Minzhang Zheng

New online ecology of adversarial aggregates: ISIS and beyond

Tackling the advance of online threats Online support for adversarial groups such as Islamic State (ISIS) can turn local into global threats and attract new recruits and funding. Johnson et al. analyzed data collected on ISIS-related websites involving 108,086 individual followers between 1 January 1 and 31 August 2015. They developed a statistical model aimed at identifying behavioral patterns among online supporters of ISIS and used this information to predict the onset of major violent events. Sudden escalation in the number of ISIS-supporting ad hoc web groups (“aggregates”) preceded the onset of violence in a way that would not have been detected by looking at social media references to ISIS alone. The model suggests how the development and evolution of such aggregates can be blocked. Science, this issue p. 1459 Online activity of ad hoc extremist groups can be modeled, providing predictive power and potential countermeasures. Support for an extremist entity such as Islamic State (ISIS) somehow manages to survive globally online despite considerable external pressure and may ultimately inspire acts by individuals having no history of extremism, membership in a terrorist faction, or direct links to leadership. Examining longitudinal records of online activity, we uncovered an ecology evolving on a daily time scale that drives online support, and we provide a mathematical theory that describes it. The ecology features self-organized aggregates (ad hoc groups formed via linkage to a Facebook page or analog) that proliferate preceding the onset of recent real-world campaigns and adopt novel adaptive mechanisms to enhance their survival. One of the predictions is that development of large, potentially potent pro-ISIS aggregates can be thwarted by targeting smaller ones.

Multiscale dynamical network mechanisms underlying aging of an online organism from birth to death

We present the continuous-time evolution of an online organism network from birth to death which crosses all organizational and temporal scales, from individual components through to the mesoscopic and entire system scale. These continuous-time data reveal a lifespan driven by punctuated, real-time co-evolution of the structural and functional networks. Aging sees these structural and functional networks gradually diverge in terms of their small-worldness and eventually their connectivity. Dying emerges as an extended process associated with the formation of large but disjoint functional sub-networks together with an increasingly detached core. Our mathematical model quantifies the very different impacts that interventions will have on the overall lifetime, period of initial growth, peak of potency, and duration of old age, depending on when and how they are administered. In addition to their direct relevance to online extremism, our findings may offer insight into aging in other network systems of comparable complexity for which extensive in vivo data is not yet available.

Complexity in Individual Trajectories toward Online Extremism

Society faces a fundamental global problem of understanding which individuals are currently developing strong support for some extremist entity such as ISIS (Islamic State), even if they never end up doing anything in the real world. The importance of online connectivity in developing intent has been confirmed by recent case studies of already convicted terrorists. Here we use ideas from Complexity to identify dynamical patterns in the online trajectories that individuals take toward developing a high level of extremist support, specifically, for ISIS. Strong memory effects emerge among individuals whose transition is fastest and hence may become “out of the blue” threats in the real world. A generalization of diagrammatic expansion theory helps quantify these characteristics, including the impact of changes in geographical location, and can facilitate prediction of future risks. By quantifying the trajectories that individuals follow on their journey toward expressing high levels of pro-ISIS support—irrespective of whether they then carry out a real-world attack or not—our findings can help move safety debates beyond reliance on static watch-list identifiers such as ethnic background or immigration status and/or postfact interviews with already convicted individuals. Given the broad commonality of social media platforms, our results likely apply quite generally; for example, even on Telegram where (like Twitter) there is no built-in group feature as in our study, individuals tend to collectively build and pass through the so-called super-group accounts.