The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
IEEE Transactions on Knowledge and Data Engineering
Acquisition of a vernacular gazetteer from web sources
Proceedings of the first international workshop on Location and the web
Computable social patterns from sparse sensor data
Proceedings of the first international workshop on Location and the web
Digital Footprinting: Uncovering Tourists with User-Generated Content
IEEE Pervasive Computing
Eigenplaces: Segmenting Space through Digital Signatures
IEEE Pervasive Computing
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Anonymizing user location and profile information for privacy-aware mobile services
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
An Alignment Approach for Context Prediction Tasks in UbiComp Environments
IEEE Pervasive Computing
Automatic Analysis of Geotagged Photos for Intelligent Tourist Services
IE '10 Proceedings of the 2010 Sixth International Conference on Intelligent Environments
Ubiquitous Advertising: The Killer Application for the 21st Century
IEEE Pervasive Computing
Location-Related Privacy in Geo-Social Networks
IEEE Internet Computing
Exploring trajectory-driven local geographic topics in foursquare
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
You are where you e-mail: using e-mail data to estimate international migration rates
Proceedings of the 3rd Annual ACM Web Science Conference
When a city tells a story: urban topic analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Studying inter-national mobility through IP geolocation
Proceedings of the sixth ACM international conference on Web search and data mining
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Social networks attract lots of new users every day and absorb from them information about events and facts happening in the real world. The exploitation of this information can help identifying mobility patterns that occur in an urban environment as well as produce services to take advantage of social commonalities between people. In this paper we set out to address the problem of extracting urban patterns from fragments of multiple and sparse people life traces, as they emerge from the participation to social network. To investigate this challenging task, we analyzed 13 millions Twitter posts (3 GB) of data in New York. Then we test upon this data a probabilistic topic models approach to automatically extract urban patterns from location-based social network data. We find that the extracted patterns can identify hotspots in the city, and recognize a number of major crowd behaviors that recur over time and space in the urban scenario.