Event Detection and Tracking in Social Streams

By Hassan Sayyadi, Matthew F. Hurst, Alexey Maykov. International Conference on Web and Social Media, 2009

333 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/224e457fde0e97a080ce2385496784ee9d193a02

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Hip and trendy: Characterizing emerging trends on Twitter (2011, background): “While trend and event detection in news and blog posts has been studied in depth (Allan, 2002; Kleinberg, 2003; Sayyadi, Hurst, & Maykov, 2009), the detection of trends on Twitter is a topic that is still in its infancy (Petrovic, Osborne, & Lavrenko, 2010; Sakaki et al., 2010).”
Detection and Resolution of Rumours in Social Media (2017, methodology): “For instance, [Sayyadi et al. 2009] describe an event detection and tracking approach based on keyword graphs.”
Crowd sensing of traffic anomalies based on human mobility and social media (2013, background): “, tweets) as real-time social streams and focus on analyzing the features of keywords in the specific context to detect events [8, 9, 5].”
Sensing Trending Topics in Twitter (2013, methodology): “[29] builds a term cooccurrence graph, whose nodes are clustered using a community detection algorithm based on betweenness centrality.”
Processing Social Media Messages in Mass Emergency (2014, background): “For instance, Sayyadi et al. [2009] introduce a retrospective NED approach that overlays a graph over the documents, based on word co-occurrences.”