Online Library

VOX-Pol: An Introduction

The increasing use of online social networks (OSNs) by extremists for the spread of radicalization have been a great concern for law enforcement agencies across the world. Today, they are being increasingly used by radical groups for spreading ideologies, recruitment, influencing and planning their activities. However, many of such groups remain hidden within the social fabric and can only be discovered by analyzing the related content posted by them. This article addresses the missing line of research by analyzing hidden online Radical networks along three dimensions—element level, group-level, and network level and addresses the gap which the present metrics for social network analysis fail to address as we graduate toward the dynamic network analysis. We propose new metrics to analyze the evolving topic-centric network and present our findings about the understanding of properties of such complex networks in the information network of Twitter with the existing as well the new proposed metrics.

The growing penetration of online social networks (OSNs) has emerged as a strong operational tool for spreading online radicalization by adding intensity and acceleration to the operations of radical groups. The efficiency of ways and methods proposed to measure the dynamics of radicalization are limited by the availability of models for modeling the intricacies of human dynamics and heterogeneous time varying interactions. Most of the developed algorithms and tools mainly focus on the static network structures and tend to ignore the “origin” of a new radical idea or propagation of a “weak” topic which might find its relevance in the security domain later on with the passage of time. In this article we propose a mathematical model for measuring dynamic propagation of radicalization in OSNs using Discrete Time Markov Chains by discretizing the continuous time domain. We also discuss our experimental results obtained through the information network of Twitter. The approach presented here is scalable and can easily be extended for modeling diffusion of general topics in OSNs.

The Online Social Network (OSN) has evolved as a popular platform enabling rich topic-centric interactions and serving as a medium to facilitate online radicalization (Behr et al. 2013). Keeping in view the growing need of uncovering online radicalization, we focus on the information network of Twitter and present an approach for identifying dynamic communities, which arise due to “radical” topic-centric user interactions. The approach at successive timestamps deploys evolving topic-entity maps along with evolving interaction graphs. We propose “Rate of Overlap (ROAct)” to determine the similarity among successive community timestamps. We further validate our approach using an open dataset of criminal offences in the city of Denver, Colorado. The approach presented is simple, fast, and effective for dynamic identification of topic-centric communities and, thus, will enable law enforcement agencies to identify hidden radicalization.

The increasing complexity and emergence of Web 2.0 applications have paved way for threats arising out of the use of social networks by cyber extremists (Radical groups). Radicalization (also called cyber extremism and cyber hate propaganda) is a growing concern to the society and also of great pertinence to governments & law enforcement agencies all across the world. Further, the dynamism of these groups adds another level of complexity in the domain, as with time, one may witness a change in members of the group and hence has motivated many researchers towards this field. This proposal presents an investigative data mining approach for detecting the dynamic behavior of these radical groups in online social networks by textual analysis of the messages posted by the members of these groups along with the application of techniques used in social network analysis. Some of the preliminary results obtained through partial implementation of the approach are also discussed.