algorithms working as part of their advanced search that can interpret where a user’s location is from their profile, and assumes all of their tweets come from there. This means when you search for tweets using coordinates or a city name, the search returns tweets that are geo-tagged from that location, or that were tweeted by users with that location (or somewhere close-by) in their profile.   Photo by Lucas Davies on Unsplash   As an example, when I searched for tweets near:"London" I managed to find examples of both: The first tweet is geo-tagged, and the user does not have a location as part of the profile, i.e. the tweet was found because of its geo-tag. The second tweet is not geo-tagged, and was found because the user has their location in their profile.  A geo-tagged tweet by a user without a location   A tweet with no geo-tag from a user with a location     Getting location from a scraped tweet If you want to get the user’s location having scraped the tweet, this is also possible using snscrape. In the example below I scrape 50 tweets from within 10km of LA, store it as a DataFrame and then create a new column for the user’s location.   Looking at the first 5 rows, we can see that although not all locations are formatted as the same, they can all be interpreted as Los Angeles.    Five results for tweets from LA about pizza, with a user location column     When it doesn’t work as expected The way Twitter uses this search tag isn’t obvious, and when iterating through Counties and Unitary Authorities in England I found the results to be inconsistent. For example, when searching for tweets near:Lewisham, all the tweets appear to be geo-tagged and come from Hobart, Australia (see below); that’s more than 17,000 km away! I found that using city names worked as expected but towns, villages and even countries returned suspicious results.  Tweet supposedly from Lewisham which is in fact from Hobart, Tasmania  When using snscrape to scrape tweets by location I’d always recommend using the   geocode   tag with longitude and latitude coordinates, with a radius to bound the search area. This will provide the most accurate results possible given the data available and features at hand.
Scraping Tweets by Location in Python using snscrape
https://medium.com/swlh/how-to-scrape-tweets-by-location-in-python-using-snscrape-8c870fa6ec25
https://michaelcurrin.github.io/twitterverse/#/
Twitterverse
2017-01-01
None
<div><section><article><h2><a href="#/?id=twitterverse"></a></h2><blockquote>
<p>Store and report on Twitter conversations, from tweets to trending topics.</p></blockquote>
<p><a href="https://github.com/MichaelCurrin/twitterverse/actions"></a>
<a href="https://www.tweepy.org/"></a>
<a href="http://sqlobject.org/"></a>
<a href="https://GitHub.com/MichaelCurrin/twitterverse/tags/"></a></p><p>Application to harvest data from the Twitter API (using Python <code>tweepy</code> package) and to make that data easy to navigate, search and make sense of. SQLite is used for storage, with <code>SQLObject</code> as the ORM.</p><p>NB. This project requires <strong>Twitter API credentials</strong> (see <a href="#/installation?id=twitter-credentials">installation</a>) which takes time and effort to setup, but they are available for free.</p><h2><a href="#/?id=how-to-use-this-project"></a></h2><p>This project does a lot of things but its core is searching for tweets and getting daily trending data and storing results in a database or CSV. It uses the command-line to let you store hashtag searches and choose places to watch.</p><p>If you want to see how to see how this project does auth, search or streaming, have a look at this module.</p><ul><li><a href="https://github.com/MichaelCurrin/twitterverse/tree/master/app/lib/twitter_api">app/lib/twitter_api</a></li></ul><p>You can clone and use the code locally or reference the code that you need for your own projects. Please provide a link back to this project. A copy of the license must be included with your code as well if you copy code directly from here.</p><h2><a href="#/?id=follow-a-guide-to-tweepy"></a></h2><p>If you want to learn to use Tweepy and the Twitter API, I added tutorials and resources here.</p><ul><li><a href="https://MichaelCurrin.github.io/python-twitter-guide">https://MichaelCurrin.github.io/python-twitter-guide</a></li></ul><h2><a href="#/?id=features"></a></h2><p>This project works best if you have an aim to follow and explore certain data on Twitter. Such as:</p><p>This project allows you to fetch data related to the above. The default behavior for most is to store though some have flags to print only.</p><p>Note: You cannot actually post a tweet with this project. But the <a href="http://docs.tweepy.org/en/latest/">tweepy</a> website covers that well.</p><p>Access to the project is all through command-line utilities.</p><h3><a href="#/?id=get-tweets-and-profiles"></a></h3><p>Lookup tweets and profiles from the Twitter API. The focus of this project is searching for tweets (and getting their profiles at the same time) using a search query. If you lookup a profile directly, you can get stats, bio and their tweets.</p><p>The input needed depends on the API query type, as below:</p><table>
<thead>
<tr>
<th>API query</th>
<th>Input required</th>
<th>Equivalent browser URL</th>
</tr>
</thead>
<tbody><tr>
<td>Tweet search</td>
<td>Search query to match against tweet messages</td>
<td><code>twitter.com/search?q=QUERY</code> for phrase which may contain hashtag or spaces, OR <code>twitter.com/hashtag/HASHTAG</code> for a single hashtag.</td>
</tr>
<tr>
<td>Tweet lookup</td>
<td>ID of a tweet</td>
<td><code>twitter.com/HANDLE/status/TWEET_ID</code></td>
</tr>
<tr>
<td>Profile lookup</td>
<td>Handle or ID of the Twitter user.</td>
<td><code>twitter.com/HANDLE</code></td>
</tr>
</tbody></table>
<p>The browser URL is useful for testing a query on a small scale or to check an object exists before doing an API query for it.</p><p>If you store the data in the DB, you can then do a report on the tweets and profiles.</p><h3><a href="#/?id=stream-tweets"></a></h3><p>You can also do a live stream of a search query and print out the tweets to the console. This is not a core part of this project and so no tweets are printed to the console but note stored in the DB.</p><p>Input required:</p><ul><li>Search query to match against tweet messages.</li></ul><h3><a href="#/?id=get-trending-topics"></a></h3><p>Using the trends scripts to find what topics are trending in your country or town at the current moment. See what other places are also talking about this topics.</p><p>Reports can be done on the stored trending data.</p><p>Input required:</p><ul><li>Names of countries or towns places to get trending topic data for. Twitter allows lookup for Worldwide, 62 countries and about 400 towns/cities.</li></ul><p>There is no browser URL equivalent of looking up a trend, but on the right menu on twitter.com you can see a list of trends in your area and you can click through from there to do a search for tweets about that topic. Format: <code>twitter.com/search?q=QUERY</code>.</p><h2><a href="#/?id=setup"></a></h2><p>Follow the <a href="#/installation">Installation</a> guide to setup the application locally, including config setup.</p><h2><a href="#/?id=usage"></a></h2><h3><a href="#/?id=api"></a></h3><p>This project has a command-line interface which allows you to use the configured application, <em>without</em> writing any code or using a Python console. Access the API through scripts covered in these sections.</p><ul><li><a href="#/tweets/">Tweets</a></li><li><a href="#/trends/">Trends</a></li></ul><p>See the docs menu for development, console use and other advanced functionality.</p><h3><a href="#/?id=scheduling"></a></h3><p>If you are interested in scheduling jobs around tweets and trends but are not familiar with <code>crontab</code>, I recommended researching how to use it before following the usage docs.</p><p>See the <a href="https://github.com/MichaelCurrin/learn-to-code/tree/master/Shell/Scheduling">Scheduling</a> of my <em>Learn To Code</em> project.</p><h3><a href="#/?id=makefile"></a></h3><p>Run the following in the project for useful shortcuts through the <code>make</code> command. These are used in the sections above.</p><pre><code>$ make help</code></pre><p>Or see the <a href="https://github.com/MichaelCurrin/twitterverse/blob/master/Makefile">Makefile</a> on Github.</p><h2><a href="#/?id=project-history"></a></h2><p>This project started in 2017 out of a desire to get tweet and trend data at scale so it can be explored as reports and graphs. I find topics of interest of myself and friends. Hopefully this can be used for businesses to use to understand their campaigns or brand mentions. It has turned out to be useful for tracking social and political data too, such as the Cape Town Water Crisis and elections.</p><p>The project has been adapted to manager higher volumes of data.</p><p>Unit tests have been added but a lot more could be added.</p><p>The command-line tools could be refactored to be more consistent and easier to use.</p><h2><a href="#/?id=license"></a></h2><p>This project is license with an <a href="https://github.com/MichaelCurrin/twitterverse/blob/master/LICENSE">MIT License</a>. Feel free to use this project for non-commercial use. You can fork the project and modify it too.</p><p>No liability or warranty is provided. Responsibility lies with you for managing your database data.</p><p>See the <a href="#/twitter-api-docs/policies">Twitter Policies</a> page for info on fair use of the Twitter API and the returned data.</p><h2><a href="#/?id=credits"></a></h2><ul><li>Cover image by <a href="https://unsplash.com/@stereophototyp?utm_medium=referral&amp;utm_campaign=photographer-credit&amp;utm_content=creditBadge"></a></li><li>This docs site uses the <em>dark</em> theme of <a href="#/docsify.js.org">Docsify</a>.</li></ul></article></section></div>||||I|||| Twitterverse Docs

 Explore the Twitter conversations through users and their
 tweets and countries and their trending topics.

 Fetch content

 * Search for tweets using queries
 * Follow profiles and their tweets.
 * Follow countries and cities and their trending topics.

 Manage data

 * Manage tweet queries, watched profiles and watches locations in a DB.
 * Store fetched data in a DB using daily cron.
 * Run CSV reports.

 GitHub Getting Started

Twitterverse

 * Home
 * Installation
 * Tweets
 + Fetch tweets
 + Fetch profile tweets
 + Watch profiles
 + Watch influencers
 + Reports
 + Streaming
 * Trends
 + Seed locations
 + Basic fetch
 + Watch locations
 + Fetch watched
 + Reports
 * Reports
 * Schema
 * Advanced usage
 + Tips for using 3rd-party tools
 o SQLite
 o SQLObject
 o Tweepy
 + Development
 o Setup docs site
 o Run tests
 o Models
 * Twitter API docs
 + Search
 + Streaming
 + Policies

Twitterverse

Store and report on Twitter conversations, from tweets to trending topics.

Application to harvest data from the Twitter API (using Python tweepy package) and to make that data easy to navigate, search and make sense of. SQLite is used for storage, with SQLObject as the ORM.

NB. This project requires Twitter API credentials (see installation) which takes time and effort to setup, but they are available for free.

How to use this project

This project does a lot of things but its core is searching for tweets and getting daily trending data and storing results in a database or CSV. It uses the command-line to let you store hashtag searches and choose places to watch.

If you want to see how to see how this project does auth, search or streaming, have a look at this module.

 * app/lib/twitter_api

You can clone and use the code locally or reference the code that you need for your own projects. Please provide a link back to this project. A copy of the license must be included with your code as well if you copy code directly from here.

Follow a guide to Tweepy

If you want to learn to use Tweepy and the Twitter API, I added tutorials and resources here.

 * https://MichaelCurrin.github.io/python-twitter-guide

Features

This project works best if you have an aim to follow and explore certain data on Twitter. Such as:

This project allows you to fetch data related to the above. The default behavior for most is to store though some have flags to print only.

Note: You cannot actually post a tweet with this project. But the tweepy website covers that well.

Access to the project is all through command-line utilities.

Get tweets and profiles

Lookup tweets and profiles from the Twitter API. The focus of this project is searching for tweets (and getting their profiles at the same time) using a search query. If you lookup a profile directly, you can get stats, bio and their tweets.

The input needed depends on the API query type, as below:

API query Input required Equivalent browser URL  
Tweet search Search query to match against tweet messages twitter.com/search?q=QUERY for phrase which may contain hashtag or spaces, OR twitter.com/hashtag/HASHTAG for a single hashtag.
Tweet lookup ID of a tweet twitter.com/HANDLE/status/TWEET_ID  
Profile lookup Handle or ID of the Twitter user. twitter.com/HANDLE  


The browser URL is useful for testing a query on a small scale or to check an object exists before doing an API query for it.

If you store the data in the DB, you can then do a report on the tweets and profiles.

Stream tweets

You can also do a live stream of a search query and print out the tweets to the console. This is not a core part of this project and so no tweets are printed to the console but note stored in the DB.

Input required:

 * Search query to match against tweet messages.

Get trending topics

Using the trends scripts to find what topics are trending in your country or town at the current moment. See what other places are also talking about this topics.

Reports can be done on the stored trending data.

Input required:

 * Names of countries or towns places to get trending topic data for. Twitter allows lookup for Worldwide, 62 countries and about 400 towns/cities.

There is no browser URL equivalent of looking up a trend, but on the right menu on twitter.com you can see a list of trends in your area and you can click through from there to do a search for tweets about that topic. Format: twitter.com/search?q=QUERY.

Setup

Follow the Installation guide to setup the application locally, including config setup.

Usage

API

This project has a command-line interface which allows you to use the configured application, without writing any code or using a Python console. Access the API through scripts covered in these sections.

 * Tweets
 * Trends

See the docs menu for development, console use and other advanced functionality.

Scheduling

If you are interested in scheduling jobs around tweets and trends but are not familiar with crontab, I recommended researching how to use it before following the usage docs.

See the Scheduling of my Learn To Code project.

Makefile

Run the following in the project for useful shortcuts through the make command. These are used in the sections above.

$ make help

Or see the Makefile on Github.

Project history

This project started in 2017 out of a desire to get tweet and trend data at scale so it can be explored as reports and graphs. I find topics of interest of myself and friends. Hopefully this can be used for businesses to use to understand their campaigns or brand mentions. It has turned out to be useful for tracking social and political data too, such as the Cape Town Water Crisis and elections.

The project has been adapted to manager higher volumes of data.

Unit tests have been added but a lot more could be added.

The command-line tools could be refactored to be more consistent and easier to use.

License

This project is license with an MIT License. Feel free to use this project for non-commercial use. You can fork the project and modify it too.

No liability or warranty is provided. Responsibility lies with you for managing your database data.

See the Twitter Policies page for info on fair use of the Twitter API and the returned data.

Credits

 * Cover image by Sara Kurfeß
 * This docs site uses the dark theme of Docsify.
Store and report on Twitter conversations, from tweets to trending topics. 
  
 
 
   Application to harvest data from the Twitter API (using Python tweepy package) and to make that data easy to navigate, search and make sense of. SQLite is used for storage, with SQLObject as the ORM. NB. This project requires Twitter API credentials (see installation) which takes time and effort to setup, but they are available for free.     This project does a lot of things but its core is searching for tweets and getting daily trending data and storing results in a database or CSV. It uses the command-line to let you store hashtag searches and choose places to watch. If you want to see how to see how this project does auth, search or streaming, have a look at this module.   app/lib/twitter_api   You can clone and use the code locally or reference the code that you need for your own projects. Please provide a link back to this project. A copy of the license must be included with your code as well if you copy code directly from here.     If you want to learn to use Tweepy and the Twitter API, I added tutorials and resources here.   https://MichaelCurrin.github.io/python-twitter-guide       This project works best if you have an aim to follow and explore certain data on Twitter. Such as: This project allows you to fetch data related to the above. The default behavior for most is to store though some have flags to print only. Note: You cannot actually post a tweet with this project. But the tweepy website covers that well. Access to the project is all through command-line utilities.     Lookup tweets and profiles from the Twitter API. The focus of this project is searching for tweets (and getting their profiles at the same time) using a search query. If you lookup a profile directly, you can get stats, bio and their tweets. The input needed depends on the API query type, as below: 
API query
Input required
Equivalent browser URL
 
Tweet search
Search query to match against tweet messages
 twitter.com/search?q=QUERY for phrase which may contain hashtag or spaces, OR twitter.com/hashtag/HASHTAG for a single hashtag.
Tweet lookup
ID of a tweet
 twitter.com/HANDLE/status/TWEET_ID 
Profile lookup
Handle or ID of the Twitter user.
 twitter.com/HANDLE 
 
The browser URL is useful for testing a query on a small scale or to check an object exists before doing an API query for it. If you store the data in the DB, you can then do a report on the tweets and profiles.     You can also do a live stream of a search query and print out the tweets to the console. This is not a core part of this project and so no tweets are printed to the console but note stored in the DB. Input required:  Search query to match against tweet messages.      Using the trends scripts to find what topics are trending in your country or town at the current moment. See what other places are also talking about this topics. Reports can be done on the stored trending data. Input required:  Names of countries or towns places to get trending topic data for. Twitter allows lookup for Worldwide, 62 countries and about 400 towns/cities.  There is no browser URL equivalent of looking up a trend, but on the right menu on twitter.com you can see a list of trends in your area and you can click through from there to do a search for tweets about that topic. Format: twitter.com/search?q=QUERY.     Follow the Installation guide to setup the application locally, including config setup.         This project has a command-line interface which allows you to use the configured application, without writing any code or using a Python console. Access the API through scripts covered in these sections.   Tweets   Trends   See the docs menu for development, console use and other advanced functionality.     If you are interested in scheduling jobs around tweets and trends but are not familiar with crontab, I recommended researching how to use it before following the usage docs. See the Scheduling of my Learn To Code project.     Run the following in the project for useful shortcuts through the make command. These are used in the sections above.  $ make help  Or see the Makefile on Github.     This project started in 2017 out of a desire to get tweet and trend data at scale so it can be explored as reports and graphs. I find topics of interest of myself and friends. Hopefully this can be used for businesses to use to understand their campaigns or brand mentions. It has turned out to be useful for tracking social and political data too, such as the Cape Town Water Crisis and elections. The project has been adapted to manager higher volumes of data. Unit tests have been added but a lot more could be added. The command-line tools could be refactored to be more consistent and easier to use.     This project is license with an MIT License. Feel free to use this project for non-commercial use. You can fork the project and modify it too. No liability or warranty is provided. Responsibility lies with you for managing your database data. See the Twitter Policies page for info on fair use of the Twitter API and the returned data.      Cover image by    This docs site uses the dark theme of Docsify.
Twitterverse
https://michaelcurrin.github.io/twitterverse/#/
https://www.followersanalysis.com/
Twitter Follower Analysis, Comparison, Tracking and More
2023-01-01
None
<div><div>
 	<p>You can use FollowersAnalysis to download and analyze your followers’ data.</p>
 	<p>It provides insightful metrics such as followers' last tweet date, account age of Twitter followers, Tweets, followers and following count of Twitter account followers, verified and protected Twitter followers percentage, and list of top followers, and following that helps you make data-driven decisions.</p>
 	</div>||||I|||| * Followers Analysis & Data
 * User Tweets Analysis & Data
 * Products
 * Old Tweets
 * Pricing
 * Blog
 * ( 0 )
 * Login
 * Register


 * Home
 * Followers Analysis & Data
 * User Tweets Analysis & Data
 * Old Tweets
 * Pricing
 * Blog
 * ( 0 )
 * Login
 * Register

 Twitter Follower Analysis Following Analysis Account Analysis

 Export the list of all Twitter followers and following in CSV/Excel along with their analysis

 * Followers Analysis & Data
 * User Tweets Analysis & Data

 Download Sample Report of @katyperry

 Logging in through Twitter

 We require Twitter authentication to retrieve tweets and followers data from Twitter. Please note:

 * 1. We don't post anything on Twitter
 * 2. Your account information is 100% confidential

 Click here if not redirected.

 Followers Analysis & Data

 Get the complete list of Twitter Followers & Following Data as well as 3200 tweets of any Twitter account in CSV/Excel along with a comprehensive analytics PDF. Twitter follower analysis includes Most active and popular followers, Followers last tweet date, Followers' account age, etc.

 Download Sample Learn More

 User Tweets Analysis & Data

 Export any public Twitter user's recent 3200 tweets in a CSV/Excel datasheet and analyze it with the help of FollowersAnalysis' advanced AI-based analytical tool. The analytics include the best time to tweet, the Most mentioned keywords, Most retweeted, liked tweets and Week-Day Peak Usage Pattern, etc.

 Download Sample Learn More

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 Why Choose FollowersAnalysis?

 Get Recent 3200 Tweets and More

 Along with Twitter followers data, you can also export more than 3200 tweets of any public Twitter account with Tweet content, URLS, and more.

 * More than 3200 tweets on special request
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 * List of URLs of Images which are used in the tweets
 * List of URLs of Videos which are used in the tweets

 Comprehensive Twitter data

 Our tool performs Twitter follower analysis and provides complete data related to a Twitter account and hence furnish you with the required firepower to win the social media battle, and at the end, drive valuable conversions for your brand:

 * Get a full list of all the followers of any Twitter account along with lots of useful metadata.
 * Followers Metadata includes the Twitter id of followers, account creation date, Name, username, Profile URL, Location and many more
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 Twitter follower analysis helps you analyze twitter users and their followers with the help of our AI-based tool. We provide a detailed analysis of Twitter accounts to help you in Research and Analysis.

 * Get to know the potential reach and overall impressions created by the tweets of an individual account
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 Followers Analysis exclusive analytics and insights

 FollowersAnalysis provides lots of useful analytics that are derived from raw tweet and followers data. These analytics help our users to conduct research, build a marketing event, and design future strategy. Twitter Followers Analysis can provide you with a lot of useful insights. All the crucial analytical metrics are explained below.

 * Best Time to Tweet

 Know the best time for a user to tweet to get maximum exposure and retweets. It is calculated based on retweets gained by the tweets which were tweeted in the past.

 * Tweet Client Source

 The devices which are used to post the tweets are client sources. They are Twitter web client, Twitter for iPhone, Twitter for Android, TweetDeck, etc.

 * Week-Day Peak Usage Pattern

 Week-Day peak usage pattern is the pattern between hours of the day and days of the week which shows the tweeting pattern of Twitter users.

 * Most Retweeted and Liked Tweets

 Among all the tweets of a Twitter user, you can know its 10 most retweeted tweets and 10 most liked tweets. It helps in knowing what kind of tweets gain maximum exposure.

 * Top Twitter Followers and Following

 Know the most popular, most active, most liked followers and following. Twitter Account Analysis helps in finding out the key influencers of the industry. It helps you in getting a brief idea about how active they are on Twitter and how authentic their followers are. Statistics such as tweet reply percentage provide us with an idea of their activeness on Twitter.

 * Other Followers Analytics

 Other analytics provided by Followers analysis are Followers’ Last Tweet Date, Account Age of Followers, Tweets, Followers, Following Count of Twitter Account Followers, Followers Verified and Protected Percentage

 Features provided by Followers Analysis

 Export data to Excel/CSV

 Export large volume of raw twitter data in excel (csv) format.

 Downloadable PDF report of Analytics

 Download PDF containing various useful analytics and insights from raw Twitter data.

 Influencers search

 Find out those Twitter Followers and Following which are most impactful and influential.

 Predictive Followers analytics

 Predict the best time to tweet, the best hashtag to use, etc. with the help of existing raw Twitter data.

 Notifications and Alerts

 Get email notifications at every event happening in your FollowersAnalysis account.

 Tweet timeline and pattern

 Get a detailed timeline of all the tweets as well as their daily and weekly pattern.

 Separate Media Files

 Get a separate image and video files that contain URLs, Media ID, and Tweet IDs of all the media.

 Replies/Comments on a Tweet

 Get all the replies/comments received on any Tweet in CSV/Excel file. It contains Reply content, Tweet id etc.

 Customization

 Get Customized reports, customized tools and filtered data to fulfill your needs and requirements.

 FAQ

 * How can I see a list of someone's followers on Twitter?

 You can see a list of someone’s followers on Twitter by visiting their profile and clicking on ‘followers’. You have to scroll down until the end of the page a couple of times to load up all the people who follow the account you’re viewing. If you want to export their Twitter followers, we recommend using FollowersAnalysis.com instead of copy-pasting, as this can be a tedious task for accounts with more than 500 followers.

 * How do I Download Twitter data for research?

 FollowersAnalysis lets you download tweets from any public Twitter account, hashtag, keyword, or mention. There is no limitation to the Twitter data download, hence you can get all the tweets of any public Twitter user. The data is provided in CSV, Excel, and JSON formats.

 * How do I analyze my Twitter followers?

 You can use FollowersAnalysis to download and analyze your followers’ data.

 It provides insightful metrics such as followers' last tweet date, account age of Twitter followers, Tweets, followers and following count of Twitter account followers, verified and protected Twitter followers percentage, and list of top followers, and following that helps you make data-driven decisions.

 * How to detect fake Twitter followers?

 There are a few ways to tell if a follower is fake, a bot, or just an inactive account. It’s important to make the distinction between a real and a fake account for better engagement results.

 You can use FollowersAnalysis to get a followers report for any account. In the report, you get a list of detailed metrics that can help you identify fake Twitter followers or you can just run fake followers audit of any public Twitter account with FollowerAudit.com.

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You can use FollowersAnalysis to download and analyze your followers’ data.
 	It provides insightful metrics such as followers' last tweet date, account age of Twitter followers, Tweets, followers and following count of Twitter account followers, verified and protected Twitter followers percentage, and list of top followers, and following that helps you make data-driven decisions.
Twitter Follower Analysis, Comparison, Tracking and More
https://www.followersanalysis.com/
https://www.researchgate.net/post/How_can_I_scrape_data_from_Twitter_Advanced_search
How can I scrape data from Twitter Advanced search? | ResearchGate
2024-01-21
Cecilia Almeida; Prem Sankar Chakkingal; Nosharwan Arbab Abbasi; Stuart William Shulman; Mona Elbiely; Emma Lee; Chhinder Kaur; Solveg Nikolaisen; Gargy Gupta
<div><div><section><a href=""></a><div><div><a href="https://www.researchgate.net/post/profile/Cecilia-Almeida"></a></div><div><p></p></div></div><div><p><h2>Most recent answer</h2></p><div><div><a href="https://www.researchgate.net/post/profile/Gargy_Gupta4"></a></div><div><p></p></div></div></div><div><p><h2>Top contributors to discussions in this field</h2></p></div><div><p><strong>Get help with your research</strong></p><p>Join ResearchGate to ask questions, get input, and advance your work.</p></div><div><div><p><h2>All Answers (11)</h2></p></div><div><div><a href="https://www.researchgate.net/post/profile/Prem-Sankar-Chakkingal"></a></div><div><div><div><a href="https://www.researchgate.net/post/profile/Cecilia-Almeida"></a></div><div><p>Federal University of Pernambuco</p></div></div><div><p></p></div></div><div><div><div><a href="https://www.researchgate.net/post/profile/Nosharwan-Abbasi-2"></a></div><div><p>National University of Modren Languages Islamabad</p></div></div><div><p></p></div></div><div><div><div><a href="https://www.researchgate.net/post/profile/Cecilia-Almeida"></a></div><div><p>Federal University of Pernambuco</p></div></div><div><p></p></div></div><div><div><a href="https://www.researchgate.net/post/profile/Stuart-Shulman"></a></div><div><div><p></p></div><div><div></div><div><ul><li></li></ul></div></div></div></div><div><div><div><a href="https://www.researchgate.net/post/profile/Cecilia-Almeida"></a></div><div><p>Federal University of Pernambuco</p></div></div><div><p></p></div></div><div><div><a href="https://www.researchgate.net/post/profile/Mona-Elbiely"></a></div><div><p></p></div></div><div><div><a href="https://www.researchgate.net/post/profile/Emma-Lee-11"></a></div><div><p></p></div></div><div><div><a href="https://www.researchgate.net/post/profile/Chhinder-Kaur"></a></div><div><p>Chandigarh Group of Colleges</p></div></div><div><div><a href="https://www.researchgate.net/post/profile/Solveg-Nikolaisen"></a></div><div><p></p></div></div><div><div><a href="https://www.researchgate.net/post/profile/Gargy_Gupta4"></a></div><div><p></p></div></div></div></div><a href=""></a><div><p><h2>Similar questions and discussions</h2></p><div><a href="https://www.researchgate.net/post/post/New_scam_Do_you_want_to_be_paid_to_cite_others_research"><div><div><p>New scam: Do you want to be paid to cite others' research? </p></div><div><ul><li></li></ul></div><div><p></p></div></div></a></div></div><a href=""></a><div><p><h2>Related Publications</h2></p><div><ul><li><a href="https://www.researchgate.net/profile/Sarah-Bouraga"></a></li></ul></div></div><div><p><strong>Got a technical question?</strong></p><p>Get high-quality answers from experts. </p></div></section></div></div>||||I|||| Question
 Answers 11
 Similar questions
 Research that mentions Twitter
 Question
 Asked 25th Oct, 2017
 Cecilia Almeida
 * Federal University of Pernambuco

 How can I scrape data from Twitter Advanced search?

 Hello, I would like to collect a large amount of old data from Twitter Advanced Search (from 2014 to 2017).
 I tried using dataminer Chrome extension to scrape the data, but it crashes everytime and I have to keep checking the process. Does anyone know how to work this out?
 Thanks
 Twitter
 Share
 Facebook
 Twitter
 LinkedIn
 Reddit

 Most recent answer

 Gargy Gupta
 There is an absolutely perfect tool https://www.followersanalysis.com/ that I have been using for a long now to scrape Twitter data.
 FollowersAnalysis is a great Twitter analytics tool that allows you to extract tweets linked to any @username or term. Scrape your brand's Twitter profile's previous 3200 tweets, competitors’, influencer marketing candidates, and so on.
 Not only does FollowersAnalysis provide you access to Twitter data, but it also gives you analytical insights and predictive analytics. The detailed PDF reports offer useful information that might assist you in making data-driven decisions.
 You may also use FollowersAnalysis to examine your Twitter followers.
 If you require any further information, please contact them at contact@followersanalysis.com or utilize the chat button at the bottom right corner of their website, and they will gladly assist you.
 Go get easy access to various Twitter data and their analytical insights!
 Cite

 Top contributors to discussions in this field

 Emre Pakdemirli
 * Sağlık Bilimleri Üniversitesi Ankara Sehir Hastanesi (University of Health Sciences)
 Nino Durglishvili
 * Ivane Javakhishvili Tbilisi State University
 Ljubomir Jacić
 * Technical College Požarevac
 Nidhal Kamel Taha El-Omari
 * The World Islamic Science and Education University (WISE)
 Chuck A Arize
 * Texas A&M University-Commerce

 Get help with your research

 Join ResearchGate to ask questions, get input, and advance your work.

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 All Answers (11)

 Prem Sankar Chakkingal
 University of Kerala
 Twitter Official API has a limitation , you can't get older tweets than a week. Some tools provide access to older tweets but in the most of them you have to spend some money before. Try this python code https://github.com/Jefferson-Henrique/GetOldTweets-python
 Cite
 1 Recommendation
 Cecilia Almeida
 Federal University of Pernambuco
 Thanks for the answer, I`ll ty the code :)
 Cite
 Nosharwan Arbab Abbasi
 National University of Modren Languages Islamabad
 Agree with Prem, you can only get 7 days old data. i was facing same problem and used API but could not find any solution to get older data. So i am using code to get twitter data on real time for future use.
 Thanks
 Cite
 Cecilia Almeida
 Federal University of Pernambuco
 I solved the problem (at least partially) using Data Miner and Twitter advanced search. Data Miner has recipes for scraping tweets with the information that is visible on the browser. So I opened twitter's advanced search feature, chose the specific time-period I wanted and scraped all the data as possible: Account name, tweet Content, Date, hashtags, number of replies, number of retweets and number of likes. If you try to scrape a large amount of data, the site crashes from time to time.
 Cite
 Stuart William Shulman
 Texifter
 You could also try Sifter:
 https://sifter.texifter.com
 * 01 sifter
 1.png
 276.37 KB
 Cite
 Cecilia Almeida
 Federal University of Pernambuco
 Many thanks for the suggestion, I'll give it a try!
 Cite
 Mona Elbiely
 Damanhour University
 i need to scrape data from twitter with certain hashtag with more than 7 days can you help me i have a python code but it scrape 7 days only is there any way to solve this..
 import sys, csv, twitter
 import os
 import tweepy
 # Replace the API_KEY and API_SECRET with your application's key and secret.
 #This code is using AppAuthHandler, not OAuthHandler to get higher limits, 2.5 times.
 auth = tweepy.AppAuthHandler('tsgSktklB1UOlvsQjbC9US40Z', 'TUFBo8iqsCxESFMEctzRCppaLwvzRRITpS1zYluxQUW0tu5bJG')
 api = tweepy.API(auth, wait_on_rate_limit=True,
 wait_on_rate_limit_notify=True)
 if (not api):
 print ("Can't Authenticate")
 sys.exit(-1)
 def clean(val):
 clean = ""
 if val:
 clean = val.encode('utf-8')
 return clean
 searchQuery = '#AAPL' #Put your hasthag here
 maxTweets = 10000000000 # Some arbitrary large number
 tweetsPerQry = 100 # this is the max the API permits
 fName = 'bandung_intoleran.csv' #CSV file to store the data
 csvfile = open(fName, 'w');
 csvwriter = csv.writer(csvfile)
 count=0
 # If results from a specific ID onwards are reqd, set since_id to that ID.
 # else default to no lower limit, go as far back as API allows
 sinceId = None
 # If results only below a specific ID are, set max_id to that ID.
 # else default to no upper limit, start from the most recent tweet matching the search query.
 max_id = -1L
 tweetCount = 0
 #print("Downloading max {0} tweets".format(maxTweets))
 with open(fName, 'w') as csvfile:
 while tweetCount < maxTweets:
 try:
 if (max_id <= 0):
 if (not sinceId):
 new_tweets = api.search(q=searchQuery, count=tweetsPerQry)
 else:
 new_tweets = api.search(q=searchQuery, count=tweetsPerQry,
 since_id=sinceId)
 else:
 if (not sinceId):
 new_tweets = api.search(q=searchQuery, count=tweetsPerQry,
 max_id=str(max_id - 1))
 else:
 new_tweets = api.search(q=searchQuery, count=tweetsPerQry,
 max_id=str(max_id - 1),
 since_id=sinceId)
 if not new_tweets:
 print("No more tweets found")
 break
 for tweet in new_tweets:
 #print tweet with details, you can add other data by putting a proper reference, look at Twitter developers website;
 csvwriter.writerow([tweet.created_at,
 clean(tweet.user.screen_name),
 clean(tweet.text),
 tweet.user.created_at,
 tweet.user.followers_count,
 tweet.user.friends_count,
 tweet.user.statuses_count,
 clean(tweet.source),
 clean(tweet.user.location),
 tweet.user.geo_enabled,
 tweet.user.lang,
 clean(tweet.user.time_zone),
 tweet.retweet_count
 ]);
 tweetCount += len(new_tweets)
 #print("Downloaded {0} tweets".format(tweetCount))
 max_id = new_tweets[-1].id
 except Exception as e:
 # Just exit if any error
 print("some error : " + str(e))
 pass
 print ("Downloaded {0} tweets, Saved to {1}".format(tweetCount, fName))
 Cite
 Emma Lee
 Peking University
 You can get free data sample by Podargos, with 100% historical and real time tweets for your search criteria.
 If you have any interest in it, please access Podargos website https://www.podargos.com, or view data sample: http://goo.gl/dRxyow
 Cite
 Chhinder Kaur
 Chandigarh Group of Colleges
 you can get unlimited historical tweets with the help of twitter Scarper visit this website https://pypi.org/project/twitterscraper/0.2.7/ with the help of shortcode you can download unlimited tweets
 Cite
 1 Recommendation
 Solveg Nikolaisen
 -
 Hello! If you need to get tweets you should check https://www.tweetbinder.com/
 It has full access to Twitter data, and not only gives all of Twitter's statistics but also includes an influencer detector, the economic value of a tweet/hashtag/user, sentiment value, etc. including ALL the images under a certain hashtag (with a unique link for each one, from where you can download them). It gives both data and information presented in a comprehensible way and ready to can use.
 It lets the user analyze hashtags, keywords, accounts, back in time (historically, 7 days, 30 days), and in real-time. It has a very cool visualization panel with which you can classify all the tweets of a report as you please, even by images.
 You can also download the reports both in Excel and PDF.
 If you want you can DM me here in Research Gate or send a non-binding e-mail to solveg@tweetbinder.com, I'll be happy to share an example with you. This blog article also explains some of the features I mentioned above: https://www.tweetbinder.com/blog/tweet-binder-tweets/
 Kind regards,
 Solveg Nikolaisen
 Cite
 Gargy Gupta
 There is an absolutely perfect tool https://www.followersanalysis.com/ that I have been using for a long now to scrape Twitter data.
 FollowersAnalysis is a great Twitter analytics tool that allows you to extract tweets linked to any @username or term. Scrape your brand's Twitter profile's previous 3200 tweets, competitors’, influencer marketing candidates, and so on.
 Not only does FollowersAnalysis provide you access to Twitter data, but it also gives you analytical insights and predictive analytics. The detailed PDF reports offer useful information that might assist you in making data-driven decisions.
 You may also use FollowersAnalysis to examine your Twitter followers.
 If you require any further information, please contact them at contact@followersanalysis.com or utilize the chat button at the bottom right corner of their website, and they will gladly assist you.
 Go get easy access to various Twitter data and their analytical insights!
 Cite

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 Emre Pakdemirli
 * Sağlık Bilimleri Üniversitesi Ankara Sehir Hastanesi (University of Health Sciences)
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Most recent answer               Top contributors to discussions in this field     Get help with your research  Join ResearchGate to ask questions, get input, and advance your work.     All Answers (11)               Federal University of Pernambuco               National University of Modren Languages Islamabad               Federal University of Pernambuco                                     Federal University of Pernambuco                                  Chandigarh Group of Colleges                             Similar questions and discussions      New scam: Do you want to be paid to cite others' research?                     Related Publications             Got a technical question?  Get high-quality answers from experts.
How can I scrape data from Twitter Advanced search? | ResearchGate
https://www.researchgate.net/post/How_can_I_scrape_data_from_Twitter_Advanced_search