Detecting hot events from web search logs

  • Authors:

  • Yingqin Gu;Jianwei Cui;Hongyan Liu;Xuan Jiang;Jun He;Xiaoyong Du;Zhixu Li

  • Affiliations:

  • Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Department of Management Science and Engineering, Tsinghua University, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China

  • Venue:
  • WAIM'10 Proceedings of the 11th international conference on Web-age information management
  • Year:
  • 2010

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Abstract

Detecting events from web resources is a challenging task, attracting many attentions in recent years. Web search log is an important data source for event detection because the information it contains reflects users' activities and interestingness to various real world events. There are three major issues for event detection from web search logs: effectiveness, efficiency and the organization of detected events. In this paper, we develop a novel Topic and Event Detection method, TED, to address these issues. We first divide the whole data into topics for efficiency consideration, and then incorporate link information, temporal information and query content to ensure the quality of detected events. Finally, events detected are organized through the proposed interestingness measure as well as topics they belong to. Experiments are conducted on a commercial search engine log. The results demonstrate that our method can effectively and efficiently detect hot events and give a meaningful organization of them.