Bayesian vote weighting in crowdsourcing systems

In social collaborative crowdsourcing platforms, the votes which people give on the content generated by others is a very important component of the system which seeks to find the best content through collaborative action. In a crowdsourced innovation platform, people vote on innovations/ideas generated by others which enables the system to synthesize the view of the crowd about an idea. However, in many such systems gaming or vote spamming as it is commonly known is prevalent. In this paper we present a Bayesian mechanism for weighting the actual vote given by a user to compute an effective vote which incorporates the voters history of voting and also what the crowd is thinking about the value of the innovation. The model results into some interesting insights about social voting systems and new avenues for gamification.