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Paper Abstract and Keywords
Presentation 2014-06-14 13:30
Early Detection of Disasters with Contextual Information on Twitter
Shota Saito (Univ. of Tokyo), Yohei Ikawa (IBM), Hideyuki Suzuki (Univ. of Tokyo), Akiko Murakami (IBM) NLC2014-2
Abstract (in Japanese) (See Japanese page) 
(in English) Twitter, a recently growing micro-blogging service, offers opportunities to analyze real-time events by its real-time nature. Particularly, it is highly valuable if real-time disasters in a real world can be detected by Twitter. However, we have to search for the disaster on Twitter manually, or we cannot know the details or the contexts of the disaster if we use previous porposed event detection methods. Therefore we propose a method for an early detection of disasters with contextual information. In this study, we assume that huge disasters make a topic which is composed of several words, and that the expression of tweets mentioning that disaster diverge. Based on the assumputions, we propose to make a graph of cooccurence of the words appearing in Twitter, and to detect real disasters by independent source measure, which measures how much the expressions between each tweet mentioning a same topic diverge. We demonstrate our technique on real data from Twitter and show that our method can detect reasonable disasters before media reports.
Keyword (in Japanese) (See Japanese page) 
(in English) Social Media / Event Detection / Graph / Independent Source Measure / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 81, NLC2014-2, pp. 7-12, June 2014.
Paper # NLC2014-2 
Date of Issue 2014-06-07 (NLC) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NLC  
Conference Date 2014-06-14 - 2014-06-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLC 
Conference Code 2014-06-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Early Detection of Disasters with Contextual Information on Twitter 
Sub Title (in English)  
Keyword(1) Social Media  
Keyword(2) Event Detection  
Keyword(3) Graph  
Keyword(4) Independent Source Measure  
1st Author's Name Shota Saito  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Yohei Ikawa  
2nd Author's Affiliation IBM Research (IBM)
3rd Author's Name Hideyuki Suzuki  
3rd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
4th Author's Name Akiko Murakami  
4th Author's Affiliation IBM Research (IBM)
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Date Time 2014-06-14 13:30:00 
Presentation Time 25 
Registration for NLC 
Paper # IEICE-NLC2014-2 
Volume (vol) IEICE-114 
Number (no) no.81 
Page pp.7-12 
#Pages IEICE-6 
Date of Issue IEICE-NLC-2014-06-07 

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