Social Influence and Radicalization: A Social Data Analytics Study
This addresses the challenge of countering extremist activities through social data analytics, but it appears incremental as it builds on existing influence maximization and analytics work.
The paper tackles the problem of understanding and detecting online radicalization by analyzing social data and influence flow, introducing a pipeline called iRadical with algorithms and an extensible architecture that is publicly available.
The confluence of technological and societal advances is changing the nature of global terrorism. For example, engagement with Web, social media, and smart devices has the potential to affect the mental behavior of the individuals and influence extremist and criminal behaviors such as Radicalization. In this context, social data analytics (i.e., the discovery, interpretation, and communication of meaningful patterns in social data) and influence maximization (i.e., the problem of finding a small subset of nodes in a social network which can maximize the propagation of influence) has the potential to become a vital asset to explore the factors involved in influencing people to participate in extremist activities. To address this challenge, we study and analyze the recent work done in influence maximization and social data analytics from effectiveness, efficiency and scalability viewpoints. We introduce a social data analytics pipeline, namely iRadical, to enable analysts engage with social data to explore the potential for online radicalization. In iRadical, we present algorithms to analyse the social data as well as the user activity patterns to learn how influence flows in social networks. We implement iRadical as an extensible architecture that is publicly available on GitHub and present the evaluation results.