Department of Computer Science, Aalto University, Espoo, Finland.
Department of Management and Engineering, Linköping University, Linköping, Sweden.
J Med Internet Res. 2023 Apr 12;25:e41319. doi: 10.2196/41319.
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. A substantial portion of these discussions occurs openly on social media platforms. This allows us to closely monitor the opinions of different groups and their changes over time.
This study investigated posts related to COVID-19 vaccines on Twitter (Twitter Inc) and focused on those that had a negative stance toward vaccines. It examined the evolution of the percentage of negative tweets over time. It also examined the different topics discussed in these tweets to understand the concerns and discussion points of those holding a negative stance toward the vaccines.
A data set 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 classifier to identify the tweets with a negative stance toward COVID-19 vaccines. A total of 5163 tweets were used to train the classifier, of which a subset of 2484 tweets was manually annotated by us and made publicly available along with this paper. We used the BERTopic model to extract the topics discussed within the negative tweets and investigate them, including how they changed over time.
We showed that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine rollouts. We identified 37 topics of discussion and presented their respective importance over time. We showed that popular topics not only consisted of conspiratorial discussions, such as 5G towers and microchips, but also contained legitimate concerns around vaccination safety and side effects as well as concerns about policies. The most prevalent topic among vaccine-hesitant tweets was related to the use of messenger RNA and fears about its speculated negative effects on our DNA.
Hesitancy toward vaccines existed before the COVID-19 pandemic. However, given the dimension of and circumstances surrounding the COVID-19 pandemic, some new areas of hesitancy and negativity toward COVID-19 vaccines have arisen, for example, whether there has been enough time for them to be properly tested. There is also an unprecedented number of conspiracy theories associated with them. 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, the discussed topics, and how they change over time is essential for policy makers and public health authorities to provide better in-time information and policies to facilitate the vaccination of the population in future similar crises.
自 COVID-19 大流行以来，疫苗一直是公众讨论的重要话题。关于疫苗的讨论存在两极分化，一些人认为疫苗是结束大流行的重要措施，而另一些人则犹豫不决或认为它们有害。这些讨论的很大一部分是在社交媒体平台上公开进行的。这使我们能够密切监测不同群体的意见及其随时间的变化。
本研究调查了 Twitter（Twitter Inc.）上与 COVID-19 疫苗相关的帖子，并重点关注对疫苗持负面立场的帖子。它研究了随着时间的推移，负面推文的百分比的演变。它还研究了这些推文中讨论的不同主题，以了解对疫苗持负面立场的人的关注和讨论点。
收集了 2020 年 3 月 1 日至 2021 年 7 月 31 日期间与 COVID-19 疫苗相关的 16713238 条英语推文数据集。我们使用 scikit-learn Python 库应用支持向量机分类器来识别对 COVID-19 疫苗持负面立场的推文。使用 5163 条推文对分类器进行训练，其中 2484 条推文由我们手动注释，并与本文一起公开提供。我们使用 BERTopic 模型提取负面推文中讨论的主题，并对其进行调查，包括它们随时间的变化。
我们表明，随着疫苗的推出，对 COVID-19 疫苗的负面情绪随着时间的推移而降低。我们确定了 37 个讨论主题，并展示了它们随时间的相对重要性。我们表明，热门话题不仅包括关于 5G 塔和微芯片的阴谋论讨论，还包括关于疫苗安全性和副作用以及政策的合理担忧。在对疫苗犹豫不决的推文中，最常见的主题与信使 RNA 的使用以及对其对我们 DNA 的推测负面影响的担忧有关。
在 COVID-19 大流行之前，人们对接种疫苗犹豫不决。然而，鉴于 COVID-19 大流行的规模和情况，对 COVID-19 疫苗的负面情绪和犹豫出现了一些新的领域，例如，是否有足够的时间对它们进行适当的测试。也有大量与它们相关的阴谋论。我们的研究表明，即使是不受欢迎的意见或阴谋论，当与 COVID-19 疫苗等广受欢迎的话题结合时，也可能会广泛传播。了解关注、讨论的话题以及它们随时间的变化对于政策制定者和公共卫生当局来说至关重要，以便在未来类似的危机中为民众提供更好的实时信息和政策，促进疫苗接种。
