DeepFakE: improving fake news detection using tensor decomposition-based deep neural network

Abstract

Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity of the news articles being shared on social media. Nowadays, the problem of fake news has gained massive attention from research communities and needed an optimal solution with high efficiency and low efficacy. Existing detection methods are based on either news-content or social-context using user-based features as an individual. In this paper, the content of the news article and the existence of echo chambers (community of social media-based users sharing the same opinions) in the social network are taken into account for fake news detection. A tensor representing social context (correlation between user profiles on social media and news articles) is formed by combining the news, user and community information. The news content is fused with the tensor, and coupled matrix-tensor factorization is employed to get a representation of both news content and social context. The proposed method has been tested on a real-world dataset: BuzzFeed. The factors obtained after decomposition have been used as features for news classification. An ensemble machine learning classifier (XGBoost) and a deep neural network model (DeepFakE) are employed for the task of classification. Our proposed model (DeepFakE) outperforms with the existing fake news detection methods by applying deep learning on combined news content and social context-based features as an echo-chamber.