Artificial Intelligence-Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: COVID-19; Facebook; Twitter; artificial intelligence; deep learning; health informatics; infodemiology; natural language processing; public health; sentiment analysis; social media; vaccination.

Figure 1

Hierarchical hybrid-ensemble–based artificial intelligence model and data pipeline for thematic sentiment analysis. BERT: Bidirectional Encoder Representations from Transformers, VADER: Valence Aware Dictionary for Sentiment Reasoning.

Figure 2

Averaged weekly trends in Facebook sentiments for (A) the United Kingdom and (B) the United States.

Figure 3

Average weekly trends in Twitter sentiments for (A) the United Kingdom and (B) the United States.

Figure 4

Geospatial mapping of averaged social media public sentiments in the United States (left) and the United Kingdom (right) toward COVID-19 vaccines (1 or green: positive sentiments, 0: neutral sentiments, and -1 or red: negative sentiments).

Figure 5

Overall averaged sentiments: (A) Twitter sentiments in the United Kingdom, (B) Twitter sentiments in the United States, (C) Facebook sentiments in the United Kingdom, and (D) Facebook sentiments in the United States.