Not cool, calm or collected: Using emotional language to detect COVID-19 misinformation

Gabriel Asher, Phil Bohlman, Karsten Kleyensteuber
3/27/2023
cs.CLcs.LGcs.SI

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Abstract

COVID-19 misinformation on social media platforms such as twitter is a threat to effective pandemic management. Prior works on tweet COVID-19 misinformation negates the role of semantic features common to twitter such as charged emotions. Thus, we present a novel COVID-19 misinformation model, which uses both a tweet emotion encoder and COVID-19 misinformation encoder to predict whether a tweet contains COVID-19 misinformation. Our emotion encoder was fine-tuned on a novel annotated dataset and our COVID-19 misinformation encoder was fine-tuned on a subset of the COVID-HeRA dataset. Experimental results show superior results using the combination of emotion and misinformation encoders as opposed to a misinformation classifier alone. Furthermore, extensive result analysis was conducted, highlighting low quality labels and mismatched label distributions as key limitations to our study.

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Cite this paper

@article{asher2023cool,
  title  = {Not cool, calm or collected: Using emotional language to detect COVID-19 misinformation},
  author = {Gabriel Asher and Phil Bohlman and Karsten Kleyensteuber},
  year   = {2023},
  eprint = {2303.16777},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.16777v1}
}

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