2021/06/08 by Md. Faisal Ahmed, Md Faisal Ahmed, Zalish Mahmud +11
Computer Science · Psychology · #Advanced Malware Detection Techniques #Bullying, Victimization, and Aggression #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Machine Learning (cs.LG) #Social and Information Networks (cs.SI) #cs.CL #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2106.04506
9 pages, 9 figures, 3 tables
arxiv created 2021/06/08 · openalex publication_date 2021/06/08 · arxiv updated 2021/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Cyberbullying or Online harassment detection on social media for various major languages is currently being given a good amount of focus by researchers worldwide. Being the seventh most speaking language in the world and increasing usage of online platform among the Bengali speaking people urge to find effective detection technique to handle the online harassment. In this paper, we have proposed binary and multiclass classification model using hybrid neural network for bully expression detection in Bengali language. We have used 44,001 users comments from popular public Facebook pages, which fall into five classes - Non-bully, Sexual, Threat, Troll and Religious. We have examined the performance of our proposed models from different perspective. Our binary classification model gives 87.91% accuracy, whereas introducing ensemble technique after neural network for multiclass classification, we got 85% accuracy.