AI-Powered Cyberbullying Detection in Indian Languages: An Ensemble Approach With XAI

Manav Shah

Vellore Institute of Technology, Vellore

Dr. Ranjithkumar S

Professor, Vellore Institute of Technology, Vellore

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http://doi.org/10.37648/ijrst.v16i01.007

Abstract

References

1. Muneer, A., Alwadain, A., Ragab, M. G., & Alqushaibi, A. (2023). Cyberbullying detection on social media using stacking ensemble learning and enhanced BERT. Information, 14(8), 467. https://doi.org/10.3390/info14080467
2. López-Vizcaíno, M. F., Nóvoa, F. J., Carneiro, V., & Cacheda, F. (2021). Early detection of cyberbullying on social media networks. Future Generation Computer Systems, 118, 219–229. https://doi.org/10.1016/j.future.2021.01.006
3. Jain, V., Kumar, V., Pal, V., & Vishwakarma, D. K. (2021, April). Detection of cyberbullying on social media using machine learning. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1091–1096). IEEE. https://doi.org/10.1109/ICCMC51019.2021.9417777
4. Sultan, T., Jahan, N., Basak, R., Jony, M. S. A., & Nabil, R. H. (2023). Machine learning in cyberbullying detection from social-media image or screenshot with optical character recognition. International Journal of Intelligent Systems and Applications (IJISA), 15(2), 1–13. https://doi.org/10.5815/ijisa.2023.02.01
5. Alduailaj, A. M., & Belghith, A. (2023). Detecting Arabic cyberbullying tweets using machine learning. Machine Learning and Knowledge Extraction, 5(1), 29–42. https://doi.org/10.3390/make5010018
6. Obaid, M. H., Guirguis, S. K., & Elkaffas, S. M. (2023). Cyberbullying detection and severity determination model. IEEE Access, 11, 110723–110735. https://doi.org/10.1109/ACCESS.2023.3321237
7. Xingyi, G., & Adnan, H. (2024). Potential cyberbullying detection in social media platforms based on a multi-task learning framework. International Journal of Data and Network Science, 8(1), 25–34. https://doi.org/10.5267/j.ijdns.2023.10.021
8. Fati, S. M., Muneer, A., Alwadain, A., & Balogun, A. O. (2023). Cyberbullying detection on Twitter using deep learning-based attention mechanisms and continuous bag of words feature extraction. Mathematics, 11(16), 3567. https://doi.org/10.3390/math11163567
9. Ahmed, M. T., Akter, N., Rahman, M., Das, D., Azm, T., & Rashed, G. (2023). Multimodal cyberbullying meme detection from social media using deep learning approach. International Journal of Computer Science and Information Technology (IJCSIT), 15(4), 27–37. https://doi.org/10.5121/ijcsit.2023.15403
10. Rani, M. U., Ramesh, M. A., Srinivas, M. G., Ganesh, M. S., & Lakshmi, M. D. V. (2023). Detection of cyberbullying on social media. Journal of Engineering Sciences, 14(4), 1–15. https://doi.org/10.36897/jes.2023.14.04.01
11. Awate, V., Bagad, V., Jadhav, S., & Jadhao, B. (2023). Detection of cyberbullying on social media using machine learning. Advancement in Image Processing and Pattern Recognition, 6(2), 6–12.
12. Muhariya, A., Riadi, I., Prayudi, Y., & Saputro, I. A. (2023). Utilizing K-means clustering for the detection of cyberbullying within Instagram comments. Ingénierie des Systèmes d’Information, 28(4).
13. Huang, H., & Qi, D. (2023). Cyberbullying detection on social media. Higher Education and Oriental Studies, 3(1).
14. Balakrishnan, V., & Ng, S. K. (2023). Personality and emotion based cyberbullying detection on YouTube using ensemble classifiers. Behaviour and Information Technology, 42(13), 2296–2307. https://doi.org/10.1080/0144929X.2022.2079756
15. Nahar, K. M., Alauthman, M., Yonbawi, S., & Almomani, A. (2023). Cyberbullying detection and recognition with type determination based on machine learning. Computers, Materials & Continua, 75(3).
16. Dewani, A., Memon, M. A., Bhatti, S., Sulaiman, A., Hamdi, M., Alshahrani, H., Alghamdi, A., & Shaikh, A. (2023). Detection of cyberbullying patterns in low resource colloquial Roman Urdu microtext using natural language processing, machine learning, and ensemble techniques. Applied Sciences, 13(4), 2062. https://doi.org/10.3390/app13042062
17. Mehta, H., & Passi, K. (2022). Social media hate speech detection using explainable artificial intelligence (XAI). Algorithms, 15(8), 291. https://doi.org/10.3390/a15080291
18. Neelakandan, S., Sridevi, M., Chandrasekaran, S., Murugeswari, K., Pundir, A. K. S., Sridevi, R., & Bheema, T. (2022). Deep learning approaches for cyberbullying detection and classification on social media.
19. Ejaz, N., Razi, F., & Choudhury, S. (2024). Towards comprehensive cyberbullying detection: A dataset incorporating aggressive texts, repetition, peerness, and intent to harm. Computers in Human Behavior, 153, 108123. https://doi.org/10.1016/j.chb.2023.108123
20. Sultan, D., Omarov, B., Kozhamkulova, Z., Kazbekova, G., Alimzhanova, L., Dautbayeva, A., Zholdassov, Y., & Abdrakhmanov, R. (2023). Review of machine learning techniques in cyberbullying detection.
21. Roy, P. K., & Mali, F. U. (2022). Cyberbullying detection using deep transfer learning. Complex & Intelligent Systems, 8, 5449–5467. https://doi.org/10.1007/s40747-022-00756-6
22. Luo, Y., Zhang, X., Hua, J., & Shen, W. (2021). Multi-featured cyberbullying detection based on deep learning. In 2021 16th International Conference on Computer Science and Education (ICCSE) (pp. 746– 751). IEEE. https://doi.org/10.1109/ICCSE52927.2021.9548577
23. Alam, K. S., Bhowmik, S., & Prosun, P. R. K. (2021). Cyberbullying detection: An ensemble-based machine learning approach. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 710–715). IEEE. https://doi.org/10.1109/ICICV52345.2021.9515567
24. Dewani, A., Memon, M. A., & Bhatti, S. (2021). Cyberbullying detection: Advanced preprocessing techniques and deep learning architecture for Roman Urdu data. Journal of Big Data, 8(1), 1–20. https://doi.org/10.1186/s40537-021-00504-3
25. Mahat, M. (2021). Detecting cyberbullying across multiple social media platforms using deep learning. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 299–301). IEEE. https://doi.org/10.1109/ICACITE51222.2021.9538727
26. Jain, N., Hegde, A., Jain, A., Joshi, A., & Madake, J. (2021). Pseudo-conventional approach for cyberbullying and hate-speech detection. In 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) (pp. 1–8). IEEE. https://doi.org/10.1109/ICAC353735.2021.9591367
27. Yadav, Y., Bajaj, P., Gupta, R. K., & Sinha, R. (2021). A comparative study of deep learning methods for hate speech and offensive language detection in textual data. In 2021 IEEE 18th India Council International Conference (INDICON) (pp. 1–6). IEEE. https://doi.org/10.1109/INDICON52577.2021.9701347
28. Lee, E., Rustam, F., Washington, P. B., El Barakaz, F., Aljedaani, W., & Ashraf, I. (2022). Racism detection by analysing differential opinions through sentiment analysis of tweets using stacked ensemble GCRNN model. IEEE Access, 10, 9717–9728. https://doi.org/10.1109/ACCESS.2022.3143125
29. Berrimi, M., Moussaoui, A., Oussalah, M., & Saidi, M. (2020). Attention based networks for analysing inappropriate speech in Arabic text. In 2020 4th International Symposium on Informatics and Its Applications (ISIA) (pp. 1–6). IEEE. https://doi.org/10.1109/ISIA50717.2020.9311357
30. Dubey, K., Nair, R., Khan, M. U., & Shaikh, S. (2020). Toxic comment detection using LSTM. In 2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) (pp. 1– 8). IEEE. https://doi.org/10.1109/ICAECC48674.2020.9312322
31. d’Sa, A. G., Illina, I., & Fohr, D. (2020). BERT and fasttext embeddings for automatic detection of toxic speech. In 2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA) (pp. 1–5). IEEE. https://doi.org/10.1109/OCTA51217.2020.9312707
32. Yuvaraj, N., Chang, V., Gobinathan, B., Pinagapani, A., Kannan, S., Dhiman, G., & Rajan, A. R. (2021). Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification. Computers & Electrical Engineering, 92, 107186. https://doi.org/10.1016/j.compeleceng.2021.107186

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