# Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter

arXiv: https://arxiv.org/abs/2003.11906 · HTML: https://mlsanity.com/paper/2003.11906/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives](https://mlsanity.com/paper/2106.08423/) (2021)
- [Cascade-based Echo Chamber Detection](https://mlsanity.com/paper/2208.04620/) (2022)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Learning Ideological Embeddings from Information Cascades](https://mlsanity.com/paper/2109.13589/) (2021)
- [Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic](https://mlsanity.com/paper/2108.10735/) (2021)
- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches](https://mlsanity.com/paper/2109.09014/) (2021)
- [Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach](https://mlsanity.com/paper/2307.04668/) (2023)
- [From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora](https://mlsanity.com/paper/2403.14298/) (2024)
- ["Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media](https://mlsanity.com/paper/2206.13456/) (2022)
- [Revisiting the Objective of Echo Chamber Detection](https://mlsanity.com/paper/2609.18545/) (2026)

## Abstract (arXiv)

The reappearance of measles in the US and Europe, a disease considered eliminated in early 2000s, has been accompanied by a growing debate on the merits of vaccination on social media. In this study we examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant. We focus on Italy, one of the countries most affected by the latest measles outbreaks. We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers". The structure of these communities differs as well, with skeptics arranged in a tightly connected cluster, and advocates organizing themselves around few authoritative hubs. At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers (attaining 95% cross-validated accuracy). Insights of this study provide several avenues for potential future interventions, including network-guided targeting, accounting for the political context, and monitoring of alternative sources of information.