A framework for social media information flow analytics in cyberspace and physical space

Abstract

Communication in online social media naturally forms information flow from senders to receivers. With the proliferation of location‐aware social media services such as Twitter, information flow shaped on these platforms is becoming more deeply and extensively embedded in the physical world. Therefore, growing interest has been raised in discovering the spatiotemporal patterns of information flow in location‐aware social media, aiming for a holistic understanding of the social dynamics in the nested cyber and physical world. This article addresses this interest by proposing a framework for information flow analytics based on a spatiotemporal clustering method designed for large data streams with location information. The framework was implemented as an open‐source tool, and its performance was examined by two case studies using Twitter data streams. It is suggested that this framework has well addressed the new requirements and challenges arising from social media data streams, effectively visualized information flow on maps, and demonstrated its feasibility of supporting downstream spatiotemporal analysis and applications.