Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering

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Title: Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering
Authors: Ifrim, Georgiana
Shi, Bichen
Brigadir, Igor
Permanent link: http://hdl.handle.net/10197/7546
Date: 8-Apr-2014
Online since: 2016-04-05T09:00:55Z
Abstract: Twitter has become as much of a news media as a social network, and much research has turned to analysing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in [16]. We show that aggressive lettering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2014 ACM
Keywords: Event detectionTwitterSocial mediaDigital journalismNews aggregation
Other versions: http://www.snow-workshop.org/2014/
Language: en
Status of Item: Peer reviewed
Conference Details: Second Workshop on Social News on the Web (SNOW), Seoul, Korea, 8 April 2014
Appears in Collections:Insight Research Collection

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