A Machine Learning Approach to Fake News Detection
Abstract
In today’s world, social media platforms are important means of information diffusion, and people trust
themwithout questioning their authenticity. Social media is a
majorfactor in propagating fake news. Thus, to mitigate the
conse- quences of fake news, we create an NLP model to
differentiate fake and real news. Here machine learning
algorithms has been used for enhancing fake news detection
performance with NLP. Models trained using max entropy
classifier, where news content is scanned for sentences that could indicate the news is fake based on existing NLP libraries. TF-IDF weighting is used to score certain pieces of text, so that detection is fast on any updates or incoming messages due to its fast computation time and high recall rate (low mistake rate). Here the proposed project’s purpose is to detect fake and misleading news from social media
networks
Keywords:
FakeNews, Recurrent Neural Network, TF-IDF, NLP
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Copyright (c) 2023 International Journal on Emerging Research Areas
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