Slides from the Social Graph Symposium panel

Some introductory slides from a panel session at the Social Graph Symposium.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

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

RESEARCH: Insights from the latest social graph studies
Moderator: Eric Siegel – President at Prediction Impact and Conference Chair at Predictive Analytics World
Speakers:
Sharad Goel – Research Scientist at Yahoo
Ho John Lee – Principal Program Manager at Microsoft
DJ Patil – Chief Scientist at LinkedIn
Marc Smith – Chief Social Scientist at Connected Action Consulting Group

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