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

dc.contributor.authorMeyer, Dylan
dc.contributor.authorTao, Jie
dc.contributor.authorKris, Alison
dc.date.accessioned2021-12-24T17:18:36Z
dc.date.available2021-12-24T17:18:36Z
dc.date.issued2022-01-04
dc.description.abstractThe 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.
dc.format.extent10 pages
dc.identifier.doihttps://doi.org/10.24251/HICSS.2022.039
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79369
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCollaboration for Data Science
dc.subjectdeep learning
dc.subjectgenerative adversarial networks
dc.subjecthuman in the loop analysis
dc.subjectmisinformation correction
dc.subjectnatural language processing
dc.titleTune Down the Misinformation, Please: Generating Corrective Messages for COVID-19 Misinformation
dc.type.dcmitext

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