Social Influence and Radicalization: A Social Data Analytics Study

TL;DR

This paper explores how social data analytics and influence maximization can be used to understand and detect online radicalization, introducing a new analytical pipeline called iRadical.

Contribution

It presents a novel social data analytics pipeline, iRadical, with algorithms for analyzing influence flow and user activity patterns related to radicalization.

Findings

iRadical effectively analyzes influence flow in social networks.

Algorithms identify patterns associated with online radicalization.

The architecture is scalable and publicly available.

Abstract

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.