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Paper Abstract and Keywords
Presentation 2022-06-25 11:20
Estimating tweet directivity using linguistic features
Hidenori Kiyomoto, Kongmeng Liew, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki (NAIST) DE2022-4
Abstract (in Japanese) (See Japanese page) 
(in English) With the Covid-19 pandemic, governments and local authorities are often required to provide accurate and prompt information using social media. For the communication of such information, it is important for social media users themselves to know if they fall within the targeted demographics of these communications (age, gender, etc.), which we label here as ‘directivity’. Previous studies have mainly focused on examining the attributes of the information provider, but to our knowl- edge, there have not been any studies that examine the attributes of targeted users (receivers). In this study, we first assumed that tweets by magazine publishers are crafted for their targeted readership. We then collected tweets from the official Twitter accounts of these magazines and manually labeled the target age and gender of each magazine to create a dataset of tweet directivity. Using this dataset, we then classified the target user demographic (age group and gender) of these tweets through machine learning. We analyzed the results of this experiment and discussed the usefulness of our quantitative estimates of directivity.
Keyword (in Japanese) (See Japanese page) 
(in English) Social Computing / Natural Language Processing / Risk Communication / Twitter / Directivity / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 88, DE2022-4, pp. 19-24, June 2022.
Paper # DE2022-4 
Date of Issue 2022-06-17 (DE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction
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)
Download PDF DE2022-4

Conference Information
Committee DE  
Conference Date 2022-06-24 - 2022-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Musashino University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Social Computing 
Paper Information
Registration To DE 
Conference Code 2022-06-DE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimating tweet directivity using linguistic features 
Sub Title (in English)  
Keyword(1) Social Computing  
Keyword(2) Natural Language Processing  
Keyword(3) Risk Communication  
Keyword(4) Twitter  
Keyword(5) Directivity  
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1st Author's Name Hidenori Kiyomoto  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Kongmeng Liew  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
3rd Author's Name Shuntaro Yada  
3rd Author's Affiliation Nara Institute of Science and Technology (NAIST)
4th Author's Name Shoko Wakamiya  
4th Author's Affiliation Nara Institute of Science and Technology (NAIST)
5th Author's Name Eiji Aramaki  
5th Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2022-06-25 11:20:00 
Presentation Time 15 minutes 
Registration for DE 
Paper # DE2022-4 
Volume (vol) vol.122 
Number (no) no.88 
Page pp.19-24 
#Pages
Date of Issue 2022-06-17 (DE) 


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