School of Computer Science, University of Guelph, Guelph, Ontario, Canada.
School of Computer Science, University of Guelph, Guelph, Ontario, Canada.
Int J Infect Dis. 2021 Jul;108:256-262. doi: 10.1016/j.ijid.2021.05.059. Epub 2021 May 27.
We identified public sentiments and opinions toward the COVID-19 vaccines based on the content of Twitter.
We retrieved 4,552,652 publicly available tweets posted within the timeline of January 2020 to January 2021. Following extraction, we identified vaccine sentiments and opinions of tweets and compared their progression by time, geographical distribution, main themes, keywords, posts engagement metrics and accounts characteristics.
We found a slight difference in the prevalence of positive and negative sentiments, with positive being the dominant polarity and having higher engagements. The amount of discussion on vaccine rejection and hesitancy was more than interest in vaccines during the course of the study, but the pattern was different in various countries. We found the accounts producing vaccine opposition content were partly Twitter bots or political activists while well-known individuals and organizations generated the content in favour of vaccination.
Understanding sentiments and opinions toward vaccination using Twitter may help public health agencies to increase positive messaging and eliminate opposing messages in order to enhance vaccine uptake.
我们根据 Twitter 上的内容来确定公众对 COVID-19 疫苗的看法和意见。
我们检索了 2020 年 1 月至 2021 年 1 月期间发布的 4552652 条公开可用的推文。提取后,我们确定了推文的疫苗情绪和意见,并按时间、地理分布、主要主题、关键词、帖子参与度指标和账户特征对其进行了比较。
我们发现积极情绪和消极情绪的流行程度略有不同,积极情绪是主要的极性,具有更高的参与度。在研究过程中,对疫苗排斥和犹豫的讨论量超过了对疫苗的兴趣,但在不同国家的模式不同。我们发现,发布疫苗反对内容的账户部分是 Twitter 机器人或政治活动家,而知名个人和组织则发布了支持接种疫苗的内容。
使用 Twitter 了解疫苗接种的看法和意见可能有助于公共卫生机构增加积极信息,消除反对信息,以提高疫苗接种率。