Detection of Unusually Crowded Places through Micro-Blogging Sites

Recently, micro-blogging sites such as Twitter have garnered a great deal of interests as an advanced form of blogging, where individuals can share their experiences, thoughts, feelings, etc, in real time. Additionally, mobile device based micro-blogging applications are now enabling the incorporation of extremely precise location information in the form of GPS-based coordinates. With the enormous number of micro-blogs being published all over the world, the resulting social media presents a novel dataset that can be used to survey our society on a global or local scale. In this paper, we propose an effective method for the detection of unusual crowding in physical locations. This method achieves the extraction of useful and interesting movement patterns reflecting the occurrence of critical events in a geographic region. In order to accomplish this, we analyze common patterns of occurrence in each region over a specified time period employing K-means based micro-blog clustering. Furthermore, we contrast unusual occurrence patterns with the movement patterns of micro-bloggers. Finally, we present an experimental evaluation of the proposed method using a real dataset collected from Twitter.