CRCS Lunch Seminar

Date: Monday, November 19, 2012
Time: 12:00pm – 1:30pm
Place: Maxwell Dworkin 119

Speaker:   Arpita Ghosh, Cornell University

Title: Social Computing and User Generated Content: A Game-theoretic Approach

Abstract:  Social computing is now ubiquitous on the Web, with user-generated contributions on sites like Amazon and Yelp, Q&A forums like Y! Answers or StackOverflow, blogs and YouTube forming a growing fraction of the content consumed by Web users. But while there is a large amount of user-generated content online, not all of it is of the same quality. What can we understand, using an economic approach, about what incentive schemes elicit high quality contributions, as well as adequate participation, in such systems?

We provide a game-theoretic model with strategic, attention-motivated contributors within which to address the problem of incentivizing high-quality user-generated content. We first use this model to investigate the widely-usedrank-order allocation, where users’ contributions are displayed on a webpage in decreasing order of their ratings: such an allocation of attention constitutes a mechanism, which can influence the quality of content produced by attention-motivated contributors. We show that this rank-order mechanism elicits high quality contributions— in a very strong sense— while also achieving high participation in equilibrium: the lowest quality that can arise in anymixed strategy equilibrium of the rank-order mechanism becomes optimal as the amount of available attention diverges. Additionally, these equilibrium qualities are higher (with probability tending to 1 in the limit of diverging attention) than those elicited in equilibrium by a more equitable proportional mechanism, which distributes attention in proportion to the number of positive ratings a contribution receives. We then move on to crowdsourcing environments with non-diverging rewards, such as the contests hosted by Innocentive or TopCoder, as well as crowdsourced content as in online Q\&A forums, and use a model with endogenous entry to analyze incentivizing high quality in these settings. Unlike models which treat participation as an exogenous choice, the expected number of participants here can be increased by subsidizing entry, potentially improving the expected value of the best contribution. However, we show that free entry is, in fact, dominated by taxing entry— making all entrants pay a small fee which is rebated to the winner can improve the quality of the best contribution over a winner-take-all contest with no taxes.

Based on joint work with Patrick Hummel (EC’11) and Preston McAfee (WWW’11, WWW’12).

Bio: Arpita Ghosh is an Associate Professor of Information Science at Cornell University. Her research focuses on algorithms and mechanism design in the context of strategic behavior on the Web, particularly social computing, user-generated content, and crowdsourcing, and markets and mechanisms for privacy.  She holds a PhD from Stanford University.