College of Computing and Informatics, Drexel University, Philadelphia, PA, United States.
Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan.
J Med Internet Res. 2021 Oct 21;23(10):e30765. doi: 10.2196/30765.
As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter.
The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media.
To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity.
After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy.
This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.
随着当地卫生机构对多种 COVID-19 疫苗授予紧急使用授权并在多个国家接种,为确保通过接种实现群体免疫,赢得公众对这些疫苗的信任至关重要。一种衡量公众对疫苗接种目标的看法以提高疫苗接种率的方法是分析社交媒体,如 Twitter。
本研究旨在通过分析美国开始接种疫苗的 60 天内社交媒体上有关疫苗的讨论,了解公众对 COVID-19 疫苗的看法。我们使用主题检测和情绪分析相结合的方法,确定了公众在社交媒体上表达的不同类型的疫苗关注。
为了更好地了解公众情绪,我们从 2020 年 12 月 16 日开始整整收集了 60 天的推文,这些推文包含与 COVID-19 疫苗相关的标签或关键词。我们检测并分析了这些推文的不同讨论主题及其情绪内容。使用非负矩阵分解识别疫苗主题,使用 Valence Aware Dictionary 和 sEntiment Reasoner 情绪分析库以及使用来自转换器嵌入的句子双向编码器表示并使用余弦相似性将嵌入与不同情绪进行比较来识别情绪内容。
在去除所有重复项和转发后,在 60 天的时间内共收集了 794886 条推文。主题建模产生了 50 个主题;其中,我们选择了 12 个拥有最高推文量的主题进行分析。疫苗的管理和获取是公众关注的主要问题之一。此外,我们将每个主题中的推文分为 1 种情绪中的 5 种,并发现恐惧是推文的主导情绪,其次是喜悦。
本研究不仅关注可能导致疫苗犹豫的负面情绪,还关注对疫苗的正面情绪。通过识别积极和消极情绪,我们能够了解公众对疫苗的总体反应以及与疫苗相关的新闻事件。这些结果对于制定传播权威健康信息的计划以及更好地进行沟通以建立理解和信任非常有用。