Anonymity, Membership-Length and Postage Frequency as Predictors of Extremist Language and Behaviour among Twitter Users.

Sutch, Hollie; Carter, Pelham

The rise in participation of social media networks is accompanied with a corresponding rise in online extremism. The present research was carried out to ascertain whether anonymity, membership length and postage frequency are predictors of online extremism. A total of 205 Twitter accounts and 102,290 tweets were examined. To address the research question, both a corpus linguistic analysis (CLA) and content analysis (CA) were conducted. The former looked at extreme words associated with Islam and the latter looked at four types of extremist behaviour (extreme pro-social, extreme anti-social, extreme anti-social prejudicial biases and extreme radical behaviours). Keyness tests demonstrated that extreme words were most significantly associated with Twitter accounts with high anonymity, low membership length and low postage frequency. A series of multiple regressions found that anonymity significantly predicted four types of extremist behaviour. Membership length only predicted extreme anti-social behaviour and postage frequency did not display any significant predictive power for any of the four types of extremist behaviour. These results suggest that anonymity, membership length and postage frequency differ in terms of predicting extremist language and behaviour.

ANONYMITY; SOCIAL participation; LINGUISTIC analysis; BEHAVIOR; EXTREMISTS

International Journal of Cyber Criminology, 2019, Vol 13, Issue 2, p439

0974-2891

Academic Journal

10.5281/zenodo.3707789