This study presents an iterative methodological approach for the extension and evaluation of taxonomies applied to peer-to-peer (P2P) cryptocurrency forums in the dark web, a domain characterised by high semantic homogeneity and low thematic diversity. Drawing on a corpus of 23,642 posts, a rule-based deterministic pipeline was implemented to design an initial taxonomy focused on transactional intention, traded assets and payment mechanisms. Subsequently, non-classifiable cases were examined statistically using n-grams to detect empirical evidence of emerging subclasses, which led to the incorporation of new categories (primarily exchange-platform and forum ) derived from the behaviour of the corpus itself. This extension eliminated ambiguous categories (other/unclear) and substantially improved classification coverage, showing that taxonomies in P2P domains should be understood as evolutionary artefacts rather than fixed structures. Under this definition, the present taxonomy evolves through a documented cycle of deterministic classification, ambiguity diagnosis, candidate extraction from residual cases, and constrained human-in-the-loop consolidation. Finally, co-occurrence analysis and lexical clustering revealed four differentiated functional communities, confirming that these forums do not operate as broad discursive spaces but as transactional infrastructures oriented towards operational execution. The results provide a traceable framework for explainable semantic classification in crypto environments, with direct implications for cyber intelligence and digital forensic analysis. • Proposes an iterative and reproducible framework for extending taxonomies in semantically homogeneous domains. • Combines deterministic rule-based classification with analysis of unclassified cases to guide ontology refinement. • Eliminates residual ambiguity through data-driven taxonomic extensions without relying on opaque probabilistic models. • Shows that P2P cryptocurrency forums act as transactional infrastructures rather than thematic discursive spaces. • Validates the approach on 23,642 posts achieving full intent coverage and improved classification performance.
Medina-Merodio et al. (Fri,) studied this question.