Karami Amir, Zhu Michael, Goldschmidt Bailey, Boyajieff Hannah R, Najafabadi Mahdi M
School of Information Science, University of South Carolina, Columbia, SC 29208, USA.
Department of Psychology, University of South Carolina, Columbia, SC 29208, USA.
Vaccines (Basel). 2021 Sep 23;9(10):1059. doi: 10.3390/vaccines9101059.
The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.
了解公众对新冠疫苗的反应是控制新冠疫情大流行的关键成功因素。为了解公众反应，有必要探索公众舆论。传统调查成本高、耗时久，涉及的健康主题有限，且获取的数据规模小。推特能为了解公众对新冠疫苗的看法提供绝佳机会。当前研究提出一种方法，利用计算和人工编码方法收集并分析大量推文，以更全面地看待新冠疫苗。本研究使用基于机器学习规则的方法识别推文的情感倾向，发现主要话题，探索时间趋势，并通过统计检验比较负面和非负面推文的话题，揭示具有负面和非负面情感的推文的热门话题。我们的研究结果表明，2020年11月至2021年2月期间，公众对新冠疫苗的负面情绪呈下降趋势。我们发现，2020年11月至2021年2月期间，推特用户讨论了从疫苗接种点到2020年美国大选等广泛话题。研究结果表明，在大多数话题的权重方面，负面和非负面推文之间存在显著差异。我们的结果还表明，负面和非负面推文有不同的话题优先级和关注点。这项研究表明，推特数据可用于探索公众对新冠疫苗的看法。
