Invariant Event Tracking on Social Networks

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• 作者：Sayan Unankard (17) (18)
  Xue Li (18)
  Guodong Long (19)
  
  17. Information Technology Division ; Maejo University ; Chiang Mai ; Thailand
  18. School of ITEE ; The University of Queensland ; Brisbane ; Australia
  19. Centre for Quantum Computation and Intelligent Systems ; University of Technology Sydney ; Sydney ; Australia
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9050
• 期：1
• 页码：517-521
• 全文大小：730 KB
• 参考文献：1. Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: CIKM, pp. 155鈥?64 (2012)
  2. Palla, G, Der茅nyi, I, Farkas, I, Vicsek, T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435: pp. 814-818 CrossRef
  3. Takaffoli, M., Sangi, F., Fagnan, J., Zaiane, O.R.: Modec - modeling and detecting evolutions of communities. In: ICWSM, pp. 626鈥?29 (2011)
  4. Unankard, S., Li, X., Sharaf, M.A.: Emerging event detection in social networks with location sensitivity. World Wide Web Journal, 1鈥?5 (2014)
  
• 作者单位：Database Systems for Advanced Applications
• 丛书名：978-3-319-18122-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

When an event is emerging and actively discussed on social networks, its related issues may change from time to time. People may focus on different issues of an event at different times. An invariant event is an event with changing subsequent issues that last for a period of time. Examples of invariant events include government elections, natural disasters, and breaking news. This paper describes our demonstration system for tracking invariant events over social networks. Our system is able to summarize continuous invariant events and track their developments along a timeline. We propose invariant event detection by utilizing an approach of Clique Percolation Method (CPM) community mining. We also present an approach to event tracking based on the relationships between communities. The Twitter messages related to the 2013 Australian Federal Election are used to demonstrate the effectiveness of our approach. As the first of this kind, our system provides a benchmark for further development of monitoring tools for social events.