Tune Down the Misinformation, Please: Generating Corrective Messages for COVID-19 Misinformation

Date
2022-01-04
Authors
Meyer, Dylan
Tao, Jie
Kris, Alison
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The ongoing COVID-19 pandemic drastically changed our lives in multiple aspects, one of which is the reliance on social media during quarantine, both for social interaction and information-seeking purposes. However, the wide dissemination of misinformation on social media has impacted public health negatively. Previous studies on COVID-19 misinformation mainly focused on exploration of impacts and explanation of motivations, with few exceptions. In this study, we propose an analytical pipeline that generates corrective messages toward COVID-19 misinformation in a semi-automatic fashion, and then evaluate it against a large amount of data. Both the automated and manual evaluation results suggest the efficiency of the proposed pipeline, which can be used in combination with human intelligence by individuals and public health organizations in fighting COVID-19 misinformation.
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Collaboration for Data Science, deep learning, generative adversarial networks, human in the loop analysis, misinformation correction, natural language processing
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10 pages
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Proceedings of the 55th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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