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Fake News Detection on YouTube Using Related Video Information
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관련 동영상 정보를 활용한 YouTube 가짜뉴스 탐지 기법

논문 기본 정보

Type
Academic journal
Author
Junho Kim (국민대학교) Yongjun Shin (강원대학교) Hyunchul Ahn (국민대학교)
Journal
Korea Intelligent Information Systems Society Journal of Intelligence and Information Systems Vol.29 No.3 KCI Accredited Journals
Published
2023.9
Pages
19 - 36 (18page)

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Fake News Detection on YouTube Using Related Video Information
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Research history (3)

2023

Fake News Detection on YouTube Using Related Video Information
Junho Kim ,  Yongjun Shin ,  Hyunchul Ahn Journal of Intelligence and Information Systems 2023.09 Academic journal
관련 동영상 정보를 활용한 YouTube 가짜뉴스 탐지 기법
김준호 ,  신용준 ,  안현철 한국지능정보시스템학회 학술대회논문집 2023.05 Proceeding

2022

Related Video 정보를 활용한 YouTube 가짜뉴스 탐지 기법
김준호 ,  신용준 ,  안현철 한국지능정보시스템학회 학술대회논문집 2022.11 Proceeding
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Abstract· Keywords

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As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world’s leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.
#가짜뉴스 탐지 #유튜브 #합성곱 신경망 #관련 동영상 #Fake News #YouTube #Doc2vec #Convolutional Neural Network #Related Video

Contents

1. 서론
2. 이론적 배경
3. 연구모델
4. 실증 분석
5. 결론
참고문헌(References)
Abstract

References (25)

Add References
김유나. (2021). ‘한강 의대생 사건’ 보도, 언론의 부끄러운 자화상. 관훈저널, 63(3), 79-86. google schola 박성수, 이건창. (2019). 효과적인 가짜 뉴스 탐지를 위한 텍스트 분석과 네트워크 임베딩 방법의 비교 연구. 디지털융복합연구, 17(5), 137-143. google schola 염정윤, 정세훈. (2019). 가짜뉴스 노출과 전파에 영향을 미치는 요인, 한국언론학보, 63(1), 7-45. google schola 이동호, 이정훈, 김유리, 김형준, 박승면, 양유준, 신웅비. (2018). 딥러닝 기법을 이용한 가짜뉴스 탐지. 한국정보처리학회 학술대회논문집, 25(1), 384-387. google schola 이원상. (2019). 허위사실유포에 대한 형법의 대응방안 고찰 - 소위 ‘가짜뉴스(fake news)’를 중심으로 -. 형사법의 신동향, 62, 35-68. google schola

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