The moral embeddedness of cryptomarkets: text mining feedback on economic exchanges on the dark web
Ana Macanovic, Wojtek Przepiorka
Abstract
Abstract Reputation systems promote cooperation in large-scale online markets for illegal goods. These so-called cryptomarkets operate on the Dark Web, where legal, social, and moral trust-building mechanisms are difficult to establish. However, for the reputation mechanism to be effective in promoting cooperation, traders have to leave feedback after completed transactions in the form of ratings and short texts. Here we investigate the motivational landscape of the reputation systems of three large cryptomarkets. We employ manual and automatic text mining methods to code 2 million feedback texts for a range of motives for leaving feedback. We find that next to self-regarding motives and reciprocity, moral norms (i.e. unconditional considerations for others’ outcomes) drive traders’ voluntary supply of information to reputation systems. Our results show how psychological mechanisms interact with organizational features of markets to provide a collective good that promotes mutually beneficial economic exchange.