A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Moving Phenomenon: Aggregation and Analysis of Geotime-Tagged Contents on the Web
W2GIS '09 Proceedings of the 9th International Symposium on Web and Wireless Geographical Information Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
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With being coupled with geographic location, microblogging messages have become more important information resources to share observations and opinions about the real world via social media. As a result, we are getting much interested in comprehending situations related to natural and/or social events using those messages. In the recent, certain works have focused on showing trending topics that can represent snapshots of certain situations with spatial and temporal contexts on the basis of geo-tagged microblogging messages. They, however, have difficulty in tracking and comparing topic changes and movements in a spatiotemporal domain because of the separated geo-spatial and temporal components. This demonstration introduces mTrend, which constructs and visualizes spatiotemporal trends of topics, named as "topic movements", on the basis of the geo-tagged Tweets. In particular, its interactive visual mining tool allows users to intuitively understand the differences between topic movements over space and time. In the demonstration, we present the comparisons of topic movements using a few keywords related to the Great East Japan Earthquake.