Misinformation spreading on Facebook
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Abstract (arXiv)
Social media are pervaded by unsubstantiated or untruthful rumors, that contribute to the alarming phenomenon of misinformation. The widespread presence of a heterogeneous mass of information sources may affect the mechanisms behind the formation of public opinion. Such a scenario is a florid environment for digital wildfires when combined with functional illiteracy, information overload, and confirmation bias. In this essay, we focus on a collection of works aiming at providing quantitative evidence about the cognitive determinants behind misinformation and rumor spreading. We account for users' behavior with respect to two distinct narratives: a) conspiracy and b) scientific information sources. In particular, we analyze Facebook data on a time span of five years in both the Italian and the US context, and measure users' response to i) information consistent with one's narrative, ii) troll contents, and iii) dissenting information e.g., debunking attempts. Our findings suggest that users tend to a) join polarized communities sharing a common narrative (echo chambers), b) acquire information confirming their beliefs (confirmation bias) even if containing false claims, and c) ignore dissenting information.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2017.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 2.
4 papers.
- 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigationcited by 11
- 2025Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAPcited by 3
- 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directionscited by 3
- 2021A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsAppcited by 0
- 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions29 Sep 2025
- 2025Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP16 May 2025
- 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation9 Dec 2021
- 2021A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp22 Sep 2021