Mapping Extremist Communities: A Social Network Analysis Approach

In the domain of computer science, the last years have witnessed the improvement of social network analysis at scale. One of the most challenging aspects of social network analysis is community detection; analysts use a variety of tools to visualise the spontaneous group structure emerging from interactions and friendship relations in multi-million-user networks. This visualisation, combined with influencer detection and automated text analysis tools such as topic detection, enables the analyst to grasp most of the complexity of a social network. This computer-science-oriented study explores three lines of research concerning online extremism. First, about the emerging narratives and the topics that can be found on open platforms. We show that many actors actively use terror-group related terms; most cannot be directly tied to any specific organisation. A second axis concerns the connections between platforms: the information space has no central point as content is shared across platforms. However, the links reveal clusters of locations: we observe a group of Pakistan-India conflict mentions, and a cluster of US alt-right websites, transforming terrorism into a migration problem. The third axis relates to the social media landscape structure. We rely on a combination of document-level topic modelling and graph analysis to detect and explore the social data, visualising the types of groups that are active on the topic. Among the results, we found a small botnet circulating a pro-Daesh pamphlet and a set of grassroot reactions that managed to moderate a controversial pro-Jihadi post on Reddit.

Tags: automated text analysis tools, botnet, Narratives, Social Network Analysis, Social Networks, topic detection