Ingredient Extraction from Text in the Recipe Domain.

Arkin Dharawat –<EMAIL_ADDRESS>
Chris Doan –<EMAIL_ADDRESS>

##  1 Abstract

In recent years, there has been an increase in the number of devices with
virtual assistants (e.g: Siri, Google Home, Alexa) in our living rooms and
kitchens. As a result of this, these devices receive several queries about
recipes. All these queries will contain terms relating to a ”recipe-domain”
i.e: they will contain dish-names, ingredients, cooking times, dietary
preferences etc. Extracting these recipe-relevant aspects from the query thus
becomes important when it comes to addressing the user’s information need. Our
project focuses on extracting ingredients from such plain-text user
utterances. Our best performing model was a fine-tuned BERT which achieved an
F1-score of $95.01$. We have released all our code in a GitHub repository
111https://github.com/ArkinDharawat/ingredient_extraction.

##  2 Introduction

Exploration of new recipes and new cuisines is an activity engaged in by
people ever since search engines have become ubiquitous. A report from Google
shows that ”59% of 25- to 34-year-olds cook with either their smartphones or
tablets handy” 222https://www.thinkwithgoogle.com/future-of-marketing/digital-
transformation/cooking-trends-among-millennials/. A user might have several
reasons behind doing this which can range from simple curiosity, an acitvity
with friends or to satisfying hunger. Given the fact that virtual assistants
are present in almost all our devices, it is not quite far-fetched to assume
that user’s will want to address their recipe-related information need through
these agents. To address a user’s information need in this restricted domain
it is important to understand the vocabulary and extract relevant information
from the query.

Extracting aspects from user queries has been an active research area in the
fields of Information Retrieval (IR) and Natural Language Processing (NLP).
E.g: Extraction of clinical entities from user queries [1]. Earlier approaches
in extractions take advantage of the query or text conforming to a certain
structure or used well-known unsupervised methods [2]. Virtual assistants at
home take the user utterance and convert it to text using Automatic Speech
Recognition (ASR). These queries made by the user are short and do not conform
to any particular structure, they may also have minor spelling error based on
the performance of the ASR model.

We choose to focus on ingredients since they can’t be detected by simple hard-
coding (e.g: make an exhaustive list which can done in the case of dietary
preferences) or use a regex that conforms to some structure (e.g: in the case
of cooking times.). Our task can be described as extraction of ingredients
from a raw user query in the recipe domain. Our approach to this problem is
framed as an extraction of entities (ingredients), commonly also known as
Named Entity recognition (NER). We use NER techniques and models where the
ingredients are our desired entities and the rest of the text is to be
ignored. Thus our goal is the correct extraction of ingredients from a user
text.
To achieve this we have taken the following steps, all which will be covered
in detail later:

  1. 1.

Created and annotated our own dataset by using publicly available data on
recipes.

  2. 2.

Pre-processed and cleaned available datasets. Combined our dataset with other
similarly annotated data.

  3. 3.

Trained NER models: one using traditional NLP techniques and another using
deep-learning and transformers.

  4. 4.

Generate our own test data and test the performance of our models on unseen
real-world data.

Finally, We evaluate the performance of our models and analyse areas of
improvements for interested researchers.

##  3 Background/Related Work

We looked into several techniques of information extraction for food/recipes.

In the FoodIE paper [3], named entity recognition is performed by building on
and improving semantic tagging using the UCREL Semantic Analysis System
(USAS). The USAS is an automatic semantic tagger which has many categories
related to food. FoodIE uses semantic tags and its own rule based system to
extract food phrase chunks from recipe data. The model was evaluated on 200
food recipes and achieved a F1 score of 0.9593 on food entity tagging. Most
errors occur with false negatives classifications regarding brand name used in
recipes, colloquial names, and dual part-of-speech of certain words.

A Stanford paper regarding information extraction from recipes was also
reviewed[4] where a maximum entropy Markov model was used to extract many food
labels like ingredients, utensil, etc from recipe data. The model evaluated
well for ingredients but did not do well for utensils.

Although this area has been explored, what we discovered was that several of
the datasets were trained on recipe text and relied on some structure of the
text. E.g: FoodIE is a rule-based method that relies on the ingredients having
a certain POS tag to determine if a word could be an ingredient.

##  4 Approach

Our problem statement can be formally defined as follows: Given a dataset $D$
where each element is tuple that can be defined as $(t_{i},L_{i})$. $t_{i}$ is
the recipe text and $L_{i}$ are the annotations in the form of
$[(s_{0},e_{0})\cdots(s_{n_{i}},e_{n_{i}})]$ where $s_{i}$ indicates the start
of the span, $e_{i}$ indicates the end of the span and $n_{i}$ are the total
number of annotations for $t_{i}$. Our model $f$ takes in $t_{i}$ only and
outputs the list of start-end spans for the labels.

###  4.1 Dataset

As mentioned in our proposal, we planned to use two datasets. Currently, we
are using the following publicly-available datasets:

  1. 1.

FoodBase [5]: This dataset consists of recipes with their text and annotated
ingredients. It contains 1K human annotated recipes and 12K annotated by a
heuristic.

  2. 2.

Food.com: A publicly available dataset of Food.com available on Kaggle
333https://www.kaggle.com/shuyangli94/food-com-recipes-and-user-interactions.
Each row is a recipe with number of steps, text of the steps, ingredients etc.
We will use this dataset to generate text with the ingredients labelled by
their indices.

We are using these for training and evaluation with a train/eval/test split of
$80/10/10$.
We used the NLTK [6] to do the text pre-processing. Our pre-processing for
FoodBase required us to parse and tokenize the XML data and then convert it
into a JSON format suitable for SpaCy. However, this wasn’t a streamlined
process since we encountered several issues:

  1. 1.

Overlapping spans: Several of the annotations were overlapping e.g: ’cream
cheese’ sometimes had two annotations one was for ’cream’ and another was for
’cream cheese’.

  2. 2.

Duplicate annotations: Several times ingredients were annotated twice-thrice
i.e: the same span start-end pairs occurred more than once.

In the case for the Food.com dataset we only had the recipe-text and list of
ingredients. We had to manually annotate some of the data together. To achieve
this we used a free annotation tool, Doccano [7]. We setup a local docker
container where we uploaded a cleaned and formatted version of our dataset. We
each took turns annotating as much text as we could.
Here are the statistics for each of our datasets:

Dataset | Total Rows | Total labels
---|---|---
Foodbase | 22669 | 271565
Food.com | 106 | 1100
Table 1: Dataset statistics

The following are top 5 ingredients for Foodbase:

Ingredient | Count
---|---
Sugar | 10580
Flour | 9355
Butter | 8437
Salt | 8342
Eggs | 7114
Table 2: Foodbase ingredients

Top 5 ingredients for Food.com:

Ingredient | Count
---|---
Butter | 53
Salt | 42
Water | 36
Cheese | 34
Flour | 33
Table 3: Food.com ingredients

As we can notice some of our most common ingredients are butter, salt, flour
and sugar which makes sense since these would occur in a lot of dishes. For
ease of reference we’ll refer to the combination of FoodBase and Food.com as
FoodCombined. Additionally, we also had to convert the rows in FoodCombined to
specific formats for SpaCy and BERT. Our SpaCy model used a simple json with
the accompanied spans the BERT model used a csv with ”Insider-Outside-
Beginning” (IOB) format. This differentiatde from the SpaCy that took a single
INGREDIENT entity tag while for the BERT model we needed two specific tags for
ingredients: ”B-ING and I-ING”. This allows the model to learn chunks of
tokens as ingredients like ”ground beef”. ”B-ING” indicates the start of a new
chunk of ingredients and ”I-ING” indicates the inside of a chunk. In the
”ground beef” example beef would be tagged as ”B-ING” and beef would be tagged
as ”I-ING”. This add some complexity to our training since the chunking of
ingredients allows our model to interpret more than single token-ed
ingredients but can be susceptible to over fitting to our trainings data and
the chunks that exist in the corpus.

It is always good practice to evaluate your model on unseen data which is
usually refereed to as the test set. However, in our case there was a small
discrepancy between the test set and what we expected in the real-world. Our
dataset consisted of annotated recipes, the text for each row consisted of a
few sentences and had more than a single ingredient. The user utterances that
are made to the virtual assistants usually are only a single sentence along
with one to three ingredients mentioned.

To generate a dataset that was closer to the real world, we decided to use
Checklist [8], an NLP testing framework. It is important to note that so far
Checklist has only been used for testing text classification models by
generating and perturbing text based on certain templates. We took advantage
of it’s generative functionality to generate $500$ test cases. These test
cases were generated by a variety of templates we came up with, these include:

  1. 1.

”I would like a dish with {ing}”,

  2. 2.

”Show me {ing} recipes”,

  3. 3.

”Can I see recipes with {ing}”,

  4. 4.

”I want {ing} in my recipe”

The ”{ing}” in these templates is replaced by the ingredient which we sample
from a large ingredient dataset from Kaggle, called Recipe Ingredients Dataset
444https://www.kaggle.com/kaggle/recipe-ingredients-dataset. Some generated
examples are: ”Can I see recipes with bacon?”, ”I would like a dish with
peanut oil” and ”I want soy sauce in my recipe” etc. For each of these
examples we had to manualy calculate the span for each ingredient based on the
length of the template and placement of ”{ing}”.
We wanted to generate data with two to three ingredients as well however the
generation of the spans became too complicated and error-prone. This is
something else we would investigate in the future.

###  4.2 Model

####  4.2.1 SpaCy

We are working with SpaCy, a free open-source industrial-strength natural
language processing library for Python. SpaCy allows for us to use their model
framework on custom data and for our use case we are using their NER model
framework to correctly identify ingredients in the training recipe data.

SpaCy’s NER model is based on the transition-based parser model and the name
of its model is ”TransitionBasedParser”. Transition-based parsers go through a
document and creates a word to word relationship graph of each word in the
document. This parsing and relationship graphing allows for better
understanding of the semantic reasoning and structure of the document [9]. The
parser begins with an initial state of parsing the document alongside the
buffer of all the tokens in the document, and the state is defined as the
current set of relationships the parsed words of the document have to one
another. The transition based parser then chooses the best scoring state
transition operation at each step of the algorithm. State transitions include
reading the next token in the buffer, and assigning relationships to words in
the parser. Scoring is done by mapping the states to features and taking the
dot product with a learn-able weight matrix and summing them up. For SpaCy’s
TransitionBasedParser actions assign tags to words in a state at each step
(ie: puts a word into a specific category of entities) [9]. SpaCy’s
implementation also allows for the use of a feed-forward neural network that
takes the state representation from the document and calculates the score
using the feed forward net.

We pass in our ingredient-annotated recipe training data to train the
TransitionBasedParser. From our training step we have a custom entity
extractor for our intended problem goal. We can then use this model to
evaluate and extract entities from testing data and real samples. The SpaCy
model was trained and validated on splits of the FoodCombined dataset with
varying hyper-parameter settings. Due to the long training time of the
training data, small changes between training sessions were made. The final
hyper-parameters included a learning rate of 0.001, a hidden layer width of
64, and batch size equal to 500.

####  4.2.2 BERT

The Bidirectional Encoder Representations from Transformers (BERT) is a paper
and model proposed by Google AI [10]. BERT is a transformer model that uses
bidirectional training as opposed to left-to-right training or a right-to-left
training that might occur in an Long Short-term Memory (LSTM) network.

A Transformer is a natural language model that focuses on a special mechanism
called attention to capture important information in text data [11].
Transformer’s use an encoder that takes input text as an input and generates
embeddings of each word with attention based and positional based vectors.
Attention based vectors for each word allow for the model to learn what other
words in the same input text are important to that specific word. Using the
context from position embeddings and attention embeddings the encoder’s output
is used as input for the decoder’s input one-by-one.

The BERT model is similar to the transformer but it uses only the encoder as
its language model alongside some changes to the vanilla transformer to get a
bidirectional language model. BERT implements a masked-language model to hide
k percent of the input to the encoder in order to guess the values of the
missing words and Next Sentence Prediction pairs sentences together in
training to allow for the BERT language model to learn long-term dependencies
across sentences. BERT can be used for entity recognition by processing the
outputs of the model into a linear layer to classify the labels for each word
token [10].

We use the SimpleTransformer library to use its BERT model. The BERT model was
trained using various hyper-parameters that were tuned between training
sessions. The final model trained on the entire FoodCombined corpus had the
following parameters: learning rate set to $5\times 10^{-5}$ and batch size
equal to 32 in 3 epochs.

##  5 Experiment

###  5.1 Process

The goal of the research is to gather a large corpus of recipe data annotated
with variations of ”ingredient” tags in order to combine them and use models
trained on our corpus to generalize and perform ingredient/food extraction
from voice assistant plain-text queries. Our experimentation included using
SpaCy’s transition based model as a baseline and then to use BERT’s
transformer based model architecture on our training and evaluating task. The
FoodCombined was shuffled and split into a 80:20 train test validation split
for training and validation purposes only. Testing on both models used a held
out test set of the Checklist created data.

###  5.2 Evaluation Metric

To evaluate the accuracy of our model using the average correct guess over
total word count was not suitable for the data since there are far more non-
ingredient words in the dataset than there are ingredient words. This causes
an imbalance in the dataset and the best way to measure the performance of our
model would be through precision, recall, and F1 score. These metrics use true
positives (words that are ingredients from the ground truth are marked as
ingredients correctly), false positives (words that are not words are
incorrectly labelled as ingredients), false negatives (ingredients from the
ground truth are not marked as ingredients), and true negatives.

Precision is the fraction of true positives over the total number of
positives. In our problems scope a true positive would be correctly
identifying a word as an ingredient when it is an ingredient.

$Precision=\frac{TruePositives}{TruePositives+FalsePositives}$ (1)

Recall is the fraction of true positives over the sum of true positives and
false negatives. In the scope of our problem, this describes the ratio of how
many words we correctly label as ingredients out of the total number of words
marked as ingredients in the ground truth.

$Recall=\frac{TruePositives}{TruePositives+FalseNegatives}$ (2)

F1 score is the harmonic mean of precision and recall. F1 score helps
determine the accuracy of the model when the distribution of ingredient labels
and not ingredient labels are imbalanced.

$F_{1}=2*\frac{Recall*Precision}{Recall+Precision}$ (3)

###  5.3 Results: Metrics

First we trained the SpaCy model on 2000 steps of batch size equal to 500. We
then evaluated our trained model on the validation and test splits of data. In
Table 4 and 5 you can see the SpaCy NER model achieves a validation F1 score
of 96.53%. Its Recall and Precision scores are also similarly at 96.69% and
96.67% respectively. SpaCy’s performance on the held-out test set achieved
only a F1 score of 55.97% with lower Recall score of 49.20% and Precision of
64.91%. The lower Recall on the test set indicates that the model was not
extracting ingredient tags from words that should have been ingredients. The
overall accuracy of the test set was very low compared to the validation set.

Figure 1: BERT Training Loss over 3 Epochs

To use the power of deep neural networks and transformers we use the BERT
model architecture to help us extract ingredient data from textual data.
Similar to the SpaCy training and evaluation, we ran BERT for 3 epochs (about
33 steps) on batch size of 32. In Figure 2. we have plotted the training loss
over the 3 epochs. The loss decreased exponentially on the first 5-7 steps of
training and from there it decreased with semi-abrupt increase in training
loss. Smaller learning rates were used but did not achieve the same validation
results as our selected learning rate of $5\times 10^{5}$.

Validation results on the BERT model outperformed SpaCy in all three
evaluation metrics from Table 4. The biggest performance gain was on the
generalization of the BERT model trained on the FoodCombined to the held-out
Cheklist test dataset. Here we see similar performance on the test set as we
see with the validation set: an F1 score of 95.01%, Recall of 95.20%, and
Precision score of 94.82%.

Model | F1 score | Recall | Precision
---|---|---|---
SpaCy NER | 96.53 | 96.39 | 96.67
BERT | 98.55 | 98.69 | 98.40
Table 4: Validation data set F1 Score, Recall and Precision Model | F1 score | Recall | Precision
---|---|---|---
SpaCy NER | 55.97 | 49.20 | 64.91
BERT | 95.01 | 95.20 | 94.82
Table 5: Test set data F1 Score, Recall, and Precision

###  5.4 Results: Examples

In this section, we will examine and analyze some misclassifications done by
our best performing model, BERT. The words highlighted in red are examples of
false negatives and those in blue are the false positives. Orange words are
misclassified as in they are labelled as one entity but there ground truth is
a different non-empty entity.

Example: ”Pierce pork all over with a carving fork. Rub salt then liquid smoke
over meat . Place roast in a slow cooker. Cover, and cook on Low for 16 to 20
hours, turning once during cooking time. Remove meat from slow cooker , and
shred , adding drippings”.

The interesting point to note here is that our model misclassified ”Pierce
pork” as a single ingredient, whereas in the ground-truth the only ingredient
present here is ”pork”. We believe this because of the data the model was
trained. Some example of words that we noticed followed pork were, ”ground
pork”, ”grilled pork”, ”salted pork” etc, in all these the description
followed by pork was the entire ingredient.

Example: ”Heat honey and peanut butter together over low heat until creamy.
Remove from heat and cool . Mix the rest of the ingredients into the
honey/peanut butter mixture , except coconut. Shape into balls. Roll in
coconut”.

As you can see our model couldn’t find the ingredient, which in this case was
”honey/peanut butter mixture”. We believe this is because our BERT model is
accustomed to seeing ingredients which only consist of one-two tokens,
sometimes three. So a four token ingredient would be an unseen use-case for
our model. The token ”honey/peanut” is also an interesting find because this
is only specific to recipe data and would definitely not be found in plain-
text AI assistant queries. Another future consideration would to rid these
token examples from the data all-together since it does not provide any useful
information for our goal.

Example: ”In a small bowl combine quick oats, margarine, and boiling water.
cover and allow margarine to melt, and mixture to cool a bit. mix remaining
cake ingredients and mix well. spread into 9x13 greased and lightly floured
cake pan . bake at 350f for approximately 35 minutes or until done . for icing
: mix icing ingredients until well blended . spread over cooled cake and put
under broiler until coconut”.

This is an interesting example since our model labelled oats, margarine and
cake as ingredients whereas in the ground-truth only oats was mentioned. In
this case oats is incorrect since our model marked it as a ‘I-ING‘ thereby
making it an intermediate token for an ingredient whereas the correct label
was ‘B-ING’, the beginning token of the ingredient. Cake is a simple
misclassification. However, as we can observe margarine is a case where our
model is in fact correct and our ground-truth is incorrect.

##  6 Conclusion & Future Work

In this report, we studied the task of extracting ingredients from raw text
that is obtained from users looking to satisfy some kind of information need
when it comes to food / recipes. To make train our models we combined and
annotated datasets from various sources and to make them robust automated
generation of real-world unseen data. We performed experiments and trained two
different kinds of models: a traditional parser based one from SpaCy and a
deep-learning transformer based model like BERT.
As we can see from Table 5, BERT gives the best F1 score of 95.01. However,
there are still improvements we can make. In future work, interested
researchers can pursue the following.

  1. 1.

Generate Noisy data: We also looked at a paper in ASR [12] to determine how we
can generate noisy data similar to what it would look when generated by a
text-to-speech model, to make our NER models ore robust. As mentioned earlier,
The main application of this model will be in the TaskBot competition where
the model will received text converted using ASR as its input.

  2. 2.

Generate data with more ingredients: As mentioned, the usage of the Checklist
framework didn’t quite give us the kind of data we wanted to generate, A
future direction to pursue would be building a testing framework similar to
Checklist that can generate data for NER models and can handle multiple
entities and their annotations.

  3. 3.

Use more types of tags: We only tried to extract ingredients in this work,
however it is not far-fetched to work on a model that can extract other
aspects of a recipe along with ingredients. Some of these aspects could be:
dietary restrictions, cooking utensils and cuisines. We have yet to come
across a dataset that combines all of these since adding these annotations
would require significant human effort.

## References

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  * [4] R. Agarwal and K. Miller, “Information extraction from recipes,” https://nlp.stanford.edu/courses/cs224n/2011/reports/rahul1-kjmiller.pdf.
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# Who is bragging more online? A large scale analysis of bragging in social
media

###### Abstract

Bragging is the act of uttering statements that are likely to be positively
viewed by others and it is extensively employed in human communication with
the aim to build a positive self-image of oneself. Social media is a natural
platform for users to employ bragging in order to gain admiration, respect,
attention and followers from their audiences. Yet, little is known about the
scale of bragging online and its characteristics. This paper employs
computational sociolinguistics methods to conduct the first large scale study
of bragging behavior on Twitter (U.S.) by focusing on its overall prevalence,
temporal dynamics and impact of demographic factors. Our study shows that the
prevalence of bragging decreases over time within the same population of
users. In addition, younger, more educated and popular users in the U.S. are
more likely to brag. Finally, we conduct an extensive linguistics analysis to
unveil specific bragging themes associated with different user traits.

Keywords: bragging, social media, computational sociolinguistics,
computational linguistics

Who is bragging more online? A large scale analysis of bragging in social
media

Mali Jin$\diamondsuit$, Daniel Preoţiuc-Pietro$\spadesuit$, A. Seza
Doğruöz$\clubsuit$, Nikolaos Aletras$\diamondsuit$
---
$\diamondsuit$University of Sheffield, $\spadesuit$Bloomberg, $\clubsuit$ LT3,
IDLab, Universiteit Gent
{m.jin<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>

Abstract content

## 1\. Introduction

Bragging (self-praise) is a speech act that involves disclosing positive
content about oneself or their close network Dayter (2014, 2018) such as
achievements, possessions or feelings Scopelliti et al. (2015); Matley (2018).
It falls in the category of self-presentation strategies with the goal of
building and establishing a positive social image of one self Goffman et al.
(1978); Jones et al. (1982); Jones (1990); Bak et al. (2014). This desire for
attaining a positive self-image is a key driver of human behavior Baumeister
(1982); Leary and Kowalski (1990); Sedikides (1993); Tetlock (2002), as it can
bring benefits like admiration, respect, attention by other people and
personal rewards Gilmore and Ferris (1989); Hogan (1982); Schlenker (1980).
Bragging as a behavior was found to be related to user traits including
personality and it can differ based on communication strategies favored by the
participants to the bragging act Miller et al. (1992); Van Damme et al.
(2017); Sezer et al. (2018).

Social media presents a unique environment to study bragging (and self-
presentation strategies more generally) as users’ craft and control the
information they expose to their audience. Further, due to the nature of
online interactions, this information can represent a significant part of what
most of the audience knows about a person. Thus, users on social media
platforms pay great attention to building their online social image Chou and
Edge (2012); Michikyan et al. (2015); Halpern et al. (2017) and studies have
shown that online self-presentation is predominantly positive Matley (2018);
Chou and Edge (2012); Lee-Won et al. (2014). Moreover, social media platforms
include functionality that rewards and promotes positive statements such as
likes or positive reactions to users’ posts Reinecke and Trepte (2014) which
often are used to quantify impact on the platform Lampos et al. (2014); Mu et
al. (2024). Traditionally, bragging is considered a high risk act Van Damme et
al. (2017); Brown and Levinson (1987); Holtgraves (1990) and can lead to the
opposite effect than intended, such as dislike or decreased perceived
competence Matley (2018); Jones et al. (1982); Sezer et al. (2018). However,
bragging is acceptable and even desired in certain online contexts Dayter
(2018). According to Dayter (2018); Matley (2020); Ren and Guo (2020) self-
promotion is pervasive on social media. Bragging in particular is more
frequent on social media than face-to-face interactions Ren and Guo (2020).
Table 1 shows examples of bragging and non-bragging tweets.

Type |  Text
---|---
Bragging |  Just impressed myself with how much French I think I understood! One semester at KC FTW!
Non-bragging |  Glad to hear that! Well done Jim!
Table 1:  Example of bragging and non-bragging tweets.

To date, bragging behavior was quantitatively studied in specific contexts
through manual analyses of small data sets of hundreds of posts Dayter (2014,
2018); Matley (2018); Tobback (2019); Rüdiger and Dayter (2020). This paper
aims to provide the first large scale quantitative study on the prevalence of
online bragging using a longitudinal data set that includes over 1 million
English tweets posted by a group of 2,685 Twitter users in the U.S. over ten
years. We identify bragging through Natural Language Processing (NLP) methods,
including using a state-of-the-art neural model Jin et al. (2022) for
identifying bragging in social media posts. Our main findings are as follows:

  * •

Bragging steadily decreases over time in the past 10 years within the same
group of users.

  * •

Bragging is more prevalent among users who are female, generally younger, more
educated, have a higher income and are more popular on Twitter.

  * •

Male users and users with higher income brag more about leisure activities.
Female users and users with lower education focus more on themselves when
bragging. Bragging by older users and users who have higher education are more
likely to involve others. Emojis are more frequently used by female and
younger users while bragging.

## 2\. Related Work

#### Bragging as a Social Behavior

Bragging enhances one’s positive social image and can trigger high-quality
interactions with other users (Miller et al., 1992). Therefore, it is
acceptable and even desirable on social media to some extent (Dayter, 2018).
However, bragging is also widely considered as a face-threatening act for
speakers according to the modesty maxim (Leech, 2016) and politeness theory as
it threatens both positive and negative face (Brown and Levinson, 1987). As a
result, it may damage user likability if it is negatively perceived by the
audience (Matley, 2018). Thus, social psychology and linguistic studies have
mostly focused on investigating the impact of self-promotion for identifying
its pragmatic strategies (e.g., shifting focus to others who are closely
related to them). These strategies help the speakers/users to mitigate the
social risk and negative effects caused by bragging (Scopelliti et al., 2015;
Tobback, 2019; Matley, 2020).

#### Analysis of Bragging

The use of pragmatic strategies for bragging has been analyzed qualitatively
by linguists and psychologists across languages such as Mandarin (Wu, 2011),
English (Speer, 2012; Dayter, 2021), Spanish (Maíz-Arévalo, 2021), or Russian
(Dayter, 2021). In social media, Dayter (2014) identified a series of
overlapping strategies in a small ballet community on Twitter. Matley (2018)
examined the pragmatic function of hashtags (e.g., #brag, #humblebrag) used by
Instagram users and Tobback (2019) studied the impact of bragging about
professional skills on LinkedIn. Also, the frequency of different bragging
strategies used by regular people (Ren and Guo, 2020) and celebrities (Guo and
Ren, 2020) has been examined in Weibo (a Chinese social media platform).
Furthermore, the emotional influence of bragging from the audience perspective
was studied by Scopelliti et al. (2015). More recently, Jin et al. (2022)
introduced the task of automatically identifying bragging and classifying its
types on Twitter using computational methods.

#### Language Use and User Traits

Previous work in computational sociolinguistics showed that user traits (e.g.,
age, gender and personality) and popularity correlate with language use and
online behavior (Preoţiuc-Pietro et al., 2015b; Nguyen et al., 2016; Preoţiuc-
Pietro et al., 2017; Preoţiuc-Pietro and Ungar, 2018; Villegas et al., 2023).
Schwartz et al. (2013) found significant variations in language on Facebook
with age, gender and personality while Preoţiuc-Pietro et al. (2015c) examined
the correlations between user income and a variety of factors such as profile
features, demographic and psychological traits.

Studies on differences in self-disclosure have focused on gender through
different platforms (Valkenburg et al., 2011; Sheldon, 2013; Altenburger et
al., 2017; Farinosi and Taipale, 2018; Wang et al., 2021), where most of them
have suggested that women disclose more about themselves than men do. Also,
racial background was investigated in self-disclosure behavior (Chen, 1995;
Rui and Stefanone, 2013). Bak et al. (2012) investigated the relationship
between self-disclosure and user relationship strength (e.g., strong and weak)
on Twitter, while Moon et al. (2016) examined the relationship between
narcissism (i.e., a personality trait reflecting a grandiose and inflated
self-concept) and self-promoting on Instagram. To the best of our knowledge,
no previous study has attempted to study the relationship between bragging and
social factors.

## 3\. Measuring Bragging Prevalence

### 3.1. Data for Model Training

We use a publicly available data set developed by Jin et al. (2022) to train a
bragging classifier and it consists of 6,696 English tweets and each tweet is
manually annotated as bragging or non-bragging. In total, there are 544
bragging tweets and 2,838 non-bragging tweets in the training set; 237
bragging tweets and 3,077 non-bragging tweets in the development and test set
(see Table 2).111This data set is also manually annotated according to six
bragging types (i.e., Achievement, Action, Feeling, Trait, Possession,
Affiliation). We only use the binary label because of the small number of
examples in each bragging type which results in low performance on type
prediction. This can introduce noise in our analysis.

Label | Training set | Dev/Test set | All
---|---|---|---
Bragging | 544 (16.09%) | 237 (7.15%) | 781 (11.66%)
Non-bragging | 2,838 (83.91%) | 3,077 (92.85%) | 5,915 (88.34%)
Total | 3,382 | 3,314 | 6,696

Table 2:  Statistics of the bragging data set.

### 3.2. Predictive Model

We re-implement the best performing predictive model proposed by (Jin et al.,
2022) on identifying whether a tweet contains bragging or not (BERTweet-
LIWC).222We achieve similar performance on the bragging test set (72.51 macro
F1). The BERTweet-LIWC model generates word embeddings using BERTweet Nguyen
et al. (2020) and combines it with Linguistic Inquiry and Word Count (LIWC;
Pennebaker et al. (2001)) features. The feature combination is performed
through a fusion mechanism, which was originally introduced by Rahman et al.
(2020), to control the influence of word representations and linguistic
features. The joint representation of text and LIWC features is sent to the
BERTweet encoder, followed by an output binary classification layer.

### 3.3. Bragging Prevalence Metrics

Our analyses rely on computing a user-level bragging prevalence score. We
expect that the base rate of bragging will change over time. However, we aim
to compare groups of users based on their traits and we want to ensure the
bragging score is adjusted to the base rate in bragging over a certain time
period. Thus, we normalize the score over several smaller time chunks.

#### Overall Bragging Prevalence

First, we obtain the time series of total bragging tweets and total tweets
posted by all users over each month, denoted as $P=\\{p_{1},...,p_{n}\\}$ and
$Q=\\{q_{1},...,q_{n}\\}$ respectively where $n$ is the number of months in
the data set. We compute the distribution of average bragging percentage
across all users for each month $A=\\{a_{1},...,a_{n}\\}$ such that:

$\displaystyle\small A=\frac{P}{Q}$ (1)

#### User Bragging Prevalence

To compute a bragging score of an individual user $u$, we first obtain the
distributions of bragging tweets and total tweets over each month for $u$,
which are denoted as $B_{u}=\\{{b_{u}}_{1},...,{b_{u}}_{n}\\}$ and
$T_{u}=\\{{t_{u}}_{1},...,{t_{u}}_{n}\\}$ respectively. We obtain a time-
normalized bragging distribution $D_{u}=\\{d_{u1},...,d_{un}\\}$ for each user
over months by dividing each data point from the user distribution by the
overall bragging prevalence in each time window:

$\displaystyle\small D_{u}=\frac{B_{u}}{T_{u}*A}$ (2)

Finally, we average the normalized bragging distribution $D_{u}$ for each user
$l$:

$\displaystyle\small l=\frac{\sum_{i=1}^{n}{d_{u}}_{i}}{n}$ (3)

The normalized bragging percent practically compares the bragging tendency of
a single user to that of the population average in the same time range. We
normalize with time to account for possible temporal shifts in bragging
prevalence over time (see Figure 1).

Figure 1: Bragging percentage by year and month.

## 4\. Analysis Data

To analyze the social factors of bragging, we need a large set of Twitter
users associated with socio-demographic characteristics. We combine three data
sets that contain such information in the following papers as provided by the
original authors on request: the first data set (developed by Preotiuc-Pietro
et al. 2016) contains 863 users, the second data set (developed by Guntuku et
al. 2017) contains 4,568 users and the third data set (developed by Jaidka et
al. 2020) contains 938 users. All user information is obtained through self-
reports on questionnaires. All users are mapped to their self-reported gender
and age, while the users in the second data set also self-report their
education degree and annual income range. In total, our combined data set
contains 6,369 users, all from the U.S.. For details about user sampling,
please refer to the original papers. Subsequently, we collect all historical
tweets from these users resulting in more than 9.7 million tweets. We stop
data collection at the end of 2021.

### 4.1. Filtering

For computing user bragging behavior, we need to only study content that is
original and authored by the user. For this, we employ the following filtering
steps:

  * •

We filter out non-English content using the language code provided by Twitter,
as our classifier is only trained to classify English content.

  * •

We exclude replies and retweets through the parameters exposed through the
Twitter API.

  * •

We remove duplicate tweets from the same user by using the first five content
tokens (excluding numbers, usernames and hashtags), because duplicate tweets
are unlikely to be original content created by users.

  * •

All tweets that are automatically generated from third parties are filtered
out using source labels that indicate original authorship such as “Twitter Web
Client“ or “Twitter for Android". We found that tweets with source labels such
as “The Sims 4 Game" and “Paradise Island 2" are likely to be generated
automatically and would negatively impact our analysis (e.g., “I played the
Sandy Caps mini game in Paradise Island 2, and my score was: 68
#ParadiseIsland2 #GameInsight”).

  * •

Finally, we remove all users that have posted fewer than 20 tweets, as
computing an average bragging ratio across a very small number of tweets
results in unreliable estimates.

In total, our analysis data contains 2,685 users with 1,031,276 tweets, with
each user having 384 posts on average.

### 4.2. Text Processing

We pre-process the collected tweets by lower-casing and tokenizing using
TweetTokenizer from NLTK Toolkit Bird et al. (2009). We also replace all URLs
and username mentions with a single word token <URL> and <USER> respectively.

### 4.3. Computing Bragging Ratio

We use the bragging predictive model to identify the category (bragging or
non-bragging) of all 1,031,276 individual tweets in our analysis data set.
Then we compute the normalized bragging ratio for each user using Equation 2
and Equation 3.

To investigate the performance of the model on the analysis data, we manually
evaluated a batch of 100 tweets across different users and years and found
that the model achieved 78.55 macro F1, which was a higher absolute
performance number on the test data set than the one reported in Jin et al.
(2022) (72.51 macro F1). Also, we notice that it is easier for the model to
misclassify non-bragging tweets as bragging in the analysis data set due to
positive sentiment or gratitude expressions in the text (e.g., “Just installed
box.net iOS app and signed up for a FREE 50gb lifetime cloud space account .
Thx <USER>! #gaetc #gaetcsmackdown #freeapp”); while bragging tweets are
misclassified as non-bragging are mostly due to indirect expressions (e.g.,
“#MyBestFriendsKnow that I am that one friend <URL>”). This is in line with
the error analysis in Jin et al. (2022). In general, mispredicted tweets are
evenly distributed over the entire data coverage period except for the years
2012 and 2018. Among these error cases, 40% of tweets are posted by males and
60% of these are posted by females; while 55% of them are generated by users
born before 1988 and 45% of them are generated by those born after 1988.

### 4.4. User Demographic Traits

To examine the relationship between bragging behavior online and the following
user demographic traits, we use the following attributes.

  * •

Gender. There are 859 self-identified males (33.81%) and 1,678 self-identified
females (66.19%). Self-identified non-binary users represented a very small
number of the total participants, hence were removed from the analysis.

  * •

Age. Reported as the year of birth. The age reported throughout the paper
refers to user age as of the end of 2021.

  * •

Education. It contains six categories: (1) users who have not completed high
school; (2) high school or equivalent; (3) associate’s degree or equivalent;
(4) bachelor’s degree or equivalent; (5) master’s degree or equivalent; and
(6) doctoral degree or equivalent.

  * •

Income. The annual yearly income of a user in U.S. dollars is divided into
eight categories: (1) below 10K; (2) 10-25K; (3) 25-40K; (4) 40-60K (5)
60-75K; (6) 75-100K; (7) 100-200K; and (8) above 200K.

### 4.5. User Popularity Traits

Popularity reflects other people’s interest in users’ accounts or posts. We
quantify this using the following metrics: (1) the number of followers (the
higher, the more popular); (2) the ratio between friends or users being
followed and followers (the lower, the more popular) and (3) the number of
times a user was listed (the higher, the more popular). Note that, for
computing the correlation between popularity metrics and bragging, we log
scale the values such that their distribution are closer to a Gaussian
distribution. We collect the popularity user information at the end of 2021,
consistent with the cut-off date for the data collection.

Table 3 shows statistics of the demographic and popularity traits in our data.
The trait distributions in the data set are presented in Figure 2.

(a) Age.

(b) Education.

(c) Income.

(d) Log-scaled no. followers.

(e) Friends/followers.

(f) Log-scaled no. listings.

Figure 2: Histograms of user socio-demographic traits and popularity.

Trait | Mean | Median
---|---|---
Demographic Traits
Age | 35.99 | 33
User Impact
No. Followers | 22,951.06 | 186
No. Friends/No. Followers | 2.62 | 1.58
No. Listings | 47.72 | 2.0

Table 3:  Statistics of user socio-demographic traits and popularity.

## 5\. Results and Discussion

### 5.1. Bragging Prevalence over Time

We first look at the frequency of bragging on Twitter through our data set and
its dynamics over time. The mean bragging ratio is 0.1114 and the median
bragging ratio is 0.0968 (0.0020 and 0.0013 after normalization) for all
users. This shows that bragging is a common occurrence in original tweets from
U.S. users, with users bragging on average in 1 out of 9 of their tweets.
Next, we study the temporal dynamics of bragging. Figure 1 shows the bragging
percentage over time across the ten years of the data set. This shows that
overall the bragging percentage is slowly decreasing with time. The decline in
bragging behavior on Twitter during the study period may be attributed to
various factors such as a shift in focus to other social media platforms.
Thus, it would be challenging to ascertain the exact reasons based solely on
our Twitter data. Additionally, this finding highlights the need for temporal
normalization of the bragging ratio, as users who published more recently
could be skewed to having a lower overall bragging ratio.

### 5.2. When to Brag and How

Next, we investigate the time (e.g., day of the week, time of the day) of
bragging and the linguistic patterns around these times. Overall, we do not
find any significant correlations between bragging rate and either the time of
day, when split into ranges, or the day of the week (e.g., weekday vs. weekend
$\rho=0.02$).

However, even if the bragging rate is similar, we also explore if the
expressions of bragging across days of the week and times of the day vary
using a linguistic analysis. For this, we use a unigram (i.e., token) feature
analysis to identify words and themes associated with bragging by computing
the correlations between the distribution of each unigram across posts and the
label of the post (i.e., bragging or not bragging). Then, we use univariate
Pearson correlation to rank the unigrams similar to Schwartz et al. (2013).

#### Day of the Week

Table 4 presents the top-20 unigram features correlated with bragging in
weekdays vs. weekends. For this analysis, we normalize the creation time of
each post to the local time by using the timezone difference which was
inferred from the zip code that users have provided.

Weekday vs. Weekend
---
Unigram | r | Unigram | r
class | .050 | sunday | .034
professor | .028 | night | .029
semester | .028 | friday | .028
job | .026 | church | .028
campus | .024 | <user> | .026
classes | .023 | #livepd | .025
#tbt | .023 | weekend | .025
thursday | .021 | we | .024
interview | .021 | state | .024
exam | .021 | watching | .020
office | .020 | bar | .020
bio | .020 | game | .020
monday | .020 | won | .019
killed | .019 | football | .019
grow | .019 | drinking | .019
vote | .019 | rewards | .018
internship | .018 | afternoon | .018
ago | .018 | party | .018
teeth | .017 | racing | .018
gym | .017 | jam | .017
Table 4: Unigram feature correlations with bragging between weekday and
weekend, sorted by Pearson correlation (r). All correlations are significant
at $p<.001$, two-tailed t-test.

To demonstrate the face validity of the analysis, we first observe that
bragging statements from both weekdays and weekends involve words related to
time (e.g., thursday, sunday, night, weekend, afternoon). Secondly, we observe
that users mostly brag about their school life or work on weekdays (e.g.,
class, professor, interview, office, internship). For example, a user mentions
his/her professor when bragging.

> T1: “My professor for accounting saw what I carry all day and said I must
> have great upper body strength lol”.

Another popular bragging topic on weekdays is about going to the gym. Bragging
on weekends usually focuses on certain entertainment, recreation and worship
activities (e.g., church, watching, bar, football, drinking, party) or church
attendance (church) (see example T2).

> T2: “Yay, I’m picking up my cute new glasses tomorrow. Now I can rock them
> at Mom’s party tomorrow”.

In addition, this could also involve activities that are done as part of a
group, as exemplified by the first person plural pronoun we.

#### Time of Day

Table 5 shows the unigrams associated with bragging at different times in a
day. Similar to the day of the week analysis, our findings demonstrate face
validity since the top correlated tokens are related to the time of bragging
(e.g., morning, yesterday, tonight, 2:30).

06:00-09:00 | 09:00-12:00 | 12:00-15:00 | 15:00-18:00 | 18:00-21:00 | 21:00-00:00 | 00:00-03:00 | 03:00-6:00
---|---|---|---|---|---|---|---
Unigram | r | Unigram | r | Unigram | r | Unigram | r | Unigram | r | Unigram | r | Unigram | r | Unigram | r
morning | .131 | morning | .042 | lunch | .053 | dinner | .040 | dinner | .042 | tomorrow | .049 | i | .055 | sleep | .069
last | .055 | coffee | .040 | just | .029 |  $<$url$>$ | .025 | #piano | .031 | tonight | .047 | night | .044 | awake | .069
breakfast | .055 | class | .037 | afternoon | .023 | store | .022 | #music | .026 | bed | .040 | sleep | .043 | morning | .069
today | .051 | last | .031 | shopping | .020 | pizza | .021 | tonight | .024 | i | .038 | midnight | .041 | early | .058
day | .048 | today | .029 | basically | .020 | came | .021 | beer | .022 | night | .029 | bed | .032 | up | .057
up | .046 | lunch | .028 | outside | .020 | bags | .020 | tomorrow | .020 | #livepd | .026 |  | .032 | 4am | .051
sleep | .044 | yesterday | .028 | shop | .020 | fitness | .018 | #musicmonday | .020 | midnight | .026 | 2:30 | .031 | 5:30 | .047
coffee | .044 | woke | .028 | break | .020 | mail | .018 | pizza | .021 | win | .025 | drunk | .031 | 6:30 | .042
school | .044 | breakfast | .027 | cleaning | .019 | published | .018 | #classicalmusic | .021 | life | .025 | tonight | .030 | covet | .041
yesterday | .043 | classes | .025 | food | .019 | rain | .017 | wine | .021 | absolutely | .023 | friends | .030 | singles | .039

Table 5: Unigram feature correlations with bragging between different time
periods in a day, sorted by Pearson correlation (r). All correlations are
significant at $p<.001$, two-tailed t-test.

Next, we observe that bragging about eating or sharing food such as coffee,
lunch, pizza and beer is popular at all times except at night (see example
T3).

> T3: “Just made some lamb burgers with homemade tzatziki sauce. Starting to
> feel confident about my cooking skills.”.

Also, bragging in the morning is usually about things that happened the
previous day (e.g., yesterday, sleep) or study/work (e.g., school, class,
“Secured a B in my principles of marketing class this semester!!!”) while
bragging in the afternoon or evening involves a wide variety of recreational
activities (e.g., shopping, fitness, #music, “Finally, newest member of planet
fitness!”). Finally, many users tend to brag about their upcoming activities
in the evening (e.g., tomorrow, morning).

### 5.3. Bragging Prevalence and Demographic Factors

Next, we explore the relationship between bragging rate and user demographic
traits. To understand what types of users brag more than others in U.S.
Twitter, we perform a correlation analysis by computing the Pearson
correlation coefficient between user traits and the user-level bragging metric
described in Equation 2 and Equation 3. The results are summarized in the top
part of Table 6.

Trait | r | $p_{unc}$ | $p_{corr}$
---|---|---|---
User Demographics
Gender (♀-1, ♂-0) | 0.10 |  $<$.001 |  $<$.001
Age | -0.16 |  $<$.001 |  $<$.001
Education | 0.14 |  $<$.001 |  $<$.001
Income | 0.07 |  $<$.003 |  $<$.002
User Popularity
No. Followers | 0.12 |  $<$.001 |  $<$.001
No. Friends/Followers | -0.10 |  $<$.001 |  $<$.001
No. Listings | 0.09 |  $<$.001 |  $<$.001
Table 6: Pearson correlations between user-level traits and their bragging
metric. $p_{unc}$ and $p_{corr}$ refer to uncorrected and corrected
(Bonferroni correction) p-values.

We first analyze gender and age. According to our analysis, gender and age are
strongly associated with the bragging percentage ($p<.001$). Note that
correlations around 0.1 with such a large sample size (N=2,685) are highly
significant and in terms of magnitude in line with correlations between other
well known linguistic variables and traits Carey et al. (2015); Holgate et al.
(2018).

Thus, we examine the rest of the demographic traits by controlling for gender
and age using partial correlation Preoţiuc-Pietro et al. (2015a), where
education level and annual income are represented in the ordinal scale
described in Section 4.4. The main findings are:

  * •

Gender is significantly correlated with bragging in the sense that female
users brag more than male users ($r=0.10$, $p<.001$). This is consistent with
the findings of previous studies related to self-presentation Sheldon (2013);
Wang et al. (2021) and it can be explained by the fact that women show more
interest in developing friendships online Holmes (2013), which can be
accomplished by positive self-presentation.

  * •

There is a significant association between age and bragging, with younger
users bragging more than older ones ($r=-0.16$, $p<.001$). This might result
from younger people’s desire to increase their status among peers and peer
approval MacIsaac et al. (2018). Social comparison was found to occur more
frequently in younger age groups than in older ones McAndrew and Jeong (2012),
which explains why younger users tend to create positive self-presentations
online Yau and Reich (2019).

  * •

Users with higher education levels tend to brag more ($r=0.14$, $p<.001$).
This is might be explained by the fact that users with a higher educational
level tend to express more joy which could include self-disclosure statements
Volkova and Bachrach (2015).

  * •

Users with higher income significantly brag more frequently online ($r=0.07$,
$p<.003$) than users with lower incomes. Users with higher income were found
to be more likely to produce positive tweets Volkova and Bachrach (2015).
Previous work also suggested that rich people are characterized by a self-
focused and narcissistic personality Leckelt et al. (2019). This causes them
to produce more content that is related to self-promotion in social media
Buffardi and Campbell (2008); Moon et al. (2016).

  * •

Higher income and education are positively correlated with older age (income:
$r=0.16$, $p<.001$; education: $r=0.30$, $p<.001$). Older age however has an
inverse relationship to bragging than income and education. This highlights a
divergence along these traits, where users who are either highly educated or
young are likely to brag more.

We further explore the relationship between language use and bragging behavior
across different demographic characteristics.

#### Gender

Table 7 (left column) shows the top-25 unigrams correlated with gender. We
observe that male users mostly brag about their partners (e.g., wife), but
also mention other users (e.g., $<$user$>$). As illustrated in example T4,
popular bragging topics for males are entertainment achievements such as games
(e.g., #dnd, #league, #twitch, playing) and sports (e.g., tournament, win,
football, championship, teams).

> T4: “I’m so genuinely happy I witnessed that. #NationalChampionship”.

On the other hand, female users prefer to brag with first person pronouns
(e.g., my, me, i). They also brag about personal traits (e.g., hair), feelings
(e.g., love, happy) and their partners (e.g., boyfriend). For example, a
female user mentions her haircut appointment in example T5.

> T5: “HOORAY!! I finally got a haircut appointment!! It’ll be 64 days since
> my last cut (I normally go every 5 weeks so this’ll be close to double my
> normal tim, and yeah, it feels like I have about twice as much hair to
> cut!!)”.

According to McAndrew and Jeong (2012), women spend more energy than men in
presenting themselves for impression management. Furthermore, bragging by
females usually contains female-related terms (e.g., ♀) or positive emoticons
(e.g., :), :p) and emojis (e.g., , ) to strengthen the meaning of their posts.
This corroborates findings that positive emojis are used more frequently in
positive contexts Derks et al. (2008). Results are also consistent with the
observation that female users communicate using more emotional exchanges Gefen
and Ridings (2005). They also use emojis more often than men Chen et al.
(2017); Prada et al. (2018).

Male vs. Female | Born after 1988 vs. before 1988
---|---
Unigram | r | Unigram | r | Unigram | r | Unigram | r
$<$url$>$ | .073 | my | .083 | i | .099 |  $<$url$>$ | .141
$<$user$>$ | .071 | so | .063 | my | .069 |  $<$user$>$ | .079
game | .061 |  | .060 |  | .069 | ! | .077
team | .047 | :) | .059 | me | .057 | join | .036
games | .040 | :p | .058 | so | .050 | #livepd | .033
#dnd | .036 |  | .048 |  | .050 | local | .032
#league | .036 | hair | .048 | m | .049 | kids | .031
wife | .034 | mom | .043 |  | .047 | our | .031
tournament | .034 |  | .042 |  | .041 | we | .031
podcast | .033 |  | .041 | class | .040 | wife | .031
win | .033 | bed | .041 | exam | .039 | daughter | .030
#livepd | .033 | love | .040 | college | .038 | via | .029
football | .033 |  | .040 | semester | .038 | w | .029
#twitch | .033 |  | .040 | lol | .037 | play | .028
fantasy | .033 | me | .039 | myself | .036 | #dnd | .028
great | .032 |  | .037 | life | .036 | pe | .028
stream | .032 | ♀ | .036 |  | .036 | inboxdollars | .028
aw_prints | .031 |  | .035 | ve | .035 | app | .028
inboxdollars | .031 |  | .034 | best | .035 | challenge | .027
championship | .030 |  | .034 |  | .035 | covet | .026
playing | .030 |  | .033 |  | .035 | show | .026
teams | .030 | because | .033 |  | .035 | awesome | .025
congratulations | .029 | i | .033 |  | .035 | #positive | .025
app | .029 | happy | .033 | like | .034 | photo | .025
bout | .029 | boyfriend | .033 | mom | .034 | #i_am | .024

Table 7: Unigram feature correlations with bragging between gender and age,
sorted by Pearson correlation (r). All correlations are significant at
$p<.001$, two-tailed t-test.

#### Age

In terms of age, we split the users into two age groups (born before 1988 and
after 1988) using the population median. Table 7 (right column) shows the
top-15 unigram features correlated with age. We notice that younger users
(born after 1988) brag more about themselves (e.g., i, my, me, myself) and
school life (e.g., class, exam, college, semester). Example (T12) indicates
how a younger user brags about his/her exam result.

> T12: _“Also somehow got a 90 on my soils exam… how…”_.

Also, they use more emojis in their bragging posts, which is consistent with
the fact that in general younger users tend to use more emojis than older ones
do in social media Prada et al. (2018).

The group of users above median age brag more about collective activities
(e.g., our, we) and their affiliation such as family members (e.g., kids,
wife, daughter), which suggests older people are more family-focused and
engage more with family activities McAndrew and Jeong (2012). In example
(T13), a user in this group mentions his/her daughter while bragging.

> T13: _“My daughter is hands down the coolest person I know!”_.

#### Education & Income:

Table 8 (left column) presents the top 15 unigrams in bragging posts
correlated with higher and lower education. We observe that people with higher
levels of education use a smaller number of emojis. As illustrated in example
(T14), they brag more about their jobs (e.g., student, conference) and
activities involving food (e.g., beer, delicious).

> T14: “Glad I’ve been working on my Adobe Suite skills this year. I’m going
> to be making a very special obituary poster for my uncle’s celebration of
> life to highlight testimonials from friends and family ”.

Furthermore, people with higher education mention others (e.g., <user>, our,
their) when bragging. As illustrated in example (T15), This is in contrast
with lower educated users who focus on their personal traits, possessions or
activities (e.g., i, my, look, got).

> T15: “I look cute as hell in this hoodie”.

Higher vs. Lower Education | Higher vs. Lower Income
---|---
Unigram | r | Unigram | r | Unigram | r | Unigram | r
$<$user$>$ | .062 | i | .074 |  $<$user$>$ | .061 | :) | .043
students | .040 | lol | .059 |  $<$url$>$ | .051 | my | .030
$<$url$>$ | .039 | my | .055 | aw_prints | .032 | lol | .030
our | .030 |  | .043 |  $\star$congrats | .030 | got | .026
$\star$congrats | .029 | m | .038 | #iteachk | .029 | #livepd | .023
pe | .025 |  | .035 | pe | .029 | work | .023
ss | .025 | look | .033 | #piano | .029 | cory | .022
season | .025 |  | .032 |  $\star$#hc | .027 | covet | .022
zak | .025 | got | .032 | students | .026 | renee | .021
conference | .024 |  | .031 | #music | .024 | makes | .021
beer | .024 |  | .030 | team | .024 | boyfriend | .021
#piano | .023 |  $\star$#10bw | .028 | golf | .024 | yay | .021
their | .023 |  | .027 | win | .024 | m | .020
delicious | .023 | baby | .027 |  | .023 | then | .019
student | .021 |  | .026 | bet | .023 | kiss | .019

Table 8: Unigram feature correlations with bragging between higher and lower
education level annual income, sorted by Pearson correlation (r). All
correlations are significant at $p<.001$, two-tailed t-test. $\star$congrats,
$\star$#10bw, $\star$#hc is congratulations, #10billionwives,
#happyclassrooms.

Similar income and education levels share a similar language (e.g. $<$user$>$,
students, congratulations in higher education and higher income, my, lol,
baby, boyfriend in lower education and lower income) (see Table 8 right column
for the top 15 unigrams in bragging posts correlated with higher and lower
income). In addition, bragging expressions from higher income users are more
related to entertainment events such as #music, golf compared with higher
education (see example (T16)).

> T16: “Great golf lesson with <USER> at the PAGA! Already see the results.
> Now-practice to see them more often. #GonnaBreak80”.

### 5.4. Bragging Prevalence and User Popularity

Finally, we investigate whether bragging prevalence is related to user
popularity on Twitter. Table 6 shows that users who are more popular on
Twitter are likely to brag more across all three popularity metrics ($r$
between $0.09$ and $0.12$, $p<.001$), when controlled for gender and age.

It is possible that users with many followers (e.g., micro- or macro-
influencers) tend to interact with and try to maintain followers or obtain
more followers Guo and Ren (2020) by establishing a positive social image
through bragging. It is expected that users with higher follower counts are
less likely to have an audience that knows them personally and thus they aim
to shape their perception through self-presentation strategies. This is in
contrast with the users who have fewer friends and followers and who do not
feel the need to use self-presentation strategies such as bragging to their
close network. Past research showed that users are more willing to share
content with positive sentiment with people that share a weak relationship
(e.g., online followers) than with actual real-life friends Bak et al. (2012).

The linguistic feature analysis did not result in any clear patterns or
meaningful insights into the expression of bragging in terms of user
popularity.

## 6\. Conclusion

We have presented the first large-scale quantitative study of bragging on
social media, focusing on understanding the prevalence, temporal dynamics and
user factors impacting bragging prevalence. Our analysis of more than 1
million English Twitter posts from users in the U.S. showed that bragging
slowly becomes less prevalent with time, it is more prevalent during the
weekends and daytime and that females, younger users, users with higher
education, income and popularity tend to brag more. Furthermore, we uncovered
using linguistic methods the prevalent topics distinctive of each user group.

For future work, we plan to compare how bragging is perceived towards building
a positive self-image, reactions to bragging and cultural differences in
bragging behavior across locations and languages.

## 7\. Ethics Statement

Our work has received approval from the Ethics Committee of our institution
and complies with the Twitter data policy for
research.333https://developer.twitter.com/en/developer-terms/agreement-and-
policy

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