Earticle

초록

영어

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information―both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people’s willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review
2.1. Fake News and the COVID-19 Infodemic
2.2. Fake News Detection Using Deep Learning
2.3. Content Analysis for Fake News
Ⅲ. Research Framework
3.1. Phase 1: Fake News Detection Model
3.2. Phase 2: Content analysis of news
Ⅳ. Analysis and Result
4.1. Results of Prediction Model
4.2. Results of Topic Modeling
4.3. Results of Linguistic Analysis
Ⅴ. Discussion and Conclusion
Acknowledgements

키워드

저자

• Olga Chernyaeva [ Ph.D. Student, College of Business Administration, Pusan National University, Korea ]
• Taeho Hong [ Professor, College of Business Administration, Pusan National University, Korea ] Corresponding Author
• YongHee Kim [ Associate Professor, College of Business Administration, Pusan National University, Korea ]
• YoungKi Park [ Associate Professor, School of Business, George Washington University, USA ]
• Gang Ren [ Assistant Professor, School of Business, Hefei University of Technology, China ]
• Jisoo Ock [ Assistant Professor, College of Business Administration, Pusan National University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658