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Automatic identification and classification of bragging in social media

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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.
Keywords
Computational Sociolinguistics, Social Media, Bragging, lt3

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MLA
Jin, Mali, et al. “Automatic Identification and Classification of Bragging in Social Media.” Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, edited by Smaranda Muresan et al., Association for Computational Linguistics, 2022, pp. 3945–59, doi:10.18653/v1/2022.acl-long.273.
APA
Jin, M., Preoțiuc-Pietro, D., Doğruöz, A. S., & Aletras, N. (2022). Automatic identification and classification of bragging in social media. In S. Muresan, P. Nakov, & A. Villavicencio (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (pp. 3945–3959). https://doi.org/10.18653/v1/2022.acl-long.273
Chicago author-date
Jin, Mali, Daniel Preoțiuc-Pietro, A. Seza Doğruöz, and Nikos Aletras. 2022. “Automatic Identification and Classification of Bragging in Social Media.” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, edited by Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, 3945–59. Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.273.
Chicago author-date (all authors)
Jin, Mali, Daniel Preoțiuc-Pietro, A. Seza Doğruöz, and Nikos Aletras. 2022. “Automatic Identification and Classification of Bragging in Social Media.” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, ed by. Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, 3945–3959. Association for Computational Linguistics. doi:10.18653/v1/2022.acl-long.273.
Vancouver
1.
Jin M, Preoțiuc-Pietro D, Doğruöz AS, Aletras N. Automatic identification and classification of bragging in social media. In: Muresan S, Nakov P, Villavicencio A, editors. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics; 2022. p. 3945–59.
IEEE
[1]
M. Jin, D. Preoțiuc-Pietro, A. S. Doğruöz, and N. Aletras, “Automatic identification and classification of bragging in social media,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 2022, pp. 3945–3959.
@inproceedings{8743470,
  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.}},
  author       = {{Jin, Mali and Preoțiuc-Pietro, Daniel and Doğruöz, A. Seza and Aletras, Nikos}},
  booktitle    = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}},
  editor       = {{Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline}},
  isbn         = {{9781955917216}},
  keywords     = {{Computational Sociolinguistics,Social Media,Bragging,lt3}},
  language     = {{eng}},
  location     = {{Dublin, Ireland}},
  pages        = {{3945--3959}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{Automatic identification and classification of bragging in social media}},
  url          = {{http://dx.doi.org/10.18653/v1/2022.acl-long.273}},
  year         = {{2022}},
}

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