Detecting Untrue Information On Social Media Using Machine Learning
European Journal of Molecular & Clinical Medicine,
2023, Volume 10, Issue 4, Pages 1266-1271
AbstractThese days, a lot of information is exchanged on social media, and it can be difficult to tell what information is accurate and what information is false. As soon as they read the content, people start sharing their problems or ideas without first checking your validity. Its spread is a result of this as well. The most common sources of misleading and unverified information are rumours and fake stories, which should be exposed as soon as possible to prevent their unexpected effects. Online forums are where most smart phone users choose to read tales. News websites disseminate breaking news and offer a source of confirmation. How to spread news and articles on social media platforms like WhatsApp groups, Facebook pages, Twitter, and other tiny blogs and social networking sites is the subject at hand. It is risky for the general population to take these rumours and news stories seriously. There is an urgent need to put an end to rumours, especially in growing nations like India, and to concentrate on legitimate, established issues. This essay demonstrates a paradigm and a technique for gathering misleading information. The proposed model's outcomes are contrasted with those of other models. The suggested model performs well and explains the results' accuracy to a maximum of 93.6% accuracy.
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