Detecting Misleading Information on COVID-19

IEEE Access

Mohamed K. Elhadad¹  Kin Fun Li¹  Fayez Gebali¹ 

[1] Department of Electrical and Computer Engineering, University of Victoria, V8W 2Y2, Victoria, Canada;

关键词: Coronavirus;    COVID-19;    fake news detection;    infodemic;    misleading information;    pandemic;   

DOI  :  10.1109/ACCESS.2020.3022867

来源: DOAJ

【 摘 要 】

This article addresses the problem of detecting misleading information related to COVID-19. We propose a misleading-information detection model that relies on the World Health Organization, UNICEF, and the United Nations as sources of information, as well as epidemiological material collected from a range of fact-checking websites. Obtaining data from reliable sources should assure their validity. We use this collected ground-truth data to build a detection system that uses machine learning to identify misleading information. Ten machine learning algorithms, with seven feature extraction techniques, are used to construct a voting ensemble machine learning classifier. We perform 5-fold cross-validation to check the validity of the collected data and report the evaluation of twelve performance metrics. The evaluation results indicate the quality and validity of the collected ground-truth data and their effectiveness in constructing models to detect misleading information.

【 授权许可】

Unknown