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Online platforms experience a tension between decentralisation and incentivesto steer user behaviour, which are usually implemented through digitalreputation systems. We provide a statistical characterisation of the userbehaviour emerging from the interplay of such competing forces in StackOverflow, a long-standing knowledge sharing platform. Over the 11 years coveredby our analysis, we find that the platform's user base consistentlyself-organise into specialists and generalists, i.e., users who focus theiractivity on narrow and broad sets of topics, respectively. We relate theemergence of these behaviours to the platform's reputation system with a seriesof data-driven models, and find specialisation to be statistically associatedwith a higher ability to post the best answers to a question. Our findings arein stark contrast with observations made in top-down environments - such asfirms and corporations - where generalist skills are consistently found to bemore successful.

Quantifying the relationship between specialisation and reputation in an online platform