비지도 학습 기반 가짜뉴스 생성 프레임워크 개발 및 데이터셋 품질 평가 지표의 효과성 연구
Development of an Unsupervised Learning-Based Fake News Generation Framework and Study the Effectiveness of Dataset Quality Evaluation Metrics
- 발행
- 2024 등록KCI API에는 발행일 항목이 없어 원문 등록일을 쓰고 있습니다. 실측으로 발행보다 최대 6년 늦습니다 — 재수집하면 발행연월로 바뀝니다.
- 소속·발행
- 고려대학교 산업경영공학
- 출처
- 국내 KCI
- DOI
- 10.7232/JKIIE.2024.50.1.036
- 원문
- 원문 보기 ↗
개념
키워드
Natural Language Processing, Fake News Generation, Evaluation Metrics, Natural Language Processing, Fake News Generation, Evaluation Metrics
초록
With the advent of the internet and social media, fake news has become easily generated and disseminated, causing severe societal issues. The development of robust fake news detection models to mitigate the damages incurred from fake news necessitates a substantial volume of high-quality fake news data. However, there exists a significant shortage of publicly available datasets that embody such quality. In this study, we propose an unsupervised learning-based framework for generating fake news, solely leveraging real news data. This proposed framework is structured into four key phases: similar news exploration, fake news title generation, labeling error filtering, and data quality verification. Moreover, we introduce a novel set of evaluation metrics to assess the quality of the generated fake news data in terms of reliability, diversity, and complexity. Through extensive experiments, we have validated the effectiveness of the proposed framework and evaluation metrics, paving the way for more nuanced approaches to the creation and analysis of fake news datasets.
 이 논문은 2023년도 정부의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업(NRF-2022R1A2C2005455)의 성과물임. 또한, 이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로