Passing while bot: Fake news network role analysis from twitter data
Richard E. Gardner III, Carter ButtsSocial influence bot networks—or SIBNs, are networks of online bot accounts that masquerade as regular everyday people, groups, and organizations. These social actors are not only tasked with entering a social space with their true nature left undetected but must also effectively influence the conversations, thoughts, opinions, and ideas within these spaces. We explore the social behaviors of a Kremlin-linked fake news SIBN tasked with influencing social and political events through Twitter discourse around the time of the 2016 U.S. presidential election. For years, the 2,752 bot accounts identified in this SIBN masqueraded as gun-owning housewives, young Black Lives Matter activists, Twitter-adjuncts for obsolete local news companies, non-existent political organizations, and more before their eventual detection and termination by Twitter. Here we explore the tactics employed by SIBN bots to assume these roles using the metadata from the SIBN’s tweets as well as the mention, reply, and retweet behavior of the bot network as a whole. To link the roles these bots assume with the differences in their information transmission behavior, we make comparisons between SIBN bots’ reply, mention, and retweet behavior and their role similarity to observe to what extent the engagement in these roles aids in improving legitimation tactics and the spread of fake news.