Measuring the value of specific contributions users make to a community and using that measurement to influence participation.

• Developed algorithms for computing the value of ratings both prospectively and retrospectively.
• Designed, conducted, and wrote up an experiment to test whether making the value of a contribution explicit increased contribution and if the beneficiary of the contribution (self-versus other) made a difference. This paper was accepted at the ACM Conference on Computer Supported Cooperative Work.
• Designed (and will carry out) an experiment to test the effect of revealing value to the users, and of the effect of the size and type of the beneficiary group, on user contribution.
• Our initial experiment showed, as predicted by theory, that individuals contributed more when they were reminded that their contributions were unique. However, a puzzling result was that reminding individuals that their ratings help either themselves or others prompted less rather than more rating.
• Developed new algorithms for assessing the value of a specific rating contribution to a community, both retrospectively for past contributions and prospectively for potential ones.
• Designed new interfaces to make this information visible to users.
• Conducted experiments where we asked subscribers to MovieLens to rate additional movies as part of a campaign. In prior experiments in the series, we attempted to signal both the beneficiary of contributions and their value through persuasive email messages sent to subscribers. In the newest experiments, we modified the user interface to MovieLens so that the beneficiary of a rating and its value are shown as an icon associated with each movie. We developed algorithms to assess the value of a rating, which we defined as improvement in accuracy of predictions about a movie for a particular target group if a subscriber rated it. We showed subscribers the relative value of rating 100 movies they were most likely to have seen for each of four target groups -- the subscriber him or herself, the average movie lens subscriber, someone who liked the movie genres the subscriber liked, and someone who like genres the subscriber did not like.
• Subscribers participating in the rating campaign rated approximately 7.3% of the movies shown to them. Their likelihood of rating a movie doubled when they believed that they were helping other subscribers who liked genres they liked and increased by about 50% when they believed they were helping the typical MovieLens subscriber. However, they did not increase the number of rating they made when they believed that doing so would help themselves or would help subscribers who liked genres they did not like.
• Expanded upon prior work in 'value of information' analysis to present new and more focused algorithms for assessing the value of a rating contribution. 
• We have extended our exploration of algorithmic approaches to assessing actual and potential value of contributions to a user and to the community as a whole.
• The first experiment found that users were more likely to rate items that were indicated as high value.  Users self reported that they would most prefer to rate items that were high value for themselves; in practice, however, they actually preferentially rated items that were marked as being of high value for other users.  In other results, users chose to rate items that were of high value to small groups of similar people, in preference both to dissimilar groups, and to the population as a whole.  We have not yet completed data analysis on the second experiment.

Title  [6]	Authors [7]	Appears In	Publication Date [8]	Date added [9]
Group theory for social engineering: Promises and pitfalls [10]	Robert Kraut	Presented at the annual meeting of the Academy of Management	2004	01.28.06
Influence in Ratings-Based Recommender Systems: An Algorithm-Independent Approach [11]	Al Mamunur Rashid, George Karypis, John Riedl	Proceedings of SIAM 2005 Data Mining Conference	2005	01.28.06
Modeling Member Motivation and Participation in Online Communities [12]	Ren, Y., and Kraut, R.	Proceeding, Academy of Management Conference	2006	07.13.06
Motivating Participation by Displaying the Value of Contribution [13]	Al Mamunur Rashid, Kimberly Ling, Regina D Tassone, Paul Resnick, Robert Kraut, John Riedl	Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems	2006	01.25.06
Think different: increasing online community participation using uniqueness and group dissimilarity [14]	Pamela J. Ludford, Dan Cosley, Dan Frankowski, and Loren Terveen	Proceedings of the SIGCHI conference on Human factors in computing systems	2004	01.28.06
Using Social Psychology to Motivate Contributions to Online Communities [15]	Gerard Beenen, Kimberly Ling, Xiaoqing Wang, Klarissa Chang, Dan Frankowski, Paul Resnick, Robert E. Kraut	Proceedings of ACM CSCW 2004 Conference on Computer Supported Cooperative Work	2004	01.28.06
Using social psychology to motivate contributions to online communities. Journal of Computer-Mediated Communication [16]	Ling, K., Beenen, G., Ludford, P., Wang, X., Chang, K., Cosley, D., Frankowski, D., Terveen, L., Rashid, A. M., Resnick, P., and Kraut, R	Journal of Computer-Mediated Communication	2005	01.28.06