Abstract
The increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making social media toxic. Homophobia and transphobia constitute offensive comments against LGBT + community. It becomes imperative to detect and handle these comments, to timely flag or issue a warning to users indulging in such behaviour. However, automated detection of such content is a challenging task, more so in Dravidian languages which are identified as low resource languages. Motivated by this, the paper attempts to explore applicability of different deep learning models for classification of the social media comments in Malayalam and Tamil languages as homophobic, transphobic and non-anti-LGBT + content. The popularly used deep learning models-Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) using GloVe embedding and transformer-based learning models (Multilingual BERT and IndicBERT) are applied to the classification problem. Results obtained show that IndicBERT outperforms the other implemented models, with obtained weighted average F1-score of 0.86 and 0.77 for Malayalam and Tamil, respectively. Therefore, the present work confirms higher performance of IndicBERT on the given task on selected Dravidian languages.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Upadhyay, I.S., Srivatsa, K.A., Mamidi, R.: Sammaan@ lt-edi-acl2022: ensembled transformers against Homophobia and Transphobia. In: Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pp. 270–275 (2022)
Nozza, D.: Nozza@ LT-EDI-ACL2022: ensemble modeling for homophobia and transphobia detection. In: Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pp. 258–264 (2022)
Bhandari, V., Goyal, P.: bitsa_nlp@ lt-edi-acl2022: leveraging pretrained language models for detecting homophobia and transphobia in Social Media Comments. arXiv preprint arXiv:2203.14267. (2022)
García-Díaz, J., Caparrós-Laiz, C., Valencia-García, R.: UMUTeam@ LT-EDI-ACL2022: Detecting homophobic and transphobic comments in Tamil. In: Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pp. 140–144 (2022)
Ashraf, N., Taha, M., Abd Elfattah, A., Nayel, H.: Nayel@ lt-edi-acl2022: homophobia/transphobia detection for Equality, Diversity, and Inclusion using Svm. In: Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pp. 287–290 (2022)
Maimaitituoheti, A.: ABLIMET@ LT-EDI-ACL2022: a RoBERTa based approach for homophobia/transphobia detection in social media. In: Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pp. 155–160 (2022)
Swaminathan, K., Bharathi, B., Gayathri, G.L., Sampath, H.:Ssncse_nlp@ lt-edi-acl2022: homophobia/transphobia detection in multiple languages using Svm classifiers and Bert-based Transformers. In: Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pp. 239–244 (2022)
Chakravarthi, B.R., et al. Dataset for identification of homophobia and transophobia in multilingual YouTube comments. arXiv preprint arXiv:2109.00227
Khan, S., et al.: HCovBi-caps: hate speech detection using convolutional and Bi-directional gated recurrent unit with Capsule network. IEEE Access 10, 7881–7894 (2022)
Khan, S., et al.: BiCHAT: BiLSTM with deep CNN and hierarchical attention for hate speech detection. J. King Saud Univ.-Comput. Inform. Sci. 34(7), 4335–4344 (2022)
Sap, M., Card, D., Gabriel, S., Choi, Y., Smith, N.A.: The risk of racial bias in hate speech detection. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 1668–1678 (2019)
Koufakou, A., Pamungkas, E. W., Basile, V., Patti, V.: HurtBERT: incorporating lexical features with BERT for the detection of abusive language. In: Fourth Workshop on Online Abuse and Harms, pp. 34–43. Association for Computational Linguistics (2020)
Susanty, M., Rahman, A.F., Normansyah, M.D., Irawan, A.: Offensive language detection using artificial neural network. In: 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), pp. 350–353. IEEE (2019)
Wiedemann, G., Ruppert, E., Jindal, R., Biemann, C.: Transfer learning from lda to bilstm-cnn for offensive language detection in twitter. arXiv preprint arXiv:1811.02906 (2018)
Cheng, L., Li, J., Silva, Y. N., Hall, D. L., Liu, H.: Xbully: Cyberbullying detection within a multi-modal context. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 339–347 (2019)
Balakrishnan, V., Khan, S., Fernandez, T., Arabnia, H.R.: Cyberbullying detection on twitter using Big Five and Dark Triad features. Personal. Individ. Differ. 141, 252–257 (2019)
Hani, J., Mohamed, N., Ahmed, M., Emad, Z., Amer, E., Ammar, M.: Social media cyberbullying detection using machine learning. Int. J. Adv. Comput. Sci. Appl. 10(5) (2019). https://doi.org/10.14569/IJACSA.2019.0100587
Kakwani, D., Kunchukuttan, A., Golla, S., Gokul, N. C., Bhattacharyya, A., Khapra, M. M., Kumar, P.: IndicNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4948–4961 (2020)
Devlin, J., Chang, M. W., Lee, K.,Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Acknowledgement
This work is supported by an extramural research grant by HPE Aruba Centre for Research in Information Systems at BHU (No. M-22-69 of BHU).
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 paper
Cite this paper
Sharma, D., Gupta, V., Singh, V.K. (2023). Detection of Homophobia & Transphobia in Malayalam and Tamil: Exploring Deep Learning Methods. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2022. Communications in Computer and Information Science, vol 1798. Springer, Cham. https://doi.org/10.1007/978-3-031-28183-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-28183-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28182-2
Online ISBN: 978-3-031-28183-9
eBook Packages: Computer ScienceComputer Science (R0)