The Journal of Machine Learning Research
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
Mining topics on participations for community discovery
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages
PACIFICVIS '12 Proceedings of the 2012 IEEE Pacific Visualization Symposium
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Spatial Interestingness Measures for Co-location Pattern Mining
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
Co-occurrence prediction in a large location-based social network
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Models and techniques for the extraction and analysis of communities from social network data have become a major area of research. Most of the prominent approaches exploit the link structure among users based on, e.g., information about followers or the exchange of messages among users. However, there are also other types of information that are useful for extracting communities from social network data, such as geographic information associated with postings and users. In this paper, we present a novel approach to discover so-called regional communities. Motivated by the fact that more and more postings to social networks include the geo-location of users, we claim that communities also form even if their users do not necessarily interact but are posting (similar) messages in both spatial and temporal proximity. To discover such regional communities we propose a generative probabilistic model based on spatial latent Dirichlet allocation (SLDA) that unveils not only regional communities but also topics associated with these communities. We demonstrate the effectiveness of our approach using Twitter data and compare the properties of communities detected that way with communities discovered by approaches using link graphs.