Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 7, 2021
Date Accepted: Jul 11, 2021
Date Submitted to PubMed: Aug 12, 2021
Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines
ABSTRACT
Background:
The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines.
Objective:
The timely rollout of COVID-19 vaccination by extracting the latest public opinion, attitudes, and behavioral intentions to tailor promotional programs for different populations.
Methods:
We conducted a retrospective cohort study on a dataset of COVID-19 vaccine-related tweets posted from November 01, 2020, to January 31, 2021. We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling. F1 values were used as the primary outcome to compare the performance of machine learning and transfer learning models. The main topics in tweets were extracted by latent Dirichlet allocation (LDA) analysis.
Results:
We collected 2,678,372 tweets related to COVID-19 vaccines with 841,978 unique users and annotated 5,000 tweets. The F1 values of transfer learning models were 0.792 [0.789, 0.795], 0.578 [0.572, 0.0.584], and 0.614 [0.606, 0.622] for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we identified 10 main topics and relevant terms for tweets in each of the following categories (positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions).
Conclusions:
We provided a method to automatically analyze the public understanding of COIVD-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.