{"id":238350,"date":"2016-05-17T00:00:00","date_gmt":"2016-05-17T07:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/geoburst-real-time-local-event-detection-in-geo-tagged\/"},"modified":"2018-10-16T20:07:06","modified_gmt":"2018-10-17T03:07:06","slug":"geoburst-real-time-local-event-detection-in-geo-tagged","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/geoburst-real-time-local-event-detection-in-geo-tagged\/","title":{"rendered":"GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The real-time discovery of local events (e.g., protests, crimes, disasters) is of great importance to various applications, such as crime monitoring, disaster alarming, and activity recommendation. While this task was nearly impossible years ago due to the lack of timely and reliable data sources, the recent explosive growth in geo-tagged tweet data brings new opportunities to it. That said, how to extract quality local events from geo-tagged tweet streams in real time remains largely unsolved so far. We propose GEOBURST, a method that enables effective and real-time local event detection from geo-tagged tweet streams. With a novel authority measure that captures the geo-topic correlations among tweets, GEOBURST first identifies several pivots in the query window. Such pivots serve as representative tweets for potential local events and naturally attract similar tweets to form candidate events. To select truly interesting local events from the candidate\u00a0 list, GEOBURST further summarizes continuous tweet streams and compares the candidates against historical activities to obtain spatiotemporally bursty ones. Finally, GEOBURST also features an\u00a0 updating module that finds new pivots with little time cost when the query window shifts. As such, GEOBURST is capable of monitoring continuous streams in real time. We used crowdsourcing to\u00a0 evaluate GEOBURST on two real-life data sets that contain millions of geo-tagged tweets. The results demonstrate that GEOBURST significantly outperforms state-of-the-art methods in precision, and is orders of magnitude faster.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The real-time discovery of local events (e.g., protests, crimes, disasters) is of great importance to various applications, such as crime monitoring, disaster alarming, and activity recommendation. While this task was nearly impossible years ago due to the lack of timely and reliable data sources, the recent explosive growth in geo-tagged tweet data brings new opportunities [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"SIGIR 2016","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. 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