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Event Detection System Using Twitter Data

트위터를 이용한 이벤트 감지 시스템

  • Received : 2016.10.31
  • Accepted : 2016.12.09
  • Published : 2016.12.31

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.

최근 소셜 네트워크 사용자들이 늘어나면서, 각 지역에서 관심 받고 있는 사회적인 이슈나 재해 등과 같은 이벤트에 대한 정보들이 소셜 미디어 사이트를 통해 실시간으로 빠르게 대량으로 게시되고 있으며, 사회적 파급효과도 매우 커지고 있다. 본 논문에서는 지역정보를 가진 트위터 데이터를 이용하여 특정 시간, 지역에 사용자들이 관심을 가지고 있는 이벤트를 탐지하는 방법을 제안하고자 한다. 이를 위해 트위터 스트리밍 API를 이용해 데이터를 수집하고, 트윗의 키워드들의 시간에 따른 빈도수를 분석하여 정상적인 패턴과 다른 패턴을 가진 키워드를 이벤트로 추출하고, 같은 이벤트에 대한 키워드들을 군집화 하기 위해 co-occurrence 그래프를 이용하여 이벤트 감지 시스템을 구현하였다. 그리고 실험을 통해 제안한 기법의 유효성을 검증한다.

Keywords

References

  1. 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.
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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

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