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

has been cited by the following article:

• TITLE: Social Media Cyberbullying Detection on Political Violence from Bangla Texts Using Machine Learning Algorithm

  AUTHORS: Md. Tofael Ahmed, Almas Hossain Antar, Maqsudur Rahman, Abu Zafor Muhammad Touhidul Islam, Dipankar Das, Md. Golam Rashed

  KEYWORDS: Cyberbullying, Bangla Texts, Political Issues, Machine Learning, Random Forest, Social Media

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.15 No.4, November 6, 2023

  ABSTRACT: 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%.