Choudhury, Najeefa NikhatNeogi, Parom GuhaFahim, Anjel HaidarKhan, FaisalKhan, Fahim KabirFaisal, Md. Fahim2025-01-202025-01-20©20242024-05ID 20101562ID 19101093ID 19101557ID 19101161http://hdl.handle.net/10361/25216Cataloged from PDF version of thesis.Includes bibliographical references (pages 46-48).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.As more people use social media, toxic language and cyberbullying become more common with the Bengali-speaking community particularly. The complexity of Bangla text data makes it difficult for traditional natural language processing (NLP) algorithms to identify harmful content. This study proposes a machine learningbased solution that recognizes and categorizes harmful language and “Cyberbullying in Bangla text on social media”, leveraging BanglaBERT’s advanced features. As more people use social media, toxic language and cyberbullying are on the rise, with the Bengali-speaking minority particularly vulnerable. The proposed machine learning-based solution achieved 94% testing accuracy in detecting and categorizing cyberbullying and offensive language on digital platforms that support Bengali.60 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.CyberbullyingDetectionInternetLanguageNLPCyberbullying.Computer crimes.Natural language processing (Computer science).Thesis