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.

翻译：诸如推特等社交媒体平台上的COVID-19虚假信息对有效的疫情管理构成威胁。先前关于推特COVID-19虚假信息的研究忽视了推特中常见的语义特征（如强烈情感）的作用。因此，我们提出了一种新型COVID-19虚假信息检测模型，该模型同时使用推文情感编码器和COVID-19虚假信息编码器来预测推文是否包含COVID-19虚假信息。我们的情感编码器在一个新型标注数据集上进行了微调，而COVID-19虚假信息编码器则在COVID-HeRA数据集的子集上进行了微调。实验结果表明，与仅使用虚假信息分类器相比，结合情感编码器和虚假信息编码器取得了更优的结果。此外，我们还进行了广泛的结果分析，指出标签质量低下和标签分布不匹配是本研究的两个主要局限性。