Truth Discovery Algorithm on Big Data Social Media Tweets

Counterfeit news has become a significant area of research in an assortment of orders including semantics and software engineering. In this work, explanation of how the issue is moved closer from the perspective of basic language taking care of, with the target of building a system to perceive deception in news. The main challenge in this area is gathering quality data, i.e., events of fake and certifiable reports on a reasonable scattering of subjects. 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.