The content of Online Illicit Marketplaces (OIMs) on the Dark Web has been studied using different approaches. For example, qualitative ethnographic studies to understand the markets' social dynamics have produced interesting descriptions of specific sites, contexts and behaviours [1]; but given the large amount of data exchanged on several markets a manual qualitative analysis can only provide a partial view of these systems. Therefore, more quantitative approaches have been used (e.g., to provide an overview of the exchanges and social dynamic within the Silk Road OIM [2]), and to classify the information exchanged online (e.g., clustering forum posts depending on the function of the post or the type of drug [3]). These quantitative methods can scale to larger datasets, but they cannot extract the rich behavioural information that can be obtained through manual inspection of the data. In this poster we show our experience combining both, statistical inference methods and blockmoldeing techniques approaches, into a single analysis pipeline able to provide a more complete understanding of the social dynamics occurring on these types of online participatory forums.