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  5. Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering
 
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Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering

Author(s)
Ifrim, Georgiana  
Shi, Bichen  
Brigadir, Igor  
Uri
http://hdl.handle.net/10197/7546
Date Issued
2014-04-08
Date Available
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2014 ACM
Subjects

Event detection

Twitter

Social media

Digital journalism

News aggregation

Web versions
http://www.snow-workshop.org/2014/
Language
English
Status of Item
Peer reviewed
Conference Details
Second Workshop on Social News on the Web (SNOW), Seoul, Korea, 8 April 2014
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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insight_publication.pdf

Size

255.01 KB

Format

Adobe PDF

Checksum (MD5)

3bf2fa93b49819de18a1264514cc08ea

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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