Development of Truth Detection Method to Solve Fake News in Social Media Micro-Blogs

Counterfeit news has become a significant subject of research in an assortment of orders including semantics and software engineering. In this work, clarification of how the issue is drawn nearer from the point of view of common language handling, with the objective of building a framework to consequently recognize falsehood in news. The principle challenge in this line of research is gathering quality information, i.e., occasions of phony and genuine news stories on a fair dispersion of themes. In this paper, a unique truth detection system with similar words concepts is added to the scalable and robust truth discovery system used earlier. By the use of similar words concepts, the manipulated fake news can be detected much easier and faster. The features add up same meaning words which are compared using Jaccard algorithm in the main algorithm to detect more number of fake news with reliability score. The reliability score is calculated by combining independent score, attitude score and uncertainty score. The implemented software is found to be having better accuracy and results compared to existing truth detection methods.