Synergies Between Search and Social Metrics

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My talk from: New Metrics for New Media:
Analytics for Social Media and Virtual Worlds

http://mediax.stanford.edu/WSI/metrics.html

@Media X Stanford 2009

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  • Me: Y! Research after Northwestern, PhD CS, Adjunct at Medill, CMEX @ Ames, Studio Art instructor for 5 years during my BS/MS in AI
  • How do people become aware of services and applications, and integrate them into their lives? What are tipping points for awareness and for increased usage?How/when/when do people share knowledge (digital content)?(3) How is the increase in information appliances like smart phones and always-on internet displays in the home and in public spaces changing knowledge sharing and internet service use?
  • This goes beyond A+B testing & hill climbing.
  • Are they really watching or talking to each other?
  • Reciprocity means nothing for core search as we know it
  • Synergies Between Search and Social Metrics

    1. Synergies BetweenSearch and Social Metrics<br />@ mediaX • Stanford University<br />David A. Shamma • Yahoo! Research<br />
    2. Internet Experiences Group (IEG)<br />Yahoo! Research<br />
    3. Internet ExperiencesYahoo! Research<br />Elizabeth Churchill<br />M Cameron Jones<br />Ayman<br />
    4. IEG Research Areas<br />
    5. IEG Research Areas<br />
    6. Methods<br />Experience measurement through method triangulation:<br /><ul><li>Literature/business analysis
    7. Site reviews
    8. Data visualization
    9. Surveys
    10. Field studies
    11. Prototypes
    12. Activity log analysis</li></ul>Psychology, Sociology, Anthropology, <br />Linguistics, Computer Science, Design <br />
    13. Why are these people here?<br />Dolores Park, San Francisco July 2006.<br />
    14. People Like Video<br />FIFA World Cup Final 2006.<br />
    15. Legacy Web Video<br />
    16. Legacy Metrics<br /><ul><li>Page Views
    17. Playback
    18. Tags
    19. Links/Embed
    20. Favorites
    21. Comments
    22. People
    23. Shares</li></li></ul><li>Yahoo! Zync<br />
    24. What to Collect to measure engagement?<br />Type of event (Zync player command or a normal chat message)<br />Anonymous hash (uniquely identifies the sender and the receiver, without exposing personal account data)<br />URL to the shared video<br />Timestamp for the event<br />The player time(with respect to the specific video) at the point the event occurred<br />The number of characters and the number words typed (for chat messages)<br />Emoticons used in the chat message<br />
    25. A Short Movie<br />
    26. Percent of actions over time.<br />
    27. Volume of actions over time.<br />
    28. Chat follows the video!<br />CHAT<br />
    29. Social Networks <br />
    30. Boundary Objects<br />
    31. Core Stats (opt-in)<br />April 1, 2009 through April 7, 2009 (inclusive).<br />3.25 million events (URLs, chat, volumes, pause events). <br />24,258 users • 24,506 sessions<br />Of these users, 35.29% (μ=2:02, σ= 2:72,σ2= 7:40) of the users engaged in more than one session during that week<br />76,762 URLs, 23% shared in more than one session.<br />Over 99% of the shared videos came from YouTube.<br />Approximately 2% of all the URLs sent within Yahoo! Messenger at-large.<br />
    32. Reciprocity<br />43.6% of the sessions the invitee played at least one video back to the session’s initiator.<br />77.7% sharing reciprocation<br />Pairs of people often exchanged more than one set of videos in a session.<br />In the categories of Nonprofit, Technology and Shows, the invitees shared more videos to the initiator (5:4, 9:7, and 5:2 respectably).<br />
    33. What are they doing?<br />Dolores Park, San Francisco July 2006.<br />
    34. Now they would be Tweeting!<br />Dolores Park, San Francisco July 2006.<br />
    35. People Tweet While They Watch<br />
    36. CurrentTV: Hack the Debate<br />
    37. Tweet Crawl<br />Three hashtags: #current #debate08 #tweetdebate<br />97 mins debate + 53 mins following = 2.5 hours total. <br />3,238 tweets from 1,160 people.<br />1,824 tweets from 647 people during the debate.<br />1,414 tweets from 738 people post debate.<br />577 @ mentions (reciprocity!)<br />266 mentions during the debate<br />311 afterwards.<br />Low RT: 24 retweets in total<br />6 during<br />18 afterwards.<br />
    38. 1st Presidential Debate Sampled Tweet Volume over time.<br />
    39. 1st Presidential Debate Sampled Tweet Volume over time.<br />CHAT<br />
    40. Video Segmentation<br />
    41. #tags as boundary objects<br />
    42. Hey @newshour!<br />
    43. High Centrality<br />
    44. Topics follow but not aligned<br />
    45. In Search of Social…<br />Instrument for Engagement<br />Privacy<br />Volume of Interaction<br />Search for Expression<br />Crawling samples<br />Re-query for missed data<br />Identify pragmatics of the medium<br />Twitter can be content free<br />Twitter isn&apos;t necessarily social<br />Find meaningful reciprocity<br />Merge Classical Methods with Social Metrics<br />Model social interaction as it is represented and beyond<br />
    46. thanks to echu, lyndon, mcjones!<br />Fin.<br />aymans@acm.org • http://shamurai.com<br />

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