Criminal clickbait: a panel data analysis on the attractiveness of online advertisements offering stolen data - Madarie, Renushka; de Poot, Christianne; Weulen Kranenbarg, Marleen (Frontiers in Big Data, 2023)
Introduction Few studies have examined the sales of stolen account credentials on darkweb markets. In this study, we tested how advertisement characteristics affect the popularity of illicit online advertisements offering account credentials. Unlike previous criminological research, we take a novel approach by assessing the applicability of knowledge on regular consumer behaviours instead of theories explaining offender behaviour. Methods We scraped 1,565 unique advertisements offering credentials on a darkweb market. We used this panel data set to predict the simultaneous effects of the asking price, endorsement cues and title elements on advertisement popularity by estimating several hybrid panel data models. Results Most of our findings disconfirm our hypotheses. Asking price did not affect advertisement popularity. Endorsement cues, including vendor reputation and cumulative sales and views, had mixed and negative relationships, respectively, with advertisement popularity. Discussion Our results might suggest that account credentials are not simply regular products, but high-risk commodities that, paradoxically, become less attractive as they gain popularity. This study highlights the necessity of a deeper understanding of illicit online market dynamics to improve theories on illicit consumer behaviours and assist cybersecurity experts in disrupting criminal business models more effectively. We propose several avenues for future experimental research to gain further insights into these illicit processes.