Rasel, Annajiat AlimSadeque, Farig YousufAhmed, IstiakPrima, Shanzida Binta AkramBaptee, Tahsin AnzumAfroz, MehrinShanto, Ariful Islam2023-09-242023-09-2420232023-04ID 21241066ID 19101174ID 22141041ID 21241078ID 19101369http://hdl.handle.net/10361/21171Cataloged from PDF version of thesis.Includes bibliographical references (pages 29-31).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.Nowadays, social networking sites like Facebook and Twitter have become an sig- nificant impact on our lives . We use such sites to remain in touch with one another and as a source of news to stay informed about current events. As a result, we frequently see news articles with click-bait headlines from various web portals that lack authenticity. The majority of these sites that share these sorts of links are used to manipulate people and spread false propaganda. We intended to utilize both traditional machine learning algorithms and deep learning algorithms on manually annotated data-sets to create effective approaches for spotting Bangla fake news on online media that included about 8,500 pieces of news data. In particular, in this project, we used classic machine learning algorithms for text classification such as “Naive Bayes Classifier”, “Support Vector Machines (SVM)”, “K-Nearest Neigh- bor (KNN)” as well as other classification-based algorithms such as “Decision Tree (DT)”, “Logistic Regression (LR)”, “Random Forest” and “AdaBoost”. We have also used deep learning models based on Feed-Forward Neural Networks such as “Convolutional Neural Network (CNN)” as well as a variety of Recurrent Neural Networks (RNN) such as “Long-Short Term Memory (LSTM)”, “Gated Recurrent Unit (GRU)” to detect fake news on online media. To conclude, our research focused on developing precise strategies for spotting fake news on social media sites.31 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.Conventional machine learning modelsDeep learning modelsClassic machine learning algorithmsFeed-forward neural networksRecurrent Neural Networks (RNN)Machine learningCognitive learning theoryNeural networks (Computer science)Thesis