Faces of radicalism: Differentiating between violent and non-violent radicals by their social media profiles. (March 2021) - Wolfowicz, Michael; Perry, Simon; Hasisi, Badi; Weisburd, David
Abstract: Objectives Social media platforms such as Facebook are used by both radicals and the security services that keep them under surveillance. However, only a small percentage of radicals go on to become terrorists and there is a worrying lack of evidence as to what types of online behaviors may differentiate terrorists from non-violent radicals. Most of the research to date uses text-based analysis to identify "radicals" only. In this study we sought to identify new social-media level behavioral metrics upon which it is possible to differentiate terrorists from non-violent radicals. Methods: Drawing on an established theoretical framework, Social Learning Theory, this study used a matched case-control design to compare the Facebook activities and interactions of 48 Palestinian terrorists in the 100 days prior to their attack with a 2:1 control group. Conditional-likelihood logistic regression was used to identify precise estimates, and a series of binomial logistic regression models were used to identify how well the variables classified between the groups. Findings: Variables from each of the social learning domains of differential associations, definitions, differential reinforcement, and imitation were found to be significant predictors of being a terrorist compared to a nonviolent radical. Models including these factors had a relatively high classification rate, and significantly reduced error over base-rate classification. Conclusions Behavioral level metrics derived from social learning theory should be considered as metrics upon which it may be possible to differentiate between terrorists and non-violent radicals based on their social media profiles. These metrics may also serve to support textbased analysis and vice versa. Highlights: Comparing the Facebook behaviors of terrorists and non-violent radicals using a matched case-control design. Testing the predictive quality of factors derived from Social Learning Theory. Terrorists are more likely to post about a friend's terrorist attack already committed. Terrorists engage in more content sharing and non-violent radicals in more text-based posting. Automated detection approaches should integrate behavior-level factors to improve accuracy and reduce false-positives.