Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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
Space-time dynamics of topics in streaming text
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Visualization of Real-World Events with Geotagged Tweet Photos
ICMEW '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops
Social event detection with interaction graph modeling
Proceedings of the 20th ACM international conference on Multimedia
Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development
IEEE Transactions on Knowledge and Data Engineering
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Popular microblogging service has attracted much attention around the world recently. With tremendous amount of tweets published each day, social event detection is becoming one of the most challenging research topics, especially for geographical social event. This paper proposes a novel geographical social event detection approach by mining geographical temporal pattern and analyzing the content of tweets. For the tweets published by users in the geographical area at each time unit, we first estimate its geographical temporal pattern based on the alternation regularity of tweets. Furthermore, we discovery the unusual geographical area by more frequent alternation of tweet count, and adopt adaptive K-means clustering algorithm for the tweets published in the geographical area. Finally, the geographical social event is detected by the number of the tweets in the cluster. We implement and validate our approach on realistic data collected from real-world social media websites. Experimental results show that our method can detect geographical social event with better performance than traditional methods. In addition, vivid demonstration of geographical social event can be effectively performed by our method.