4th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN) 2012
November 6th, 2012
Redondo Beach, CA, USA
Co-located with ACM SIGSPATIAL GIS 2012 (http://acmgis2012.cs.umd.edu)
CALL FOR PAPERS
Social networks and social media are prevalent on the Internet and have become a hot research topic attracting many professionals from a variety of fields. By adding a location dimension, we can bring online social networks and media back to the physical world and share our real-life experiences in the virtual world conveniently. In Location Based Social Networks (LBSN), people can not only track and share location-related information with each other via mobile devices or desktop computers, but can also leverage collaborative social knowledge learned from user-generated and location-related content. As location is one of the most important properties in people’s everyday lives, LBSN bridge the gap between online societies and the physical world, enabling novel applications that have the potential to change the way we live, such as travel planning, location/friend recommendations, community discovery, human mobility modeling, and user activity analysis. The technology derived from LBSN, e.g., location trajectory mining and retrieval, can also be applied to a multitude of other research areas including biology, sociology, geography, and climatology, etc.
The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art in LBSN development and application, present their ideas and contributions, and set the future direction of LBSN research. In this Fourth offering of the workshop, we would like to broaden the focus to include location based social media more generally. Social media platforms such as Twitter represent a rich source of information about individual and group behavior and much of this data has location information associated with it, either explicitly or implicitly.
Spatial and spatio-temporal data mining in user-centric scenarios
Moving object tracking, indexing and retrieval for social applications
Trajectory compressing and simplification
Trajectory mining, pattern recognition, and knowledge discovery
Location privacy and security
Uncertainty of location and trajectory in modeling, inference, and querying
Activity recognition and sensing for social applications
Location identification from sensor data for social applications
User behavior modeling using physical sensor data
Semantic meaning and knowledge discovery from location-related data
User similarity computing based on location-related information
Social structure detection from location-related data
Location and friend recommendations
Hot spots, significant places, and interesting locations detection
Location-tagged media sharing and mining
Human-computer interaction in location-based social networks
Mobile and ubiquitous computing for location-based social networks
Information retrieval in location-based social networks
Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Submitted papers can be of two types:
Regular Research Papers: these papers should report original research results or significant case studies. They should be at most 8 pages.
Position Papers: these papers should report novel research directions or identify challenging problems. They should be at most 4 pages.
Manuscripts (both research and position papers) should be submitted in PDF format according to the ACM camera-ready templates available at:
Papers must be electronically submitted at the following address:
At least one author of each accepted paper must register for the workshop. The workshop proceedings will also be part of the ACM Digital Library.
Paper Submission Due: August 17, 2012
Notification to the authors: September 17, 2012
Camera ready papers due: September 30, 2012
Jennifer Neville, Purdue University
Gabriel Ghinita, University of Massachusetts at Boston
Shawn Newsam, University of California, Merced