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Call for Papers

Workshop on Social Media Analytics (SOMA 2010)

Held in conjunction with
ACM Conference on Knowledge Discovery and Data Mining (KDD-2010)
July 25, 2010
Washington, DC

http://snap.stanford.edu/soma2010/

Papers due: May 7, 2010
Acceptance notification: May 21, 2010
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The explosion of Online Social Media in the form of user-generated content on blogs, microblogs (Twitter), discussion forums, product reviews and multimedia sharing sites presents many new opportunities and challenges to both producers and consumers of information. For producers, this user-generated content provides a rich source of implicit consumer feedback. Tracking the pulse of the ever-expanding social media outlets, enables companies to discern what consumers are saying about their products, which provides useful insight on how to improve and market products better. For consumers, the plethora of information and opinions from diverse sources helps them tap into the wisdom of crowds, to aid in making more informed decisions. Though there is a vast quantity of information available, the consequent challenge is to be able to analyze the large volumes of user-generated content and the implicit (or explicit) links between users, in order to glean meaningful insights therein. This has given rise to the emerging discipline of Social Media Analytics, which draws from Social Network Analysis, Machine Learning, Data Mining, Information Retrieval (IR), and Natural Language Processing.

We invite papers on all forms of Social Media including 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).

Topics of interest include, but are not limited to:
* Assessing relevance of blogs, posts and tweets
* Determining subjectivity in text - sentiment analysis and opinion mining
* Metrics, models and measurement of influence of blogs and individuals
* Detecting emerging topics and buzz
* Relevance classification of blogs/posts based on content and link structure
* Validating measures of influence and authority in blog/twitter networks
* Identifying topic-specific sentiment expressed in posts
* Adapting sentiment detection to different domains, e.g. by transfer learning
* Tracking topics or the evolution of topics over time
* Modeling and influencing diffusion of information in networks
* Scaling algorithms to the size of the blogosphere
* Notions of trust and reliability of information
* Coping with information overload
* Novel social media applications


Workshop Organizers
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Prem Melvile, IBM Research (pmelvil@us.ibm.com)
Jure Leskovec, Stanford University (jure@cs.stanford.edu)
Foster Provost, NYU Stern School of Business (fprovost@stern.nyu.edu)