When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People communicate with others on social media through messages and comments. So bullies use social media as a rich environment to bully others, especially on political issues. Fights over Cyberbullying on political and social media posts are common today. Most of the time, it does a lot of damage. However, few works have been done for monitoring Bangla text on social media & no work has been done yet for detecting the bullying Bangla text on political issues due to the lack of annotated corpora and morphologic analyzers. In this work, we used several machine learning classifiers & a model. That will help to detect the Bangla bullying texts on social media. For this work, 11,000 Bangla texts have been collected from the comments section of political Facebook posts to make a new dataset and labelled the data as either bullied or not. This dataset has been used to train the machine learning classifier. The results indicate that Random Forest achieves superior accuracy of 91.08%.
KeywordsCyberbullyingBangla TextsPolitical IssuesMachine LearningRandom ForestSocial Media
Rice, E., et al. (2015) Cyberbullying Perpetration and Victimization among Middle-School Students. AJPH, Washington DC, e66-e72. https://doi.org/10.2105/AJPH.2014.302393
Harsh, Liu, H., Li, J.D., et al. (2017) Sentiment Informed Cyberbullying Detection in Social Media. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Cham, 52-67.
Xue, Z., Hong, L., Yin, D. and Davison, B.D. (2009) Detection of Harassment on Web 2.0., in 1st Content Analysis in Web 2.0 (CAW 2.0), Madrid, Spain.
Huang, Q.J., Singh, V.K. and Atrey, P.K. (2014) Cyberbullying Detection Using Social and Textual Analysis. Proceedings of the 3rd International Workshop on Socially-Aware Multimedia, Orlando, 7 November 2014, 3-6. https://doi.org/10.1145/2661126.2661133
Wahbeh, Al-Kabi, M. and Abdullah, H. (2012) Comparative Assessment of the Performance of Three WEKA Text Classifiers Applied to Arabic Text. Abhath Al-Yarmouk (Basic Sciences and Engineering), 21, 15-28.
Ding, S., Zhu, H., Liu, X.-L. and Zhang, L. (2010) An Overview on Semi-Supervised Support Vector Machine. Neural Computing and Applications, 28, 969-978. https://doi.org/10.1007/s00521-015-2113-7
Liu, Z.J., et al. (2010) Study on SVM Compared with the Other Text Classification Methods. 2010 2nd International Workshop on Education Technology and Computer Science (ETCS), Wuhan, 6-7 March 2010, 219-222.
Gogoi, M. and Sarma, S.K. (2015) Document Classification of Assamese Text Using Naïve Bayes Approach. International Journal of Computer Trends and Technology, 30, 1-5.
Rajan, K., Ramalingam, V., Palaniappan, B., Ganesan, M. and Palanivel, S. (2009) Automatic Classification of Tamil Documents Using Vector Space Model and Artificial Neural Network. Expert System with Applications, 36, 10914-10918. https://doi.org/10.1016/j.eswa.2009.02.010
Nandhini, B. and Sheeba, J.I. (2015) Cyberbullying Detection and Classification Using Information Retrieval Algorithm. Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015), Unnao, 6-7 March 2015, 20. https://doi.org/10.1145/2743065.2743085
Murnion, S., Buchanan, W.J., Smales, A. and Russell, G. (2018) Machine Learning and Semantic Analysis of In-Game Chat for Cyberbullying. Computers & Security, 76, 197-213. https://doi.org/10.1016/j.cose.2018.02.016
Romsaiyud, W., Nakornphanom, K., Prasertsilp, P., Konglerd, P. and Nurarak, P. (2017) Automated Cyberbullying Detection Using Clustering Appearance Patterns. 2017 9th International Conference on Knowledge and Smart Technology (KST), Chonburi, 1-4 February 2017, 242-247. https://doi.org/10.1109/KST.2017.7886127
Noviantho, Ashianti, L. and Isa, S.M. (2017) Cyberbullying Classification Using Text Mining. 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), Semarang, 15-16 November 2017, 241-246. https://doi.org/10.1109/ICICOS.2017.8276369
Chamoun, M., Serhrouchni, A. and Haidar, B. (2017) A Multilingual System for Cyberbullying Detection: Arabic Content Detection Using Machine Learning. Advances in Science, Technology and Engineering Systems Journal, 2, 275-284. https://doi.org/10.25046/aj020634
Zhang, X., Tong, J., Vishwamitra, N., Dillon, E., Macbeth, J., Hu, H.X., Whittaker, E., Mazer, J.P. and Kowalski, R. (2016) Cyberbullying Detection with a Pronunciation-Based Convolutional Neural Network. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, 18-20 December 2016, 740-745. https://doi.org/10.1109/ICMLA.2016.0132
De Jong, F. and Dadvar, M. (2012) Cyberbullying Detection: A Step toward a Safer Internet Yard. Proceedings of the 21st International Conference on World Wide Web, Lyon, 16-20 April 2012, 121-126.
Dadvar, M., Trieschnigg, D., De Jong, F. and Ordelman, R. (2012) Improved Cyberbullying Detection Using Gender Information. Proceedings of the 12th Dutch-Belgian Information Retrieval Workshop (DIR 2012), Ghent, 24 February 2012, 23-25.
Dadvar, M., Trieschnigg, D., De Jong, F. and Ordelman, R. (2013) Improving Cyberbullying Detection with User Context. In: European Conference on Information Retrieval, Springer, Berlin, 693-696. https://doi.org/10.1007/978-3-642-36973-5_62
De Jong, F. and Dadvar, M. (2014) Experts and Machines against Bullies: A Hybrid Approach to Detecting Cyberbullies. In: Canadian Conference on Artificial Intelligence, Springer, Berlin, 275-281. https://doi.org/10.1007/978-3-319-06483-3_25
Ahmed, M.T., Rahman, M., Nur, S., Islam, A.Z.M.T. and Das, D. (2022) Natural Language Processing and Machine Learning Based Cyberbullying Detection for Bangla and Romanized Bangla Texts. TELKOMNIKA Telecommunication Computing Electronics and Control, 20, 89-97. https://doi.org/10.12928/telkomnika.v20i1.18630
Ahmed, M.T., Rahman, M., Nur, S., Islam, A., Islam, M.T. and Das, D. (2021) Deployment of Machine Learning and Deep Learning Algorithms in Detecting Cyberbullying in Bangla and Romanized Bangla Text: A Comparative Study. 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, 19-20 February 2021, 1-10. https://doi.org/10.1109/ICAECT49130.2021.9392608
Ahmed, M.T., Rahman, M., Nur, S., Islam, A.M.T. and Das, D. (2021) Introduction of PMI-So Integrated with Predictive and Lexicon Based Features to Detect Cyberbullying in Bangle Text Using Machine Learning. Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications, Jaipur, 27-28 March 2021, 685-697. https://doi.org/10.1007/978-981-16-6332-1_56
Shil, P., Saima, U., Rahman, R.M. and Islam, M.S. (2021) An Approach for Detecting Bangla Spam Comments on Facebook. 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), Khulna, 14-16 September 2021, 1-4. https://doi.org/10.1109/ICECIT54077.2021.9641358
Akther, A., Acharjee, U.K., Talukder, M.A., Islam, M. and Uddin, M.A. (2023) A Robust Hybrid Machine Learning Model for Bengali Cyber Bullying Detection in Social Media. https://doi.org/10.31224/3124
Priya and Gupta, S. (2022) Identification of Political Hate Speech Using Machine Learning-Based Text Toxicity Analysis. In: Tuba, M., Akashe, S. and Joshi, A., Eds., ICT Systems and Sustainability, Springer, Berlin, 217-236. https://doi.org/10.1007/978-981-19-5221-0_22
Kompally, P., Chakkarvarthy, S., Walczak, S. and Johnson, S. (2021) MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content. Applied Science, 11, Article No. 8701. https://doi.org/10.3390/app11188701
Wright, R.E. (1995) Logistic Regression. American Psychological Association, Washington DC, 217-244.
Hossain, M.I., Rahman, M., Ahmed, T. and Touhidul Islam, A.Z.M. (2021) Forecast the Rating of Online Products from Customer Text Review Based on Machine Learning Algorithms. International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, 27-28 February 2021, 6-10. https://doi.org/10.1109/ICICT4SD50815.2021.9396822
Xu, S., Li, Y. and Wang, Z. (2017) Bayesian Multinomial Naïve Bayes Classifier to Text Classification. In: Park, J.J., Chen, S.-C. and Choo, K.-K.R., Eds., Advanced Multimedia and Ubiquitous Engineering, Springer, Berlin, 347-352. https://doi.org/10.1007/978-981-10-5041-1_57
Wu, Q. and Zhou, D.-X. (2006) Analysis of Support Vector Machine Classification. Journal of Computational Analysis & Applications, 8, 99-119.