Detection and Analysis of Fake News Users’ Communities in Social Media

Abstract

The widespread use of social media platforms has led to an increase in the dissemination of fake news with the intention of manipulating public opinion and causing chaos and panic among the population. To address this issue, we focus on detecting the organized groups that participate together in fake news campaigns without prior knowledge of the news content or the profiles of social accounts. To this end, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial–temporal similarity graph</i> , a novel graph structure that connects social accounts that participate in the early stage of similar fake news campaigns. A community detection algorithm is applied on the similarity graph to cluster the users into communities. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">community labeling algorithm</i> to label the communities as benign or malicious based on the output of a fake news classifier. Evaluation results show that the community labeling algorithm can correctly label the communities with an accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99.61\%$</tex-math> </inline-formula> . In addition, we perform a statistical comparison analysis to identify the structural community features that are statistically significant between benign and malicious communities.