Dallo Irina, Elroy Or, Fallou Laure, Komendantova Nadejda, Yosipof Abraham
Swiss Seismological Service at ETH Zurich, ETH Zurich, Sonnegstrasse 5, 8092, Zurich, Switzerland.
Faculty of Information Systems and Computer Science, College of Law and Business, Ramat-Gan, Israel.
Sci Rep. 2023 Aug 17;13(1):13391. doi: 10.1038/s41598-023-40399-9.
The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (March 2020-March 2022) and hand-labeled 4157 tweets. We used RoBERTa to classify the complete dataset and analyzed the results. We found that (1) there are significantly more not-misinformation than misinformation tweets; (2) earthquake predictions are continuously present on Twitter with peaks after felt events; and (3) prediction misinformation tweets sometimes link or tag official earthquake notifications from credible sources. These insights indicate that official institutions present on social media should continuously address misinformation (even in quiet times when no event occurred), check that their institution is not tagged/linked in misinformation tweets, and provide authoritative sources that can be used to support their arguments against unfounded earthquake predictions.
社交媒体上错误信息的传播可能会导致不当行为,从而使灾难更加严重。在我们的研究中,我们聚焦于包含地震预测错误信息的推文,并分析了它们的传播动态。为此,我们在两年时间(2020年3月至2022年3月)内检索了82129条推文,并人工标注了4157条推文。我们使用RoBERTa对整个数据集进行分类并分析结果。我们发现:(1)非错误信息的推文数量显著多于错误信息的推文;(2)地震预测在推特上持续存在,有感事件发生后会出现峰值;(3)预测错误信息的推文有时会链接或标记来自可靠来源的官方地震通知。这些见解表明,社交媒体上的官方机构应持续处理错误信息(即使在没有事件发生的平静时期),检查其机构是否未在错误信息推文中被标记/链接,并提供可用于支持其反对毫无根据的地震预测论点的权威来源。