Quantifying the relationship between specialisation and reputation in an online platform

Abstract

Abstract Online platforms implement digital reputation systems in order to steer individual user behaviour towards outcomes that are deemed desirable on a collective level. At the same time, most online platforms are highly decentralised environments, leaving their users plenty of room to pursue different strategies and diversify behaviour. We provide a statistical characterisation of the user behaviour emerging from the interplay of such competing forces in Stack Overflow, a long-standing knowledge sharing platform. Over the 11 years covered by our analysis, we represent the interactions between users and topics as bipartite networks. We find such networks to display nested structures akin to those observed in ecological systems, demonstrating that the platform’s user base consistently self-organises into specialists and generalists, i.e., users who focus on narrow and broad sets of topics, respectively. We relate the emergence of these behaviours to the platform’s reputation system with a series of data-driven models, and find specialisation to be statistically associated with a higher ability to post the best answers to a question. We contrast our findings with observations made in top-down environments—such as firms and corporations—where generalist skills are consistently found to be more successful.