Title : WELFake Word Embedding Over Linguistic Features for Fake NewsDetection
Author : Garikapati Yuvanesh, Shaik Firoz, Shaik Mokthyar, C.Archana
Abstract :
Intentionally false material disguised as respectable journalism is a global information accuracy and integrity concern that influences opinion formation, decision making, and voting habits. Social media channels like Facebook and Twitter first propagate so-called 'fake news,' which then spreads to conventional media outlets like television and radio news. Fake news articles spread through social media often have similar language features, such as the overuse of unsupported exaggeration and the failure to properly identify referenced information, when they first appear. Results of a fake news detection investigation are reported in this article to demonstrate the effectiveness of a fake news classifier. There are many tools that may be utilised to construct a new kind of false news detector that utilises quoted attribution in a Bayesian machine learning system as one of the primary features. To put it another way, it is 63.33% accurate in detecting bogus q
