Mohammed Hasanuzzaman
ABSTRACT
We present a two-step strategy that addresses fundamental deficiencies in social media-based event detection and achieves effective local event by taking advantage of geo-located data from Twitter. While previous work has mainly relied on an analysis of tweet text to identify local events, we show how to reliably detect events using meta-data analysis of geo-tagged tweets. The first step of the method identifies several spatio-temporal clusters within the dataset across both space and time using metadata to form potential candidate events. In the second step, it ranks all the candidates by the amount of hashtag/entity inequality. We used crowdsourcing to evaluate the proposed approach on a data set that contains millions of geo-tagged tweets. The results show that our framework performs reasonably well in terms of precision and discovers local events faster.
- Hamed Abdelhaq, Christian Sengstock, and Michael Gertz. 2013. Eventweet: Online localized event detection from twitter. Proceedings of the VLDB Endowment 6, 12 (2013), 1326--1329. Google Scholar
Digital Library
- Charu C Aggarwal and Karthik Subbian. 2012. Event Detection in Social Streams. In SDM, Vol. 12. SIAM, 624--635.Google Scholar
- James W Buehler, Ruth L Berkelman, David M Hartley, and Clarence J Peters. 2003. Syndromic surveillance and bioterrorism-related epidemics. Emerging infectious diseases 9, 10 (2003), 1197.Google Scholar
- Tao Cheng and Thomas Wicks. 2014. Event detection using Twitter: a spatio-temporal approach. PloS one 9, 6 (2014), e97807.Google ScholarCross Ref
- Peacock B Evans M, Hastings N. 2000. Statistical distributions, 3rd Ed. New York: Wiley. 221 p.Google Scholar
- Martin Kulldorff. 1997. A spatial scan statistic. Communications in Statistics-Theory and methods 26, 6 (1997), 1481--1496.Google Scholar
- Florian Kunneman and Antal van den Bosch. 2014. Event detection in Twitter: A machine-learning approach based on term pivoting. In Grootjen, F.; Otworowska, M.; Kwisthout, J.(ed.), Proceedings of the 26th Benelux Conference on Artificial Intelligence. Nijmegen, the Netherlands: Radboud University, 65--72.Google Scholar
- Janette Lehmann, Bruno Gonçalves, José J Ramasco, and Ciro Cattuto. 2012. Dynamical classes of collective attention in twitter. In Proceedings of the 21st international conference on World Wide Web. ACM, 251--260. Google ScholarDigital Library
- Chenliang Li, Aixin Sun, and Anwitaman Datta. 2012. Twevent: segment-based event detection from tweets. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 155--164. Google ScholarDigital Library
- Gözde Özbal, Carlo Strapparava, Rada Mihalcea, and Daniele Pighin. 2011. A comparison of unsupervised methods to associate colors with words. In Affective Computing and Intelligent Interaction. Springer, 42--51. Google ScholarDigital Library
- Ana-Maria Popescu and Marco Pennacchiotti. 2010. Detecting controversial events from twitter. In Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, 1873--1876. Google ScholarDigital Library
- Yanxia Qin, Yue Zhang, Min Zhang, and Dequan Zheng. 2013. Feature-Rich Segment-Based News Event Detection on Twitter. In IJCNLP. 302--310.Google Scholar
- Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. 2010. Earthquake shakes Twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web. ACM, 851--860. Google ScholarDigital Library
- Jagan Sankaranarayanan, Hanan Samet, Benjamin E Teitler, Michael D Lieberman, and Jon Sperling. 2009. Twitterstand: news in tweets. In Proceedings of the 17th acm sigspatial international conference on advances in geographic information systems. ACM, 42--51. Google ScholarDigital Library
- Toshiro Tango and Kunihiko Takahashi. 2005. A flexibly shaped spatial scan statistic for detecting clusters. International journal of health geographics 4, 1 (2005), 11.Google ScholarCross Ref
- Kazufumi Watanabe, Masanao Ochi, Makoto Okabe, and Rikio Onai. 2011. Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs. In Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 2541--2544. Google ScholarDigital Library
- Jianshu Weng and Bu-Sung Lee. 2011. Event Detection in Twitter. ICWSM 11 (2011), 401--408.Google Scholar
Index Terms
- Local Event Discovery from Tweets Metadata
Recommendations
Spatio-temporal visual analysis for event-specific tweets
SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of DataTwitter is one of the most popular social networks where people use to tweet about their opinions, feelings, desires, ...etc. One of the most important and consistent behaviors of Twitter users is posting a plethora of tweets about events of different ...
Analyzing and predicting viral tweets
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide WebTwitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of ...
Analysis of Tweets Related to Cyberbullying: Exploring Information Diffusion and Advice Available for Cyberbullying Victims
The use of Twitter, especially by teenagers and young people, has raised the issue of cyberbullying. There is a lack of research into what types of advice and support are available in tweets for cyberbullying victims, and into the features influencing ...
Comments