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FeedMe: Enhancing Directed Content Sharing on the Web

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.

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FeedMe: Enhancing Directed Content Sharing on the Web

  1. Enhancing Directed Content Sharing on the Web<br />Michael Bernstein, Adam Marcus, David Karger, Rob Miller<br />mitcsail<br />mit human-computer interaction<br />
  2. Information Overload<br />
  3. You want more information.<br />
  4. Aggregate<br />Filter<br />Facet<br />Recommend<br />
  5. Friendsourced content sharing<br />Related to your research<br />
  6. Friendsourced content sharing<br />is inhibited.<br />Related to your research<br />
  7. Our goal is to encourage friendsourced content sharingby making it easier and less inhibited.<br />
  8. http://feedme.csail.mit.edu<br />Recommend recipients to reduce the time and effort for sharing<br />Surface activity via awareness indicators<br />Learn personalized models passively<br />
  9. Introduction<br />Related Work<br />Understanding Sharing<br />Supporting Sharing<br />Implementation<br />Evaluation<br />Discussion<br />Conclusion<br />
  10. Related work<br />Mediating our information access<br />Information mediators [Ehrlich and Cash 94]<br />Contact brokers [Paepcke 96]<br />Technological gatekeepers [Allen 77]<br />Information is shared via e-mail [Erdelez and Rioux 00]to educate and form rapport [Marshall and Bly 04]<br />Recommender systems focus on discovery [Resnick et al 94, Joachims et al 97]<br />Expertise recommenders focus on information needs [McDonald 00]<br />The FeedMe namesake [Burke 09, Sen 06]<br />
  11. What drives social sharing?<br />Two surveys (N=40 / N=100) on Amazon Mechanical Turk<br />Vetted for cheaters<br />Paid $0.20 / $0.05<br />Intro<br />Understanding<br />Supporting<br />Evaluation<br />Discussion<br />FeedMe<br />
  12. E-mail is still dominant<br />
  13. Recipients want more<br />When asked to agree/disagree with:“I would be interested in receiving more relevant links.”Median = 6<br />1<br />2<br />3<br />4<br />5<br />6<br />7<br />
  14. Hypotheses<br />Sharers are those who seek out large volumes of web content<br />Sharers are especially social individuals<br />
  15. What explains interest in sharing?<br />4 scales of 10 questions each<br />Sharing<br />“I often tell people I know about my favorite web sites to follow. “<br />Seeking<br />“I often seek out entertaining posts, jokes, comics and videos using the Internet. “<br />Bridging social capital“I come in contact with new people all the time.” <br />Bonding social capital<br /> “There is someone I can turn to for advice about making very important decisions.”<br />[Ellison et al. 2007]<br />
  16. Hypotheses<br />Sharers seek out large amounts of web content<br />Sharers are especially social individuals<br />β<br />p-value<br />factor<br />Seeking<br />.74<br />.001<br /><<br />.22<br />.05<br /><<br />Bridging Social Capital<br />.33<br />.01<br />Bonding Social Capital<br />Adj. R2 = 0.56<br />
  17. Can we give active content seekers the means to share more?<br />Intro<br />Understanding<br />Supporting<br />Evaluation<br />Discussion<br />FeedMe<br />
  18. Recommendations<br />Annotate each post with friends who might be interested in the content<br />
  19. Recommendations<br />Lifehacker: Share with friends using MIT’s FeedMe<br />rcm@mit.edu<br />karger@mit.edu<br />msbernst@mit.edu<br />Type a name…<br />0 FeedMes today<br />5 FeedMes today<br />1 FeedMe today<br />Add an optional comment…<br />Now<br />Later<br />
  20. Awareness indicators<br />rcm@mit.edu<br />rcm@mit.edu<br />rcm@mit.edu<br />0 FeedMes today<br />5 FeedMes today<br />Seen it already<br />Address concerns about volume:<br /> “How much are we sending them?”<br />Give an indication of whether it’s old news“Oh, somebody already sent it to them?”<br />
  21. Digests: managing volume<br />Share without overwhelming the inbox<br />Now<br />Later<br />
  22. One-click thanks<br />Low-effort recipient feedback<br />
  23. Implementation<br />
  24. Building models without recipient involvement<br />MIT HCIResearch<br />FeedMe Profile<br />rcm@mit.edu<br />rcm@mit.edu<br />rcm@mit.edu<br />MIT HCIResearch<br />Computer Science Education<br />Computer Science Education<br />
  25. Recommendation details<br />joe@sixpack.com:<br />sports: 200<br />baseball: 150<br />sox: 132<br />lacrosse: 89<br />workout: 41muscle: 30hiking: 23vitamin: 22<br />twitter: 38<br />tweet: 30<br />social: 27<br />post: 23<br />conversation: 19<br />answers: 10<br />blog: 3<br />google: 1<br />rcm@mit.edu:<br />design: 184<br />tweet: 170<br />web: 79<br />twitter: 48<br />social: 43friendfeed: 32blog: 25developer: 23<br />
  26. What impact does FeedMe haveon friendsourced sharing?<br />Two-week study for $30<br />60 Google Reader users (46 male) recruited through blogs<br />Used Google Reader daily for two weeks with FeedMe installed<br />Viewed 84,667 posts; shared 713<br />Intro<br />Understanding<br />Supporting<br />Evaluation<br />Discussion<br />FeedMe<br />
  27. 2x2 Study design<br />Recommendations (within-subjects)<br />Awareness and feedback (between-subjects)<br />vs.<br />vs.<br />vs.<br />vs.<br />
  28. Do shared posts benefit recipients? <br />Surveyed 64 recipients, who reported on 160 shared posts<br />80.4% of posts contained novel content<br /> Appreciative of having received the post<br />
  29. Are the recommendations worthwhile?<br />Speed, Keyboard-Free<br />Visual Clutter<br />
  30. Do overload indicators help?<br />rcm@mit.edu<br />rcm@mit.edu<br />We asked: “What killer feature would get you to use FeedMe more?”<br />We measured: unprompted responses regarding social inhibition<br />14 of 28 without awareness+feedback features asked for them<br />3 of 30 with awareness+feedback features asked for them<br />5 FeedMes today<br />Saw it already<br />
  31. One-click thanks<br />30.9% of shares received a thanks<br />
  32. Discussion<br />Mixed-initiative social recommender systems<br />E-mail as a delivery mechanism<br />Intro<br />Understanding<br />Supporting<br />Evaluation<br />Discussion<br />FeedMe<br />
  33. Mixed-initiative social recommenders<br />Humans filter recommendations for their friends<br />Small marginal cost:sharers have already read the article<br />AI<br />Friend<br />Recipient<br />
  34. Mixed-initiative social recommenders<br />Sharers appreciate recommendations<br />High error tolerance<br />Applications to other AI-hard problems<br />[Bernstein et al. UIST ‘09]<br />
  35. Low-priority Queue<br />E-mail as a delivery mechanism<br />“I'm pretty conservative about invading <br /> people's email space.”<br />“I feel that articles that I read are more like ambient information.”<br />
  36. Summary of contributions<br /><ul><li>Formative understanding of the process behind link sharing
  37. Leveraging social link sharing to power a content recommender
  38. Users as lightweight recommendation verification for others</li></li></ul><li>http://feedme.csail.mit.edu<br />http://bit.ly/CHIProgram2010<br />
  39. Study design<br />Between-subjects<br />Within-subjects<br />38<br />
  40. Bootstrapped Learning<br />Post Recipients<br />30.9% One-click Thanks<br />FeedMe Not Installed: 93.8%<br />FeedMe Installed: 6.2%<br />39<br />
  41. Topic relevance drives enjoyment<br />
  42. Topic relevance drives enjoyment<br />“Those who know my politics usually send me very pointed articles – no junk.”<br />“I could care less about a cat boxing.”<br />
  43. Sharing x 10<br />Seeking x 10<br />Bridging x 10<br />Bonding x 10<br />Verify scale agreement<br /> normality assumptions<br />homoscedascicity<br /> factor loading<br />Multiple regression on sharing index<br />
  44. β<br />p-value<br />factor<br />Seeking<br />.74<br />< .001<br />Bridging Social Capital<br />.22<br />< .05<br />Bonding Social Capital<br />.01<br />.33<br />Adj. R2 = 0.56<br />
  45. Hypotheses<br />Sharers seek out large amounts of web content<br />Sharers are especially social individuals<br />
  46. Hypotheses<br />Sharers seek out large amounts of web content<br />Sharers are especially social individuals<br />
  47. FeedMe’s target users<br />Sharers: firehose<br />Purposely consume volumes of content<br />Use aggregators like Google Reader<br />Recipients: drip<br />Won’t use a new tool, but read e-mail<br />
  48. Privacy<br />Learn from intersection of recommendations<br />

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