A System for new event detection

By Thorsten Brants, Francine Chen. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2003

337 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.

Parameter Free Bursty Events Detection in Text Streams (2005, methodology): “Topic detection and tracking (TDT) is the major area that tackles the problem of discovering events from a stream of news stories [2, 18, 27, 26, 3, 4, 27, 26, 21].”
Processing Social Media Messages in Mass Emergency (2014, background): “An event is the occurrence of something significant that is associated with a specific time and location [Brants et al. 2003].”
SIN: Semantic Inference Network for Few-Shot Streaming Label Learning (2022, background): “Similar scenarios occur frequently in practice, such as new event detection [4], emerging disease surveillance [5], and tagging pictures on social networks [6], which need systems to be updated accurately according to the incoming new labels [7].”
Twevent: segment-based event detection from tweets (2012, background): “The former aims to cluster documents related to the same events and then extract event-based features from the document clusters [1,2, 23–25].”
Event Detection in Social Streams (2012, background): “Much of the text data in social scenarios arises in the context of streaming applications, in which the text arrives as a continuous and massive stream of text segments [2].”
Automatic online news issue construction in web environment (2008, methodology): “Incremental TF-IDF model is widely applied to term weight calculation in TDT [3, 5, 7, 13].”