Rasel, Annajiat AlimMukta, Jannatun NoorAhmed, Md IstiaqAnoy, Shoumik MalakarNusrat, ElhamAhmed, Risat2023-03-222023-03-2220222022-05ID 18101139ID 18101191ID 18301265ID 18101713http://hdl.handle.net/10361/18001Cataloged from PDF version of thesis.Includes bibliographical references (pages 68-71).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Over the last decade, social media networks have become a vital platform for sharing and obtaining information. Nevertheless, one of the dark sides of this social media fever is rumors and fake news. Because misinformation has caused destruction around the globe, it can also be a powerful weapon in cyber warfare. Due to its easy accessibility in Bangladesh, cybercriminals and malicious forces target social media platforms to spread misinformation in the native Bangla language. However, there is no effective research or efficient tool to detect rumors in the Bangla language on social media. So, this research works on Bangla rumor detection using Machine Learning and Deep learning algorithms. This work explores different types of Machine Learning and Deep Learning techniques and relevant datasets to develop an effective technique that will help detect rumors from trending and sensitive Bangla social media posts, detect their authenticity, and efficiently provide an accurate result.71 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.RumorHoaxMisinformationFake newsClassification modelsNatural language processingCNNRNNArtificial intelligenceMachine learningCognitive learning theoryThesis