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In recent years, due to the booming development of online social networks,fake news for various commercial and political purposes has been appearing inlarge numbers and widespread in the online world. With deceptive words, onlinesocial network users can get infected by these online fake news easily, whichhas brought about tremendous effects on the offline society already. Animportant goal in improving the trustworthiness of information in online socialnetworks is to identify the fake news timely. This paper aims at investigatingthe principles, methodologies and algorithms for detecting fake news articles,creators and subjects from online social networks and evaluating thecorresponding performance. This paper addresses the challenges introduced bythe unknown characteristics of fake news and diverse connections among newsarticles, creators and subjects. This paper introduces a novel automatic fakenews credibility inference model, namely FAKEDETECTOR. Based on a set ofexplicit and latent features extracted from the textual information,FAKEDETECTOR builds a deep diffusive network model to learn the representationsof news articles, creators and subjects simultaneously. Extensive experimentshave been done on a real-world fake news dataset to compare FAKEDETECTOR withseveral state-of-the-art models, and the experimental results have demonstratedthe effectiveness of the proposed model.

FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network