FndIP: Fake News Detection on Social Media Using Incompatible Probabilistic Method

Fake News Detection continues to be a serious issue in our culture today. There are several approaches to classifying fake news. Even machine learning systems have had difficulty predicting and detecting bogus news. To fulfill the job of detecting fake news, this study uses Legitimacy, a unique categorization approach. A subject is a statement about an event, such as a headline, in our paradigm. Articles in the news may mention or expound on the alleged event. Our method works by assessing the likelihood of news stories being incompatible with a topic based on their reviewer-determined positions. The stances are agree, disagree, and discuss in appropriate circumstances where a news piece is related to a topic, with the last choice reflecting doubt. The news stories with the highest incompatibility probability values are the best candidates for being fake news, as shown empirically.