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Counterfeit news has gotten an essential subject of exploration in an assortment of solicitations including semantics and programming building. In this work, clarification of how the issue is drawn nearer from the point of view of fundamental language managing, with the objective of building a framework to subsequently see misdirection in news. The rule challenge in this line of examination is gathering quality information, i.e., occasions of phony and true reports on a sensible dispersing of subjects. In this paper, a novel truth acknowledgment system with near words thoughts is added to the versatile and overwhelming truth disclosure structure used previously. By the use of practically identical words thoughts, the controlled fake news can be recognized with much basic and snappier. The features add up same meaning words which are compared using Jaccard algorithm in the main algorithm to detect a greater 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.

Truth Detection in Social Media Posts using Jaccard Algorithm with SRTD and Word Net Concept