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Paper Abstract and Keywords
Presentation 2021-07-24 10:20
[Short Paper] COVID-19 and biased information dissemination on Twitter
Gefei Li (AIST/Waseda Univ.), Yijun Duan, Taehoon Kim, Kyoungsook Kim (AIST) DE2021-4
Abstract (in Japanese) (See Japanese page) 
(in English) This study aims to examine if Twitter users with a higher level of similarity would have a more similar distribution of biased tweets. We randomly selected 100 users from an open-access COVID-19 Tweets dataset and fetched their Twitter timelines. We pre-trained the attention-based bidirectional long short-term memory (BiLSTM) model with a Media Bias Annotation Dataset and fine-tuned this model with 3,000 manually labelled Tweets concerning coronavirus-related topics. This model was used to classify users’ Tweets and returned the distribution of biased Tweets on their timelines. Users’ similarity was measured from two aspects: profile and text similarity. Ordinary Least Squares regression (OLS) correlation analysis suggests that both profile and text similarity are statistically significant in estimating the similarity of the distribution of biased tweets on users’ timelines.
Keyword (in Japanese) (See Japanese page) 
(in English) Bias / Twitter / Social Media / Profile Similarity / Text Similarity / NLP / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 125, DE2021-4, pp. 18-21, July 2021.
Paper # DE2021-4 
Date of Issue 2021-07-17 (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 DE2021-4

Conference Information
Committee DE  
Conference Date 2021-07-24 - 2021-07-24 
Place (in Japanese) (See Japanese page) 
Place (in English) KAIT IT extension center (Hybrid) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Social Computing 
Paper Information
Registration To DE 
Conference Code 2021-07-DE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) COVID-19 and biased information dissemination on Twitter 
Sub Title (in English)  
Keyword(1) Bias  
Keyword(2) Twitter  
Keyword(3) Social Media  
Keyword(4) Profile Similarity  
Keyword(5) Text Similarity  
Keyword(6) NLP  
Keyword(7)  
Keyword(8)  
1st Author's Name Gefei Li  
1st Author's Affiliation National Institute of Advanced Industrial Science and Technology/Waseda University (AIST/Waseda Univ.)
2nd Author's Name Yijun Duan  
2nd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
3rd Author's Name Taehoon Kim  
3rd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
4th Author's Name Kyoungsook Kim  
4th Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
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Speaker Author-1 
Date Time 2021-07-24 10:20:00 
Presentation Time 15 minutes 
Registration for DE 
Paper # DE2021-4 
Volume (vol) vol.121 
Number (no) no.125 
Page pp.18-21 
#Pages 4 
Date of Issue 2021-07-17 (DE) 

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