Parameter Free Bursty Events Detection in Text Streams

By G. Fung, Jeffrey Xu Yu, Philip S. Yu, Hongjun Lu. Very Large Data Bases Conference, 2005

352 citations recorded by the source corpus.

How researchers see this paper

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

Twevent: segment-based event detection from tweets (2012, background): “The probability of observing frequency f s , t of segment s in t can be modeled by a binomial distribution [5]. where p s is the expected probability of tweets that contain segment s in a random time window.”
Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract (2015, methodology): “To achieve real-time efﬁciency, we adopt a parameter-free algorithm [Fung et al. , 2005] to identify bursty words from Twitter text streams.”
Event Detection in Twitter (2011, methodology): “Different from this work, Fung et al. model individual word’s appearance as binomial distribution, and identify burst of each word with a threshold-based heuristic (Fung et al. 2005) .”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, unknown): “This would result in a sort of signal to which standard temporal series techniques may be applied [59, 85, 222, 223, 243].”
Sensing Trending Topics in Twitter (2013, background): “[21], for instance, detects bursty terms by looking at where the frequency of the term in a given time window is positioned in the overall distribution of the number of documents containing that term.”