NLP Based Fake News Detection Using Hybrid Machine Learning Techniques

Abstract

Fake news is incorrect information. With the advent of false news on social media and other platforms, it is more important than ever to know the difference. Fake news contributes to riots, mayhem, mob violence, and other social and economic turmoil. Fake news can be created to purposely mislead or deceive readers, promote a biased point of view, a cause or goal, or just for fun. Fake news can be shared on social media, printed in publications due to political pressure, etc. Misinformation may cause public discontent, riots, and even distrust amongst individuals or nations. It also has the ability to tap into broad public sentiment in novel ways. The emergence of fake news has radically transformed news and media coverage. In recent years, people's news consuming patterns have altered. The internet has become the major source of information. However, most online content is untrustworthy and may even be deceptive. Humans can't identify certain fake news from real news. Fake news is created to mislead or deceive readers, support a biased point of view, a cause or goal, or just for fun. Fake news can be spread via unauthenticated user IDs, social media, and newspaper publishing. The proposed project will use hybrid machine learning model to detect bogus news in order to recognize the fake news easier.