Abstract
The rise of hate speech contents on social network platforms has recently become a topic of interest. There have been a lot of studies to develop systems that can automatically detect hate speech contents. In this paper, we propose a knowledge-rich solution to hate speech detection by incorporating hate speech embeddings to generate a more accurate representation of the given text. To obtain the hate speech embeddings, we construct a hate speech dictionary in a semi-supervised fashion. We conduct experiments on two popular datasets, which show that the combination of word embeddings and hate speech embeddings can produce promising results when compared with the methods that employ large-scale pre-trained language models.
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References
Warner, W., Hirschberg, J.: Detecting hate speech on the world wide web. In: Proceedings of the Second Workshop on Language in Social Media, pp. 19–26 (2012)
Davidson, T., Warmsley, D., Macy, M.W., Weber, I.: Automated hate speech detection and the problem of offensive language. In: ICWSM, pp. 512–515. AAAI Press (2017)
Gao, L., Huang, R.: Detecting online hate speech using context aware models. In: Mitkov, R., Angelova, G. (eds): Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, Varna, Bulgaria, 2–8 September 2017, pp. 260–266. INCOMA Ltd (2017)
de Gibert, O., Pérez, N., Pablos, A.G., Cuadros, M.: Hate speech dataset from a white supremacy forum. In: Fiser, D., Huang, R., Prabhakaran, V., Voigt, R., Waseem, Z., Wernimont, J., (eds.) Proceedings of the 2nd Workshop on Abusive Language Online, ALW@EMNLP 2018, Brussels, Belgium, 31 October 2018, pp. 11–20. Association for Computational Linguistics (2018)
Qian, J., Bethke, A., Liu, Y., Belding, E.M., Wang, W.Y.: A benchmark dataset for learning to intervene in online hate speech. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3–7 November 2019, pp. 4754–4763. Association for Computational Linguistics (2019)
Kovács, G., Alonso, P., Saini, R.: Challenges of hate speech detection in social media. SN Comput. Sci. 2(2), 1–15 (2021)
Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, 2–7 February 2018, pp. 5876–5883. AAAI Press (2018)
Founta, A.M., et al.: Large scale crowdsourcing and characterization of twitter abusive behavior. In: ICWSM, pp. 491–500. AAAI Press (2018)
Mandl, T., et al.: Overview of the HASOC track at FIRE 2019: hate speech and offensive content identification in Indo-European languages. In: Majumder, P., Mitra, M., Gangopadhyay, S., Mehta, P., eds.: FIRE 2019: Forum for Information Retrieval Evaluation, Kolkata, India, December 2019, pp. 14–17. ACM (2019)
Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In: SRW@HLT-NAACL, The Association for Computational Linguistics, pp. 88–93 (2016)
Gambäck, B., Sikdar, U.K.: Using convolutional neural networks to classify hate-speech. In: Waseem, Z., Chung, W.H.K., Hovy, D., Tetreault, J.R. (eds.) Proceedings of the First Workshop on Abusive Language Online, ALW@ACL 2017, Vancouver, BC, Canada, 4 August 2017, pp. 85–90. Association for Computational Linguistics (2017)
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., Kumar, R.: Predicting the type and target of offensive posts in social media. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, Volume 1 (Long and Short Papers), pp. 1415–1420. Association for Computational Linguistics (2019)
Nguyen, D.Q., Vu, T., Nguyen, A.T.: BERTweet: a pre-trained language model for English Tweets. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 9–14 (2020)
Wang, B., Ding, Y., Liu, S., Zhou, X.: YNU_Wb at HASOC 2019: ordered neurons LSTM with attention for identifying hate speech and offensive language. In: Mehta, P., Rosso, P., Majumder, P., Mitra, M. (eds.) Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation, Kolkata, India, 12–15 December 2019, Volume 2517 of CEUR Workshop Proceedings, pp. 191–198. CEUR-WS.org (2019)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019)
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Duong, P.H., Chung, C.C., Vo, L.T., Nguyen, H.T., Ngo, D. (2021). Detecting Hate Speech Contents Using Embedding Models. In: Mohaisen, D., Jin, R. (eds) Computational Data and Social Networks. CSoNet 2021. Lecture Notes in Computer Science(), vol 13116. Springer, Cham. https://doi.org/10.1007/978-3-030-91434-9_13
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