Social Graph Symposium Panel - May 2010

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Introductory slides for a panel discussion at the Social Graph Symposium, May 21, 2010

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Social Graph Symposium Panel - May 2010

  1. 1. Social Graph Symposium Panel<br />Ho John Lee | Principal Program Manager | Bing Social Search<br />
  2. 2. About me:<br />Ho John Lee<br />hojohn.lee@microsoft.com<br />twitter.com/hjl<br />Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)<br />HP Labs, MIT, Stanford, Harvard<br />Current: Bing Social Search - graph and time series analysis, data mining<br />Twitter, Facebook, new products, technical planning<br />
  3. 3. What can we do by observing social networks?<br />On the internet, no one knows you’re a dog.<br />But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests<br />
  4. 4. How many Twitter users are there?<br />from a search on twopular, May 2009<br />
  5. 5. Graph analysis for relevance and ranking<br />Spam marketing campaign<br />(teeth whitening)<br />Naturally connected community (#smx)<br />Real time relevance needs data mining to filter and rank based on history<br />Spammy communities can be highly visible<br />Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools <br />
  6. 6. Information diffusion in the graph<br />Observed incidence network of retweets in Twitter<br />Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010<br />Information flow and behaviors form an implicit interaction graph<br />
  7. 7. Topic / sentiment range, volume, trend analysis<br />What is the baseline rate of mentions / sentiment per unit time?<br />Look for changes in attention flow around a subject, location, topic<br />Watch for correlated signals from multiple sources<br />Consider source relevance and authority as well<br />
  8. 8. Applying graph analysis<br />Attention flow vs information flow<br />Leads to utility functions, cost functions<br />Variable diffusion rates by actor / network / info type<br />Predicting interests and affiliations<br />Content creation follows attention<br />Self-organized communities of attention<br />If there’s no content, you can ask for some<br />Observable propagation of information<br />
  9. 9. Clustering and fuzzing properties and identities<br /><ul><li>Frequently used terms can identify interests, affinities, latent query intent
  10. 10. But can potentially be used to identify likely individual users!
  11. 11. Infochaff – fuzzing out identity, behavior, properties</li></li></ul><li>Publication Platforms<br />Web<br />Public<br />Answers<br />Twitter<br />Facebook<br />Access<br />Community<br />Email<br />Private<br />Public<br />Community<br />Private<br />Publication<br />
  12. 12. Thank You<br />Ho John Lee<br />hojohn.lee@microsoft.com<br />twitter.com/hjl<br />
  13. 13. Thank you<br />

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