  Online platforms experience a tension between decentralisation and incentives
to steer user behaviour, which are usually implemented through digital
reputation systems. 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 find that the platform's user base consistently
self-organise into specialists and generalists, i.e., users who focus their
activity 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. Our findings are
in stark contrast with observations made in top-down environments - such as
firms and corporations - where generalist skills are consistently found to be
more successful.
