Towards Proactive Moderation of Malicious Content via Bot Detection in Fringe Social Networks

It has been long known that malicious content, e.g., fake news, originates from bots operating on fringe social networks (e.g., the now-defunct Parler) and then percolate to mainstream social networks (e.g., Twitter). It follows that effective moderation in mainstream networks depends upon proactively identifying malicious content while it becomes popular on the fringe ones. This, in turn, requires identifying the automatic bots therein. In this paper, we address the problem of detecting social bots in fringe networks and assessing their impact on individuals' opinions. Such a problem is complicated by the nature of fringe social networks, where less information on the social structure is available, i.e., there are no "friends" or "followers". Our approach is to detect bots and infer their impact from a partial sampling of the dynamical opinions expressed by individuals. The problem is then cast as a sparse recovery problem, which we will attempt to solve through algorithms with theoretical guarantees of accuracy and excellent scalability properties, e.g., logarithmic in network size. Numerical simulations are provided to corroborate our results.