Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

Abstract

Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence; (ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure; and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors.