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Pete, I., Hughes, J., Chua, Y. T., Bada, M..  2020.  A Social Network Analysis and Comparison of Six Dark Web Forums. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :484—493.

With increasing monitoring and regulation by platforms, communities with criminal interests are moving to the dark web, which hosts content ranging from whistle-blowing and privacy, to drugs, terrorism, and hacking. Using post discussion data from six dark web forums we construct six interaction graphs and use social network analysis tools to study these underground communities. We observe the structure of each network to highlight structural patterns and identify nodes of importance through network centrality analysis. Our findings suggest that in the majority of the forums some members are highly connected and form hubs, while most members have a lower number of connections. When examining the posting activities of central nodes we found that most of the central nodes post in sub-forums with broader topics, such as general discussions and tutorials. These members play different roles in the different forums, and within each forum we identified diverse user profiles.

Dondio, P., Longo, L..  2014.  Computing Trust as a Form of Presumptive Reasoning. Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on. 2:274-281.

This study describes and evaluates a novel trust model for a range of collaborative applications. The model assumes that humans routinely choose to trust their peers by relying on few recurrent presumptions, which are domain independent and which form a recognisable trust expertise. We refer to these presumptions as trust schemes, a specialised version of Walton's argumentation schemes. Evidence is provided about the efficacy of trust schemes using a detailed experiment on an online community of 80,000 members. Results show how proposed trust schemes are more effective in trust computation when they are combined together and when their plausibility in the selected context is considered.