Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 20, 2022
Date Accepted: Sep 15, 2022
Date Submitted to PubMed: Sep 29, 2022
Fine-Tuned Sentiment Analysis of COVID-19 Vaccine Related Social Media Data: A Comparative Study
ABSTRACT
Background:
The emergence of the novel coronavirus (COVID-19), and the necessary separation of populations led to an unprecedented number of new social media users seeking information related to the pandemic. Nowadays, with an estimated 4·5 billion users worldwide, social media data offer an opportunity for near to real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policy.
Objective:
Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter, harvested from January 1, 2020, to March 1, 2022.
Methods:
To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict sentiments of approximately 9·5 million Tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3,600 Tweets and then augmented our dataset by the method of back-translation. Text sentiment for each social media platform was then classified and compared.
Results:
Our results determined that the average sentiment expressed on Twitter was more negative than positive and the sentiment expressed on Reddit was more positive than negative. Though average sentiment was found to vary between these social media platforms, both displayed similar behavior at key vaccine-related developments during the pandemic.
Conclusions:
Considering this similar behavior demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can utilize to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety, fear, etc.), there is an urgency in understanding the public perspective and attitude toward shared falsities. Education delivery systems tailored to population expressed sentiment could aid in clarifying such misinformation.
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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.