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The Islamic State in Iraq and Syria (ISIS), like no other terrorist organization before, has used Twitter and other social media channels to broadcast its message, inspire followers, and recruit new fighters. Though much less heralded, ISIS opponents have also taken to Twitter to castigate the ISIS message. This report draws on publicly available Twitter data to examine this ongoing debate about ISIS on Arabic Twitter and to better understand the networks of ISIS supporters and opponents on Twitter. To support the countermessaging effort and to more deeply understand ISIS supporters and opponents, this study uses a mixed-methods analytic approach to identify and characterize in detail both ISIS support and opposition networks on Twitter. This analytic approach draws on community detection algorithms that help detect interactive communities of Twitter users, lexical analysis that can identify key themes and content for large data sets, and social network analysis.

Despite the ongoing need for practitioners to identify violent extremists online before their engagement in violence offline, little is empirically known about their digital footprints generally or differences in their posting behaviors compared to their non-violent counterparts particularly – especially on high-frequency posting days. Content analysis was used to examine postings from a unique sample of violent and non-violent right-wing extremists as well as from a sample of postings within a sub-forum of the largest white supremacy forum during peak and non-peak posting days for comparison purposes. Several noteworthy posting behaviors were identified that may assist in identifying credible threats online.

Although there is an ongoing need for law enforcement and intelligence agencies to identify and assess the online activities of violent extremists prior to their engagement in violence offline, little is empirically known about their online posting patterns generally or differences in their online patterns compared to non-violent extremists who share similar ideological beliefs particularly. Even less is empirically known about how their online patterns compare to those who post in extremist spaces in general. This study addresses this gap through a content analysis of postings from a unique sample of violent and non-violent right-wing extremists as well as from a sample of postings within a sub-forum of the largest white supremacy web-forum, Stormfront. Here the existence of extremist ideologies, personal grievances, and violent extremist mobilization efforts were quantified within each of the three sample groups. Several notable differences in posting patterns were observed across samples, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online. This study concludes with a discussion of the implications of the analysis, its limitations, and avenues for future research.

Although many law enforcement and intelligence agencies are concerned about online communities known to facilitate violent right-wing extremism, little is empirically known about the presence of extremist ideologies, expressed grievances, or violent mobilization efforts that make up these spaces. In this study, we conducted a content analysis of a sample of postings from two of the most conspicuous right-wing extremist forums known for facilitating violent extremism, Iron March and Fascist Forge. We identified a number of noteworthy posting patterns within and across forums that may assist law enforcement and intelligence agencies in identifying credible threats online.

Many researchers, practitioners, and policymakers are concerned about online communities that are known to facilitate violent right-wing extremism, but little is empirically known about these digital spaces in general and the developmental posting behaviors that make up these spaces in particular. In this study, group-based trajectory modeling—derived from a criminal career paradigm—was used to identify posting trajectories found in the open-access sections of the Iron March and Fascist Forge forums, both of which have gained notoriety for their members’ online advocacy of violence and acts of violence carried out by them. Multinomial logistic regression and analysis of variance were then used to assess whether posters’ time of entry into the violent forums predicted trajectory group assignment. Overall, the results highlight a number of similarities and differences in posting behaviors within and across platforms, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online. We conclude with a discussion of the implications of this analysis, followed by a discussion of study limitations and avenues for future research.

Objectives
Despite popular notions of “filter bubbles” and “echo chambers” contributing to radicalization, little evidence exists to support these hypotheses. However, social structure social learning theory would suggest a hereto untested interaction effect.
Methodology
An RCT of new Twitter users in which participants were randomly assigned to a treatment of “filter bubble” (personalization algorithm) suppression. Ego-centric network and survey data were combined to test the effects on justification for suicide bombings.
Findings
Statistically significant interaction effects were found for two proxies of the echo chamber, the E-I index and modularity. For the treatment group, higher scores on both factors decreased the likelihood for radicalization, with opposing trends in the control group.
Conclusions
The echo chamber effect may be dependent on the filter bubble. More research is needed on online network structures in radicalization. While personalization algorithms can potentially be harmful, they may also be leveraged to facilitate interventions.