Abstract
Any statement that is vituperative towards an individual or a group based on their traits like race, ethnicity, gender, sexual orientation, color, religion, nationality, or another attribute is described as hate speech. Hate speech and bullying, spreading uncontrolled might undermine society’s peace and harmony, becoming a societal issue. Especially when hate speech is used to hurt people or to hurt the respect of individuals, groups, or countries. This complicates the task since social media posts contain paralinguistic tools (e.g., emoticons and hash tags) and a lot of poor quality written text that does not follow grammatical norms. With the recent advancements in NLP, it is possible to analyze unstructured composite natural language content. The chapter first focuses on discussing various deep learning architectures such as DCNNs, Bi- LSTMs, Transformers and models like BERT and how they are applied in identifying hate speech in social media. The chapter examines the capacity of deep learning algorithms to capture hate speech on public media systematically. The chapter also reviews the accuracy of models on publicly available standard datasets. The findings of this study pave the way for more research into the discovery of spontaneous abusive conduct on social media in the future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
AI advances to better detect hate speech, ML Applications, Integrity, Meta AI: https://ai.facebook.com/blog/ai-advances-to-better-detect-hate-speech. Accessed 16 Jan 2022
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)
Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: NAACL-HLT, pp. 88–93 (2016)
Zhang, Z., Robinson, D., Tepper, J.: Detecting hate speech on twitter using a convolution-gru based deep neural network. In: European Semantic Web Conference, pp. 745–760. Springer, Cham (2018)
Cao, R., Lee, R.K.-W., Hoang, T.-A.: DeepHate: Hate Speech Detection via multi-faceted text representations. In: 12th ACM Conference on Web Science (2020). https://doi.org/10.1145/3394231.3397890
Ribeiro, A., Silva, N.: INF-HatEval at SemEval-2019 Task 5: convolutional neural networks for hate speech detection against women and immigrants on twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 420–425 (2019)
Manolescu, M., Löfflad, D., Saber, A.N.M., Tari, M.M.: TuEval at SemEval-2019 Task 5: LSTM approach to hate speech detection in English and Spanish. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 498–502 (2019)
Alonso, P., Saini, R., Kovács, G.: The North at HASOC 2019: hate speech detection in social media data. In: FIRE (Working Notes), pp. 293–299 (2019)
Ghosh, S., Chaki, A., Kudeshia, A.: Cyberbully detection using 1D-CNN and LSTM. In: Proceedings of International Conference on Communication, Circuits, and Systems, pp. 295–301. Springer, Singapore (2021)
Roy, P.K., Tripathy, A.K., Das, T.K., Gao, X.Z.: A framework for hate speech detection using deep convolutional neural network. IEEE Access 8, 204951–204962 (2020)
d’Sa, A.G., Illina, I., Fohr, D.: Classification of hate speech using deep neural networks. Rev. d’Information Sci. Tech. 25(01) 2020
Devlin, J., Chang, M., Lee, K., et al.: BERT: pre-training of deep bidirectional transformers for language understanding (2018). CoRR arXiv:abs/1810.04805
Mozafari, M., Farahbakhsh, R., Crespi, N.: A BERT-based transfer learning approach for hate speech detection in online social media, arXiv:1910.12574v1 [cs.SI] , 28 Oct 2019 hate speech detection in online social media, arXiv:1910.12574v1 [cs.SI] . Accessed 28 Oct 2019
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. Association for Computational Linguistics, San Diego, California (2016). https://doi.org/10.18653/v1/N16-2013
Davidson, T., Warmsley, D., Macy, M.W., et al.: Automated hate speech detection and the problem of offensive language (2017). CoRR arXiv:abs/1703.04009
Waseem, Z., Thorne, J., Bingel, J.: Bridging the Gaps: Multi Task Learning for Domain Transfer of Hate Speech Detection, pp. 29–55. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-78583-7_3
Bhatia, B., Verma, A., Katarya, R.: Analysing cyberbullying using natural language processing by understanding jargon in social media. arXiv:2107.08902 (2021)
Founta, A.M. et al.: Large scale crowdsourcing and characterization of twitter abusive behavior. In: Twelfth International AAAI Conference on Web and Social Media (2018)
Saravanaraj, A., Sheeba, J.I., Pradeep Devaneyan, S.: Automatic detection of cyberbullying from twitter. Int. J. Comput. Sci. Inf. Technol. Secur. (IJCSITS) (2016)
Waseem, Z., Dirk, H.: Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: Proceedings of the NAACL Student Research Workshop (2016)
Raisi, E., Huang, B.: Cyberbullying identification using participant-vocabulary consistency (2016). arXiv:1606.08084
Mozafari, M., Farahbakhsh, R., Crespi, N.: A BERT-based transfer learning approach for hate speech detection in online social media. In: International Conference on Complex Networks and Their Applications. Springer, Cham (2019)
Zahiri, S.M., Ahmadvand, A.: Crab: Class representation attentive BERT for hate speech identification in social media (2020). arXiv:2010.13028
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Gudumotu, C.E., Nukala, S.R., Reddy, K., Konduri, A., Gireesh, C. (2023). A Survey on Deep Learning Models to Detect Hate Speech and Bullying in Social Media. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-12419-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12418-1
Online ISBN: 978-3-031-12419-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)