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Viral online disinformation is misleading content that is generated to manipulate public opinion and to circulate rapidly in the digital space. Although viral disinformation has become an instrument for radicalization, the specific psychological mechanisms by which disinformation can be weaponized––wielded as mobilizing and radicalizing political tools––are not yet well-understood.

In this paper, we establish the potential of concerted disinformation efforts to impact mass radicalization and political violence, first through historical precedents of deadly disinformation campaigns, then in modern-day examples from the USA and Russia. Comparing and contrasting political effects of two recent disinformation campaigns, QAnon’s #SaveTheChildren campaign in the USA, and anti-LGBTQ disinformation campaign in Russia, this paper highlights the significance of LGBTQ contagion threat—a notion that people can be “turned” into LGBTQ through deliberate outside influence. The psychological and political consequences of such messaging, its main target audience, and vulnerability factors rendering individuals especially susceptible to its radicalizing effects are discussed.

Content analysis of the posts from eleven Facebook accounts of known Dutch and Belgian foreign fighters (in Dutch). English summary is available here: http://leidensafetyandsecurityblog.nl/articles/foreign-fighters-on-social-media-an-analysis-of-11-facebook-accounts

VOX-Pol Blog post.

VOX-Pol Blog post.

VOX-Pol Blog post.

Terrorism research has begun to focus on the issue of radicalization, or the acceptance of ideological belief systems that lead toward violence. There has been particular attention paid to the role of the Internet in the exposure to and promotion of radical ideas. There is, however, minimal work that attempts to model the ways that messages are spread or how individual participation in radical on-line communities operates. In this paper, we present a stochastic linear system to represent the evolution of contribution to a sample of 126 threads in an on-line forum where individuals discuss radical belief systems. To estimate or predict the time-varying contributions of agents for given onlineforum data, each agent’s contribution has been modeled as a state variable. We then use the expectation-maximization (EM) algorithm to identify the model parameters including the adjacency matrix of the graph constructed among participating agents along with measurement and system uncertainty levels in online-postings. Our approach reveals the identified dynamical influences among agents in the time-varying shaping of the contribution in a datadriven fashion. We use the real-world data from online-postings to demonstrate the usefulness of our approach, and its application toward on-line radicalization.