School of Journalism & Mass Communication, University of Iowa, Iowa City, Iowa, USA.
Department of Journalism, Broadcasting & Public Relations, SUNY Brockport, Brockport, New York, USA.
J Health Commun. 2024 Sep;29(9):556-565. doi: 10.1080/10810730.2024.2385638. Epub 2024 Aug 7.
Despite the robust scientific evidence affirming the safety and efficacy of COVID-19 vaccines, the proliferation of misinformation on social media platforms poses a threat by potentially exacerbating vaccine hesitancy. In response, certain social media platforms have taken measures to flag posts containing such misinformation with warning labels, aiming to dispel false beliefs. This present study employs a survey experiment ( = 304) to examine the effectiveness of two distinct warning labels - disputed and neutral warning labels - in the Twitter (the social media platform now known as X) context, specifically targeting misinformation about COVID-19 vaccines. This study investigates the nuanced effects of vaccine hesitancy on the perceived credibility of debunked misinformation posts following the application of warning flags. The results demonstrated that disputed labels significantly reduced the perceived credibility of misinformation regarding anti-COVID-19 vaccines in comparison to posts without any labeling. Nevertheless, individuals exhibiting higher levels of vaccine hesitancy tended to view the misinformation as more credible than their counterparts with lower levels of hesitancy. These findings present the efficacy of warning labels in combatting misinformation on social media platforms, particularly among those who are least hesitant about vaccination.
尽管有大量科学证据证实了 COVID-19 疫苗的安全性和有效性,但社交媒体平台上错误信息的泛滥可能会加剧疫苗犹豫,从而构成威胁。为此,某些社交媒体平台已采取措施,用警告标签标记包含此类错误信息的帖子,旨在消除错误信念。本研究通过调查实验(n=304),在 Twitter(即现在称为 X 的社交媒体平台)背景下,考察了两种不同警告标签(有争议和中立警告标签)的有效性,专门针对 COVID-19 疫苗错误信息。本研究调查了在应用警告标签后,疫苗犹豫对揭穿错误信息帖子的可信度的细微影响。结果表明,与没有任何标签的帖子相比,有争议的标签显著降低了人们对反 COVID-19 疫苗错误信息的可信度。然而,疫苗犹豫程度较高的个体往往认为错误信息比犹豫程度较低的个体更可信。这些发现表明,警告标签在打击社交媒体平台上的错误信息方面是有效的,特别是针对那些对疫苗接种最不犹豫的人群。