Invisible Stories That Drive Online Social Cognition

Abstract

Detection of online subversive activities, such as fake news, concerted campaigns, and bots, is getting increasingly urgent. However, without specific knowledge of underlying facts and disparate valid perspectives of a given issue, it is hard to detect subversive intent in a generic sense. To address this, we approach the problem from a “macro” perspective. Rather than asking whether a specific social media account is acting subversively, we look at the entire discourse around a trending topic, and ask whether the discourse looks “healthy” or is it showing signs of getting hijacked or dominated by one particular perspective. To do this, we break down a social discourse into its constituent narratives. Narratives are in turn modeled as latent stories or worldviews, whose visible characterizations are in the form of specific distributions over different opinions expressed in the discourse. Once the discourse is broken down into narratives, the “health” of the discourse can be addressed using various measures, such as the relative sizes of its constituent narratives, sentiment polarity of internarrative interactions, and presence or absence of dominant players within each narrative. We conduct experiments on several well-known trending topics on Twitter to identify its constituent narratives and provide a report card on the overall discourse quality. We also show how this top-down approach offers the means to delineate roles played by users as drivers of the discourse, the constituent narratives, or their component opinions, determined on the basis of dominance centrality measures and narrative affinities.