# CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

arXiv: https://arxiv.org/abs/2204.13746 · HTML: https://mlsanity.com/paper/2204.13746/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [A Network Perspective on Stratification of Multi-Label Data](https://mlsanity.com/paper/1704.08756/) (2017)
- [Explain and Predict, and then Predict Again](https://mlsanity.com/paper/2101.04109/) (2021)
- [A scikit-based Python environment for performing multi-label classification](https://mlsanity.com/paper/1702.01460/) (2017)
- [Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users](https://mlsanity.com/paper/2111.10667/) (2021)
- [Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing](https://mlsanity.com/paper/1805.05491/) (2018)
- [TGSum: Build Tweet Guided Multi-Document Summarization Dataset](https://mlsanity.com/paper/1511.08417/) (2015)

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter](https://mlsanity.com/paper/2301.06660/) (2023)
- [Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models](https://mlsanity.com/paper/2502.05610/) (2025)
- [Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions](https://mlsanity.com/paper/2404.13307/) (2024)
- [Multi-Label Classification of COVID-Tweets Using Large Language Models](https://mlsanity.com/paper/2312.10748/) (2023)
- [Examining Temporalities on Stance Detection towards COVID-19 Vaccination](https://mlsanity.com/paper/2304.04806/) (2023)
- [PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT](https://mlsanity.com/paper/2305.11536/) (2023)
- [Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification](https://mlsanity.com/paper/2506.06806/) (2025)
- [Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Media](https://mlsanity.com/paper/2312.10626/) (2023)
- [ATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data](https://mlsanity.com/paper/2405.06541/) (2024)
- [Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages](https://mlsanity.com/paper/2505.20496/) (2025)
- ["Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media](https://mlsanity.com/paper/2206.13456/) (2022)
- [ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization](https://mlsanity.com/paper/2405.06551/) (2024)
- [ICPR 2024 Competition on Multilingual Claim-Span Identification](https://mlsanity.com/paper/2411.19579/) (2024)
- [Utilising Large Language Models for Generating Effective Counter Arguments to Anti-Vaccine Tweets](https://mlsanity.com/paper/2510.16359/) (2025)
- [WhatsApp Vaccine Discourse (WhaVax): An Expert-Annotated Dataset and Benchmark for Health Misinformation Detection](https://mlsanity.com/paper/2605.12510/) (2026)

## Abstract (arXiv)

Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models. Our dataset and codes are available at: https://github.com/sohampoddar26/caves-data