Conference paper Open Access

Challenges in Automated Detection of COVID-19 Misinformation

Herrmannova, Drahomira; Thakur, Gautam; Grant, Joshua N.; Tansakul, Varisara; Eaton, Bryan; Kotevska, Olivera; Burdette, Jordan; Smyth, Martin; Smith, Monica

The COVID-19 pandemic has made the dangers of the spread of misinformation obvious but despite much global effort to curbing its spread, fake information about the pandemic keeps proliferating. In this paper, we address the development of automated methods for verification of claims about COVID-19 and discuss the challenges associated with this task. We focus on labeled data collection, limitations of existing models, and difficulties of applying misinformation detection models in practical applications. Our initial analysis indicates label imbalance may be a particular challenge for developing claim verification models and we discuss options for alleviating this issue.

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