# Debunking in a World of Tribes

arXiv: https://arxiv.org/abs/1510.04267 · HTML: https://mlsanity.com/paper/1510.04267/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2015.

## Built on by (most cited first, 10 of 10)

- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections](https://mlsanity.com/paper/2404.00141/) (2024)
- [Online Hate: Behavioural Dynamics and Relationship with Misinformation](https://mlsanity.com/paper/2105.14005/) (2021)
- [Personalized Interventions for Online Moderation](https://mlsanity.com/paper/2205.09462/) (2022)
- [The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions](https://mlsanity.com/paper/2511.08592/) (2025)
- [Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media](https://mlsanity.com/paper/2509.04823/) (2025)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)
- [Belief Is All You Need: Modeling Narrative Archetypes in Conspiratorial Discourse](https://mlsanity.com/paper/2512.10105/) (2025)

## Abstract (arXiv)

Recently a simple military exercise on the Internet was perceived as the beginning of a new civil war in the US. Social media aggregate people around common interests eliciting a collective framing of narratives and worldviews. However, the wide availability of user-provided content and the direct path between producers and consumers of information often foster confusion about causations, encouraging mistrust, rumors, and even conspiracy thinking. In order to contrast such a trend attempts to textitdebunk are often undertaken. Here, we examine the effectiveness of debunking through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users interact with proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook in the US. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. Both groups interact similarly with the information within their echo chamber. We examine 47,780 debunking posts and find that attempts at debunking are largely ineffective. For one, only a small fraction of usual consumers of unsubstantiated information interact with the posts. Furthermore, we show that those few are often the most committed conspiracy users and rather than internalizing debunking information, they often react to it negatively. Indeed, after interacting with debunking posts, users retain, or even increase, their engagement within the conspiracy echo chamber.