Identifying Dark Web clusters with temporal coherence analysis

Extremists are actively utilizing social media as propaganda to promote their ideologies. Online forums are ideal platforms to draw attention from worldwide Internet users to the timely issues and some opinions in these discussions can be threatening the public safety. It is of great interest for the intelligence to identify clusters on these forums and capture the topics of discussions and their development. Previous work in cluster identification focused on social networks constructed by the direct interactions between users utilizing link analysis techniques. However, the direct interactions between users may only capture one potential relationship between forum users. Users who share common interests may not necessarily interact with each other directly. On the other hand, they may be active in similar events simultaneously. In this paper, we propose a temporal coherence analysis approach to identify clusters of users from the Dark Web data. Users are represented as vectors of activeness and clusters are extracted with the support of temporal coherence analysis. We tested our proposed methods on both synthetic dataset and real world dataset. Using the real-world Dark Web dataset, three clusters were identified and each cluster was also associated with a specific theme. It shows that a cluster of users participating in a theme of discussion can be discovered without using any content analysis but only using temporal analysis.