Abstract
Secretary-General António Guterres launched the United Nations Strategy and Plan of Action on Hate Speech in 2019, recognizing the alarming trend of increasing hate speech worldwide. Despite extensive research, benchmark datasets for hate speech detection remain limited in volume and vary in domain and annotation. In this paper, the following research objectives are deliberated (a) performance comparisons between multi-task models against single-task models; (b) performance study of different multi-task models (fully shared, shared-private) for hate speech detection, considering individual dataset as a separate task; (c) what is the effect of using different combinations of available existing datasets in the performance of multi-task settings? A total of six datasets that contain offensive and hate speech on the accounts of race, sex, and religion are considered for the above study. Our analysis suggests that a proper combination of datasets in a multi-task setting can overcome data scarcity and develop a unified framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
References
Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 759–760 (2017)
Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 11, pp. 512–515 (2017)
Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: 2011 Proceedings of the International Conference on Weblog and Social Media. Citeseer (2011)
Do, H.T.T., Huynh, H.D., Van Nguyen, K., Nguyen, N.L.T., Nguyen, A.G.T.: Hate speech detection on Vietnamese social media text using the bidirectional-LSTM model. arXiv preprint arXiv:1911.03648 (2019)
Fortuna, P., Bonavita, I., Nunes, S.: Merging datasets for hate speech classification in Italian. In: EVALITA@ CLiC-it (2018)
Fortuna, P., Nunes, S.: A survey on automatic detection of hate speech in text. ACM Comput. Surv. (CSUR) 51(4), 1–30 (2018)
de Gibert, O., Perez, N., García-Pablos, A., Cuadros, M.: Hate speech dataset from a white supremacy forum. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), Brussels, Belgium, October 2018, pp. 11–20. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/W18-5102. https://www.aclweb.org/anthology/W18-5102
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. arXiv preprint arXiv:1802.06893 (2018)
Maity, K., Saha, S.: BERT-capsule model for cyberbullying detection in code-mixed Indian languages. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds.) NLDB 2021. LNCS, vol. 12801, pp. 147–155. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80599-9_13
Maity, K., Saha, S., Bhattacharyya, P.: Emoji, sentiment and emotion aided cyberbullying detection in Hinglish. IEEE Trans. Comput. Soc. Syst. 10, 2411–2420 (2022)
Malik, J.S., Pang, G., van den Hengel, A.: Deep learning for hate speech detection: a comparative study. arXiv preprint arXiv:2202.09517 (2022)
Mandl, T., et al.: Overview of the HASOC track at FIRE 2019: hate speech and offensive content identification in Indo-European languages. In: Proceedings of the 11th Forum for Information Retrieval Evaluation, pp. 14–17 (2019)
Mehdad, Y., Tetreault, J.: Do characters abuse more than words? In: Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 299–303 (2016)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Nockleby, J.T.: Hate speech in context: the case of verbal threats. Buff. L. Rev. 42, 653 (1994)
i Orts, Ò.G.: Multilingual detection of hate speech against immigrants and women in Twitter at SemEval-2019 task 5: frequency analysis interpolation for hate in speech detection. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 460–463 (2019)
Paul, S., Saha, S.: CyberBERT: BERT for cyberbullying identification. Multimed. Syst. 28, 1897–1904 (2020)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Preoţiuc-Pietro, D., Liu, Y., Hopkins, D., Ungar, L.: Beyond binary labels: political ideology prediction of Twitter users. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 729–740 (2017)
Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. In: 2011 10th International Conference on Machine Learning and Applications and Workshops, vol. 2, pp. 241–244. IEEE (2011)
Rizoiu, M.A., Wang, T., Ferraro, G., Suominen, H.: Transfer learning for hate speech detection in social media. arXiv preprint arXiv:1906.03829 (2019)
Simanjuntak, D.A., Ipung, H.P., Nugroho, A.S., et al.: Text classification techniques used to faciliate cyber terrorism investigation. In: 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 198–200. IEEE (2010)
Talat, Z., Thorne, J., Bingel, J.: Bridging the gaps: multi task learning for domain transfer of hate speech detection. In: Golbeck, J. (ed.) Online Harassment. HIS, pp. 29–55. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78583-7_3
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, San Diego, California, June 2016, pp. 88–93. Association for Computational Linguistics (2016). http://www.aclweb.org/anthology/N16-2013
Watanabe, H., Bouazizi, M., Ohtsuki, T.: Hate speech on Twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6, 13825–13835 (2018)
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., Kumar, R.: SemEval-2019 task 6: identifying and categorizing offensive language in social media (OffensEval). arXiv preprint arXiv:1903.08983 (2019)
Zimmerman, S., Kruschwitz, U., Fox, C.: Improving hate speech detection with deep learning ensembles. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018 (2018)
Acknowledgements
The Authors would like to acknowledge the support of Ministry of Home Affairs (MHA), India, for conducting this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Maity, K., Balaji, G., Saha, S. (2024). Towards Analyzing the Efficacy of Multi-task Learning in Hate Speech Detection. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14452. Springer, Singapore. https://doi.org/10.1007/978-981-99-8076-5_23
Download citation
DOI: https://doi.org/10.1007/978-981-99-8076-5_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8075-8
Online ISBN: 978-981-99-8076-5
eBook Packages: Computer ScienceComputer Science (R0)