Event Detection and Tracking in Social Streams
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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Sayyadi et al. (2009) described an event detection and tracking approach based on keyword graphs, building a graph to detect communities and emerging events, then using event keywords to track new tweets.
“For instance, Sayyadi et al. (2009) describe an event detection and tracking approach based on keyword graphs.They build a graph of keywords to detect communities and subsequently newly emerging events.They then use the set of keywords associated with an event to track new incoming tweets.”
From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Sayyadi et al. 2009
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“For instance, [Sayyadi et al. 2009] describe an event detection and tracking approach based on keyword graphs.”
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- Authors
- Sayyadi, H., Hurst, M., & Maykov, A.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2009-03-20
- Volume
- 3
- Issue
- 1
- Pages
- 311-314
- ISSN
- 2334-0770
- 2162-3449
- Cited by
- 124
- References
- 0
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