The variant of efforts avoiding strain: successful correction of a scientific discourse related to COVID-19

IF 2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Journal of Computational Social Science Pub Date : 2023-10-26 DOI:10.1007/s42001-023-00223-w
Dongwoo Lim, Fujio Toriumi, Mitsuo Yoshida, Mikihito Tanaka, Kunhao Yang
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Abstract

Abstract This study focuses on how scientifically accurate information is disseminated through social media, and how misinformation can be corrected. We have identified examples on Twitter where scientific terms that have been widely misused have been rectified and replaced by scientifically accurate terms through the interaction of users. The results show that the percentage of accurate terms (“variant” or “COVID-19 variant”) being used instead of the inaccurate terms (“strain”) on Twitter has already increased since the end of December 2020. This was about a month before the release of an official statement by the Japanese Association for Infectious Diseases regarding the accurate terminology, and the use of terms on social media was faster than it was in television. Some Twitter users who quickly started using the accurate term were more likely to retweet messages sent by leading influencers on Twitter, rather than messages sent by traditional media or portal sites. However, a few Twitter users continued to use wrong terms even after March 2021, even though the use of the accurate terms was widespread. This study empirically verified that self-correction occurs even on Twitter, and also suggested that influencers with expertise can influence the direction of public opinion on social media.
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努力避免紧张的变体:成功纠正与COVID-19相关的科学论述
本研究的重点是科学准确的信息如何通过社交媒体传播,以及如何纠正错误信息。我们在推特上发现了一些例子,在这些例子中,通过用户的互动,被广泛滥用的科学术语已经被纠正,并被科学准确的术语所取代。结果显示,自2020年12月底以来,推特上使用准确术语(“变体”或“COVID-19变体”)取代不准确术语(“菌株”)的比例已经有所增加。大约一个月后,日本传染病协会发布了一份关于准确术语的官方声明,社交媒体上术语的使用速度比电视上要快。一些很快开始使用这个准确术语的推特用户更有可能转发由推特上的主要影响者发送的消息,而不是传统媒体或门户网站发送的消息。然而,即使在2021年3月之后,一些推特用户继续使用错误的术语,尽管正确的术语被广泛使用。本研究实证验证了即使在Twitter上也会出现自我纠正,也表明具有专业知识的网红可以影响社交媒体上的舆论方向。
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来源期刊
Journal of Computational Social Science
Journal of Computational Social Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
6.20
自引率
6.20%
发文量
30
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