# Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation

arXiv: https://arxiv.org/abs/2604.09626 · HTML: https://mlsanity.com/paper/2604.09626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [We Don't Speak the Same Language: Interpreting Polarization through Machine Translation](https://mlsanity.com/paper/2010.02339/) (2020)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive](https://mlsanity.com/paper/2301.12534/) (2023)
- [Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections](https://mlsanity.com/paper/2305.05094/) (2023)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://mlsanity.com/paper/2109.03587/) (2021)
- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy](https://mlsanity.com/paper/2404.10259/) (2024)
- [Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election](https://mlsanity.com/paper/2101.10112/) (2021)
- [Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs](https://mlsanity.com/paper/2403.10707/) (2024)
- [Rater Cohesion and Quality from a Vicarious Perspective](https://mlsanity.com/paper/2408.08411/) (2024)
- [ARTICLE: Annotator Reliability Through In-Context Learning](https://mlsanity.com/paper/2409.12218/) (2024)
- [Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling](https://mlsanity.com/paper/2310.07078/) (2023)
- [Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech](https://mlsanity.com/paper/2502.09004/) (2025)
- [Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial](https://mlsanity.com/paper/2504.20519/) (2025)
- [AI as We Describe It: How Large Language Models and Their Applications in Health are Represented Across Channels of Public Discourse](https://mlsanity.com/paper/2511.03174/) (2025)

## Abstract (arXiv)

A significant gap exists in datasets regarding post-COVID-19 vaccination experiences, particularly ``vaccine buyer's remorse''. Understanding the prevalence and nature of vaccine regret, whether based on personal or vicarious experiences, is vital for addressing vaccine hesitancy and refining public health communication. In this paper, we curate a novel dataset from a large YouTube news corpus capturing COVID-19 vaccination experiences, and construct a benchmark subset focused on vaccine regret, annotated by a politically diverse panel to account for the subjective and often politicized nature of the topic. We utilize large language models (LLMs) to identify posts expressing vaccine regret, analyze the reasons behind this regret, and quantify its occurrence in both first and second-person accounts. This paper aims to (1) quantify the prevalence of vaccine regret; (2) identify common reasons for this sentiment; (3) analyze differences between first-person and vicarious experiences; and (4) assess potential biases introduced by different LLMs. We find that while vaccine buyer's remorse appears in only <2% of public discourse, it is disproportionately concentrated in vaccine-skeptic influencer communities and is predominantly expressed through first-person narratives citing adverse health events.
