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Paper Abstract and Keywords
Presentation 2021-01-20 13:25
A Study on the Impact of Deleting Links in a Twitter Social Network for Suppressing Information Diffusion
Shiori Furukawa, Sho Tsugawa (University of Tsukuba) CQ2020-67
Abstract (in Japanese) (See Japanese page) 
(in English) While a lot of beneficial information are available on social media, spread of harmful information such as fake news has been a serious issue. For suppressing the diffusion of harmful information, significant efforts of many researchers have been devoted. As a countermeasure against the spread of harmful information, link deletion strategies in social networks have been studied. Link deletion strategies have been shown to be effective for reducing the size of information cascades generated with synthetic information diffusion models on a given social network. In this paper, we evaluate the effectiveness of an existing link deletion strategy called NetMelt for suppressing information diffusion on Twitter by utilizing log data of retweet diffusion. Consequently, our results show that even when deleting 50% of links detected with the NetMelt from a Twitter social network, the size of tweet cascades after link deletion is estimated as only 50% of its original size, which suggests a limitation of a link deletion strategy for suppressing information diffusion on Twitter.
Keyword (in Japanese) (See Japanese page) 
(in English) Social network / Social media / Information diffusion / Link deletion / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 314, CQ2020-67, pp. 38-42, Jan. 2021.
Paper # CQ2020-67 
Date of Issue 2021-01-13 (CQ) 
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 CQ2020-67

Conference Information
Committee CQ CBE  
Conference Date 2021-01-20 - 2021-01-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AR/VR, Broadcasting Service, Video/Voice Services Quality, High Realistic, User Behavior/Psychology, User Experience, Media Quality, Network Quality and QoS Control, Networks and Communications at Disaster, User Behavior, Machine Learning, Video Communication, etc. 
Paper Information
Registration To CQ 
Conference Code 2021-01-CQ-CBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on the Impact of Deleting Links in a Twitter Social Network for Suppressing Information Diffusion 
Sub Title (in English)  
Keyword(1) Social network  
Keyword(2) Social media  
Keyword(3) Information diffusion  
Keyword(4) Link deletion  
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1st Author's Name Shiori Furukawa  
1st Author's Affiliation University of Tsukuba (University of Tsukuba)
2nd Author's Name Sho Tsugawa  
2nd Author's Affiliation University of Tsukuba (University of Tsukuba)
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Speaker Author-1 
Date Time 2021-01-20 13:25:00 
Presentation Time 20 minutes 
Registration for CQ 
Paper # CQ2020-67 
Volume (vol) vol.120 
Number (no) no.314 
Page pp.38-42 
#Pages 5 
Date of Issue 2021-01-13 (CQ) 

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