To find interesting, personally relevant web content, people rely on friends and colleagues to pass links along as they encounter them. In this paper, we study and augment link-sharing via e-mail, the most popular means of sharing web content today. Armed with survey data indicating that active sharers of novel web content are often those that actively seek it out, we developed FeedMe, a plug-in for Google Reader that makes directed sharing of content a more salient part of the user experience. FeedMe recommends friends who may be interested in seeing content that the user is viewing, provides information on what the recipient has seen and how many emails they have received recently, and gives recipients the opportunity to provide lightweight feedback when they appreciate shared content. FeedMe introduces a novel design space within mixed-initiative social recommenders: friends who know the user voluntarily vet the material on the user’s behalf. We performed a two-week field experiment (N=60) and found that FeedMe made it easier and more enjoyable to share content that recipients appreciated and would not have found otherwise.

1. Enhancing Directed Content Sharing on the Web Michael Bernstein, Adam Marcus, David Karger, Rob Miller mitcsail mit human-computer interaction

2. Information Overload

3. You want more information.

4. Aggregate Filter Facet Recommend

5. Friendsourced content sharing Related to your research

6. Friendsourced content sharing is inhibited. Related to your research

7. Our goal is to encourage friendsourced content sharingby making it easier and less inhibited.

8. http://feedme.csail.mit.edu Recommend recipients to reduce the time and effort for sharing Surface activity via awareness indicators Learn personalized models passively

9. Introduction Related Work Understanding Sharing Supporting Sharing Implementation Evaluation Discussion Conclusion

10. Related work Mediating our information access Information mediators [Ehrlich and Cash 94] Contact brokers [Paepcke 96] Technological gatekeepers [Allen 77] Information is shared via e-mail [Erdelez and Rioux 00]to educate and form rapport [Marshall and Bly 04] Recommender systems focus on discovery [Resnick et al 94, Joachims et al 97] Expertise recommenders focus on information needs [McDonald 00] The FeedMe namesake [Burke 09, Sen 06]

11. What drives social sharing? Two surveys (N=40 / N=100) on Amazon Mechanical Turk Vetted for cheaters Paid $0.20 / $0.05 Intro Understanding Supporting Evaluation Discussion FeedMe

12. E-mail is still dominant

13. Recipients want more When asked to agree/disagree with:“I would be interested in receiving more relevant links.”Median = 6 1 2 3 4 5 6 7

14. Hypotheses Sharers are those who seek out large volumes of web content Sharers are especially social individuals

15. What explains interest in sharing? 4 scales of 10 questions each Sharing “I often tell people I know about my favorite web sites to follow. “ Seeking “I often seek out entertaining posts, jokes, comics and videos using the Internet. “ Bridging social capital“I come in contact with new people all the time.” Bonding social capital “There is someone I can turn to for advice about making very important decisions.” [Ellison et al. 2007]

16. Hypotheses Sharers seek out large amounts of web content Sharers are especially social individuals β p-value factor Seeking .74 .001 < .22 .05 < Bridging Social Capital .33 .01 Bonding Social Capital Adj. R2 = 0.56

17. Can we give active content seekers the means to share more? Intro Understanding Supporting Evaluation Discussion FeedMe

18. Recommendations Annotate each post with friends who might be interested in the content

19. Recommendations Lifehacker: Share with friends using MIT’s FeedMe rcm@mit.edu karger@mit.edu msbernst@mit.edu Type a name… 0 FeedMes today 5 FeedMes today 1 FeedMe today Add an optional comment… Now Later

20. Awareness indicators rcm@mit.edu rcm@mit.edu rcm@mit.edu 0 FeedMes today 5 FeedMes today Seen it already Address concerns about volume: “How much are we sending them?” Give an indication of whether it’s old news“Oh, somebody already sent it to them?”

21. Digests: managing volume Share without overwhelming the inbox Now Later

22. One-click thanks Low-effort recipient feedback

23. Implementation

24. Building models without recipient involvement MIT HCIResearch FeedMe Profile rcm@mit.edu rcm@mit.edu rcm@mit.edu MIT HCIResearch Computer Science Education Computer Science Education

25. Recommendation details joe@sixpack.com: sports: 200 baseball: 150 sox: 132 lacrosse: 89 workout: 41muscle: 30hiking: 23vitamin: 22 twitter: 38 tweet: 30 social: 27 post: 23 conversation: 19 answers: 10 blog: 3 google: 1 rcm@mit.edu: design: 184 tweet: 170 web: 79 twitter: 48 social: 43friendfeed: 32blog: 25developer: 23

26. What impact does FeedMe haveon friendsourced sharing? Two-week study for $30 60 Google Reader users (46 male) recruited through blogs Used Google Reader daily for two weeks with FeedMe installed Viewed 84,667 posts; shared 713 Intro Understanding Supporting Evaluation Discussion FeedMe

27. 2x2 Study design Recommendations (within-subjects) Awareness and feedback (between-subjects) vs. vs. vs. vs.

28. Do shared posts benefit recipients? Surveyed 64 recipients, who reported on 160 shared posts 80.4% of posts contained novel content Appreciative of having received the post

29. Are the recommendations worthwhile? Speed, Keyboard-Free Visual Clutter

30. Do overload indicators help? rcm@mit.edu rcm@mit.edu We asked: “What killer feature would get you to use FeedMe more?” We measured: unprompted responses regarding social inhibition 14 of 28 without awareness+feedback features asked for them 3 of 30 with awareness+feedback features asked for them 5 FeedMes today Saw it already

31. One-click thanks 30.9% of shares received a thanks

32. Discussion Mixed-initiative social recommender systems E-mail as a delivery mechanism Intro Understanding Supporting Evaluation Discussion FeedMe

33. Mixed-initiative social recommenders Humans filter recommendations for their friends Small marginal cost:sharers have already read the article AI Friend Recipient

34. Mixed-initiative social recommenders Sharers appreciate recommendations High error tolerance Applications to other AI-hard problems [Bernstein et al. UIST ‘09]

35. Low-priority Queue E-mail as a delivery mechanism “I'm pretty conservative about invading people's email space.” “I feel that articles that I read are more like ambient information.”

37. Leveraging social link sharing to power a content recommender

39. Study design Between-subjects Within-subjects 38

40. Bootstrapped Learning Post Recipients 30.9% One-click Thanks FeedMe Not Installed: 93.8% FeedMe Installed: 6.2% 39

41. Topic relevance drives enjoyment

42. Topic relevance drives enjoyment “Those who know my politics usually send me very pointed articles – no junk.” “I could care less about a cat boxing.”

43. Sharing x 10 Seeking x 10 Bridging x 10 Bonding x 10 Verify scale agreement normality assumptions homoscedascicity factor loading Multiple regression on sharing index

44. β p-value factor Seeking .74 < .001 Bridging Social Capital .22 < .05 Bonding Social Capital .01 .33 Adj. R2 = 0.56

45. Hypotheses Sharers seek out large amounts of web content Sharers are especially social individuals

46. Hypotheses Sharers seek out large amounts of web content Sharers are especially social individuals

47. FeedMe’s target users Sharers: firehose Purposely consume volumes of content Use aggregators like Google Reader Recipients: drip Won’t use a new tool, but read e-mail

48. Privacy Learn from intersection of recommendations