Fake or not? Automated detection of COVID-19 misinformation and disinformation in social networks and digital media

Izzat Alsmadi (), Natalie Manaeva Rice and Michael J. O’Brien
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Izzat Alsmadi: Texas A&M University–San Antonio
Natalie Manaeva Rice: University of Tennessee
Michael J. O’Brien: Texas A&M University

Computational and Mathematical Organization Theory, 2024, vol. 30, issue 3, No 1, 187-205

Abstract: Abstract With the continuous spread of the COVID-19 pandemic, misinformation poses serious threats and concerns. COVID-19-related misinformation integrates a mixture of health aspects along with news and political misinformation. This mixture complicates the ability to judge whether a claim related to COVID-19 is information, misinformation, or disinformation. With no standard terminology in information and disinformation, integrating different datasets and using existing classification models can be impractical. To deal with these issues, we aggregated several COVID-19 misinformation datasets and compared differences between learning models from individual datasets versus one that was aggregated. We also evaluated the impact of using several word- and sentence-embedding models and transformers on the performance of classification models. We observed that whereas word-embedding models showed improvements in all evaluated classification models, the improvement level varied among the different classifiers. Although our work was focused on COVID-19 misinformation detection, a similar approach can be applied to myriad other topics, such as the recent Russian invasion of Ukraine.

Keywords: Coronavirus; COVID-19; Disinformation; Learning models; Misinformation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10588-022-09369-w

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