Personality Traits and Echo Chambers on Facebook
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Abstract (arXiv)
In online social networks, users tend to select information that adhere to their system of beliefs and to form polarized groups of like minded people. Polarization as well as its effects on online social interactions have been extensively investigated. Still, the relation between group formation and personality traits remains unclear. A better understanding of the cognitive and psychological determinants of online social dynamics might help to design more efficient communication strategies and to challenge the digital misinformation threat. In this work, we focus on users commenting posts published by US Facebook pages supporting scientific and conspiracy-like narratives, and we classify the personality traits of those users according to their online behavior. We show that different and conflicting communities are populated by users showing similar psychological profiles, and that the dominant personality model is the same in both scientific and conspiracy echo chambers. Moreover, we observe that the permanence within echo chambers slightly shapes users' psychological profiles. Our results suggest that the presence of specific personality traits in individuals lead to their considerable involvement in supporting narratives inside virtual echo chambers.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jun 2016.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 2; 2025: 1.
9 papers.
- 2022An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changescited by 15
- 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigationcited by 12
- 2022Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubblescited by 8
- 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
- 2022Cascade-based Echo Chamber Detectioncited by 7
- 2024Polarization of Autonomous Generative AI Agents Under Echo Chamberscited by 7
- 2021Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Languagecited by 6
- 2024What distinguishes conspiracy from critical narratives? A computational analysis of oppositional discoursecited by 3
- 2023The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Datasetcited by 0
- 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness10 Apr 2025
- 2024What distinguishes conspiracy from critical narratives? A computational analysis of oppositional discourse15 Jul 2024
- 2024Polarization of Autonomous Generative AI Agents Under Echo Chambers19 Feb 2024
- 2023The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset29 Aug 2023
- 2022Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles18 Oct 2022
- 2022Cascade-based Echo Chamber Detection9 Aug 2022
- 2022An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes25 Mar 2022
- 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation9 Dec 2021
- 2021Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language13 Sep 2021