Event Detection and Tracking in Social Streams

Authors

  • Hassan Sayyadi University of Maryland-College Park
  • Matthew Hurst Microsoft Live Labs
  • Alexey Maykov Microsoft Live Labs

Keywords:

Event Detection, Clustering, Community detection, Social media, News

Abstract

Events and stories can be characterized by a set of descriptive, collocated keywords. Intuitively, documents describing the same event will contain  similar sets of keywords,  and the graph of keywords for a document collection will contain clusters individual events. In this paper we build a network of keywords based on their co-occurrence in documents. We propose and develop a new event detection algorithm which creates a keyword graph and uses community detection methods analogous to those used for social network analysis to discover and describe events. Constellations of keywords describing an event may be used to find related articles. We also use the proposed algorithm to analyze events and track stories in social streams.

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Published

2009-03-20

How to Cite

Sayyadi, H., Hurst, M., & Maykov, A. (2009). Event Detection and Tracking in Social Streams. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 311-314. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/13970