# Automatic Identification and Classification of Bragging in Social Media

Mali Jin1, Daniel Preoţiuc-Pietro2, A. Seza Doğruöz3, Nikolaos Aletras1
1University of Sheffield, 2Bloomberg, 3Ghent University
{mjin6<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>

###### Abstract

Bragging is a speech act employed with the goal of constructing a favorable
self-image through positive statements about oneself. It is widespread in
daily communication and especially popular in social media, where users aim to
build a positive image of their persona directly or indirectly. In this paper,
we present the first large scale study of bragging in computational
linguistics, building on previous research in linguistics and pragmatics. To
facilitate this, we introduce a new publicly available data set of tweets
annotated for bragging and their types. We empirically evaluate different
transformer-based models injected with linguistic information in (a) binary
bragging classification, i.e., if tweets contain bragging statements or not;
and (b) multi-class bragging type prediction including not bragging. Our
results show that our models can predict bragging with macro F1 up to 72.42
and 35.95 in the binary and multi-class classification tasks respectively.
Finally, we present an extensive linguistic and error analysis of bragging
prediction to guide future research on this topic.111Data is available here:
https://archive.org/details/bragging_data

##  1 Introduction

The desire to be viewed positively is a key driver of human behavior
(Baumeister, 1982; Leary and Kowalski, 1990; Sedikides, 1993; Tetlock, 2002)
and creating a positive image often leads to personal rewards (Gilmore and
Ferris, 1989; Hogan, 1982; Schlenker, 1980). Self-presentation strategies are
means for individuals to build and establish this positive social image to
meet their goals (Goffman et al., 1978; Jones et al., 1982; Jones, 1990; Bak
et al., 2014a). Bragging (or self-praise) is one of the most common strategies
and involves disclosing a positively valued attribute about the speaker or
their in-group (Dayter, 2014, 2018).

Social media platforms tend to promote self-presentation tendencies (Chen et
al., 2016) and allow users to craft an idealized self-image of themselves
(Chou and Edge, 2012; Michikyan et al., 2015; Halpern et al., 2017). Self-
presentation online is predominantly positive (Chou and Edge, 2012; Lee-Won et
al., 2014; Matley, 2018). Furthermore, self-promotion is acceptable and even
desired in certain online contexts (Dayter, 2018). This is also amplified by
social media platforms through the presence of 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). Bragging in particular was found
to be more frequent on social media than face-to-face interactions (Ren and
Guo, 2020).

Type |  Definition |  Tweet
---|---|---
Achievement |  Concrete outcome obtained as a result of the tweet author’s actions. These may include accomplished goals, awards and/or positive change in a situation or status (individually or as part of a group). |  _Finally got the offer! Whoop!!_
Action |  Past, current or upcoming action of the user that does not have a concrete outcome. |  _Guess what! I met Matt Damon today!_
Feeling |  Feeling that is expressed by the user for a particular situation. |  _Im so excited that I am back on my consistent schedule. I am so excited for a routine so I can achieve my goals!!_
Trait |  A personal trait, skill or ability of the user. |  _To be honest, I have a better memory than my siblings_
Possession |  A tangible object belonging to the user. |  _Look at our Christmas tree! I kinda just wanna keep it up all year!_
Affiliation |  Being part of a group (e.g. family, fanclub, university, team, company etc.) and/or a certain location including living in a city, neighborhood or country. |  _My daughter got first place in the final exam, so proud of her!_
Not Bragging |  The tweet is not about bragging or (a) there is not enough information to determine that the tweet is about bragging; (b) the bragging statements belong to someone other than the author of the tweet; (c) the relationship between the author and people or things mentioned in the tweet are unknown. |  _Glad to hear that! Well done Jim!_
Table 1: Bragging taxonomy together with type definitions and examples of
tweets.

However, bragging is considered a high risk act (Brown and Levinson, 1987;
Holtgraves, 1990; Van Damme et al., 2017) and can lead to the opposite effect
than intended, such as dislike or decreased perceived competence (Jones et
al., 1982; Sezer et al., 2018; Matley, 2018). It is, thus, paramount to
understand the types of bragging and strategies to mitigate the face-threat
introduced by bragging as well as how effective the self-presentation attempt
is (Herbert, 1990). Table 1 shows examples of a non-bragging and bragging
statements grouped in six types under a taxonomy that we propose in this paper
based on previous linguistic research Dayter (2018); Matley (2018).

Despite its pervasiveness and importance in online communication, bragging has
yet to be studied at scale in computational (socio) linguistics. The ability
to identify bragging automatically is important for: (a) linguists to better
understand the context and types of bragging through empirical studies
(Dayter, 2014; Ren and Guo, 2020); (b) social scientists to analyze the
relationship between bragging and personality traits, online behavior and
communication strategies (Miller et al., 1992; Van Damme et al., 2017; Sezer
et al., 2018); (c) online users to enhance their self-presentation strategies
(Miller et al., 1992; Dayter, 2018); (d) enhancing NLP applications such as
intent identification (Wen et al., 2017) and conversation modeling.

In this paper, we aim to bridge the gap between previous work in pragmatics
and the computational study of speech acts. Our contributions are:

  * •

A new publicly available data set containing a total of 6,696 English tweets
annotated with bragging and their types;

  * •

Experiments with transformer-based models combined with linguistic features
for bragging identification (binary classification) and bragging type
classification (seven classes);

  * •

A qualitative linguistic analysis of markers of bragging in tweets and the
model behavior in predicting bragging.

##  2 Related Work

#### Bragging as a Speech Act

Bragging as a speech act is considered a face-threatening act to positive face
(i.e. the desire to be liked) under politeness theory Brown and Levinson
(1987). It is directly oriented to the speaker and may threaten their
likeability if the bragging is perceived negatively, while also may affect
hearer’s face by implying that their feelings are not valued by the speaker
(Matley, 2018). Bragging online plays an important role in self-presentation
and its pervasiveness challenges classic politeness theories, such as the
modesty maxim (Leech, 2016) and the self-denigration maxim (Gu, 1990). Thus,
research in social psychology and linguistics has mostly focused on
identifying the pragmatic strategies for bragging that mitigate face threat
and their impact of likeability and perceived competence, which the speakers
aim to increase with this self-presentation strategy.

#### Bragging Strategies

Modest and sincere self-presentation styles are more likely to be perceived
positively (Sedikides et al., 2007). Bragging framed as mere information-
sharing, but with positive connotation to the speaker, can make the speaker be
perceived as more likeable (Miller et al., 1992). It can also be perceived
negatively and causes greater aggression when it involves boasting, elements
of competitiveness, use of superlatives and explicit comparisons to others
(Miller et al., 1992; Hoorens et al., 2012; Scopelliti et al., 2015; Matley,
2018). In addition, competence related statements are more likely to be
negatively perceived than those based on warmth (e.g. the ability to form
connections with others) (Van Damme et al., 2017). Common mitigation
strategies include speaker’s attempts to deny compliments, shifting focus to
persons closely related to them, reframing bragging as praise from a third
party, admitting the bragging act through disclaimers (e.g. using #brag) or
expressing it as a complaint Wittels (2011); Sezer et al. (2018), question,
narration or sharing Dayter (2018); Matley (2018); Ren and Guo (2020). The
success of self-presentation strategies are also impacted by the social
context (Tice et al., 1995) or speaker identity (Paramita and Septianto,
2021).

#### Analysis of Bragging

Bragging has been studied in the context of a small ballet community (Dayter,
2014), a pick-up artist forum (Rüdiger and Dayter, 2020) and a small set of
WhatsApp conversations (Dayter, 2018). On social media, Matley (2018) studied
the functional use of hashtags (e.g. #brag, #humblebrag) in Instagram posts,
Tobback (2019) examined bragging strategies on LinkedIn, Ren and Guo (2020)
investigated bragging and its pragmatic functions in Chinese social media and
Matley (2020) studied impact of mitigating bragging through irony showing that
bragging was negatively perceived. However, all these studies rely on manual
analyses of small data sets (e.g. $<$300 posts).

#### Speech Acts in NLP

Speech acts have been studied in NLP with examples including politeness
(Danescu-Niculescu-Mizil et al., 2013), complaints (Preoţiuc-Pietro et al.,
2019; Jin and Aletras, 2020, 2021), humor (Yang et al., 2021), parody
(Maronikolakis et al., 2020), irony (Bamman and Smith, 2015), deception (Chen
et al., 2020) and self-disclosure (Bak et al., 2012; Levontin and Yom-Tov,
2017; Ravichander and Black, 2018). Self-disclosure is closer to bragging as
it is related to revealing personal information about oneself. It is usually
employed to improve or maintain relationships (Bak et al., 2012) as measured
through conversation frequency (Bak et al., 2014b). On the other hand,
bragging is about aspects that are positively valued by the audience with the
goal of improving the speaker’s self-image. Bak et al. (2014a) aim to predict
different levels of self-disclosure statements, from general to sensitive;
while Wang et al. (2021) examine gender differences in self-promotion by
Congress members on Twitter. Bragging also involves in some cases possessions
(Chinnappa and Blanco, 2018).

##  3 Bragging Data

###  3.1 Bragging Definition & Types

#### Definition

Bragging is a speech act which explicitly or implicitly attributes credit to
the speaker for some good (e.g.possession, skill) that is positively valued by
the speaker and their audience (Dayter, 2014). A bragging statement should
clearly express what the author is bragging about.

#### Types

We generalize and extend the bragging types based on the definitions by Dayter
(2018) and Matley (2018). The former summarizes them as accomplishments and
some aspects of self; while the latter includes everyday achievements (e.g.
cooking) and personal qualities. We divide the ‘some aspects of self’ category
into two categories, namely ‘Possession’ and ‘Trait’ respectively. We also add
an ‘Affiliation’ category for bragging involving a group to which the speaker
belongs. In total, we consider six bragging types and a non-bragging category.
Table 1 shows the definitions of each type.

#### Classification Tasks

Given the taxonomy above, we define two classification tasks: (i) _binary_
bragging prediction (i.e. if a tweet contains a bragging statement or not);
and (ii) seven-way _multiclass_ classification for predicting if a tweet
contains one of the six bragging types or no bragging at all.

###  3.2 Data Collection

To the best of our knowledge, there is no other data set available for our
study. We use Twitter for data collection as tweets are openly available for
research and widely used in other related tasks, e.g. predicting sentiment
(Rosenthal et al., 2017), affect (Mohammad et al., 2018), sarcasm (Bamman and
Smith, 2015), stance (Mohammad et al., 2016).

#### Random Sampling

We select tweets for annotation by randomly sampling from the 1% Twitter feed
one day per month from January 2019 to December 2020 (approximately 10k tweets
per day) to ensure diversity using the Premium Twitter Search API for academic
research.222https://tinyurl.com/2p8wnure

#### Keyword-based Sampling

To give a model access to more positive examples of bragging statements for
training, we use a keyword-based sampling method that increases the hit rate
of bragging, following previous work on labeling infrequent linguistic
phenomena, e.g. irony (Mohammad et al., 2018) or hate speech Waseem and Hovy
(2016).

We build queries based on indicators of positive self-disclosure (e.g. _I,
just_) Dayter (2018) and stylistic indicators, e.g. positive emotion words,
present tense verbs Bazarova et al. (2013). As the frequency of these keywords
is high, we construct multi-word queries consisting of a personal pronoun and
an indicator. In addition, we use a short list of curated bragging-related
hashtags.333The queries are: {[_I, proud_], [_I, glad_], [_I, happy_], [_I,
best_], [_I, amazed_], [_I, amazing_], [_I, excellent_], [_I, just_], [_I’m,
proud]_ , [_I’m, glad_], [_I’m, happy_], [_I’m, best_], [_I’m, amazed_],
[_I’m, amazing_], [_I’m, excellent_], [_me, proud_], [_my, best_], #_brag_ ,
#_bragging_ , #_humblebrag_ , #_humble_ , #_braggingrights_}. After annotating
1,000 tweets, we compute the percentage of bragging tweets for each keyword
and remove from sampling tweets with less than 5% (i.e. [_I, amazed_], [_I’m,
amazing_], [_I’m, best_], [_my, best_], [_I, excellent_], #_humble_).

We initially collected around 6K and 368K tweets using hashtags and multi-word
queries respectively. We obtain over 9k tweets by keeping all tweets collected
using hashtags and sample 1% from those collected using multi-word queries to
balance the two types.

#### Data Filtering

After collecting tweets, we exclude those with duplicate or no meaningful
textual content (e.g. only @-mentions or images). We only focus on English
posts and filter out non-English ones using the language code provided by
Twitter. We also exclude retweets and quoted tweets, as these do not typically
express the thoughts of the user who retweeted them. Moreover, we exclude 131
tweets containing a URL in the text because these were related to
advertisements based on initial results from our annotation calibration
rounds. This resulted in a total of 6,696 tweets which is of similar size with
data sets recently released for social NLP Oprea and Magdy (2020); Chung et
al. (2019); Beck et al. (2021); Mendelsohn et al. (2021).

Label | Training set | Dev/Test set | All
---|---|---|---
| (Keyword sampling) | (Random sampling) |
Binary |  |  |
Bragging | 544 (16.09%) | 237 (7.15%) | 781 (11.66%)
Not Bragging | 2838 (83.91%) | 3077 (92.85%) | 5915 (88.34%)
Multi-class |  |  |
Achievement | 166 (4.91%) | 71 (2.14%) | 237 (3.54%)
Action | 127 (3.76%) | 58 (1.72%) | 185 (2.76%)
Feeling | 39 (1.15%) | 27 (0.82%) | 66 (0.99%)
Trait | 91 (2.69%) | 48 (1.45%) | 139 (2.08%)
Possession | 58 (1.72%) | 28 (0.84%) | 86 (1.28%)
Affiliation | 63 (1.86%) | 5 (0.15%) | 68 (1.01%)
Not Bragging | 2838 (83.91%) | 3077 (92.85%) | 5915 (88.34%)
Total | 3382 | 3314 | 6696

Table 2: Bragging data set statistics.

###  3.3 Annotation and Quality Control Process

We manually annotate tweets for providing a solid benchmark and foster future
research. All authors of the paper have significant experience in linguistic
annotation. We run three calibration rounds of 100 tweets each, where all
annotated all tweets and discussed disagreements, until a Krippendorf’s Alpha
above 0.80 in the seven-class task was reached.

To monitor quality, a subset of 1,564 tweets were annotated by two annotators
or more in case of disagreements. If a tweet fits into multiple bragging
types, we assign the more prominent one.444For example, we annotate “New
car✓New crib✓New barbershop✓20 years young” as ‘Possession’ because bragging
is mostly about possessions (crib, car, barbershop). The annotation is based
only on the actual text of the tweet without considering additional modalities
(e.g. images), context or replies. This is similar to the information
available to predictive models during training. We selected the final label as
the majority vote and a final label was assigned after consensus in cases of
three different votes.555We experimented on training models using the subset
annotated by a single annotator compared to multiple annotators and find no
significant differences (see Appendix A). The full task guidelines, examples
and interface are presented in Appendix B.

The inter-annotator agreement between two annotations of all tweets is: (a)
percentage agreement: 89.03; (b) Krippendorf’s Alpha (Krippendorff, 2011)
(7-class): 0.840; (c) Krippendorf’s Alpha (binary): 0.786. Agreement values
are between the upper part of the substantial agreement band and the perfect
agreement band (Artstein and Poesio, 2008). The final data set consists of
6,696 tweets with one of the seven classes. Before annotation, the keyword-
based and randomly sampled tweets were shuffled to not induce frequency bias.
Data set statistics are shown in Table 2, including statistics across the two
sampling strategies. The model performance curve by varying the training set
size indicates that annotating more data is not likely to lead in substantial
improvements in bragging prediction (see Figure 3 in Appendix).

Class | Self-disclosure (%) | Non-self-disclosure (%)
---|---|---
Bragging | 31.63 | 68.37
Non-bragging | 24.04 | 75.96
Achievement | 31.65 | 68.35
Action | 27.57 | 72.43
Feeling | 31.82 | 68.18
Trait | 36.69 | 63.31
Possession | 29.07 | 70.93
Affiliation | 35.29 | 64.71
Non-bragging | 24.04 | 75.96
Total | 24.93 | 75.07

Table 3: Percentages of self-disclosure class across bragging classes

###  3.4 Self-disclosure in Bragging

We conduct an analysis of the relationship between self-disclosure and
bragging as they are closely related. We use self-disclosure lexicon by Bak et
al. (2014a) to assign each tweet in our data set a label (i.e. self-disclosure
or non-self-disclosure). The percentages of self-disclosure across each
bragging type are shown in Table 3. We also used self-disclosure models as a
predictor for bragging in early experimentation but the results are omitted
due to the low performance.

###  3.5 Data Splits

We use the keyword sampled data for training and the random data for
development and testing (in the ratio of 2:8) because the latter is
representative of the real distribution of tweets (see Table 2).

##  4 Predictive Models

We evaluate vanilla transformer-based models Vaswani et al. (2017) and further
leverage external linguistic information to improve them.

#### BERT, RoBERTa and BERTweet

We experiment with Bidirectional Encoder Representations from Transformers
(BERT; Devlin et al. (2019)), RoBERTa (Liu et al., 2019) and BERTweet (Nguyen
et al., 2020). RoBERTa is a more robust variant of BERT that obtains better
results on a wide range of tasks. BERTweet is pre-trained on English tweets
using RoBERTa as basis and achieves better performance on Twitter tasks
(Nguyen et al., 2020). We fine-tune BERT, RoBERTa and BERTweet for binary and
multiclass bragging prediction by adding a classification layer that takes the
[CLS] token as input.

#### BERTweet with Linguistic Features

We inject linguistic knowledge that could be related to bragging to the
BERTweet model with a similar method proposed by Jin and Aletras
(2021),666Early experimentation with simply concatenating or applying
attention resulted in lower performance. that was found to be effective on
complaint severity classification, a related pragmatics task. The method is
adapted from Rahman et al. (2020), which integrates multimodal information
(e.g. audio, visual) in transformers using a fusion mechanism called
Multimodal Adaption Gate (MAG). MAG integrates multimodal information to text
representations in transformer layers using an attention gating mechanism for
modality influence controlling. We first expand vectors of linguistic
information to a comparable size to the embeddings fed to the pre-trained
transformer. We, then, use MAG to concatenate contextual and linguistic
representations after the embedding layer of the transformer similar to Rahman
et al. (2020). The output is sent to a pre-trained BERTweet encoder for fine-
tuning followed by an output layer.

We experiment with these linguistic features:

  * •

NRC: The NRC word-emotion lexicon contains a list of English words mapped to
ten categories related to emotions and sentiment Mohammad and Turney (2013).
We represent each tweet as a 10-dimensional vector where each element is the
proportion of tokens belonging to each category.

  * •

LIWC: Linguistic Inquiry and Word Count Pennebaker et al. (2001) is a
dictionary-based approach to count words in linguistic, psychological and
topical categories. We use LIWC 2015 to represent each tweet as a
93-dimensional vector.

  * •

Clusters: We use Word2Vec clusters proposed by Preoţiuc-Pietro et al. (2015)
to represent each tweet as a 200-dimensional vector over thematic subjects.

##  5 Experimental Setup

#### Text Processing

We pre-process text by lowercasing, replacing all username mentions with
placeholder tokens _@USER_ and emojis with words using
demojize.777https://pypi.org/project/emoji/ We also remove hashtags that are
used as keywords (e.g. _#brag_) in data collection. Finally, we tokenize the
text using TweetTokenizer.888https://www.nltk.org/api/nltk.tokenize.html

Model | Precision | Recall | Macro-F1 | Precision | Recall | Macro-F1
---|---|---|---|---|---|---
| Bragging Classification (Binary) | Bragging and Type Classification (7 class)
Majority Class | 46.42 | 50.00 | 48.15 | 13.26 | 14.29 | 13.76
LR-BOW | 54.53 | 63.16 | 52.68 | 18.52 | 20.02 | 18.59
BiGRU-Att | 55.93 $\pm$ 1.53 | 51.41 $\pm$ 0.47 | 51.29 $\pm$ 1.40 | 18.32 $\pm$ 0.10 | 26.16 $\pm$ 3.41 | 19.19 $\pm$ 0.31
BERT | 64.24 $\pm$ 1.40 | 65.91 $\pm$ 3.32 | 64.58 $\pm$ 0.80 | 24.16 $\pm$ 1.15 | 39.66 $\pm$ 4.84 | 26.85 $\pm$ 0.81
RoBERTa | 66.53 $\pm$ 0.29 | 68.43 $\pm$ 2.05 | 67.34 $\pm$ 1.02 | 28.99 $\pm$ 0.61 | 45.90 $\pm$ 3.59 | 32.82 $\pm$ 0.65
BERTweet | 70.43 $\pm$ 0.16 | 72.62 $\pm$ 0.89 | 71.44 $\pm$ 0.43 | 30.82 $\pm$ 0.75 | 47.25 $\pm$ 2.68 | 34.86 $\pm$ 0.79
BERTweet-NRC | 72.89 $\pm$ 1.26 | 70.95 $\pm$ 0.96 | 71.80 $\pm$ 0.49 | 30.95 $\pm$ 0.54 |  47.98 $\pm$ 1.12 | 34.36 $\pm$ 0.19
BERTweet-LIWC | 72.65 $\pm$ 0.20 | 72.21 $\pm$ 0.43 |  $\textbf{72.42}^{\dagger}$ $\pm$ 0.31 | 32.06 $\pm$ 2.42 | 46.68 $\pm$ 7.45 | 34.83 $\pm$ 0.79
BERTweet-Clusters | 71.26 $\pm$ 2.27 | 72.53 $\pm$ 1.91 | 71.60 $\pm$ 0.21 |  32.51 $\pm$ 1.36 | 46.97 $\pm$ 2.36 |  35.95 $\pm$ 0.54
Table 4: Macro precision, recall and F1-Score (± std. dev. for 3 runs) for
bragging prediction (binary and multiclass). Best results are in bold.
$\dagger$ indicates significant improvement over BERTweet (t-test, p$<$0.05).

#### Baselines

#### Majority Class:

As a first baseline, we label all tweets with the label of the majority class.

#### LR-BOW:

We train a Logistic Regression with bag-of-words using L2 regularization.

#### BiGRU-Att:

We also train a bidirectional Gated Recurrent Unit (GRU) network (Cho et al.,
2014) with self-attention (Tian et al., 2018). Tokens are first mapped to
GloVe embeddings (Pennington et al., 2014) and then passed to a bidirectional
GRU. Subsequently, its output is passed to a self-attention layer and an
output layer for classification.

#### Hyperparameters

For BiGRU-Att, we use 200-dimensional GloVe embeddings (Pennington et al.,
2014) pre-trained on Twitter data. The hidden size is $h$ = 128 where $h\in$
{64, 128, 256, 512} with dropout $d$ = .2, $d\in$ {.2, .5}. We use Adam
optimizer (Kingma and Adam, 2015) with learning rate $l$ = 1e-2, $l\in$ {1e-3,
1e-2, 1e-1}. For BERT, RoBERTa and BERTweet, we use the base cased model (12
layers and 109M parameters, 12 layers and 125M parameters and 12 layers and
135M parameters accordingly) and fine-tune them with learning rate $l$ = 3e-6,
$l\in$ {1e-4, 1e-5, 5e-6, 3e-6, 1e-6}. For BERTweet with linguistic features,
we project these to vectors of size $l_{NRC}$ = 200, $l_{LIWC}$ = 400,
$l_{Clusters}$ = 768, $l\in$ {10, 93, 200, 400, 600, 768}. For MAG, we use the
default parameters from Rahman et al. (2020). For multi-class classification,
we apply class weighting due to the imbalanced data and set the training epoch
to $n$ = 40, $n\in$ {15, 20, 25, 30, 35, 40, 45, 50, 55, 60,}. The maximum
sequence length is set to 50 covering 95% of tweets in the training set. We
use a batch size of 32.

#### Training and Evaluation

We train each model three times using different random seeds and report the
mean Precision, Recall and F1 (macro). We apply early stopping during training
based on the dev loss. The experiments with linguistic features are performed
with the best pre-trained transformer in each of the two classification tasks.

##  6 Results

#### Binary Bragging Classification

Table 4 (left) shows the predictive performance of all models on predicting
bragging (i.e. binary classification). Overall, BERTweet models with
linguistic information achieve better overall performance. Transformer models
perform substantially above the _majority class baseline_ (+23.29 F1) and
above Logistic Regression (+18.76). BERTweet (71.44 F1) performs better than
BERT (64.58 F1) and RoBERTa (67.34 F1), which illustrates the advantage of
pre-training on English tweets for this task.

Performance is further improved (+0.98 F1) by using LIWC features alongside
BERTweet, which indicates that injecting extra linguistic information benefits
bragging identification. We speculate that this is because a bragging
statement usually contains particular terms (e.g. personal pronouns, positive
terms) or involves at least one certain aspect or theme (e.g. reward or
property), which can be captured by linguistic features (e.g. feature I and
ACHIEVE in LIWC). Combining lexicons lead to worse results than using a single
one, so we refrain from reporting these results for clarity.

#### Multi-class Bragging Classification

Table 4 (right) shows the predictive performance of all models on multiclass
bragging type prediction including not bragging. We again find that pre-
trained transformers substantially outperform the majority class baseline
(+21.1 F1) and logistic regression (+16.27 F1). In line with the binary
results, we find that BERTweet (34.86 F1) performs best out of all
transformers. BERTweet-Clusters outperforms all models (35.95 F1), which
indicates that topical information helps to identify different types of
bragging. Each bragging type might be particularly specialized to certain
topics (e.g. weight loss in ‘Achievement’ category).

Bragging | Non-Bragging | Bragging type
---|---|---
| Achievement | Action | Feeling | Trait | Possession | Affiliation
Feature | r | Feature | r | Feature | r | Feature | r | Feature | r | Feature | r | Feature | r | Feature | r
Unigrams and LIWC
AUTHENTIC | 0.149 | CLOUT | 0.109 | FOCUSPAST | 0.200 | get | 0.146 | happy | 0.228 | APOSTRO | 0.197 | own | 0.211 | FAMILY | 0.276
my | 0.127 | YOU | 0.089 | Number | 0.157 | trip | 0.128 | POSEMOE | 0.218 | COGPROC | 0.181 | buy | 0.175 | CLOUT | 0.271
I | 0.122 | DISCREP | 0.078 | Analytic | 0.153 | RELATIV | 0.119 |  | 0.191 | FOCUSPRESENT | 0.179 | bought | 0.149 | proud | 0.263
TONE | 0.104 | NEGEMO | 0.077 | finished | 0.150 | ready | 0.114 | blessed | 0.190 | cute | 0.159 | car | 0.146 | rights | 0.215
FOCUSPAST | 0.102 | SOCIAL | 0.076 | 3 | 0.133 | him | 0.114 | AFFECT | 0.184 | PRONOUN | 0.157 | bedroom | 0.144 | SOCIAL | 0.209
WC | 0.100 | FOCUSPRESENT | 0.070 | WORK | 0.132 | happen | 0.105 | feels | 0.176 | take | 0.143 | extra | 0.144 | amazing | 0.205
RELATIV | 0.090 | INFORMAL | 0.056 | managed | 0.130 | FOCUSFUTURE | 0.105 | love | 0.169 | COMPARE | 0.143 | xr | 0.142 |  | 0.197
TIME | 0.081 | COGPROC | 0.056 | over | 0.129 | fun | 0.102 | sunrise | 0.166 | ANGER | 0.138 | macbook | 0.055 | law | 0.185
during | 0.078 | ANGER | 0.056 | under | 0.119 | gave | 0.097 | weighted | 0.162 | I | 0.137 | new | 0.139 | team | 0.182
ACHIEVE | 0.075 | just | 0.054 | beat | 0.112 | hours | 0.096 | july | 0.159 | if | 0.137 | afford | 0.139 | OTHERP | 0.181
PREP | 0.073 | your | 0.052 | race | 0.104 | before | 0.095 | time | 0.159 | SWEAR | 0.134 | PERIOD | 0.106 | words | 0.164
managed | 0.072 | IPRON | 0.051 | office | 0.103 | sitting | 0.095 | truly | 0.156 | am | 0.133 | HOME | 0.105 | teams | 0.164
REWARD | 0.069 | ? | 0.043 | possible | 0.103 | VERB | 0.094 | BIO | 0.147 | PPRON | 0.132 | DASH | 0.084 | #baseball | 0.164
row | 0.068 | not | 0.038 | 5 | 0.101 | PREP | 0.089 | CERTAIN | 0.143 | me | 0.130 | I | 0.077 | fan | 0.163
got | 0.067 | why | 0.037 | SIXLTR | 0.100 | INGEST | 0.085 | TONE | 0.140 | look | 0.122 | DISCREP | 0.071 | MALE | 0.160
POS (Unigrams and Bigrams)
PRP_VBD | 0.104 | NNP | 0.081 | CD_NNS | 0.198 | DT_NNP | 0.139 | RB_JJ | 0.183 | VBP | 0.252 | $_CD | 0.161 | FW_, | 0.164
VBD | 0.093 | VB | 0.061 | VBD | 0.171 | VBP_TO | 0.124 | VBP_IN | 0.174 | PRP | 0.193 | $ | 0.130 | VB_VBD | 0.161
CD_NNS | 0.077 | RB_VB | 0.056 | CD | 0.164 | IN_: | 0.117 | VB_RBR | 0.161 | PRP_VBP | 0.191 | NN_PDT | 0.130 | CC_UH | 0.159
PRP$ | 0.074 | NNP_NNP | 0.049 | NNS | 0.145 | VBP_WP | 0.116 | JJR_WRB | 0.161 | VBP_JJ | 0.162 | NNS_UH | 0.122 | VBZ_DT | 0.151
VBD_DT | 0.062 | VBP_PRP | 0.048 | VBD_DT | 0.141 | NNP_UH | 0.116 | RB_VBZ | 0.146 | UH_DT | 0.150 | SYM_: | 0.114 | DT_RBS | 0.146
NN_IN | 0.061 | VBZ | 0.039 | PRP_VBD | 0.132 | NFP_NNP | 0.116 | CC_JJ | 0.143 | VBP_DT | 0.150 | VBZ_JJ | 0.110 | UH_NNP | 0.145
IN_CD | 0.060 | MD | 0.035 | NN_IN | 0.132 | NNP | 0.116 | VBD_: | 0.131 | RB_VB | 0.149 | VB_PRP$ | 0.109 | ._SYM | 0.138
IN_PRP$ | 0.060 | NNP_VBZ | 0.033 | IN_CD | 0.130 | NNP_NNS | 0.114 | ._VBG | 0.123 | MD | 0.149 | PRP$_JJ | 0.109 | NFP_CC | 0.137
PRP$_NN | 0.058 | RB_RB | 0.031 | VBN | 0.129 | TO_VB | 0.109 | UH_WP | 0.118 | MD_VB | 0.134 | ._VBD | 0.109 | PRP_PRP$ | 0.136
VBD_PRP$ | 0.057 | MD_PRP | 0.031 | VB_JJR | 0.109 | TO | 0.107 | POS_RB | 0.118 | CC_WP | 0.131 | NN_PRP$ | 0.106 | NN_NN | 0.135

Table 5: Feature correlations including unigrams (lowercase), LIWC
(uppercase), part-of-speech (POS) unigrams and bigrams with bragging and non-
bragging tweets (left) and bragging tweets grouped in six types (right),
sorted by Pearson correlation (r). All correlations are significant at
$p<.01$, two-tailed t-test.

##  7 Analysis

#### Linguistic Feature Analysis

We analyze the linguistic features i.e. unigrams, LIWC and part-of speech
(POS) tags associated with bragging and its types in all tweets of our data
set. For this purpose, we first tag all tweets using the Twitter POS Tagger
(Derczynski et al., 2013). Each tweet is represented as a bag-of-words
distribution over POS unigrams and bigrams to reveal distinctive syntactic
patterns of bragging and their types. For each unigram, LIWC and POS feature,
we compute correlations between its distribution across posts and the label of
the post. Then, we use the method introduced by Schwartz et al. (2013) to rank
the features using univariate Pearson correlation with words normalized to sum
up to unit for each tweet.

Table 5 (left) presents the top 15 features from unigrams (lowercase) and LIWC
(uppercase) and top 10 features from POS unigrams and bigrams correlated with
bragging and non-bragging tweets. We notice that the top words in the bragging
category can be classified into (a) personal pronouns (e.g. my, I) that
usually indicate the author of the bragging statement; (b) words related to
time (e.g. FOCUSPAST, TIME, during); and (c) words related to a specific
bragging target (e.g. RELATIV, ACHIEVE, REWARD, managed). These findings are
in line with the indicators of positive self-disclosure by Dayter (2018) and
Bazarova et al. (2013). Furthermore, personal pronouns followed by a verb in
past tense (PRP_VBD) is common in bragging (e.g. I forgot what it’s like to be
good at school. Today I finished a thing we were doing so fast that everyone
around me started asking ME for help instead of the prof :’))

Table 5 (right) presents the top 15 features from unigrams (lowercase) and
LIWC (uppercase) correlated with bragging tweets grouped in six types. We
observe that Achievement statements usually involve verbs that are in past
tense or indicate a result (e.g. FOCUSPAST, finished, beat). A POS pattern
common in Achievement statements is a cardinal number followed by nouns in
plural (CD_NNS), similar to its unigram and LIWC features (NUMBER, 3, 5) (e.g.
I made a total of 5 dollars from online surveys wooo). It is worth noting that
one of the prevalent LIWC features for Action is FOCUSFUTURE. This is because
the user may brag about a planned action (e.g. @USER You know what? I’m going
to make some PizzaRolls Brag). Most of the top words in Feeling express
emotion or sensitivity (e.g. happy, blessed), which is consistent with the top
POS feature, RB_JJ (e.g. absolutely chuffed, so happy). In Trait category,
words are mostly pronouns (e.g. I, PRP, PRP_VBP) and verbs (e.g. VBP, VBP_JJ).
Words appear frequently in Possession category are actions related to purchase
(e.g. own, buy) and nouns related to a tangible object (e.g. car, bedroom). In
addition, users usually show off the value of their possessions using
statements that involve currency signs ($) or currency signs followed by a
number ($_CD) (e.g. I just signed a new three-year contract and I’ll be
getting 235 anytime minutes per month. Plus, the company is going to throw in
a phone for just $ 49 per month. I’ll bet you can’t beat that deal!). Finally,
top words in Affiliation category involve positive feeling towards belonging
to a group (e.g. proud, amazing) and nouns related to it (e.g. FAMILY, team).

Class | Mean | Median
---|---|---
Achievement | 3.06 | 3.00
Action | 0.91 | 0
Feeling | 0.50 | 0
Trait | 2.38 | 2.00
Possession | 2.00 | 0.50
Affiliation | 5.50 | 2.00
Table 6: Mean and median Twitter favorites across bragging classes on a sample
set of the data.

#### Bragging and Post Popularity

We also analyze the association between bragging posts and the number of
favorites/retweets they receive by other users. Similar to the previous
linguistic feature analysis, we use univariate Pearson correlation to compute
the correlations between the log-scaled favorites/retweets number of each
tweet and its label (i.e. bragging or non-bragging) by controlling the numbers
of followers and friends of the user who post the tweet. Our results show that
the number of favorites is positively correlated with bragging (see Appendix
Figure 5) while there is no correlation between bragging and the number of
retweets.

We further explore the popularity of different bragging types. We randomly
analyze a set of 443 tweets containing 56 bragging statements, where the
follower and friend number of users are within a similar range: from 100 to
500 followers and from 500 to 1000 friends ($r$ = 0.19, $p$ < .01). We compute
the mean and median Twitter favorites across the six bragging classes (see
Table 6). We observe that bragging statements about Affiliation such as family
members or sports teams are more likely to receive considerable amount of
favorites with the mean of 5.5. For example, 14 users favorite the tweet This
maybe is a little, but I’m SO proud of my research group. We represent so many
different personality types, cultures, ways of thinking, etc, and every single
member of my lab (all 21 of them). We speculate this is because praising the
group that one belongs to instead of oneself as a bragging strategy enables
users be perceived as more likeable. Furthermore, bragging about Achievement
is generally marked as favorite by other users with the median of 3, where
bigger achievements in the content such as job offers may receive more
favorites (e.g. tweet Scored 80 % on my thesis. Rather proud of that given the
circumstances: new baby; pandemic; late topic change due to lockdown; minimal
uni support because of furloughs; and an international move. was marked as
favorite 15 times).

#### Class Confusion Analysis

Figure 1: Confusion matrix of annotator agreement on seven bragging
categories.

Figure 2: Confusion matrix of the best performing model on multi-class
bragging classification, i.e. BERTweet-Clusters

Figure 2 presents the confusion matrix of human agreement on seven classes
normalized over the actual values (rows). We observe that Non-bragging (97%),
Achievement (81%) and Action (78%) have high agreement, consistent with the
class frequency. Affiliation (77%), Possession (76%) and Trait (72%) have
comparable percentages as these are easily associated with a bragging target
or group. The Feeling category has the lowest percentage mostly caused by
misclassification to the Action category. This is due to the fact that both
types are not associated to a concrete outcome by definition, with the feeling
class linked to a feeling linked to an action. Thus, it makes the boundary
between bragging about the action or the feeling associated to the action more
challenging to interpret. The next most frequent confusion is between
possession and achievement, which usually arises when a tangible possession is
involved and the annotators disagree if the author was bragging about the
actual possession or the action that lead to the author obtaining that
possession (e.g. @USER I just got some stealth 300 easily the best headset
I’ve ever had going from astro to turtle beach was a night and day
difference).

Figure 2 presents the confusion matrix between bragging type predictions from
the best performing model, BERTweet-Clusters, on the multi-class
classification task. First, we observe that the model is more likely to
misclassify other classes as the dominant class, Non-bragging. Secondly, the
most unambiguous classes are Non-bragging (87%) and Achievement (52%), which
are in line with human agreement. Also, the model is good at identifying Trait
(50%) and Possession (46%) due to the particular bragging targets (e.g.
personalities, skills or tangible objects). Furthermore, we notice that the
percentages of Action (31%) and Feeling (37%) are low. We speculate this is
because they share more similarities with other classes (e.g. involving
actions). This might also explain the high percentage of misclassified data
points between Action and Achievement, Feeling and Action. Lastly, the model
often confuses Affiliation with Feeling likely because the terms that express
positive feelings (e.g. ‘proud’, ) also appear frequently in Affiliation (see
Table 5).

#### Error Analysis

Finally, we perform an error analysis to examine the behavior and limitations
of our best performing model (i.e. BERTweet-LIWC for binary classification and
BERTweet-Clusters for multi-class classification) and identify pathways to
improve the task modeling.

We first start with the binary bragging classification. We observe that non-
bragging tweets containing positive sentiment are easy to be misclassified as
bragging and even if such tweets involve something valued positively by
authors, the purpose is usually to express recommendation, compliment or
appreciation to others:

> T1: _@USER paid for my new bottle of vodka & I Love Her with all my heart _

Another frequent error happens when non-bragging tweets contain popular
bragging targets such as achievement-oriented (e.g. weight loss, marathon) or
possession-oriented (e.g. car, electronics):

> T2: _4 spaces left on my budget weight loss program. £ 5 a week!???_

Bragging often involves contextual understanding that goes beyond word use and
require deep understanding of the context to determine the label. For example,
common terms such as first, finally, just often appear in both non-bragging
(T3) and bragging (T4) tweets:

> T3: _just cleaned my cats’ toilets_
> T4: _It happened again! I just completed 30 minutes of meditation with
> @USER. Just sitting and resting in presence._

Models also fail to detect bragging mainly because it is indirect or there are
no typical trigger terms, so they lean on pre-training to contextualize:

> T5: _9 hr drives feel like nothing now lol_

Some bragging statements use additional mitigation strategies, e.g. re-framing
the bragging statement as irony, as a complaint or invoking praise from a
third party:

> T6: _I find it strange how I was always the weird one in school and irl but
> online people think im cool for some reason_

Finally, we highlight some representative examples of model confusion between
bragging types. One example is when users’ actions lead or not to a concrete
result. In this example the model predicted Action, but the actual label is
Achievement:

> T7: _not to appropriate the gang escapes culture but me n my parents just
> did an escape room n actually got out?_

Another example is an Action misclassified as Possession. This usually happens
when a common phrase indicative of a certain type of bragging (a new dish)) is
invoked as part of an action:

> T8: _I had a new dish "egusi" it’s so damn good! Love Nigerian food!_

Other errors occur when multiple types of bragging are present (e.g. feeling
and action) but the label expresses the more salient type, such as the feeling
highlighted in this example:

> T9: _Literally had the best time with the girls last night, don’t think I’ve
> drank that much in my life?_

##  8 Conclusion

We presented the first computational approach to analyzing and modeling
bragging as a speech act along with its types in social media. We introduced a
publicly available annotated data set in English collected from Twitter. We
experimented using transformer models combined with linguistic information on
binary bragging and multiclass bragging type prediction. Finally, we presented
an extensive analysis of features related to bragging statements and an error
analysis of the model predictive behavior. In future work, we plan to study
the extent to which bragging is used across various locations Sánchez Villegas
et al. (2020); Sánchez Villegas and Aletras (2021) and languages and how it is
employed by users across contexts.

## Acknowledgements

We would like to thank Ari Silburt, Danae Sánchez Villegas, Yida Mu, and all
the anonymous reviewers for their valuable feedback.

## Ethics Statement

Our work has received approval from the Ethics Committee of the Department of
Computer Science at the University of Sheffield (No 037572) and complies with
Twitter’s data policy for
research.999https://developer.twitter.com/en/developer-terms/agreement-and-
policy

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##  Appendix A Impact of Multiple Annotations

Table 7 shows the performance of binary bragging classification of the best
performing model (BERTweet-LIWC) on two different subsets of the test data:
one annotated by a single annotator (2,130 tweets) and the other annotated by
two or more annotators until consensus is reached (522 tweets). The results
show that the same model tested on the two different subsets of test data lead
to similar results. This shows there is no quantitative difference between the
data sets annotated by two or more annotators when compared to a single
annotator.

Data set | Precision | Recall | Macro-F1
---|---|---|---
Single Annotation | 73.81 | 71.78 | 72.74
Multiple Annotations | 68.24 | 83.31 | 73.23
Entire set | 72.92 | 72.81 | 72.86
Table 7: Precision, Recall and macro F1-Score obtained by the same best
performing model (BERTweet-LIWC) for binary classification on two different
subsets of training data, annotated either by a single annotator or by
multiple annotators. Figure 3: Learning curve for performance across each
bragging type.

##  Appendix B Guidelines and Annotation Interface

Figure 4: Screenshot of annotation interface on our platform.

Thank you for your participation in our study. During our experiment, we will
ask you to read and evaluate a tweet which may include a bragging or a praisal
statement.

#### Instructions

You need to identify whether or not a tweet includes a bragging statement.

#### Bragging

Bragging is a speech act which explicitly or implicitly attributes credit to
the speaker for some ‘good’ (possession, accomplishment, skill, etc.) which is
positively valued by the speaker and the potential audience. As such, bragging
includes announcements of accomplishments, explicit positive evaluations of
some aspect of self and other types defined below. A bragging statement should
clearly express what the author is bragging about (i.e. the target of
bragging).

If the tweet is about bragging, decide on the category where the tweet belongs
to from the following categories:

#### Achievement

The act of bragging is about a concrete outcome obtained as a result of the
tweet author’s actions. These results may include achievements, awards,
products, and/or positive change in a situation or status (individually or as
part of a group).

Examples:

  * •

Finally got that offer! Whoop!!

  * •

Our team won the championship

#### Action

The act of bragging is about a past, current or upcoming action of the user
that does not have a concrete outcome

Examples:

  * •

Hanging at Buffalo Wild Wings with @user for the #ILLvsASU game.
#BraggingRights

  * •

Guess what! I met Matt Damon today!

#### Feeling

The act of bragging is about a feeling that is expressed by the user for a
particular situation.

Example:

  * •

Im so excited that I am back on my consistent schedule. I am so excited for a
routine so I can achieve my goals!!

#### Trait

The act of bragging is about a personal trait, skill or ability of the user .

Examples:

  * •

To be honest, I have a better memory than my siblings

  * •

I look great after losing weight

#### Possession

The act of bragging is about a tangible object belonging to the user.

Example:

  * •

Look at our Christmas tree! I kinda just wanna keep it up all year!

#### Affiliation

The act of bragging is about being part of a group (e.g. family, team, org
etc.) and/or a certain location including living in a city, neighborhood or
country, enrolled into a university, supporting a team, working in a company
etc.

Example:

  * •

My daughter got first place in the final exam, so proud of her!

#### Not bragging

If the tweet is not about bragging, then select "No. This is not a bragging
statement."

Examples:

  * •

One of the best books I’ve ever read

  * •

hahahahahaha

  * •

You gotta admit, that’s some mighty awesome aim!

  * •

Vote in the poll below for your book of choice!

  * •

I think this is great

  * •

dear everyone announcing they are at "Friendsgiving", we get it, you have
friends

  * •

In case you didn’t know, Adam Silver is in charge

  * •

I feel terrible

  * •

I don’t know why you are celebrating

  * •

This is exactly what is going on!

  * •

I love you

Select "No. This is not a bragging statement", also in cases when:

  * •

there is not enough information to determine that the tweet is about bragging

  * •

the bragging statements belong to someone other than the author of the tweet

  * •

the relationship between author and people/things mentioned in the tweet are
unknown:

    * –

This kid is smart

    * –

That was an amazing stream

    * –

Kudos to mike Dunleavy! It’s hard to get a franchise record ANYTHING in
Chicago

  * •

the post is about the act of bragging:

    * –

We want to hear you brag!

    * –

Trump isn’t Bragging anymore as his tradewar hits the stockmarket hard

    * –

Dudes are getting too cocky these days. Them lil labels and that dar don’t
impress everyone. brag differently

#### Not available

Finally, if the tweet is not available or displayed, or is in a language other
than English, please select the "Not available" option.

#### Other considerations

Please verify the content of hashtags as these may give clues towards the
category of the tweet. The judgment should be made only based on the given
content of the tweet - please do not search the tweet on Twitter or online in
order to identify additional context.

Figure 5: Pearson correlation between Twitter favorite number and bragging by
controlling the number of followers and friends. All correlations are
significant at $p$ < .01, two-tailed t-test.