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.
【저자키워드】 COVID-19, SARS-CoV-2, coronavirus, pandemic, social media, WHO, infodemic, text mining, social networks, Text classification, Fake news detection, misleading information, web mining, 【초록키워드】 Effectiveness, Validity, epidemiological, information, World Health Organization, Machine learning algorithms, Classifier, Performance metrics, collected data, detection system, Seven, identify, detect, collected, build, Obtaining, UNICEF, United Nation,