From awareness to repartee: sharing location within social groups
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 2009 International Workshop on Location Based Social Networks
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Towards better TV viewing rates: exploiting crowd's media life logs over Twitter for TV rating
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Crowd-powered TV viewing rates: measuring relevancy between tweets and TV programs
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome
IEEE Transactions on Intelligent Transportation Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
SpinRadar: a spontaneous service provision middleware for place-aware social interactions
Personal and Ubiquitous Computing
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Recent location-based social networking sites are attractively providing us with a novel capability of monitoring massive crowd lifelogs in the real-world space. In particular, they make it easier to collect publicly shared crowd lifelogs in a large scale of geographic area reflecting the crowd's daily lives and even more characterizing urban space through what they have in minds and how they behave in the space. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing crowd lifelogs in urban area over the social networking sites. In order to collect crowd behavioral data, we exploit the most famous microblogging site, Twitter, where a great deal of geo-tagged micro lifelogs emitted by massive crowds can be easily acquired. We first present a model to deal with crowds' behavioral logs on the social network sites as a representing feature of urban space's characteristics, which will be used to conduct crowd-based urban characterization. Based on this crowd behavioral feature, we will extract significant crowd behavioral patterns in a period of time. In the experiment, we conducted the urban characterization by extracting the crowd behavioral patterns and examined the relation between the regions of common crowd activity patterns and the major categories of local facilities.