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:
- How do we collect massive amounts of social media data and what techniques can be used for correcting for the effects and biases arising from incomplete and missing data?
- What methods can be used to extract and track the flow of interesting pieces of information that spread and diffuse among the users? How can we identify the subset of content that is discussing not only a specific entity, but higher level concepts? %that are topically relevant?
- Having identified the subset of relevant content, how do we identify the most authoritative or influential authors? How do we quantify the influence of users on the adoption and spread of different topics? How do we maximize the overall influence?
- How do we tease apart emerging topics of discussion from the constant chatter in the blogosphere and other social media? How do we extract and model the temporal patterns by which information grows and fades over time?
- How do we predict popularity of memes and other pieces of information that spread through the social media networks?
- The information spreads via implicit networks. How do we identify and infer such networks of influence and diffusion? How do we discover implicit links between users?
- How does sentiment flow through networks and how does polarization occur?
- How do we overcome the information overload and provide users with rich and coherent experience?
- How to deal with unreliable and often conflicting information? What notions of trust are appropriate?
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 will be held at International World Wide Web Conference in Hyderabad, India on Tuesday March 29 2011.
Tutorial outline
- Part 1: Information flow in social media (slides)
- Collecting social media data
- Extracting and tracking the flow of relevant information
- Correcting for the effects of missing and incomplete data
- Predicting and modeling the flow of information
- Identifying networks of information flow
- Part 2: Rich user interactions (slides)
- Predicting and recommending links in network
- Modeling tie strenght
- Modeling trust and distrust, frieds and foes
- How users evaluate one another and the social media content
Tutorial slides
Tutorial slides are available:
- Part 1: Flow of information through networks
- Part 2: Rich user interactions
Who should attend
Since social media data arises in so many different areas of data mining and predictive analytics, this 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 five best paper awards, won the ACM KDD cup and topped the Battle of the Sensor Networks competition.
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