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11.11.2020 | Special Issue Paper

CyberBERT: BERT for cyberbullying identification

BERT for cyberbullying identification

verfasst von: Sayanta Paul, Sriparna Saha

Erschienen in: Multimedia Systems | Ausgabe 6/2022

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Abstract

Cyberbullying can be delineated as a purposive and recurrent act, which is aggressive in nature, done via different social media platforms such as Facebook, Twitter, Instagram, and others. A state-of-the-art pre-training language model, BERT (Bidirectional Encoder Representations from Transformers), has achieved remarkable results in many language understanding tasks. In this paper, we present a novel application of BERT for cyberbullying identification. A straightforward classification model using BERT is able to achieve the state-of-the-art results across three real-world corpora: Formspring (\(\sim 12\hbox {k}\) posts), Twitter (\(\sim 16\hbox {k}\) posts), and Wikipedia (\(\sim 100\hbox {k}\) posts). Experimental results demonstrate that our proposed model achieves significant improvements over existing works, in comparison with the slot-gated or attention-based deep neural network models.

Vorheriger Artikel ParaCap: paraphrase detection model using capsule network

Nächster Artikel Deep learning based cyber bullying early detection using distributed denial of service flow

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https://cyberbullying.org/.

Peter, K.S., et al.: Cyberbullying: Its nature and impact in secondary school pupils. J. Child Psychol. Psychiatry 49(4), 376–385 (2008)CrossRef

Devlin, J., Chang, M.W., Lee, K., Toutanova K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: arXiv preprint arXiv:1810.04805 (2018)

Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: Fifth International AAAI Conference on Weblogs and Social Media (2011)

Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. Int. Conf. Mach. Learn. Appl. Workshop 2, 241–244 (2011)

Djuric, N., et al.: Hate speech detection with comment embeddings. In: Proceedings of the 24th International Conference on World Wide Web. pp. 29–30 (2015)

Badjatiya, P., et al.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 759–760 (2017)

Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Comput. Sec. 90, 101710 (2020)CrossRef

Raisi, E., Huang, B.: Cyberbullying identification using participant-vocabulary consistency. In: arXiv preprint arXiv:1606.08084 (2016)

Squicciarini, A., et al.: Identification and characterization of cyberbullying dynamics in an online social network. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 280–285 (2015)

10.

Aggarwal, A., et al.: Classification of fake news by fine-tuning deep bidirectional transformers based language model. EAI Endorsed Transactions on Scalable Information Systems Online First. EAI, Ghent (2020)

11.

Lee, J., et al.: BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2020)

12.

Sergio, G.C., Lee, M.: Stacked DeBERT: All Attention in Incomplete Data for Text Classification. In: arXiv preprint arXiv:2001.00137 (2020)

13.

Mozafari, M., Farahbakhsh, R., Crespi, N.: A BERT-based transfer learning approach for hate speech detection in online social media. International Conference on Complex Networks and Their Applications, pp. 928–940. Springer, Berlin (2019)

14.

Pavlopoulos, J., et al.: Convai at semeval-2019 task 6: Offensive language identification and categorization with perspective and bert. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 571–576 (2019)

15.

Waseem, Z., Hovy, D.: Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In: Proceedings of the NAACL student research workshop, pp. 88–93 (2016)

16.

Wulczyn, E., Thain, N., Dixon, L.: Ex machina: Personal attacks seen at scale. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1391–1399 (2017)

17.

Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRefMATH

18.

Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: arXiv preprint arXiv:1503.02531 (2015)

19.

Agrawal, S., Awekar, A.: Deep learning for detecting cyberbullying across multiple social media platforms. European Conference on Information Retrieval, pp. 141–153. Springer, Berlin (2018)

20.

Dietteric, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)CrossRef

21.

Nuzzo, R.: Scientific method: Statistical errors. Nat. News 5067487(487), 150 (2014)CrossRef

Titel: CyberBERT: BERT for cyberbullying identification
BERT for cyberbullying identification
verfasst von: Sayanta Paul
Sriparna Saha
Publikationsdatum: 11.11.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Multimedia Systems / Ausgabe 6/2022
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-020-00710-4

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