Online Library

VOX-Pol Guest Lecture Series, Spring 2024: Data Collection in Online Terrorism and Extremism Research

There has been a growing interest among researchers, practitioners, and policymakers to analyze the online activities of extremists and terrorists. As studies in this research area have increased, various data collection techniques have emerged to address key research questions, ranging from manual extraction to computational tools to collect online information. This lecture examines the strengths and limitations of commonly used data collection methods in online terrorism and extremism research. We draw from our research experiences and highlight ethical dilemmas with collection methods in practice. We then set forth suggestions for progressing research in this space. For more information on data collection in online terrorism and extremism research, see our recent article in Studies in Conflict & Terrorism as part of a special issue on the strengths and struggles of different methods of research on radicalization, extremism, and terrorism: https://www.tandfonline.com/doi/full/10.1080/1057610X.2024.2361957?src=exp-la

Ryan Scrivens is an Assistant Professor in the School of Criminal Justice at Michigan State University. He is also an Associate Director at the International CyberCrime Research Centre at Simon Fraser University and a Research Fellow at the VOX-Pol Network of Excellence. He is the recipient of the 2022 Early Career Impact Award from the American Society of Criminology’s Division on Terrorism and Bias Crimes.

Steven Chermak is a Professor in the School of Criminal Justice at Michigan State University. He is the co-creator of several databases including the Extremist Crime Database (ECDB), the Extremist Cybercrime Database (ECCD), and The American School Shooting Study (TASSS). He is the recipient of the 2021 Distinguished Scholar Award from the American Society of Criminology’s Division on Terrorism and Bias Crimes.

VOX-Pol Blog post.

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.

VOX-Pol Blog post.

In recent years, researchers have shown a vested interest in developing advanced information technologies, machine-learning algorithms, and risk-assessment tools to detect and analyze radical content online, with increased attention on identifying violent extremists or measuring digital pathways of violent radicalization. Yet overlooked in this evolving space has been a systematic examination of what constitutes radical posting behaviors in general. This study uses a sentiment analysis-based algorithm that adapts criminal career measures – and is guided by communication research on social influence – to develop and describe three radical posting behaviors (high-intensity, high-frequency, and high-duration) found on a sub-forum of the most conspicuous right-wing extremist forum. The results highlight the multi-dimensional nature of radical right-wing posting behaviors, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify 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.