# 医生发声：从一场支持疫苗接种的推特活动中学到的经验教训
#DoctorsSpeakUp: Lessons learned from a pro-vaccine Twitter event.
作者信息
Hoffman Beth L, Colditz Jason B, Shensa Ariel, Wolynn Riley, Taneja Sanya Bathla, Felter Elizabeth M, Wolynn Todd, Sidani Jaime E
机构信息
Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, 130 De Sotto Street, Pittsburgh, PA 15261, United States; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, School of Medicine, 1218 Scaife Hall, 35505 Terrace Street, Pittsburgh, PA 15261, United States; Center for Behavioral Health, Media, and Technology, University of Pittsburgh, School of Medicine, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States.
Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, School of Medicine, 1218 Scaife Hall, 35505 Terrace Street, Pittsburgh, PA 15261, United States; Center for Behavioral Health, Media, and Technology, University of Pittsburgh, School of Medicine, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States.
出版信息
Vaccine. 2021 May 6;39(19):2684-2691. doi: 10.1016/j.vaccine.2021.03.061. Epub 2021 Apr 13.
BACKGROUND
In response to growing anti-vaccine activism on social media, the #DoctorsSpeakUp event was designed to promote pro-vaccine advocacy. This study aimed to analyze Twitter content related to the event to determine (1) characteristics of the Twitter users who authored these tweets, (2) the proportion of tweets expressing pro-vaccine compared to anti-vaccine sentiment, and (3) the content of these tweets.
METHODS
Data were collected using Twitter's Filtered Streams Interface, and included all publicly available tweets with the "#DoctorsSpeakUp" hashtag on March 5, 2020, the day of the event. Two independent coders assessed a 5% subsample of original tweets (n = 966) using a thematic content analysis approach. Cohen's κ ranged 0.71-1.00 for all categories. Chi-square and Fisher's exact tests were used to examine associations between tweet sentiment, type of account, and tweet content (personal narrative and/or statement about research or science). Accounts were analyzed for likelihood of being a bot (i.e. automated account) using Botometer.
RESULTS
Of 847 (87.7%) relevant tweets, 244 (28.8%) were authored by a Twitter user that identified as a parent and 68 (8.0%) by a user that identified as a health professional. With regard to sentiment, 167 (19.7%) were coded as pro-vaccine and 668 (78.9%) were coded as anti-vaccine. Tweet sentiment was significantly associated with type of account (p < 0.001) and tweet content (p = 0.001). Of the 575 unique users in our dataset, 31 (5.4%) were classified as bots using Botometer.
CONCLUSIONS
Our results suggest a highly coordinated response of devoted anti-vaccine antagonists in response to the #DoctorsSpeakUp event. These findings can be used to help vaccine advocates leverage social media more effectively to promote vaccines. Specifically, it would be valuable to ensure that pro-vaccine messages consider hashtag use and pre-develop messages that can be launched and promoted by pro-vaccine advocates.
背景
为应对社交媒体上日益增长的反疫苗激进主义，“医生发声”活动旨在促进支持疫苗的宣传。本研究旨在分析与该活动相关的推特内容，以确定：（1）撰写这些推文的推特用户的特征；（2）表达支持疫苗与反对疫苗情绪的推文比例；（3）这些推文的内容。
方法
使用推特的过滤流接口收集数据，包括2020年3月5日活动当天带有“#医生发声”主题标签的所有公开可用推文。两名独立编码员采用主题内容分析方法对原始推文的5%子样本（n = 966）进行评估。所有类别的科恩kappa系数在0.71至1.00之间。使用卡方检验和费舍尔精确检验来检验推文情绪、账户类型和推文内容（个人叙述和/或关于研究或科学的陈述）之间的关联。使用Botometer分析账户是否为机器人账户（即自动账户）的可能性。
结果
在847条（87.7%）相关推文中，244条（28.8%）由自称为家长的推特用户撰写，68条（8.0%）由自称为健康专业人员的用户撰写。在情绪方面，167条（19.7%）被编码为支持疫苗，668条（78.9%）被编码为反对疫苗。推文情绪与账户类型（p < 0.001）和推文内容（p = 0.001）显著相关。在我们的数据集中的575个唯一用户中，31个（5.4%）使用Botometer被归类为机器人账户。
结论
我们的结果表明，在“医生发声”活动中，坚定的反疫苗反对者做出了高度协调的回应。这些发现可用于帮助疫苗倡导者更有效地利用社交媒体来推广疫苗。具体而言，确保支持疫苗的信息考虑主题标签的使用，并预先制定可由支持疫苗的倡导者发布和推广的信息将很有价值。
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