Preventing Fake News Propagation in Social Networks Using a Context Trust-Based Security Model.

Online Social Networks (OSN) security issues have been extensively researched in the past decade. Information is posted and shared by individuals and organizations in social networks in huge quantities. One of the most important non-resolved topics is the Fake News propagation problem. Fake news propagates because of several reasons, one of which is non-trustworthy users. These users, some with malicious intentions, and some with low social media awareness, are the ones actually spreading misleading information. As this occurs, other users, that are valid reliable users, are exposed to false information. In our previous research we have devised a comprehensive Trust-based model that can handle this problem from the user Trust aspect. The model involves Access Control for the direct circle of friends and Flow Control for the friends' networks. In this paper we use this model as a basis for the purpose of prevention of Fake News. We add context awareness and user profiling by analyzing the user's activity in the network (posts, shares, etc.), and then use Machine Learning to detect these problematic users by analyzing data items that are fake or misleading. This addition creates a much more accurate picture of OSN users and their data and helps revealing the sources of the Fake News propagation and can prevent it. These aspects of the model create a strong reliable OSN data infrastructure.