The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using Google Fact Check Explorer API we identified 226 unique COVID-19 false stories for March 2020. These were clustered into six types of misinformation (cures, virus, vaccine, politics, conspiracy theories, and other). Applying the 226 classifiers to the Twitter sample we identified 690,004 tweets. Instead of running the sentiment on all tweets we manually coded a random subset of 100 tweets for each classifier to increase the validity, reducing the dataset to 2,097 tweets. We found that only a minor part of the entire dataset was related to misinformation. Also, misinformation in general does not lean towards a certain emotional valence. However, looking at comparisons of emotional valence for different types of misinformation uncovered that misinformation related to “virus” and “conspiracy” had a more negative valence than “cures,” “vaccine,” “politics,” and “other.” Knowing from existing studies that negative misinformation spreads faster, this demonstrates that filtering for misinformation type is fruitful and indicates that a focus on “virus” and “conspiracy” could be one strategy in combating misinformation. As emotional contexts affect misinformation spreading, the knowledge about emotional valence for different types of misinformation will help to better understand the spreading and consequences of misinformation.

COVID-19 错误信息在社交媒体上的传播可能会对人们的行为产生严重影响。在本文中，我们调查了 Twitter 上与 COVID-19 危机相关的错误信息的情绪表达，以及情绪效价是否因错误信息的类型而异。我们收集了 2020 年 3 月的 17,463,220 条英文推文，其中包含 76 个与 COVID-19 相关的主题标签。使用 Google Fact Check Explorer API，我们确定了 2020 年 3 月的 226 个独特的 COVID-19 虚假故事。这些被归为六种错误信息（治疗、病毒、疫苗） 、政治、阴谋论等）。将 226 个分类器应用于 Twitter 样本，我们确定了 690,004 条推文。我们没有在所有推文上运行情绪，而是为每个分类器手动编码 100 条推文的随机子集以提高有效性，将数据集减少到 2,097 条推文。我们发现整个数据集中只有一小部分与错误信息有关。此外，错误信息一般不会倾向于某种情绪效价。然而，通过比较不同类型错误信息的情绪效价发现，与“病毒”和“阴谋”相关的错误信息比“治疗”、“疫苗”、“政治”和“其他”具有更负面的效价。从现有研究中得知负面错误信息传播速度更快，这表明过滤错误信息类型是卓有成效的，并表明关注“病毒”和“阴谋”可能是打击错误信息的一种策略。由于情绪环境会影响错误信息的传播，