Characterisation of Users’ Behaviours towards Fake News through the Analysis of their Networks

Abstract

The detection and analysis of fake news and its origins has become a main task associated with the overall objective of social media regulation in recent years. The majority of work was towards detecting misinformation with some focus on analysing the flow of fake news over social networks. However, there is less attention on understanding the characteristics of social media users who consume these fake news. In this work, we investigate the possibility of predicting users’ reactions towards fake news and defining some network characteristics for each users group. We utilised a set of fact-checking websites in the Arab world that report social media posts spreading fake news and the interactions with them. We defined three sets of users: 1) Spreaders, who spread fake news, 2) Checkers, who constantly share fact-checked news, and 3) Refuters, who respond to fake-news posts declaring their inaccuracy. We build a classifier that uses users’ network graph to predict their reactions with an accuracy exceeding 93%. We applied further analysis for the most effective features of each users group and noticed that spreaders interact with more accounts that use their mother tongue and more accounts that get suspended while checkers and refuters interact with more foreign accounts and news-reporting entities.