Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study

• URL: http://arxiv.org/abs/2211.15407v1
• Date: Mon, 17 Oct 2022 16:22:18 GMT
• Title: Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study
• Authors: Chad A Melton, Brianna M White, Robert L Davis, Robert A Bednarczyk, Arash Shaban-Nejad
• Abstract summary: This study investigated and compared public sentiment related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter. We created a fine-tuned DistilRoBERTa model to predict sentiments of approximately 9.5 million Tweets and 70 thousand Reddit comments. Our results determined that the average sentiment expressed on Twitter was more negative (52% positive) than positive.
• Score: 0.7874708385247353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This 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. 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 3600 Tweets and then augmented our dataset by the method of back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python and the Huggingface sentiment analysis pipeline. Our results determined that the average sentiment expressed on Twitter was more negative (52% positive) than positive and the sentiment expressed on Reddit was more positive than negative (53% positive). Though average sentiment was found to vary between these social media platforms, both displayed similar behavior related to sentiment shared at key vaccine-related developments during the pandemic. Considering this similar trend in shared sentiment 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. Comprehensive educational delivery systems tailored to the population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.

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