The document summarizes a AAAI Spring Symposium on Social Information Processing that took place in 2008. It discusses how collective human actions on the social web can organize knowledge and allow problems to be solved beyond any individual's capabilities. It also notes challenges in analyzing the massive, dynamic, heterogeneous data generated on social media platforms and opportunities to leverage this data for prediction, innovation and discovery.

1. Social Information Processing March 26-28, 2008 AAAI Spring Symposium Stanford University 010011011 01100001 11110 011 0101110011010 0111011010 001001 01111 00001001111010 011011010101 01001 01111 01100001001111010 100101111 00110 1001 10110101110100100 1010100100 1101 0010 00010111010011

2. Definition Social Information Processing is an activity through which collective human actions organize knowledge process which allows us to collectively solve problems far beyond any individual’s capabilities a new information processing paradigm enabled by the Social Web

3. The Social Web The Social Web is a collection of technologies, practices and services that turn the Web into a platform for users to create and use content in a social context Authoring tools  blogs Collaboration tools  wikis, Wikipedia Tagging systems  del.icio.us, Flickr, CiteULike Social networking  Facebook, MySpace, Essembly Collaborative filtering  Digg, Amazon, Yahoo answers

4. Social Web features Users create content Articles, opinions, creative products Users annotate content Metadata (e.g., tags) Ratings Users create connections Between content and metadata Between content or metadata and users Among users (social networks) Users interact Discuss and rate content

5. Social Web is interesting Social Web as a complex dynamical system Complex collective behavior emerges from actions taken by many users Patterns emerge on large scale Variety of interactions between users Coordination, collaboration, conflict … Network vs environment-mediated

6. Social Web is interesting Social Web as a knowledge-generating system Users express personal knowledge (through articles, tags, links, …) or modify knowledge expressed by others Tailor information to individual user … Personalization and recommendation … or combine users’ knowledge to create a knowledgebase Wikipedia, wikis folksonomy FAQs, …

7. Social Web is interesting Social Web as a problem-solving system By exposing human activity, Social Web allows users to harness the power of collective intelligence to solve problems Manage the commons Help the visually impaired get around in new places Figure out who to trust

8. Social Web is interesting Lots of data for empirical studies Large-scale experimenation Social Web is amenable to analysis Design systems for optimal performance

9. Social Web is challenging Social Web is enormous and growing rapidly Some popular sites have >1 million users and >1 billion objects 2G/day of “authored” content 10-15G/day of user generated content [From Andrew Tomkins, Yahoo! Research] Need new computational techniques to process massive data

10. Social Web is challenging Social Web is highly dynamic New users and content Links are created and destroyed Need new computational approaches to deal with dynamic data

11. Social Web is challenging Social Web is highly heterogeneous Variety of content and media types Variety of information domains Needs to be even more heterogeneous Ability to express knowledge at different granularity levels Micro-tagging: tag data within pages Ability to express more complex knowledge Specify relations: e.g., semantics of links Need algorithms to combine heterogeneous data

12. Social Web is challenging Social Web is highly diverse User participation has power law distribution User expertise has power law distribution Need approaches that go beyond ‘wisdom of crowds’ to combine knowledge from users Averaging is not always the best solution How do we best exploit diversity? Understand incentives for user participation Methods for improving content/metadata quality

13. Social Web is challenging Social Web as a computational platform Prediction Innovation and discovery

14. Schedule - Wednesday 9:00-9:15 Welcome 9:15-10:30am Invited talk Bernardo Huberman Social Dynamics in the Age of the Web 10:30-11am Break 11-12:30pm Technical Session: Moderator Cosma Shalizi Ed Chi Augmented Social Cognition Tad Hogg Solving the organizational free riding problem Riley Crane Viral, Quality, and Junk Videos on YouTube 12:30-2pm - Lunch 3:30-4pm - Break 2-3:30pm Technical Session: Moderator Kristina Lerman Yi-Ching Huang You Are What You Tag Julia Stoyanovich Leveraging Tagging to Model User Interests in del.icio.us Steve Whittaker Temporal Tagging 4-5:30pm Technical Session: Moderator David Gutelius Georg Groh Implicit Social Network Construction in Web Portals Elizeu Santos-Neto Content Reuse and Interest Sharing Matt Smith Social Capital in the Blogosphere: A Case Study 6-7pm – AAAI Reception

15. Schedule - Thursday 9:00-10:30am Invited talk Brian Skyrms Signaling Games 10:30-11am Break 11-12:30pm Technical Session: Moderator Ed Chi John Nicholson The Blind Leading the Blind Cosma Shalizi Social Media as Windows on the Social Life of the Mind Luc Steels Social tagging in community memories 12:30-2pm - Lunch 2-3:30pm Technical Session: Moderator Tina Eliassi-Rad Aram Galstyan Influence Propagation in Modular Networks Adam Anthony Generative Models for Clustering: The Next Generation Peter Pirolli A Probabilistic Model of Semantics 3:30-4pm - Break 4-5:30pm Technical Session: Moderator Tad Hogg Hak-Lae Kim Building a Tag Sharing Service with the SCOT Ontology Yu Zhang Mining Target Marketing Groups From Users’Web of Trust Sihem Amer-Yahia Reviewing the Reviewers 5:45-7:30pm – AAAI Plenary Session

16. Schedule - Friday 9-10:30am Technical Session: Moderator Chris Diehl Dennis Wilkinson Multiple Relationship Types in Online Communities and Social Networks Tina Eliassi-Rad Finding Mixed-Memberships in Social Networks 10:30-11am - Break 11-12:30pm - Wrap up – Open to all

17. Posters John Nicholson Collaborative Route Information Sharing for the Visually Impaired Tad Hogg and Gabor Szabo Diversity of Online Community Activities Cosma Shalizi, Kristina Klinkner and Marcelo Camperi Measuring Shared Information and Coordinated Activity in a Network Anon Plangprasopchok and Kristina Lerman On constructing shallow taxonomies from social annotations Gustavo Glusman Users, photos, groups, words: analyzing mixed networks on flickr Praveen Paritosh Freebase

18. Thanks to Organizing committee Kristina Lerman, David Gutelius, Bernardo Huberman, Srujana Merugu Program committee Jim Blythe, Arindam Banerje, Sugato Basu, Jack Park, Scott Golder, Paolo Massa, Cosma Shalizi, Ed Chi, Tad Hogg, Chris Diehl, Sihem Amer-Yahia Participants