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Exploratory Data Analysis and Implications for Detecting Fake News Related to COVID-19

Title
Exploratory Data Analysis and Implications for Detecting Fake News Related to COVID-19
Alternative Author(s)

Oh, Miae

Publication Year
2023-02-01
Publisher
Korea Institute for Health and Social Affairs
Citation
Health and Welfare Policy Forum 2023.2 No.316, pp.68-80
Abstract
False information and fake news about infectious diseases can be a huge threat, as they involve human lives and can cause a lot of social confusion. In fact, more than 800 people died around the world from January to March 2020 due to fake news related to COVID-19. In a sea of information, identifying fake news becomes increasingly important, and machine learning methods can be used as an effective tool for detecting fake news.
This study employed exploratory data analysis to detect fake news related to COVID-19. Additionally, it looks at the potential difficulties and implications of employing machine learning to identify fake news.
URI
https://doi.org/10.23062/2023.02.6
ISSN
1226-3648
DOI
10.23062/2023.02.6
KIHASA Research
Subject Classification
General social security > Health and welfare digitization
General social security > Social security statistics
Health care > Future disease risks
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