In our experiment, we used the publicly available Bangla text dataset (44,001 comments) and got the highest performance ever published works on it. The model achieved the most elevated accuracy rate of 98.57% and 98.82% in binary and multilabel classification to detect cyberbullying on social media in the Bengali language. Our best performance findings are more effective than any previous effort in identifying and categorizing bullying in the Bengali language. As a result, we might use our model to correctly classify Bengali bullying in online bullying detection systems, protecting people from being the targets of social bullying. https://www.sciencedirect.com/science/article/pii/S2949719123000249 Share this: Click to print (Opens in new window) Print Click to share on Facebook (Opens in new window) Facebook Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Reddit (Opens in new window) Reddit Click to share on WhatsApp (Opens in new window) WhatsApp Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Like this:Like Loading... Post navigation Review on Countering Extremism and Hate Speech. August 2023 (II/II) | Policyinstitute.net Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data (arXiv)