Abstract

Abstract: In Social media, it is a popular medium for the dissemination of real-time news all over the world. Easy and quickinformation proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender,and societal beliefs are engaged in social media websites. Despite these favorable aspects, a significant disadvantage comes in theform of fake news, as people usually read and share information without caring about its genuineness. It is imperative to researchmethods for the authentication of news. To address this issue, this article proposes a two-phase benchmark model namedFakeNews based on word embedding (WE) over linguistic features for fake news detection using machine learning classification.The first phase preprocesses the data set and validates the veracity of news content by using linguistic features. The second phasemerges the linguistic feature sets with WE and applies classification. To validate its approach, this article also carefully designs anovel FakeNewsdata set with approximately thousands articles, which incorporates different data sets to generate an unbiasedclassification output.Keywords-Fake News, User Profile, Trust Analysis, machine learning, Social Media.