  Decentralized content curation is the process through which uploaded posts
are ranked and filtered based exclusively on users' feedback. Platforms such as
the blockchain-based Steemit employ this type of curation while providing
monetary incentives to promote the visibility of high quality posts according
to the perception of the participants. Despite the wide adoption of the
platform very little is known regarding its performance and resilience
characteristics. In this work, we provide a formal model for decentralized
content curation that identifies salient complexity and game-theoretic measures
of performance and resilience to selfish participants. Armed with our model, we
provide a first analysis of Steemit identifying the conditions under which the
system can be expected to correctly converge to curation while we demonstrate
its susceptibility to selfish participant behaviour. We validate our
theoretical results with system simulations in various scenarios.
