TY  - JOUR
PY  - 2021//
TI  - Faces of radicalism: differentiating between violent and non-violent radicals by their social media profiles
JO  - Computers in human behavior
A1  - Wolfowicz, Michael
A1  - Perry, Simon
A1  - Hasisi, Badi
A1  - Weisburd, David
SP  - e106646
EP  - e106646
VL  - 116
IS  - 
N2  - 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. <br><br>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. <br><br>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. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 0747-5632
UR  - http://dx.doi.org/10.1016/j.chb.2020.106646
ID  - ref1
ER  -