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

  1. Amir Hussain
  2. Ahsen Tahir
  3. Zain Hussain
  4. Zakariya Sheikh
  5. Mandar Gogate
  6. Kia Dashtipour
  7. Azhar Ali
  8. Aziz Sheikh

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Abstract

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.

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  1. Version published to 10.2196/26627
  2. Version published to 10.2196/preprints.26627
  3. ScreenIT

    SciScore for 10.1101/2020.12.08.20246231: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    It is important to consider the limitations of our data sources and techniques, and related challenges and opportunities they present for required future research. Whilst we attempted to gauge country-wide public sentiments in the UK and US, by analysing English-language posts on both Facebook and Twitter, our data may not be representative of the broader population. Users are known to differ in their social-media platform preference and usage, based on their socio-demographics (e.g. age, socio-economic status, political affiliation). Vaccinations are likely to be preferentially targeted at older populations and possibly ethnic minorities, communities which have historically lower rates of vaccination uptake (e.g. [33,34]). Further exploration is therefore imperative to increase our understanding of public perception towards vaccines, and their underlying behavioral determinants [35]. Social network analysis [36] can be used in conjunction with DL methods to effectively identify sources of fake news/misinformation and their social networks, to help deal with ‘infodemic’ challenges [37]. Demographic information, such as age, gender, race and geographic origin can also be inferred from user social-media profiles using AI techniques [38]. This can help categorise distinct groups and inform the development of demographic-level engagement and tailored communications strategies to promote diversity and inclusion in vaccination campaigns. These can also effectively account for the f...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  4. Version published to 10.1101/2020.12.08.20246231v1 on medRxiv