Abstract
As we know internet and social media usages are increasing day by day. Sometimes users take social media as a medium to use hateful and abusive comments that may rude and dis-respectful for others. So it is important to detect the toxicity and remove it from the social media. As social media users are in millions so it is impossible for filtering out the toxic comments manually, and hence there is a need for a method to filter out the toxic comments and make social media cleaner and safer to use. This paper aims to detect toxic comments in social media using cellular automata based LSTM (Long Short-Term Memory) model. Our approach produces 97.43% of F1_score without using any kind of pre-trained word embeddings or language models.
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References
Akash, G., Kumar, H., Bharathi, D.: Toxic comment classification using transformers. In: Proceedings of the \(11^{th}\) Annual International Conference on Industrial Engineering and Operations Management Singapore, pp. 1895–1905 (2021)
Beniwal, R., Maurya, A.: Toxic comment classification using hybrid deep learning model. In: Karuppusamy, P., Perikos, I., Shi, F., Nguyen, T.N. (eds.) Sustainable Communication Networks and Application. LNDECT, vol. 55, pp. 461–473. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-8677-4_38
Chu, T., Jue, K., Wang, M.: Comment abuse classification with deep learning. https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2762092
Georgakopoulos, S.V., Tasoulis, S.K., Vrahatis, A.G., Plagianakos, V.P.: Convolutional neural networks for toxic comment classification. In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence, pp. 1–6 (2018)
Jigsaw (2018). https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data
Kunupudi, D., Godbole, S., Kumar, P., Pai, S.: Toxic language detection using robust filters. SMU Data Sci. Rev. 3(2), 12 (2020)
von Neumann, J.: The Theory of Self-Reproducing Automata. Burks, A.W. (ed.) University of Illinois Press, Urbana, London (1966)
Vaidya, A., Mai, F., Ning, Y.: Empirical analysis of multi-task learning for reducing identity bias in toxic comment detection. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 683–693 (2020)
Wang, K., Yang, J., Wu, H.: A Survey of Toxic Comment Classification Methods. arXiv preprint arXiv:2112.06412 (2021)
Wolfram, S.: Theory and Applications of Cellular Automata. World Scientific, Singapore (1986)
Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on web 2.0. In: Proceedings of the Content Analysis in the WEB, vol. 2, pp. 1–7 (2009)
Zhao, Z., Zhang, Z., Hopfgartner, F.: A Comparative Study of Using Pre-trained Language Models for Toxic Comment Classification, pp. 500–507. Association for Computing Machinery, Inc. (2021). https://doi.org/10.1145/3442442.3452313
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Elizabeth, M.J., Parsotambhai, S.M., Hazari, R. (2022). Cellular Automata Enhanced Machine Learning Model for Toxic Text Classification. In: Chopard, B., Bandini, S., Dennunzio, A., Arabi Haddad, M. (eds) Cellular Automata. ACRI 2022. Lecture Notes in Computer Science, vol 13402. Springer, Cham. https://doi.org/10.1007/978-3-031-14926-9_31
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DOI: https://doi.org/10.1007/978-3-031-14926-9_31
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