Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 9 of its 39 references that are in the map. First posted on arXiv 17 Oct 2022.
Builds on
9 of 39 references.
References · 9
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- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
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- 2021Data Expansion using Back Translation and Paraphrasing for Hate Speech Detectioncited by 20
- 2021Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidencecited by 18
- 2021Combat COVID-19 Infodemic Using Explainable Natural Language Processing Modelscited by 17
- 2022Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countriescited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2023Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysiscited by 2
- 2026Who Shapes Brazil's Vaccine Debate? Semi-Supervised Modeling of Stance and Polarization in YouTube's Media Ecosystemcited by 1
- 2025Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Reviewcited by 0
- 2025Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insightscited by 0
- 2026Who Shapes Brazil's Vaccine Debate? Semi-Supervised Modeling of Stance and Polarization in YouTube's Media Ecosystem4 Mar 2026
- 2025Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights12 May 2025
- 2025Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review23 Mar 2025
- 2023Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis23 Feb 2023