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Paper Abstract and Keywords
Presentation 2025-01-30 10:40
Analyzing the Characteristics of Sustained Influential Users Using Long-Term Twitter Data
Harutaka Yamada, Sho Tsugawa (Tsukuba Univ.) CQ2024-70
Abstract (in Japanese) (See Japanese page) 
(in English) With the increasing prominence of social networking services (SNS) as platforms for information gathering, companies have increasingly adopted influencer marketing by partnering with influencers to promote their products or services. For effective marketing, it is preferable to identify and collaborate with long-term influencers who maintain strong influence over extended periods, rather than those whose influence is limited to a specific point in time. This study addresses the challenge of identifying long-term influencers from among those who exhibit strong influence at a given time. Using data on information diffusion through retweets from approximately 130,000 Twitter users over a nine-month period, we investigate the characteristics of long-term influencers. Furthermore, we develop a model to identify long-term influencers based on several features derived from social networks and retweet logs. The results show that our models successfully identify long-term influencers.
Keyword (in Japanese) (See Japanese page) 
(in English) Influencer / Social Network / Social Media / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 368, CQ2024-70, pp. 18-23, Jan. 2025.
Paper # CQ2024-70 
Date of Issue 2025-01-23 (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 CQ2024-70

Conference Information
Committee CQ CBE  
Conference Date 2025-01-30 - 2025-01-31 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Computational Social Science, Human Computer Interaction, Data Analysis, User Behaviour, Media Quality, etc. 
Paper Information
Registration To CQ 
Conference Code 2025-01-CQ-CBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analyzing the Characteristics of Sustained Influential Users Using Long-Term Twitter Data 
Sub Title (in English)  
Keyword(1) Influencer  
Keyword(2) Social Network  
Keyword(3) Social Media  
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1st Author's Name Harutaka Yamada  
1st Author's Affiliation University of Tsukuba (Tsukuba Univ.)
2nd Author's Name Sho Tsugawa  
2nd Author's Affiliation University of Tsukuba (Tsukuba Univ.)
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Speaker Author-1 
Date Time 2025-01-30 10:40:00 
Presentation Time 20 minutes 
Registration for CQ 
Paper # CQ2024-70 
Volume (vol) vol.124 
Number (no) no.368 
Page pp.18-23 
#Pages 6 
Date of Issue 2025-01-23 (CQ) 

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