The Wisdom of Crowds
A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
Internet-scale collection of human-reviewed data
Proceedings of the 16th international conference on World Wide Web
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A few bad votes too many?: towards robust ranking in social media
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Click fraud resistant methods for learning click-through rates
WINE'05 Proceedings of the First international conference on Internet and Network Economics
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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.