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

My slides from the Real Time Search Panel at SES Chicago last week

Although real time search is fairly new, as we end 2009, the ability to index and search fresh results is rapidly becoming a commodity, with Bing, various startups, and now Google all integrating status feeds from social networking services. The next set of challenges in 2010 will be around providing better relevance, information discovery, and topic exploration for social search, using signals from the dynamic behavior of users and their interaction with the social and topic graphs.

I gave a short talk on real time and social search for a panel at SES Chicago last week. I’ve been heads down for the past few months working on Bing Twitter Search, so now that the first launch is out the door it was a nice chance to talk with people about some of the work we’re doing. There was a lot of interest in the sentiment, trend, and social graph analysis slides (9 and 10). I will write about those in a separate post, but wanted to get the presentation up for those who have been asking about it.

What’s Different about Real Time and Social Search – HJL Slides For SES Chicago Dec 09

View more presentations from Ho John Lee.

What’s Different about Real Time and Social Search – HJL Slides For SES Chicago Dec 09 – Presentation Transcript

  1. What’s different about real time and social search?
    Ho John Lee
    Principal Program Manager
    Bing Social Search
    Search Engine Strategies
    Chicago – December 7, 2009
  2. What’s Real Time Search Good For, Anyway?
  3. Twitter is Great for Watching Uninformed Panics Unfold Live
    …or finding balloons
    http://xkcd.com/574/
  4. Some characteristics of Twitter / Social media
    Immediacy, Sentiment, Brevity
    Not always accurate
    Feelings, reactions, impressions
    Context is often essential to determine meaning
    Gestural – @user, #hashtag, RT, favorites, follows
    Self-organizing communities of attention and authority
    Content follows attention
    People talk about what others are talking about
    Observations and commentary from everywhere
    If there’s no content, you can ask for some
    Extreme head and tail coverage
    Low relevance “noise” can become “signal” in aggregate
  5. Your product or brand could suddenly be at the center of a huge conversation
    Tiger Woods
    Balloon Boy
    Breaking Story
    Persistent Story
    Big Story
    Bigger Story
  6. Some characteristics of Real time / Social Search
    • Real time and social search is qualitatively different from traditional web search
    • Differences in ranking, relevance, use model
    • Social graph, user behavior, location, event correlation and other input signals
    • Real time search is frequently about discovery, not search per se
    • “what is everyone talking about”, followed by “what are people saying about ”
    • Top real time and social search results will usually differ from top web search results
  7. Bing Twitter Search at a glance
    Top Tweets
    Top Shared Links
    Tweets/Sentiment per link
    Adult /Spam filter; Tweets/Links ranking & relevance
  8. Bing Fall 2009: Twitter vertical, News, MSN, Maps
    MSN Local Edition
    Page 2: Tweets or Links
    Page 1: Tweets & Links
    Twitter Answer on News SERP
    MSN Hot Topics
  9. Topic / sentiment range, volume, trend analysis
    What is the baseline rate of mentions / sentiment per unit time?
    Changes in attention flow around a subject, location, topic
    Watch for correlated signals from multiple sources
    Consider source relevance and authority as well
  10. Graph analysis for relevance and ranking
    Spam marketing campaign
    Naturally connected community
    Spammy communities are highly visible – don’t be part of one!
  11. Bing Twitter Maps Demo
  12. To rise above the noise, there is more to do as search gets more social
    Plus…
  13. Thank You
    Ho John Lee
    hojohn . lee @ microsoft.com
    twitter.com/hjl
The session was moderated by Barbara Coll, CEO, WebMama.com Inc., with panelists Bill Fischer, Co-Founder & Director, Workdigital, Ltd., Rob Walk, Managing Partner, NovaRising, Nathan Stoll, Co-Founder, Aardvark, and  Ho John Lee, Principal Program Manager, Social and Real Time Search, Microsoft Bing.