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Brazilian Social Media Anti-vaccine Information Disorder Dataset -- Telegram (2020-2025)

João Phillipe Cardenuto, Ana Carolina Monari, Michelle Diniz Lopes, Leopoldo Lusquino Filho, Anderson Rocha

TL;DR

The study tackles the rise of vaccine hesitancy in Brazil driven by online misinformation by constructing a large-scale Telegram dataset collected from 119 public anti-vaccine channels spanning 2020–2025. It employs a Telethon-based data collection pipeline, language identification, and a Portuguese prompt-driven LLM (Sabiá-3) to label vaccine-related content, resulting in a dataset of nearly 4 million posts with rich metadata and media links. The contribution includes a detailed data schema, anonymization and ethical safeguards, and open data access under CC BY-NC 4.0, enabling NLP, social science, and network-analysis research to understand and counter misinformation. The work provides a foundation for evidence-based, empathetic public health communication and policy design to counter vaccine misinformation in Brazil and similar contexts.

Abstract

Over the past decade, Brazil has experienced a decline in vaccination coverage, reversing decades of public health progress achieved through the National Immunization Program (PNI). Growing evidence points to the widespread circulation of vaccine-related misinformation -- particularly on social media platforms -- as a key factor driving this decline. Among these platforms, Telegram remains the only major platform permitting accessible and ethical data collection, offering insight into public channels where vaccine misinformation circulates extensively. This data paper introduces a curated dataset of about four million Telegram posts collected from 119 prominent Brazilian anti-vaccine channels between 2020 and 2025. The dataset includes message content, metadata, associated media, and classification related to vaccine posts, enabling researchers to examine how false or misleading information spreads, evolves, and influences public sentiment. By providing this resource, our aim is to support the scientific and public health community in developing evidence-based strategies to counter misinformation, promote trust in vaccination, and engage compassionately with individuals and communities affected by false narratives. The dataset and documentation are openly available for non-commercial research, under strict ethical and privacy guidelines at https://doi.org/10.25824/redu/5JIVDT

Brazilian Social Media Anti-vaccine Information Disorder Dataset -- Telegram (2020-2025)

TL;DR

The study tackles the rise of vaccine hesitancy in Brazil driven by online misinformation by constructing a large-scale Telegram dataset collected from 119 public anti-vaccine channels spanning 2020–2025. It employs a Telethon-based data collection pipeline, language identification, and a Portuguese prompt-driven LLM (Sabiá-3) to label vaccine-related content, resulting in a dataset of nearly 4 million posts with rich metadata and media links. The contribution includes a detailed data schema, anonymization and ethical safeguards, and open data access under CC BY-NC 4.0, enabling NLP, social science, and network-analysis research to understand and counter misinformation. The work provides a foundation for evidence-based, empathetic public health communication and policy design to counter vaccine misinformation in Brazil and similar contexts.

Abstract

Over the past decade, Brazil has experienced a decline in vaccination coverage, reversing decades of public health progress achieved through the National Immunization Program (PNI). Growing evidence points to the widespread circulation of vaccine-related misinformation -- particularly on social media platforms -- as a key factor driving this decline. Among these platforms, Telegram remains the only major platform permitting accessible and ethical data collection, offering insight into public channels where vaccine misinformation circulates extensively. This data paper introduces a curated dataset of about four million Telegram posts collected from 119 prominent Brazilian anti-vaccine channels between 2020 and 2025. The dataset includes message content, metadata, associated media, and classification related to vaccine posts, enabling researchers to examine how false or misleading information spreads, evolves, and influences public sentiment. By providing this resource, our aim is to support the scientific and public health community in developing evidence-based strategies to counter misinformation, promote trust in vaccination, and engage compassionately with individuals and communities affected by false narratives. The dataset and documentation are openly available for non-commercial research, under strict ethical and privacy guidelines at https://doi.org/10.25824/redu/5JIVDT
Paper Structure (15 sections, 5 figures, 5 tables)

This paper contains 15 sections, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Preliminary Vaccine Fake News Analysis
  3. Data Collection and Curation
  4. Data Source and Selection
  5. Data Collection Tool
  6. Data Acquisition
  7. Data Curation and Processing
  8. Data Description and Structure
  9. Data Schema
  10. Descriptive Statistics
  11. Potential Uses and Applications
  12. Data Availability
  13. Ethical Considerations and Anonymization
  14. Limitations
  15. Conclusion

Figures (5)

  • Figure 1: Distribution of fact-checking articles related to vaccines per agency published during 2024.
  • Figure 2: Distribution of reported Brazilian vaccine-related fake news by social media platform during 2024.
  • Figure 3: Data collection pipeline. Telethon collects information for each monitored channel, saves it on a SQL database, and updates its engagement metrics up to seven days after its initial collection.
  • Figure 4: Language distribution of the collected posts
  • Figure 5: Number of messages collected per month during the data collection period. The last point represents June of 2025, after which we stopped collecting the data.