Detection of Topical Influence in Social Networks via Granger-Causal Inference: A Twitter Case Study

Abstract

With the ever-increasing importance of computer-mediated communication in our everyday life, understanding the effects of social influence in online social networks has become a necessity. In this work, we argue that cascade models of information diffusion do not adequately capture attitude change, which we consider to be an essential element of social influence. To address this concern, we propose a topical model of social influence and attempt to establish a connection between influence and Granger-causal effects on a theoretical and empirical level. While our analysis of a social media dataset finds effects that are consistent with our model of social influence, evidence suggests that these effects can be attributed largely to external confounders. The dominance of external influencers, including mass media, over peer influence raises new questions about the correspondence between objectively measurable information diffusion and social influence as perceived by human observers.