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. 1 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). 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) on-3945 Type Definition Tweet Achievement Concrete outcome obtained as a result of the tweet author's actions. line 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. 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 per-sons 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 (Rdiger 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 (Preotiuc-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 (Chin-nappa and Blanco, 2018). 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. 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. 2 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. 3 After annotating 1,000 tweets, we compute the percentage 2 https://tinyurl.com/2p8wnure 3 The 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 }. 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). 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. 4 The 4 For example, we annotate New car  New crib  New 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 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. 5 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 (Krippen-dorff, 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). 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 barbershop  20 years young as Possession' because bragging is mostly about possessions ( crib , car , barbershop ). 5 We experimented on training models using the subset annotated by a single annotator compared to multiple annotators and find no significant differences (see Appendix A). 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). 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 pretrained 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), 6 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. 6 Early experimentation with simply concatenating or applying attention resulted in lower performance. 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 Preotiuc-Pietro et al. (2015) to represent each tweet as a 200-dimensional vector over thematic subjects. Text Processing We pre-process text by lower-casing, replacing all username mentions with placeholder tokens @USER and emojis with words using demojize. 7 We also remove hashtags that are used as keywords (e.g. #brag ) in data collection. Finally, we tokenize the text using TweetTokenizer. 8 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  {64, 128, 256, 512} with dropout d = .2, d  {.2, .5}. We use Adam optimizer (Kingma and Adam, 2015) with learning rate l = 1e-2, l  {1e-3, 1e-2, 1e-1}. For BERT , 7 https://pypi.org/project/emoji/ 8 https://www.nltk.org/api/nltk. 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  {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  {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 im-balanced data and set the training epoch to n = 40, n  {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. 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 pretraining 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). 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 3950 Bragging Non-Bragging Braggingtype Achievement Action Feeling Trait Possession Affiliation Feature r Feature r Feature r Feature r Feature r Feature r Feature r Feature r UnigramsandLIWC 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 ? 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 ). 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 fa-3951 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. vorites/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 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 3952 Figure 1: Confusion matrix of annotator agreement on seven bragging categories. 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! 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: 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! 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? 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 (Snchez Villegas et al., 2020; Snchez Villegas and Aletras, 2021) and languages and how it is employed by users across contexts. We would like to thank Ari Silburt, Danae Snchez Villegas, Yida Mu, and all the anonymous reviewers for their valuable feedback. 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. 9 References Ron Artstein and Massimo Poesio.
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