지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 874 - 882 (9page)
- DOI
- 10.7840/kics.2024.49.6.874
이용수
초록· 키워드
오류제보하기
With the proliferation of smartphones and the advancement of internet speeds, the usage of social media has experienced a significant and rapid surge. In this evolving landscape, the predominant platform for video content is none other than YouTube. Despite operating a robust recommendation algorithm that leverages collaborative and content-based filtering technologies, YouTube grapples with the formidable challenge of generating misinformation due to its vast and diverse user base. Indeed, the scale of fake news, when examined statistically, is undeniably substantial. This misinformation has given rise to a myriad of societal issues, prominently manifesting in the form of the echo-chamber effect and the filter bubble effect. Furthermore, these effects have played a pivotal role in various instances of political exploitation, adding an extra layer of complexity to the challenges at hand. The algorithm"s inherent efficiency, coupled with widespread user engagement, has inadvertently facilitated the production and dissemination of misleading information, necessitating a comprehensive approach to address the multifaceted challenges posed by misinformation on the platform.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Current Technology and Problems with YouTube Algorithm
Ⅲ. Conclusions
References
참고문헌 (14)
참고문헌 신청
https://www.insightkorea.co.kr/news/articleView.html?idxno=112386, Dec. 30. 2023.
https://www.kpf.or.kr/front/research/selfDetail.do?seq=595768&link_g_homepage=F, Dec. 30. 2023.
S. Kim, S. Kim, and J. Ahn. “A Study on the bias and regulation of AI algorithm,” Korean Telecommun. Policy Rev., vol. 29, no. 2, pp. 111-144, 2022. (https://doi.org/10.37793/ITPR.29.2.4)
I. Kim and J. M. Kim, “YouTube algorithm and confirmation bias,” The Korean Assoc. Comput. Edu. Conf., vol. 25, no. 1(A), pp. 71-74, 2021.
P. Covington, J. Adams, and E. Sargin, “Deep neural networks for YouTube recommendations,” in Proc. 10th ACM Conf. RecSys '16, Assoc. Comput. Machinery, pp. 191-198, New York, NY, USA, 2016. (https://doi.org/10.1145/2959100.2959190)
https://www.kpf.or.kr/front/research/selfDetail.do?seq=595768&link_g_homepage=F, Dec. 30. 2023.
S. Kim, S. Kim, and J. Ahn. “A Study on the bias and regulation of AI algorithm,” Korean Telecommun. Policy Rev., vol. 29, no. 2, pp. 111-144, 2022. (https://doi.org/10.37793/ITPR.29.2.4)
I. Kim and J. M. Kim, “YouTube algorithm and confirmation bias,” The Korean Assoc. Comput. Edu. Conf., vol. 25, no. 1(A), pp. 71-74, 2021.
P. Covington, J. Adams, and E. Sargin, “Deep neural networks for YouTube recommendations,” in Proc. 10th ACM Conf. RecSys '16, Assoc. Comput. Machinery, pp. 191-198, New York, NY, USA, 2016. (https://doi.org/10.1145/2959100.2959190)
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