ABSTRACT
We propose a system for detecting local events in the real-world using geolocation information from microblog documents. A local event happens when people with a common purpose gather at the same time and place. To detect such an event, we identify a group of Twitter documents describing the same theme that were generated within a short time and a small geographic area. Timestamps and geotags are useful for finding such documents, but only 0.7% of documents are geotagged and not sufficient for this purpose. Therefore, we propose an automatic geotagging method that identifies the location of non-geotagged documents. Our geotagging method successfully increased the number of geographic groups by about 115 times. For each group of documents, we extract co-occurring terms to identify its theme and determine whether it is about an event. We subjectively evaluated the precision of our detected local events and found that it had 25.5% accuracy. These results demonstrate that our system can detect local events that are difficult to identify using existing event detection methods. A user can interactively specify the size of a desired event by manipulating the parameters of date, area size, and the minimum number of Twitter users associated with the location. Our system allows users to enjoy the novel experience of finding a local event happening near their current location in real time.
- E. Amitay, N. Har';El, R. Sivan, and A. Soffer. Web-a-where: geotagging web content. In Proc. ACM SIGIR'04, pages 273--280, 2004. Google ScholarDigital Library
- Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: a content-based approach to geo-locating twitter users. In Proc. ACM CIKM'10, pages 759--768, 2010. Google ScholarDigital Library
- M. N. Hila Becker and L. Gravano. Beyond trending topics: Real-world event identification on twitter. Proc. ICWSM'11, 2011.Google Scholar
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proc. WebKDD/SNA-KDD'07, pages 56--65, 2007. Google ScholarDigital Library
- R. Lee and K. Sumiya. Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection. In Proc. LBSN'10, pages 1--10, 2010. Google ScholarDigital Library
- T. Quack, B. Leibe, and L. Van Gool. World-scale mining of objects and events from community photo collections. In Proc. CIVR'08, pages 47--56, 2008. Google ScholarDigital Library
- T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proc. WWW'10, pages 851--860, 2010. Google ScholarDigital Library
- J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling. Twitterstand: news in tweets. In Proc. GIS'09, pages 42--51, 2009. Google ScholarDigital Library
- B. Wing and J. Baldridge. Simple supervised document geolocation with geodesic grids. In Proc. ACL'11, 2011. Google ScholarDigital Library
- Q. Zhao, P. Mitra, and B. Chen. Temporal and information flow based event detection from social text streams. In Proc. AAAI'07, pages 1501--1506, 2007. Google ScholarDigital Library
Index Terms
- Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs
Recommendations
Towards a social media analytics platform: event detection and user profiling for twitter
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide WebMicroblog data differs significantly from the traditional text data with respect to a variety of dimensions. Microblog data contains short documents, SMS kind of language, and is full of code mixing. Though a lot of it is mere social babble, it also ...
A Survey of Techniques for Event Detection in Twitter
Twitter is among the fastest-growing microblogging and online social networking services. Messages posted on Twitter tweets have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing ...
Efficient Location-Based Event Detection in Social Text Streams
IScIDE 2015: Revised Selected Papers, Part II, of the 5th International Conference on Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques - Volume 9243Social networks provide a wealth of online sources about real-world events. Due to the large volume of data in social streams, the event detection suffers from high computational complexity. In this work, we present a location-based event detection ...
Comments