Sadeque, Farig YousufKhondoker, AbdullahTaufik, Enam AhmedTashik, Md. Iftekhar IslamMahmud, S M Ishtiak2024-10-212024-10-21©20242024-06ID 20301065ID 20301398ID 20301078ID 20301071http://hdl.handle.net/10361/24358Cataloged from PDF version of thesis.Includes bibliographical references (pages 57-60).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.Communal violence is intensified by the widespread use of cyber hate, leading to aggression and increased conflicts among different religious, ethnic, and social groups, creating a barrier to social harmony. This research focuses on evaluating Bengali textual data sourced from Twitter and Reddit comments. The primary objective of this study is to enhance the accuracy of detecting communal violence-inciting speech. To achieve this, we employed and fine-tuned large language models, specifically the pre-trained BanglaBERT, aiming for a significant improvement over existing detection methodologies. Improving the detection of communal violent speech will help content moderation systems to effectively moderate and remove content linked to communal violence, thereby fostering communal peace in the Bengali-speaking regions.69 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.Bengali speechHate speechReligious groupsEthnic groupsCommunal violenceComputational analysisViolent speechMachine learningNatural language processingAutomatic speech recognition.Natural language processing (Computer science).Thesis