Ashraf, Faisal BinManzoor, TahbibAraf, Md. Wahidur RahmanOmi, Monjurul SharkerAbir, Arpan DasAbir, Tanvir Ahmed2024-06-272024-06-2720222022-05ID 17101147ID 17101404ID 18301017ID 18101526ID 18301060http://hdl.handle.net/10361/23620Cataloged from PDF version of thesis.Includes bibliographical references (pages 48-49).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Social media and sharing platforms are improving in tandem with the internet. Although, humanity has bene ted from these platforms in a variety of ways. However, many people were faced with a variety of obstacles and con icts while using social media. As a result, the usage of derogatory language and hate speech has skyrocketed, posing a major threat. As a result, many varieties of machine language have been developed to overcome this challenge. Hate speech is de ned as the use of slang or insulting phrases directed against a speci c person, race, or any religion. Hate speech and other hate content can be detected using the suggested methodology on platforms like Facebook. Our goal is to develop a model that can identify such actions with more precision so that our current and future generations are not subjected to this scourge.49 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.Social mediaHate speechDetectionMethodologyMachine learningThesis