Browse > Article
http://dx.doi.org/10.7472/jksii.2016.17.6.153

Event Detection System Using Twitter Data  

Park, Tae Soo (Dept. of Software, Gachon Univ.)
Jeong, Ok-Ran (Dept. of Software, Gachon Univ.)
Publication Information
Journal of Internet Computing and Services / v.17, no.6, 2016 , pp. 153-158 More about this Journal
Abstract
As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.
Keywords
Event Detection; Social Network; Social Media Contents;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Weng, Jianshu, and Bu-Sung Lee. "Event Detection in Twitter." ICWSM 11 (2011): 401-408. http://www.hpl.hp.com/techreports/2011/HPL-2011-98.pdf
2 Mathioudakis, Michael, and Nick Koudas. "Twittermonitor: trend detection over the twitter stream." Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, 2010. https://doi.org/101145/1807167.1807306.
3 Lee, Pei, Laks VS Lakshmanan, and Evangelos Milios. "Keysee: Supporting keyword search on evolving events in social streams." Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013. https://doi.org/10.1145/2487575.2487711   DOI
4 Marcus, Adam, et al. "Twitinfo: aggregating and visualizing microblogs for event exploration." Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 2011. http://dl.acm.org/citation.cfm?doid=1978942.1978975
5 Sankaranarayanan, Jagan, et al. "Twitterstand: news in tweets." Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, 2009. https://doi.org/10.1145/1653771.1653781   DOI
6 Phuvipadawat, Swit, and Tsuyoshi Murata. "Breaking news detection and tracking in Twitter." Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ ACM International Conference on. Vol. 3. IEEE, 2010. https://doi.org/10.1109/WI-IAT.2010.205   DOI
7 Petrovic, Sasa, Miles Osborne, and Victor Lavrenko. "Streaming first story detection with application to twitter." Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2010. http://dl.acm.org/citation.cfm?id=1858020&CFID=878446591&CFTOKEN=12113260
8 Becker, Hila, Mor Naaman, and Luis Gravano. "Selecting Quality Twitter Content for Events." ICWSM 11 (2011). http://www.cs.columbia.edu/-gravano/Papers/2011/icwsm11-b.pdf
9 Kaneko, Takamu, and Keiji Yanai. "Visual Event Mining from the Twitter Stream." Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, 2016. https://doi.org/10.1145/2872518.2889418   DOI
10 He, Qi, Kuiyu Chang, and Ee-Peng Lim. "Analyzing feature trajectories for event detection." Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2007. https://doi.org/10.1145/1277741.1277779   DOI