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This paper explores various Social Network Analysis (SNA) techniques in order to identify a range of potentially ‘important’ members of Islamic Networks within Dark Web Forums. For this experiment, we conducted our investigation on five forums collected in previous work as part of the DarkWeb Forum portal and built upon the tool support created in our previous research in order to visualise and analyse the network. Whilst existing work attempts to identify these structures through state-of-the-art Computational Linguistic techniques, our work relies on the communication metadata alone. Our analysis involved first calculating a range of SNA metrics to better understand the group members, and then apply unsupervised learning in order to create clusters that would help classify the Dark Web Forums users into hierarchical clusters. In order to create our social networks, we investigated the effect of repeated author resolution and various weighting schemes on the ranking of forum members by creating four social networks per forum and evaluating the correlation of the top n users (for n = 10; 20; 30; 40; 50 and 100). Our results identified that varying the weighting schemes created more consistent ranking schemes than varying the repeated author resolution.

This article investigates Twitter data related to the kidnapping case of two German nationals in the southern region of the Philippines by the Abu Sayyaf Group (ASG). It explores perceptions of the ASG, along with associated organizations and sentiments indicated in the tweets together with statistically significant relationships. Findings revealed that: “Rebel” and “Militant” were the most frequently used labels for the ASG; a majority of the tweets contained sentiments that assess threats such as abduction and kidnapping of hostages; and almost half contained words that indicate negotiation or concession to the demands of the captors. Logistic regression analyses on “Rebel” and “Islamist” revealed positive coefficients for these sentiments used as predictors. This meant that people who assessed threats and expressed sentiments that responders should concede to the captors’ demands were more likely to use the “Rebel” or “Islamist” labels. Rather than the two longstanding dominant narratives of the ASG as terrorists and criminals, the emerging rebel and militant labels suggest a more domestically and politically sensitive Twitter commentary than is represented in the work of the Al-Qaeda-centric paradigm exponents. These findings, along with the complex associated political and policy contexts and implications, are discussed in this article.