Temporal Formation and Evolution of Online Communities

Abstract

Researchers have already studied the identification of online communities and the possible impact or influence relationships from several perspectives. For instance, communities of users that are formed based on shared relationships and topological similarities, or communities that consist of users that share similar content. However, little work has been done on detection of communities that simultaneously share topical and temporal similarities. Furthermore, these studies have not explored the causation relationship between the communities. Causation provides systematic explanation as to why communities are formed and helps to predict future communities. This proposal will address two main research questions: i) how can communities that share topical and temporal similarities be identified, and ii) how can causation relation between different online communities be detected and modelled. We model users' behaviour towards topics of interest through multivariate time series to identify like-minded communities. Further, we employ Granger's concept of causality to infer causation between detected communities from corresponding users' time series. Granger causality is the prominent approach in time series modelling and rests on a firm statistical foundation. We assess the proposed community detection methods through comparison with the state of the art and verify the causal model through its prediction accuracy.