L. Ranaldi, A. Nourbakhsh, F. Fallucchid, FM. Zanzotto &ndash; Guglielmo Marconi University, Roma, Italy; University of Rome Tor Vergata, Roma, Italy
With the emergence of COVID-19 disease worldwide, a market of the products related to this disease formed across the Internet. By the time these goods were in short supply, many uncontrolled Dark Web Marketplaces (DWM) were active in selling these products. At the same time, Dark Web Forums (DWF) became proxies for spreading false ideas, fake news about COVID-19, and advertising products sold in DWMs. This study investigates the activities entertained in the DWMs and DWFs to propose a learning-based model to distinguish them from their related counterparts on the surface web. To this end, we propose a COVID-19 Open Source artificial INTelligence framework (C-OSINT) to automatically collect and classify the activities done in DWMs and DWFs. Moreover, we corporate linguistic and stylistic solutions to leverage the classification performance between the content found in DWMs and DWFs and two surface web sources. Our results show that using syntactic and stylistic representation outperforms the Transformer based results over these domains.