Automatic Identification and Classification of Bragging in Social Media (2022)

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.

## introduction the desire to be viewed positively is a key driver of human behavior @xcite @xcite and creating a positive image often leads to personal rewards @xcite @xcite . self-presentation strategies are means for individuals to build and establish this positive social image to meet their goals @xcite @xcite . 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 @xcite . social media platforms tend to promote selfpresentation tendencies @xcite and allow users to craft an idealized self-image of themselves (chou and @xcite @xcite . self-presentation online is predominantly positive (chou and @xcite @xcite . furthermore, self-promotion is acceptable and even desired in certain online contexts @xcite . this is also amplified by social media platforms through the presence of likes or positive reactions to users' posts @xcite which often are used to quantify impact on the platform @xcite . bragging in particular was found to be more frequent on social media than face-to-face interactions @xcite . however, bragging is considered a high risk act @xcite @xcite and can lead to the opposite effect than intended, such as dislike or decreased perceived competence @xcite @xcite . 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 @xcite . 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 @xcite ); (b) social scientists to analyze the relationship between bragging and personality traits, online behavior and communication strategies @xcite @xcite ; (c) on-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. ## 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 @xcite . 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 @xcite . bragging online plays an important role in self-presentation and its pervasiveness challenges classic politeness theories, such as the modesty maxim @xcite and the self-denigration maxim @xcite . 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 selfpresentation styles are more likely to be perceived positively @xcite . bragging framed as mere information-sharing, but with positive connotation to the speaker, can make the speaker be perceived as more likeable @xcite . 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 @xcite @xcite . 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) @xcite . 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 @xcite , question, narration or sharing @xcite @xcite . the success of self-presentation strategies are also impacted by the social context @xcite or speaker identity @xcite . ## predictive models we evaluate vanilla transformer-based models @xcite and further leverage external linguistic information to improve them. bert, roberta and bertweet we experiment with bidirectional encoder representations from transformers (bert; @xcite al. (2019) ), roberta @xcite and bertweet @xcite . 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 @xcite . 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 @xcite , 6 that was found to be effective on complaint severity classification, a related pragmatics task. the method is adapted from @xcite , 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 @xcite . the output is sent to a pre-trained bertweet encoder for fine-tuning followed by an output layer. we experiment with these linguistic features: ## experimental setup text processing we pre-process text by lowercasing, 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. ## analysis linguistic feature analysis ## 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 @xcite and languages and how it is employed by users across contexts.