UMBC ebiquity
DAPD special issue on Data Management in Social Media

DAPD special issue on Data Management in Social Media

Tim Finin, 11:22pm 23 September 2008

We (Tim Finin, Anupam Joshi, and Akshay Java) are editing a special issue of Distributed and Parallel Databases on Data Management in Social Media. Manuscripts must be submitted by January 15, 2009 and should not exceed 25 pages in length. Authors will be notified by April 15 and camera ready copy will be due May 15. Submit papers online specifying article type S.I.: Data Management for the Social Web. For more information, sett the call for papers (pdf) or contact Anupam Joshi at joshi@cs.umbc.edu.

Social Media tools like blogs, wikis and social networking sites are providing new opportunities for us to connect and interact with each other. Many social theories that could once be researched only by conducting expensive surveys can now be studied and modeled due to the easy availability of large scale social annotations and explicit description of social relationships online. The rate at which blogs, videos, bookmarks and many other user generated content is growing presents several interesting research and data management questions. The opportunity to mine social media content for analyzing opinions, sentiments and trend identification has several applications in Web search, personalization, business intelligence and national security. This special issue of the International Journal of Distributed and Parallel Databases invites original research contributions on data management in social media. Topics include but are not restricted to the following.

  • community detection and evolution in social media
  • recommendation systems
  • search in social media
  • event detection, trend identification and tracking in social media
  • influence, trust and reputation in social media
  • opinion/sentiment analysis, polarity identification
  • feed distillation and ranking blogs
  • mining microblogging and real time data
  • folksonomy, tag semantics, clustering and usage
  • advertising models for the social web
  • indexing social media content, index freshness
  • visualizing social network data
  • spam detection, social network spam and profile spam

Comments are closed.