Not cool, calm or collected: Using emotional language to detect COVID-19 misinformation
This addresses the threat of COVID-19 misinformation on platforms like Twitter for public health management, but it is incremental as it builds on prior work by adding emotional features.
The paper tackled the problem of detecting COVID-19 misinformation on social media by incorporating emotional language features, and the result showed that combining emotion and misinformation encoders yielded superior performance compared to using a misinformation classifier alone.
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.