Le Nicolette, McMann Tiana J, Wenzel Christine, Li Zhuoran, Xu Qing, Cuomo Raphael E, Yang Joshua, Mackey Tim K
Global Health Program, Department of Anthropology, University of California San Diego, San Diego, CA, USA; Global Health Policy and Data Institute, San Diego, CA, USA.
Global Health Program, Department of Anthropology, University of California San Diego, San Diego, CA, USA; Global Health Policy and Data Institute, San Diego, CA, USA; S-3 Research, San Diego, CA, USA.
Vaccine. 2025 Feb 15;47:126688. doi: 10.1016/j.vaccine.2024.126688. Epub 2025 Jan 8.
Though vaccine hesitancy and misinformation has been pervasive online, via platforms such as Twitter, little is known about the characteristics of pediatric-specific vaccine hesitancy and how online users interact with verified user accounts that may hold larger influence. Identifying specific COVID-19 pediatric vaccine hesitancy themes and online user interaction and sentiment may help inform health promotion that addresses vaccine hesitancy more effectively among parents and caregivers of pediatric populations.
Keywords were used to query the public streaming twitter application programming interface to collect tweets associated with COVID-19 pediatric vaccines. From this corpus of tweets, we used topic modeling to output 20 topic clusters of tweet content and examined the 10 most retweeted tweets from each cluster to classify for relevance to pediatric COVID-19 vaccine hesitancy topics. Tweets were inductively coded to identify specific themes. Publicly available user metadata were assessed to identify verified accounts and self-reporting of racial or ethnic identity, and parental status. Replies to tweets were coded for user sentiment. A chi-squared test was used to determine the proportion of users agreeing with misinformation tweets RESULTS: 863,007 tweets were collected between October 2020-October 2021. The 230 top tweets reviewed after outputting topic clusters accounted for 236,121 tweets and retweets. 84 unique tweets were identified as related to pediatric COVID-19 vaccine topics by verified users. Twenty three tweets (generating 44,509 retweets) contained misinformation-related themes. Seventy-one percent (n = 742) of user replies agreed with misinformation sentiment of the parent tweet. Main themes identified included vaccine development conspiracy, vaccine is experimental, and vaccine as a control tactic discussions. This study found that users who interacted with misinformation posted by verified accounts were more likely to agree than disagree with misinformation sentiment.
尽管疫苗犹豫和错误信息在网上很普遍，通过推特等平台，但对于儿科特定疫苗犹豫的特征以及在线用户如何与可能具有更大影响力的认证用户账户互动，人们知之甚少。识别特定的新冠病毒儿科疫苗犹豫主题以及在线用户互动和情绪，可能有助于为更有效地解决儿科人群父母和照顾者中的疫苗犹豫问题的健康促进提供信息。
使用关键词查询公开的推特流式应用程序编程接口，以收集与新冠病毒儿科疫苗相关的推文。从这些推文语料库中，我们使用主题建模来输出20个推文内容主题簇，并检查每个簇中转发最多的10条推文，以分类其与儿科新冠病毒疫苗犹豫主题的相关性。对推文进行归纳编码以识别特定主题。评估公开可用的用户元数据，以识别认证账户以及种族或族裔身份和父母身份的自我报告。对推文的回复进行用户情绪编码。使用卡方检验来确定同意错误信息推文的用户比例。结果：在2020年10月至2021年10月期间收集了863,007条推文。输出主题簇后审查的230条热门推文占236,121条推文和转发量。经认证用户识别出84条与儿科新冠病毒疫苗主题相关的独特推文。23条推文（产生44,509次转发）包含与错误信息相关的主题。71%（n = 742）的用户回复同意父推文的错误信息情绪。确定的主要主题包括疫苗研发阴谋、疫苗是实验性的以及疫苗作为一种控制策略的讨论。这项研究发现，与认证账户发布的错误信息互动的用户更有可能同意而不是不同意错误信息情绪。
