KDD 2011 Tutorial
Social Media Analytics
Tracking, Modeling and Predicting the Flow of Information through Networks

Tutorial information

Online social media represent a fundamental shift of how information is being produced, transferred and consumed. User generated content in the form of blog posts, comments, and tweets establishes a connection between the producers and the consumers of information.

Tracking the pulse of the social media outlets, enables companies to gain feedback and insight in how to improve and market products better. For consumers, the abundance of information and opinions from diverse sources helps them tap into the wisdom of crowds, to aid in making more informed decisions.

The tutorial investigates techniques for social media modeling, analytics and optimization:

Social Media data comes in many forms: blogs (Blogger, LiveJournal), micro-blogs (Twitter, FMyLife), social networking (Facebook, LinkedIn), wikis (Wikipedia, Wetpaint), social bookmarking (Delicious, CiteULike), social news (Digg, Mixx), reviews (ePinions, Yelp), and multimedia sharing (Flickr, Youtube). Tutorial will investigate methods and case studies for analyzing such data and extracting actionable analytics.

Tutorial outline

Tutorial Video


(click to view the video)

Tutorial slides

Tutorial slides are available:

Location

Tutorial will be held at ACM SIGKDD Conference in San Diego, CA on Sunday August 21 2011.

Tutorial will start at 8:30 go till 12:00 with a 30 min break at 10:00. Room: Manchester H.

Who should attend

Social media data arises in so many different areas of data mining and predictive analytics so the tutorial should be of theoretical and practical interest to a large part of the world-wide-web and data mining community.

The tutorial will not require prior knowledge beyond the basic concepts covered in introductory machine learning and algorithms classes.

Presenter

Jure Leskovec is an assistant professor of Computer Science at Stanford University. His research focuses on the analysis and modeling of large real-world social and information networks as the study of phenomena across the social, technological, and natural worlds. Problems he investigates are motivated by large scale data, the Web and Social Media. Jure received his PhD in Machine Learning from Carnegie Mellon University in 2008 and spent a year at Cornell University. His work received six best paper awards, won the ACM KDD cup and topped the Battle of the Sensor Networks competition.

References

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Meme-tracking and the dynamics of the news cycle
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Predicting Positive and Negative Links in Online Social Networks
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Cascading behavior in large blog graphs
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Patterns of Influence in a Recommendation Network
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On the Convexity of Latent Social Network Inference
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Correcting for Missing Data in Information Cascades
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Influentials, Networks, and Public Opinion Formation
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Patterns of Temporal Variation in Online Media
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Modeling Information Diffusion in Implicit Networks
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