Different types of COVID-19 misinformation have different emotional valence on Twitter
Different types of COVID-19 misinformation have different emotional valence on Twitter
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DOI：
10.1177/20539517211041279
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发表时间：
2021-07-01
影响因子：
8.5
通讯作者：
Bechmann, Anja
中科院分区：
文献类型：
--
作者：
Charquero-Ballester, Marina;Walter, Jessica G.;Bechmann, Anja
新冠肺炎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后果。本文考察了与新冠肺炎事件相关的错误信息在推特上的情绪表达，以及不同类型的错误信息是否具有不同的情绪效度。2020年3月，我们收集了17,463,220条带有76个新冠肺炎相关标签的英文推文。使用谷歌事实检查浏览器API，我们识别了2020年3月226个独特的新冠肺炎虚假故事。这些被归类为六种类型的错误信息(治疗、病毒、疫苗、政治、阴谋论和其他)。将226个分类器应用于Twitter样本，我们识别了690,004条tweet。我们没有对所有推文运行情感，而是为每个分类器手动编码了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，错误信息通常不会倾向于某种情绪价位。然而，通过比较不同类型的错误信息的情绪价值，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”具有更负面的价值。从现有研究得知，负面错误信息传播得更快，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，了解不同类型错误信息的情绪效价将有助于更好地理解错误信息的传播和后果。
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.