Risk Matrix for Violent Radicalization: A Machine Learning Approach (Frontiers in Psychology)

“Typically, violent extremists came from criminal but not radical backgrounds and were radicalized in late stages of their life. They were followers in terrorist groups, sought training, and were radicalized by social media. They belonged to low social strata and had problematic social relations. By contrast, non-violent but still criminal extremists were characterized by a family tradition of radicalism without having criminal backgrounds, belonged to higher social strata, were leaders in terrorist organizations, and backed terrorism by supporting activities.”

https://lnkd.in/e5wE-zNC

Leave a Reply

Your email address will not be published.

Related Post

Using Transfer-based Language Models to Detect Hateful and Offensive Language Online (Proceedings of the Fourth Workshop on Online Abuse and Harms)Using Transfer-based Language Models to Detect Hateful and Offensive Language Online (Proceedings of the Fourth Workshop on Online Abuse and Harms)

“The results indicate that the attention-based models profoundly confuse hate speech with offensive and normal language. However, the pre-trained models outperform state-of-the-art results in terms of accurately predicting the hateful

%d bloggers like this: