Public Sentiment Analysis and Topic Modeling Regarding COVID-19 Vaccines on the Reddit Social Media Platform: A Call to Action For Strengthening Vaccine Confidence

"Although the results...suggest that public sentiment in Reddit communities is overall positive regarding discussions about the Covid-19 vaccine or experiences with taking the vaccine, keywords and topics were detected that indicate some hesitancy amongst these users."

Misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy, as in the case of the rapidly developed COVID-19 vaccine. This study examined public sentiments and opinions regarding the COVID-19 vaccine using textual data harvested from Reddit, a popular social media platform in which members of "subreddits" "upvote" or "downvote" a post (consisting of links, images, videos, and/or text) based on their opinion of that post and/or leave comments.

The researchers harvested approximately 18,000 posts from 13 subreddits focusing on the COVID-19 vaccine from December 1 2020 to May 15 2021. They conducted a sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling, aggregating and analysing textual data by month to detect changes in any sentiment and latent topics.

Polarity analysis suggested that these communities expressed more positive sentiments than negative ones (56.68% positive, 27.69% negative, and 15.63% neutral) regarding the vaccine-related discussions and remained static over time. These findings could be due to the potential bias in these communities and/or related to strict Reddit community guidelines that result in the removal of certain posts, creating an echo chamber. Moreover, it is possible that the sentiment analysis reflected the nature of interaction between users rather than actual feelings about vaccination. Qualitative analysis revealed the detection of some comments that expressed negative sentiment about the vaccine but were given positive polarity (an "upvote") due to certain aspects in the text.

Topic modeling revealed that community members mainly focused on side effects rather than outlandish conspiracy theories. "This finding was expected considering many recently vaccinated people discuss and compare side effects on social media as well as in person. Due to the severity of some documented side effects and their wide media coverage, it's highly conceivable that side-effects are a major contributor to hesitancy." One topic appeared to be focused on much broader terms - i.e., information (news, source, question) - and a direct mention of concerns about vaccination. This topic also mentioned autism, most likely in reference to the antivaccine movement's fixation on the false narrative that vaccines cause autism.

Based on this experiment, the researchers suggest that leveraging textual data obtained from social media platforms could facilitate rapid and inexpensive public sentiment analysis that could inform interventions such as automated personalised messages and education delivered to individuals based on the content and sentiments from their social media posts. "High-impact personalized educational interventions providing clear, unambiguous recommendations/policies/messages on vaccine safety, efficacy, availability, accessibility, affordability, and acceptability, etc. could be impactful."

Suggestions for future research to understand how to reach populations who feel negative towards the COVID-19 vaccine include employing machine/deep learning techniques to develop an optimal system to identify misinformation and intervene within social media. Topic modeling could be used to analyse a wider variety of data sources and could contribute to an even more realistic representation of population sentiment, as well as to facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.