Blocking Fake News Propagation exploiting OSNs Users Interests and Connections

Abstract

The growth of social networking platforms has given rise to a new paradigm of communication through which a wide variety of news can reach a large audience in an easy and direct way. However, this has also contributed to the spread of false or partially true information, commonly referred to as fake news. This paper considers the scenario where a social network owner needs an automated tool to counteract this menace. Starting from three state-of-the-art algorithms for modeling information propagation on graphs, we propose a strategy that takes into account key factors influencing propagation within a social network, namely the physical connections between users and the interests they have in common. Then, we evaluate the impact of containment strategies by showing their different performances depending on how information propagation is modeled.