1. ken goldberg, gail de kosnik, kimiko ryokai (+ students) uc berkeley Opinion Space

2. Mission: To critically analyze and help shape developments in new media from para-disciplinary and global perspectives that emphasize humanities and the public interest. bcnm.berkeley.edu

5. Opinion Space

7. Open Discussion Question:

8. Opinion Space

9. Principal Component Analysis(1/4) Consider 2 propositions, each user is a point in a 2D space.

10. Principal Component Analysis (2/4) Now consider 3 propositions, each user is a point in 3D space Challenge: How to best project onto a screen for viewing data?

11. Principal Component Analysis (3/4) Different projections give different “views” of the data. PCA computes the projection that maximizes variance: Projection AProjection B

12. Principal Component Analysis (4/4) Maximum Variation Projection generalizes to n dimensions:

14. Spatial Reputations: Assumptions Goal: design a reputation system for users in Opinion Space that is as fair and resistant to manipulation as possible Assumption: users are more likely to agree with comments made by near- by users Assumption: malicious users may be more interested in promoting a certain viewpoint than their own particular comment Idea: reward / emphasize comments that promote consensus from a diversity of users (“compelling”) = negative rating = positive rating

15. Spatial Reputations: Weighting Model Distance between users Score r’ of rating Spatial weighting model for comment ratings Model: transform comment ratings to reflect how valuable they are towards finding a compelling comment r ij ’ = 1 r ij ’ = -1

16. Sybil attacks: Two types of attacks –Create opinion profile most distant from the target’s profile (easily detectable) –Create uniformly distributed profiles (much more work) False feedback: to have maximum impact when rating similar users highly, forced to misrepresent ratings of five propositions. Can’t rate truthfully and unfairly inflate neighbor’s reputation at the same time. Whitewashing: Currently no functionality for detecting and preventing whitewashing. In the future, may use IP-tracking for this purpose. Analysis: Resisting Manipulation

17. Preliminary Analysis: Empirical As of May 5, over 11,000 ratings collected on 1,200 comments Users who gave the highest rating to a comment were on average 15% closer than users who gave the worst rating Raw comment ratings Number of ratings Ratings transformed by spatial reputations Number of ratings Correlation between rank aggregation methods

18. Opinion Space

20. Opinion Space 2.0

22. ken goldberg, goldberg@berkeley.edu 090909: Neutrality Symposium Please try it: http://opinion.berkeley.edu

28. Opinion Space

29. Bias (Implicit Association) Test https://implicit.harvard.edu/implicit/

31. … … … … Collaborative collaborative control models: MultiTasking Batch

32. Online Recommender Systems Pandora Amazon Netflix

33. The EigenTaste Algorithm Offline: –Compute eigenvectors and project users onto eigen plane. –Cluster and compute average ratings for each cluster. Online: –Collect ratings for objects in gauge set –Project onto the eigen plane –Find representative cluster –Recommend objects based on average ratings within that cluster