FAKE NEWS DETECTION USING MACHINE LEARNING MULTI-MODEL METHOD
DOI:
https://doi.org/10.29121/shodhkosh.v5.i2.2024.1811Keywords:
Fake News, WhatsApp, Machine Learning, Random ForestAbstract [English]
A fake news article that originates from an WhatsApp source is known as fake news. Fake news is becoming more and more prevalent on social media and other platforms, and this is a serious worry since it has the potential to have devastating effects on society and the country. This is why there has already been a lot of research done on its detection. This study uses supervised machine learning techniques to develop a product model through research and implementation of false news detection system. To put it briefly, this work will use a Naive Bayes classifier to build a model that can identify fake news by measuring its words and phrases against a set of criteria. that uses techniques like a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix to categorize bogus news as real or false It's highly likely that the meaning of two papers with comparable word counts will be entirely different.
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Copyright (c) 2024 Laxminarayan Sahu, Dr. Bhavana Narain
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