1. Detecting Misleading Information on COVID-19
- Author
-
Elhadad, Mohamed K., Li, Kin Fun, and Gebali, Fayez
- Subjects
social networks ,text classification ,General Computer Science ,Coronavirus disease 2019 (COVID-19) ,Computer science ,social media ,media_common.quotation_subject ,Feature extraction ,Computers and Information Processing ,text mining ,02 engineering and technology ,Machine learning ,computer.software_genre ,01 natural sciences ,World health ,WHO ,infodemic ,Voting ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,010306 general physics ,fake news detection ,media_common ,SARS-CoV-2 ,business.industry ,pandemic ,General Engineering ,COVID-19 ,Ensemble learning ,misleading information ,TK1-9971 ,Coronavirus ,web mining ,Web mining ,Computational and Artificial Intelligence ,020201 artificial intelligence & image processing ,Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,computer ,Classifier (UML) - Abstract
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.
- Published
- 2020