영상정보를 활용한 소셜 미디어상에서의 가짜 뉴스 탐지: 유튜브를 중심으로
Fake News Detection on Social Media using Video Information: Focused on YouTube
- 발행
- 2023 등록KCI API에는 발행일 항목이 없어 원문 등록일을 쓰고 있습니다. 실측으로 발행보다 최대 6년 늦습니다 — 재수집하면 발행연월로 바뀝니다.
- 소속·발행
- 바이브컴퍼니
- 출처
- 국내 KCI
- 원문
- 원문 보기 ↗
개념
키워드
Fake News Detection, Video Information, Video Metadata, Facial Expression, Machine Learning, Deep Learning, Fake News Detection, Video Information, Video Metadata, Facial Expression, Machine Learning, Deep Learning
초록
Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend.
 Design/methodology/approach This study collected video clips and related information including news scripts, speakers’ facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model.
 Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news de