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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Bayesian Vote Weighting in Crowdsourcing Systems
verfasst von : Manas S. Hardas, Lisa Purvis
Erschienen in: Computational Collective Intelligence. Technologies and Applications
Verlag: Springer Berlin Heidelberg
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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.