DeepNet: An Efficient Neural Network for Fake News Detection using News-User Engagements

The rise of social media allows every user to share and immediately publish their views. Today, the problem of fake news has obtained significant consideration among researchers due to its harmful nature to deceive the people of the society. It has created an alarming situation in the world. News ecosystem evolved from a small set of trusted and regulated sources to numerous online news sources. Fake news has an adverse impact on society as it may manipulate public opinions. Thus, it is essential to investigate the credibility of news articles shared on social media outlets. In this paper, we have designed an effective deep neural network that is capable of handling not only the content of the news article but also the user-relationships in the social network. We have designed our proposed approach using tensor factorization method. A tensor expresses the social context of news articles formed by a combination of the news, user, and user-group information. Our proposed method (DeepNet) has validated on a real-world fake news dataset: BuzzFeed and Fakeddit. DeepNet outperforms from existing fake news detection methods by employing deep architecture with different kernelsizes convolutional layers combining news content and social context-based features.