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Enhancing Directed Content Sharing on the Web

Enhancing Directed Content Sharing on the Web. Michael Bernstein, Adam Marcus, David Karger , Rob Miller mit csail. mit human-computer interaction. Information Overload. You want more information. Aggregate. Filter. Facet. Recommend. Friendsourced content sharing.

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Enhancing Directed Content Sharing on the Web

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  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 factor p-value 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 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?” rcm@mit.edu rcm@mit.edu rcm@mit.edu 0 FeedMes today 5 FeedMes today Seen it already

  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. 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.” Low-priority Queue

  36. Summary of contributions • Formative understanding of the process behind link sharing • Leveraging social link sharing to power a content recommender • Users as lightweight recommendation verification for others

  37. http://feedme.csail.mit.edu http://bit.ly/CHIProgram2010

  38. Study design Between-subjects Within-subjects

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

  40. Topic relevance drives enjoyment

  41. 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.”

  42. 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

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

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

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

  46. 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

  47. Privacy Learn from intersection of recommendations

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