Enhancing Machine Learning Models in Identifying False Information
Abstract
In today's age, the dissemination of inaccurate and misleading facts on digital systems has ended up a serious problem that poses risks to people, organizations and society as a whole. Fake news spreads quicker than true information due to its bizarre and compelling content material. Fake information may have critical results, undermining the democratic system, undermining belief in the media and growing further divisions. Because of its fast spread and impact on the general public, it's far crucial to have a powerful manner to detect false information. In this paper, we pay attention to the usage of machine learning algorithms which includes SVM, Naive Bayes, and logistic regression, in addition to acquiring knowledge through deep learning models, which includes LSTM & Bi-LSTM, as well as utilising transfer learning with BERT-based approaches. Furthermore, the performance of these models will be evaluated on datasets comprising both Hindi and English languages.
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