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FAKE NEWS DETECTION USING MACHINE LEARNING

K.Santhanalakshmi¹, Roshna.R², Vasundhara Devi .S³. 

¹Associate Professor, ^2,3,Students

Department of Computer Science and Engineering, Paavai Engineering College, Namakkal.

Abstract—In recent years, the number of false news occurrences has skyrocketed. They could be created by humans, computers, or any other independent sources.

This has had a tremendous negative influence on society and people from a social and political aspect. Spammers use social media platforms to entice individuals into hazardous behavior by sending spam messages. Google Safe Browsing and Twitter's Bot Maker services have been used to battle spammers for a long time. These approaches are used to detect and block spam tweets. It is vital to research the relationship between fake news and media, as well as how fake news is becoming a big threat to civil society, in addition to analyzing and building an algorithm to recognize false news on social media platforms. Without properly determining the scope of the problem, appropriate solutions cannot be devised. According to current research, both false and true news circulate on social media in a variety of ways. People are increasingly turning to social media for news because it is more accessible, inexpensive, and visually appealing. However, it is capable of spreading "false news." Current detection algorithms are insufficient or unsuitable due to the unique nature of detecting fake news on social media. The one-of-a-kind capability of detecting fake news on social media, rendering existing detection algorithms ineffective or useless. After then, it's vital to look into secondary data. A user's social media activity is an example of secondary data. We present a simple NLP-based technique for detecting fake news.

Key words— Machine Learning, API, NLP