biostudies-literatureUnknown2(2)Kolluri N<h4>Background</h4>The volume of COVID-19-related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning-based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19-related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed.<h4>Objective</h4>The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response.<JMIR infodemiologye38756https://www.ebi.ac.uk/biostudies/studies/S-EPMC9987189biostudies-literatureCOVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic.PMC9987189Murthy DLiu YKolluri NfalseCOVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic.<h4>Background</h4>The volume of COVID-19-related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning-based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19-related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed.<h4>Objective</h4>The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response.<2022-01-01T00:00:00Z2022 Jul-Dec2026-03-17T15:58:58.016Z2025-04-04T21:08:32.763ZS-EPMC99871893711344610.2196/38756