Abstract: The extensive spread of pretend news can have a significant negative impact on individuals and society. it's brought down the authenticity of stories ecosystem because it is even more widely spread on social media than most well-liked authentic news. it's one in every of the largest problems which has the flexibility to vary opinions and influence decisions and interrupts the way during which people responds to real news. Most of the smart phone users choose to read the news via social media over internet. The news websites are publishing the news and supply the source of authentication. The question is the way to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is very harmful for the society to believe on the rumors and pretend to be a news. the requirement of an hour is to prevent the rumors especially within the developing countries like India, and concentrate on the proper, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and tongue processing, it's tried to aggregate the news and later determine whether the news is real or fake using Passive Aggressive Classifier. The results of the proposed model is compared with existing models. The proposed model is functioning well and defining the correctness of results up to 93.6% of accuracy.

Keywords: Fake News, Impact, social media, Machine Learning, Passive Aggressive Classifier.

PDF | DOI: 10.17148/IJARCCE.2021.10551

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