On the Influence Blocking Maximization for Minimizing the Spreading of Fake Information in Social Media

Abstract

Influence can be used to propagate the (fake or true) information in social media where a set of influential nodes (individuals) in social media can leverage their connections (e.g., followers in Tweeter) to impact others. Lately, most of the social interactions take place on-line where followers/members can get the information directly from their following accounts. Influential users can be used to propagate fake or false information to their followers. This paper analyzes social interactions in Twitter to studying the influence blocking maximization to minimize the propagation of fake information in social media by discovering influential users and their impact to spread fake/false information among their users/followers. Specifically, Greedy algorithm is studied to discover influence of spreading false/fake information among users in Twitter where malicious users could exploit influential users to gain more exposure to their malicious data. Results show that the influence of spreading fake or false information increases with the popularity of user through his/her followers and retweets. Furthermore, attributes such as number of followers, number of likes, retweets, etc. have huge impact on the influence for propagating the information in social media.