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Predicting behavioral profiles of online extremists through linguistic use of social roles.
- Source :
- Behavioral Sciences of Terrorism & Political Aggression; Sep2021, Vol. 13 Issue 4, p295-319, 25p
- Publication Year :
- 2021
-
Abstract
- This study explores whether social roles mentioned in jihadist literature can be used linguistically to profile the behavior of its consumers. It seeks to empirically understand extremist forums, where such ideological communication is widely disseminated, and to identify different types of user behavior through their discussions in these communities. An Arabic-language corpus was constructed with 1050 users categorized into seven behavioral profiles derived from distinct patterns of communication observed among users in forum networks. Statistical key term extraction was used to find significant social roles referenced in users' posts as these were proposed to differentiate their behavioral profiles. Multinomial logistic regression and post hoc tests were used to find the frequency of identified roles as the highest-ranking terms capable of prediction. Role terms produced high accuracy scores across classification experiments in identifying behavioral profiles (95% CI, 0.92–0.98), with varying intra- and inter-behavioral differentiation abilities emerging from the authority, religion, closeness, and conflict themes of social roles. This suggests simple and complex predictive potential exist in these constructs for separating profiles based on nuanced expressions of social roles by different types of users in extremist contexts, as was the case for distinguishing extremist organizations through their ideological communication in earlier work. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19434472
- Volume :
- 13
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Behavioral Sciences of Terrorism & Political Aggression
- Publication Type :
- Academic Journal
- Accession number :
- 152624225
- Full Text :
- https://doi.org/10.1080/19434472.2020.1775675