WhatsApp Vaccine Discourse (WhaVax): An Expert-Annotated Dataset and Benchmark for Health Misinformation Detection

TL;DR

This paper introduces WhaVax, a high-quality expert-annotated dataset of vaccine-related WhatsApp messages from Brazil, and benchmarks various models for health misinformation detection in private messaging.

Contribution

It presents a carefully constructed, expert-annotated dataset and a comprehensive analysis of misinformation patterns, along with benchmarking of multiple modeling approaches.

Findings

Large language models perform competitively in misinformation detection.

Domain alignment and data availability are critical for model performance.

The dataset exhibits complex linguistic and structural misinformation patterns.

Abstract

We introduce WhaVax, a new expert-annotated dataset of vaccine-related WhatsApp messages collected from large Brazilian public groups spanning multiple pandemic years. The dataset was constructed through a rigorous, carefully designed pipeline that integrates keyword-based data collection, semantic deduplication to remove near-duplicate content, and a multi-stage annotation protocol conducted by medical specialists. This process produced a high-quality gold-standard corpus, characterized by substantial inter-annotator agreement and strong reliability for downstream analysis. Additionally, we provide a detailed characterization of WhatsApp misinformation, revealing distinctive linguistic, structural, lexical, temporal, and group-level patterns, as well as a meaningful layer of ambiguous cases that reflect the complexity of health discourse in private messaging. We also benchmark classical models, fine-tuned Small Language Models, and zero- or few-shot Large Language Models under realistic data-scarcity constraints, demonstrating that strong embeddings and LLM approaches perform competitively, while domain alignment and data availability remain critical factors. This study provides a rare, high-quality resource to support misinformation research and computational modeling in encrypted communication environments.