Social media data
In these projects I leverage social media data to study the early stages of radicalization and participation in extremist movements. In one, I use geocoded videos uploads to explore the relationship between American military fatalities overseas and far-right mobilization. The other applies computer vision techniques to recruitment videos from groups within the Salafi Jihadi movement to study how different groups within a broader clandestine movement use rhetorical strategies to communicate to a broad pool of potential supporters.
Richard McAlexander, Rob Williams, and Michael Rubin. “They’re Still There, He’s All Gone: American Fatalities in Foreign Wars and Right-Wing Radicalization at Home.”
What explains right-wing radicalization in the US? Research shows that demographic changes and economic decline both drive support for the far-right. We contribute to this research agenda by 1) studying the elusive early stages in the process of radicalization and 2) highlighting an additional factor that contributes to right-wing radicalization in the US: the impact of foreign wars on society at home. We argue that the communities that bear the greatest costs of foreign wars are most prone to high rates of right-wing radicalization. To support this claim, we present robust correlations between participation in the far-right social media website Parler and fatalities among residents who served in the US wars in Iraq and Afghanistan. This correlation holds at both the county and census tract level, and persists after controlling for the level of military service in an area. The costs of the US’s foreign wars have important effects on domestic US politics.
Working Paper Supplemental Information
Manuscript in preparation
Rob Williams. “Mapping Extremist Networks with Visual Imagery.” Presented at the Annual Meeting of the Society for Political Methodology, Cambridge, MA, July 2019 the 2nd Annual Politics and Computational Social Science Conference, Washington, DC, August 2019.
Identifying networks of cooperation and conflict between actors in broader social movements can be a challenging task even when data are easily obtainable. When actors are involved in socially marginal movements such as extremist groups, this task becomes even more difficult due to the high degree of secrecy that surrounds communication and interaction between members. However, extremist groups such as terrorist groups often release extensive amounts of propaganda material, including video, magazines, and social media content. I focus on video propaganda and use computer vision techniques to identify points of interest within video frames and extract quantitative descriptions of them. I then find unsupervised clusters of these image fragment that I hand label e.g. guns, faces, banners, etc. I assign each point of interest in a frame to its appropriate category, and then generate counts of each category’s frequency within each video. I then rely on unsupervised clustering methods to detect groups of videos that use similar visual imagery. Extremist group propaganda materials represent an untapped potential source of information about patterns of allegiance within the broader movement as groups that are aligned with one another are likely to produce material sharing many of the same images, terms, and themes. I evaluate this method on a sample of propaganda videos produced by groups within the Salafi Jihadi movement and compare this video-derived measure of group relationships with existing qualitative work mapping these connections to validate my findings. This computer vision approach will allow researchers to identify individual terrorist groups within broader movements when the extensive information on group interactions required for traditional network analysis is unavailable.