ONLINE NEWS DETECTION ON SOCIAL MEDIA USING MACHINE LEARNING

Computer Engineering

A story that is made up with the intent to mislead or deceive the reader is referred to as fake news. Using machine Learning structures, we have proposed a solution to the challenge of detecting fake news. The dissemination of fake news has been accelerated as a result of a large number of cases of fake news. Individuals are clashing if not by far bad locators of false news because of its wide effects of the massive onsets of fake news. With all these, efforts were made to develop an automated framework for detecting false news. While these tools are being used to create a more complex full start to finish structure, we need to address more difficult cases where more reliable sources and developers are releasing fake news. Combine the information filtering mechanism with the user profiles learned from the existing collaborative filtering mechanism to create personalized news recommendations. The proposed study uses hybrid machine learning algorithms to provide online news recommendations. The system first works with Natural Language Processing (NLP) to extract and train module features. The system will suggest news based on the user's personal history and various datasets have been used to evaluate the performance of the system. The results prove that the system has more accuracy than other recommendation systems.

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