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STUDY ON MACHINE LEARNING AND DEEP LEARNING FOR FAKE NEWS DETECTION (FND) IN SOCIAL MEDIA (Conference Paper)
(Springer Science and Business Media Deutschland GmbH, 2025-02-25) Padmavathy L.; Radha N
Due to the risks associated with fake news, gathering fake information through social networks is difficult. As a result, it is now extremely difficult to evaluate fake news so that the producers can verify it through data processing media without misleading the public. There is a need for an automated technique for the detection because the news’s technical quality is in consideration. Studies currently in existence generally concentrate on using information extracted from the news content. This review’s objective is to use machine learning and deep learning techniques to clearly discuss FND on social media. (1) FND in social media by machine learning and (2) FND in social media by deep learning in data mining are the two main headings in this paper review. Lastly, the finest FND for effective results is deep learning. A Deep Neural Network (DNN) used by the best hyper-parameters has been introduced to categorize news and social context separately. FND dataset from BuzzFeed and PolitiFact by Kaggle repository has been used to validate the effectiveness of the proposed approach. Precision, recall, F-measure, and accuracy are some of the parameters applied to test the validity of the proposed model.