A naive deep nets based approach for authenticating viral textual content on social media

Abstract

Social networks in today’s life is more of necessity that helps in reducing the distance and enable people to stay connected. Networking sites are good means to broadcast news too, but quite often than desired these sites are prevalent with lies and frauds, half-truths and facts. The rapid dissemination of such information through social network sites and other online media can have instant and serious ramifications. Fake or deceptive media content and its diffusion through social networks, such as Twitter create an important and challenging problem. The objective of this work is to explore the challenges involved in identifying tweets with unpredictable media content as fake or real. Researchers have used various machine learning techniques and different kinds of classifiers, such as Naive Bayes, Decision Tree, SVM, RNN, DNN, etc. to identify the trustworthy content on Twitter. Some have perceived that how social media users support or deny rumors in breaking news stories but results are, as yet, indecisive. This paper highlights the use of Tensorflow and Neu-ralnet in R for identifying viral textual content on social network.