Implementation of Machine Learning Techniques to Identify Fake News

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Abstract

Misinformation on social media and other forms of media is widely disseminated and is a major source of concern owing to its potential to create significant social and national harm. The popularity of social media platforms has aided the spreading of fake news. This form of news, spread by electronic media, has the potential to mislead thousands of people in a brief period, causing significant damage to individuals, businesses, and society. Fake news has influenced several social media sites, including Facebook, WhatsApp, Twitter and Instagram. Fake news has the power to change a political situation, lead to disease spread, and even cause deaths. Nowadays, detecting false news has become an onerous task. Fake News are creating problems for common people to designate people who all are running our country. If we can’t stop the spread of fake news in quick time, then it will be an impossible task for future. In this work, the Machine learning classifiers include Logistic Regression, Naive Bayes, Decision Tree, SVM and Random Forest, are used to improve the accuracy of misinformation prediction. By using these classifiers, there is a high chance of getting good results.

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Keywords

Fake news, social media, Misinformation, Machine learning