Fake News Classification using Transfer Learning

The rising complexity of information communication technology has greatly affected communication through conventional broadcast media over the past decade. Smartphone applications are increasingly emasculating the new socio-economic broadcasting environment. The trend is the same in the workplace, at home and in recreation. Social networking has stolen the game and is increasingly shifting to another age, the era of “digital relationships,” in which conventional interpersonal social interactions are replaced by mobile devices and social networks. The consequences of such false information promoted by miscreants and apologists for social media are far-reaching because it has resulted in scandals in households, communities, partnerships, organizations, and culture as a whole. The purpose of this paper is to lead to the eradication of counterfeit media by the use of technology. In this article, we proposed and built a model that incorporates neural networks to identify and eradicate false phrases posted to social media networks and web forums. Also, we compared our work Elmo VNetwith current state-of the-art models. The experimental results demonstrated that the proposed Elmo VNetmodel have better accuracy rate than the existing models.