• Krishna Sharma
• Ms.Pooja Kumari
• Mr. Yash Dhankhar

Various motivations, including semantics and programming architecture, have made fake news a key topic of study. In this study, the problem is clarified from the perspective of basic language management, with the goal of creating a framework to later detect misdirection in news. Quality information, i.e. instances of fake and genuine reports on a reasonable dispersion of individuals, is the main problem in this area of research. We add a new truth acknowledgement method with near words ideas to the previously utilized flexible and overpowering truth disclosure structure. Controlled false news may be easily identified by using almost similar terms and phrases. With the same important words, Jaccard estimation is used in the fundamental calculation to identify logically number of false news with resolute quality score. Stability is determined by combining free, mien, and weakness scores. As compared to current truth ID methods, the finished writing computer programmers have higher accuracy.

Truth; big data, SRTD; Jaccard; Data Mining

"Truth Detection in Social Media Posts Using SRTD and Word Net Concept", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.e268-e271, August-2021, Available :http://www.jetir.org/papers/JETIR2108517.pdf

"Truth Detection in Social Media Posts Using SRTD and Word Net Concept", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppe268-e271, August-2021, Available at : http://www.jetir.org/papers/JETIR2108517.pdf