论文标题
Covid-19大流行期间Twitter上的疫苗话语
Vaccine Discourse on Twitter During the COVID-19 Pandemic
论文作者
论文摘要
自从Covid-19大流行即将发作以来，疫苗一直是公共话语中的重要话题。疫苗周围的讨论被两极分化，因为有些人将其视为结束大流行的重要措施，而另一些人则犹豫不决或发现它们有害。这项研究调查了与Twitter上与Covid-19疫苗有关的帖子，并着重于对疫苗有负姿态的帖子。收集了与Covid-19疫苗相关的16,713,238个英文推文的数据集，涵盖了从2020年3月1日至2021年7月31日的该期间。我们使用Scikit-Learn Python图书馆应用了支持矢量机（SVM）分类器，以识别具有负稳定性的Tweets，以识别Covid-19 vicines-19 vicines-19的负势。总共使用了5,163个推文来训练分类器，其中2484条推文由我们手动注释并公开提供。我们使用Berttopic模型来提取和调查负推文中讨论的主题以及它们如何随时间变化。我们表明，随着疫苗的推出，对COVID-19疫苗的负疫苗随着时间的推移而下降。我们确定了37个讨论主题，并随着时间的推移介绍了它们各自的重要性。我们表明，流行的主题包括阴谋讨论，例如5G塔和微芯片，但还涉及涉及疫苗接种安全性和副作用以及对政策的担忧。我们的研究表明，即使不受欢迎的观点或阴谋论与广受欢迎的讨论主题（例如Covid-19疫苗）搭配，也会变得广泛。了解问题和讨论的主题以及它们如何随着时间的变化对于政策制定者和公共卫生当局提供更好和在时间的信息和政策，以促进未来类似危机的人口接种。
Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected covering the period from March 1, 2020, to July 31, 2021. We used the Scikit-learn Python library to apply a support vector machine (SVM) classifier to identify the tweets with a negative stance toward the COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier, out of which a subset of 2,484 tweets were manually annotated by us and made publicly available. We used the BERTtopic model to extract and investigate the topics discussed within the negative tweets and how they changed over time. We show that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine roll-outs. We identify 37 topics of discussion and present their respective importance over time. We show that popular topics consist of conspiratorial discussions such as 5G towers and microchips, but also contain legitimate concerns around vaccination safety and side effects as well as concerns about policies. Our study shows that even unpopular opinions or conspiracy theories can become widespread when paired with a widely popular discussion topic such as COVID-19 vaccines. Understanding the concerns and the discussed topics and how they change over time is essential for policymakers and public health authorities to provide better and in-time information and policies, to facilitate vaccination of the population in future similar crises.
