School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom.
Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan.
J Med Internet Res. 2021 Apr 5;23(4):e26627. doi: 10.2196/26627.
Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions.
The aim of this study was to develop and apply an artificial intelligence-based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines.
Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning-based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis.
Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly.
Artificial intelligence-enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.
全球在开发和部署 COVID-19 疫苗方面的努力正在迅速推进。为了实现群体免疫，需要广泛接种疫苗，这需要公众的大力合作。因此，政府和公共卫生机构了解公众对疫苗的看法至关重要，这有助于指导教育活动和其他有针对性的政策干预措施。
本研究旨在开发并应用一种基于人工智能的方法，分析英国和美国社交媒体上公众对 COVID-19 疫苗的情绪，以更好地了解公众对 COVID-19 疫苗的态度和关注。
提取了 30 多万条与 COVID-19 疫苗相关的社交媒体帖子，包括英国的 23571 条 Facebook 帖子和美国的 144864 条，以及英国的 40268 条推文和美国的 98385 条推文，时间为 2020 年 3 月 1 日至 11 月 22 日。我们使用自然语言处理和基于深度学习的技术来预测平均情绪、情绪趋势和讨论主题。这些因素从纵向和地理空间上进行了分析，并且对感兴趣的点进行了随机选择的帖子进行人工阅读，以帮助识别潜在的主题并验证分析的结果。
在英国，总体平均积极、消极和中性情绪分别为 58%、22%和 17%，而在美国则分别为 56%、24%和 18%。公众对疫苗开发、有效性和试验的乐观情绪，以及对安全性、经济可行性和公司控制的担忧情绪都有所体现。我们将我们的发现与两国的全国性调查进行了比较，发现它们大致相关。
人工智能支持的社交媒体分析应该与调查和其他评估公众态度的传统方法一起被机构和政府采用。这种分析可以实时评估公众对 COVID-19 疫苗的信心和信任程度，帮助解决疫苗怀疑论者的担忧，并帮助制定更有效的政策和沟通策略，以最大限度地提高疫苗接种率。
