A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
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
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
Urban area characterization based on semantics of crowd activities in Twitter
GeoS'11 Proceedings of the 4th international conference on GeoSpatial semantics
On theme location discovery for travelogue services
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Identifying points of interest by self-tuning clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Crowd-based urban characterization: extracting crowd behavioral patterns in urban areas from Twitter
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Discovery of user behavior patterns from geo-tagged micro-blogs
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Lowering the barriers to large-scale mobile crowdsensing
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
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Location-based social network sites are recently attracting a great deal of attention by combing Web-based social network and the real-world location tagging in an integrated way, where people can publish their life logs about their real-world activities and share them with the public often looking for location-based information. Obviously, in terms of technological and social advance such as location sensing smartphones, experiences and thoughts by the unexpectedly growing number of the mobile users in urban area are conveniently being shared significantly impacting our ways of life experience sharing. In such context, we are able to monitor crowd's experiences through the location-based social network by collecting and analyzing crowd's numerous micro life logs to support a variety of decision makings. In this paper, we attempt to look into the crowd's urban lifestyles, which are characterizing urban areas, particularly utilizing Twitter. We provide a model to construct systems for a large-scale urban analytics with the location-based social network. We also describe our practical approach to describe urban characteristics represented by crowd's temporal behavioral patterns. In the experiment, we show an urban characterization by way of crowd's behavioral patterns, which are derived from temporal patterns of crowd behavior indirectly speculated from a massive number of collected Twitter messages. Finally, we discuss the importance of this kind of challenge amid the pervasive social network environment and some critical issues to be considered for the wide spectrum of sociological studies requiring technology-driven crowd life monitoring.