Linguist markers to early detection of radicalization in Social Networks

Abstract

Nowadays, social networks are essential communication tools that produce a large amount of information about their users and their interactions. Spreading propaganda in digital environments is a good way for various extremist groups who want to reach out with their messages, and it is considered to be an important part of the terrorist group Islamic State (IS) success in recruiting supporters from all over the world. Although propaganda is not the sole cause of radicalisation or recruitment to violent extremist ideologies, interactions on social networks can be an important component of a radicalization process due to its easy accessibility and the ability to capture and retain an individual’s interest. Even though it is not clear what role Internet and social media plays in radicalization, some previous studies have focused on measuring the risk for individuals to radicalization [1,2] and the possibility to detect individuals or groups that engage in violent extremism [3]. In this work, we focus on identifying a set of linguistic indicators that can be used to measure frustration, the perception of discrimination, and the declaration of negative and positive ideas about the Western society and Violent extremism respectively. The indicators have been tested on three different datasets: tweets by pro-ISIS users, tweets from users flagged as radicals by the Anonymous collective and a random sample of tweets gathered from the public Twitter stream. Figure 1. Density distribution of the ratio of tweets expressing positive ideas about Jihadism according to the studied indicators and their respective metrics. References [1]

Publication
1st International Caparica Conference in Translational Forensics
Antonio Gonzalez-Pardo
Antonio Gonzalez-Pardo
PhD Computer Science

Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.