Streaming first story detection with application to Twitter

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Streaming first story detection with application to Twitter (2010)

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by Sasa Petrovic , Miles Osborne , Victor Lavrenko

Venue:	IN PROCEEDINGS OF THE 11TH ANNUAL CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (NAACL HLT 2010
Citations:	113 - 6 self

BibTeX

@INPROCEEDINGS{Petrovic10streamingfirst,
    author = {Sasa Petrovic and Miles Osborne and Victor Lavrenko},
    title = {Streaming first story detection with application to Twitter},
    booktitle = {IN PROCEEDINGS OF THE 11TH ANNUAL CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (NAACL HLT 2010},
    year = {2010},
    publisher = {}
}

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Abstract

With the recent rise in popularity and size of social media, there is a growing need for systems that can extract useful information from this amount of data. We address the problem of detecting new events from a stream of Twitter posts. To make event detection feasible on web-scale corpora, we present an algorithm based on locality-sensitive hashing which is able overcome the limitations of traditional approaches, while maintaining competitive results. In particular, a comparison with a stateof-the-art system on the first story detection task shows that we achieve over an order of magnitude speedup in processing time, while retaining comparable performance. Event detection experiments on a collection of 160 million Twitter posts show that celebrity deaths are the fastest spreading news on Twitter. 1

Keyphrases

first story detection twitter post new event magnitude speedup competitive result recent rise celebrity death social medium useful information processing time stateof-the-art system traditional approach event detection web-scale corpus event detection experiment first story detection task comparable performance locality-sensitive hashing

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