# Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Media

arXiv: https://arxiv.org/abs/2312.10626 · HTML: https://mlsanity.com/paper/2312.10626/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [Classifier Chains: A Review and Perspectives](https://mlsanity.com/paper/1912.13405/) (2019)
- [CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines](https://mlsanity.com/paper/2204.13746/) (2022)

## Built on by (most cited first, 1 of 1)

- [AI-VaxGuide: An Agentic RAG-Based LLM for Vaccination Decisions](https://mlsanity.com/paper/2507.03493/) (2025)

## Abstract (arXiv)

In the realm of public health, vaccination stands as the cornerstone for mitigating disease risks and controlling their proliferation. The recent COVID-19 pandemic has highlighted how vaccines play a crucial role in keeping us safe. However the situation involves a mix of perspectives, with skepticism towards vaccines prevailing for various reasons such as political dynamics, apprehensions about side effects, and more. The paper addresses the challenge of comprehensively understanding and categorizing these diverse concerns expressed in the context of vaccination. Our focus is on developing a robust multi-label classifier capable of assigning specific concern labels to tweets based on the articulated apprehensions towards vaccines. To achieve this, we delve into the application of a diverse set of advanced natural language processing techniques and machine learning algorithms including transformer models like BERT, state of the art GPT 3.5, Classifier Chains & traditional methods like SVM, Random Forest, Naive Bayes. We see that the cutting-edge large language model outperforms all other methods in this context.