![]() ![]() 2 Among other things, network science has helped to demonstrate that the topological structure of these illicit networks has common features with that of other complex systems and social phenomena, 3 even though extremist communities are often treated as a social aberration at a policy level. 1 Over the past decade, network theory has contributed tremendously to our understanding of how and why violent extremist communities form and thrive. To understand and ultimately mitigate the threat from terrorism today, which is increasingly reliant on the massive, multivariate usage of internet-based technologies, researchers, policymakers, and practitioners require new approaches that rely on complexity science. ![]() Operating as such, this network of bots is used to lubricate and augment the Islamic State’s influence activities, including facilitating content amplification and community cultivation efforts, and connecting people with the movement based on common behaviors, shared interests, and/or ideological proximity while minimizing risk for the broader organization. We show that these bots are mainly clustered around two communities of Islamic State supporters, or “munasirun,” with one community focusing on facilitating discussion and exchange, and the other one augmenting content distribution efforts. As part of this, we examine the diverse activities of the bots to determine the extent to which they operate in synchrony with one another as well as explore their impacts. In this article, we use network science to explore the topology of the Islamic State’s “terrorist bot” network on the online social media platform Telegram, empirically identifying its connections to the Islamic State supporter-run groups and channels that operate across the platform, with which these bots form bipartite structures.
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