Browse > Article
http://dx.doi.org/10.5392/JKCA.2018.18.07.449

Graph-based Event Detection Scheme Considering User Interest in Social Networks  

Kim, Ina (충북대학교 빅데이터학과)
Kim, Minyoung (충북대학교 정보통신공학과)
Lim, Jongtae (충북대학교 정보통신공학과)
Bok, Kyoungsoo (충북대학교 정보통신공학과)
Yoo, Jaesoo (충북대학교 정보통신공학과)
Publication Information
The Journal of the Korea Contents Association / v.18, no.7, 2018 , pp. 449-458 More about this Journal
Abstract
As the usage of social network services increases, event information occurring offline is spreading more rapidly. Therefore, studies have been conducted to detect events by analyzing social data. In this paper, we propose a graph based event detection scheme considering user interest in social networks. The proposed scheme constructs a keyword graph by analyzing tweets posted by users. We calculates the interest measure from users' social activities and uses it to identify events by considering changes in interest. Therefore, it is possible to eliminate events that are repeatedly posted without meaning and improve the reliability of the results. We conduct various performance evaluations to demonstrate the superiority of the proposed event detection scheme.
Keywords
Event Detection; Social Network; Keyword Graph; Graph Clustering; User Interest;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Aldhaheri and J. Lee, "Event detection on large social media using temporal analysis," Proc. IEEE Annual Computing and Communication Workshop and Conference, pp.1-6, 2017.
2 J. Guzman and B. Poblete, "On-line relevant anomaly detection in the Twitter stream: an efficient bursty keyword detection model," Proc. ACM SIGKDD Workshop on Outlier Detection and Description, pp.31-39, 2013.
3 Y. Endo and H. Toda, "What's Hot in The Theme: Query Dependent Emerging Topic Extraction from Social Streams," Proc. International Conference on World Wide Web, pp.31-32, 2013.
4 A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden, and R. C. Miller, "Twitinfo: aggregating and visualizing microblogs for event exploration," Proc. International Conference on Human Factors in Computing Systems, pp.227-236, 2011.
5 M. Mathioudakis and N. Koudas, "Twittermonitor: trend detection over the twitter stream," Proc. ACM SIGMOD International Conference on Management of Data, pp.1155-1158, 2010.
6 A. Edouard, E. Cabrio, S. Tonelli, and N. L. Thanh, "Graph-based event extraction from twitter," Proc. International Conference Recent Advances in Natural Language Processing, pp.222-230, 2017.
7 H. Sayyadi and L. Raschid, "A graph analytical approach for topic detection," ACM Transactions on Internet Technology, Vol.13, No.2, pp.1-23, 2013.
8 B. Manaskasemsak, B. Chinthanet, and A. Rungsawang, "Graph Clustering-Based Emerging Event Detection from Twitter Data Stream," Proc. International Conference on Network, Communication and Computing, pp.37-41, 2016.
9 S. Katragadda, R. Benton, and V. Raghavan, "Framework for real-time event detection using multiple social media sources," Proc. Hawaii International Conference on System Sciences, 2017.
10 M. Thelwall, K. Buckley, and G. Paltoglou, "Sentiment in Twitter events," Journal of the Association for Information Science and Technology, Vol.62, No.2, pp.406-418, 2011.
11 A. Weiler, M. Grossniklaus, and M. H. Scholl, "Event identification and tracking in social media streaming data," Proc. Workshops of the EDBT/ICDT 2014 Joint Conference, pp.282-287, 2014.
12 G. Valkanas and D. Gunopulos, "Event Detection from Social Media Data," IEEE Data Engineering Bulletin, Vol.36, No.3, pp.51-58, 2013.