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Paper Abstract and Keywords
Presentation 2026-07-11 15:20
Methods for Extracting Opinion Distributions Based on the Differences between Generative AI Responses and Related News Articles
Ryoma Kimura, Shoichi Nakamura, Hiroki Nakayama (Fukushima Univ.) ET2026-17
Abstract (in Japanese) (See Japanese page) 
(in English) Generative AI is increasingly being used as a tool for information gathering. Although generative AI can provide concise responses that summarize key points, it may also generate plausible misinformation, making it important for users to carefully examine the validity of the generated content. Determining the validity of AI-generated responses requires considering diverse opinions based on perspectives and interpretations different from those presented in the responses themselves. However, this process can be difficult for inexperienced users. To address this issue, this study aims to develop a method to facilitate the understanding of diverse opinions by focusing on the differences between AI-generated responses and related news articles. This paper proposes methods for estimating news articles related to AI-generated responses and extracting opinion distributions based on the differences between the responses and the related articles. Furthermore, the effectiveness of the proposed method is evaluated through case studies.
Keyword (in Japanese) (See Japanese page) 
(in English) AI-generated responses / Evaluating validity / Hallucination / Opinion distributions / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 104, ET2026-17, pp. 32-37, July 2026.
Paper # ET2026-17 
Date of Issue 2026-07-04 (ET) 
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 ET2026-17

Conference Information
Committee ET  
Conference Date 2026-07-11 - 2026-07-11 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ET 
Conference Code 2026-07-ET 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Methods for Extracting Opinion Distributions Based on the Differences between Generative AI Responses and Related News Articles 
Sub Title (in English)  
Keyword(1) AI-generated responses  
Keyword(2) Evaluating validity  
Keyword(3) Hallucination  
Keyword(4) Opinion distributions  
Keyword(5)  
Keyword(6)  
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1st Author's Name Ryoma Kimura  
1st Author's Affiliation Fukushima University (Fukushima Univ.)
2nd Author's Name Shoichi Nakamura  
2nd Author's Affiliation Fukushima University (Fukushima Univ.)
3rd Author's Name Hiroki Nakayama  
3rd Author's Affiliation Fukushima University (Fukushima Univ.)
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Speaker Author-1 
Date Time 2026-07-11 15:20:00 
Presentation Time 25 minutes 
Registration for ET 
Paper # ET2026-17 
Volume (vol) vol.126 
Number (no) no.104 
Page pp.32-37 
#Pages 6 
Date of Issue 2026-07-04 (ET) 

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