Echo Chambers: Emotional Contagion and Group Polarization on Facebook
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Abstract (arXiv)
Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups -- i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models -- i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities' emotional behavior is affected by the users' involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.
Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2016.
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Citing papers per year: 2021: 6; 2022: 3; 2023: 5; 2024: 7; 2025: 5; 2026: 1.
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- 2023Aligning Language Models to User Opinionscited by 98
- 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
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- 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigationcited by 11
- 2022Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trollscited by 8
- 2024Polarization of Autonomous Generative AI Agents Under Echo Chamberscited by 7
- 2021Learning Ideological Embeddings from Information Cascadescited by 6
- 2025HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generationcited by 6
- 2021Cognitive network science for understanding online social cognitions: A brief reviewcited by 5
- 2021Retweet communities reveal the main sources of hate speechcited by 5
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- 2025Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behaviorcited by 0
- 2025When or What? Understanding Consumer Engagement on Digital Platformscited by 0
- 2025Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learningcited by 0
- 2026How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messagescited by 0
- 2026How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messages1 Jul 2026
- 2025Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning20 Oct 2025
- 2025When or What? Understanding Consumer Engagement on Digital Platforms12 Oct 2025
- 2025Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior28 Jul 2025
- 2025HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation29 Apr 2025
- 2025Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study5 Feb 2025
- 2024PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization17 Dec 2024
- 2024Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models1 Nov 2024
- 2024Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories25 Jun 2024
- 2024SoMeR: Multi-View User Representation Learning for Social Media2 May 2024
- 2024Algorithmic Fairness: A Tolerance Perspective26 Apr 2024
- 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era17 Apr 2024
15 more
- 2024Polarization of Autonomous Generative AI Agents Under Echo Chambers19 Feb 2024
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations24 Aug 2023
- 2023On the Complexity of the Bipartite Polarization Problem: from Neutral to Highly Polarized Discussions21 Jul 2023
- 2023Human-AI Coevolution23 Jun 2023
- 2023Aligning Language Models to User Opinions24 May 2023
- 2023Structural Group Unfairness: Measurement and Mitigation by means of the Effective Resistance5 May 2023
- 2022Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls17 Oct 2022
- 2022Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset6 Apr 2022
- 2022This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign14 Jan 2022
- 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation9 Dec 2021
- 2021Learning Ideological Embeddings from Information Cascades28 Sep 2021
- 2021Regulating Ruminative Web-browsing Based on the Counterbalance Modeling Approach20 Sep 2021
- 2021Countering Online Hate Speech: An NLP Perspective7 Sep 2021
- 2021Retweet communities reveal the main sources of hate speech31 May 2021
- 2021Cognitive network science for understanding online social cognitions: A brief review25 Feb 2021
- 2025HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation29 Apr 2025
- 2023Aligning Language Models to User Opinions24 May 2023
- 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
- 2023Human-AI Coevolutioncited by 27
- 2024Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Modelscited by 13
- 2021Countering Online Hate Speech: An NLP Perspectivecited by 12
- 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigationcited by 11
- 2022Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trollscited by 8
- 2021Learning Ideological Embeddings from Information Cascadescited by 6
- 2021Cognitive network science for understanding online social cognitions: A brief reviewcited by 5
- 2021Retweet communities reveal the main sources of hate speechcited by 5
- 2022Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Datasetcited by 4
- 2023Structural Group Unfairness: Measurement and Mitigation by means of the Effective Resistancecited by 4
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulationscited by 3
12 most cited of 23 All 23 (JSON)
- 2026How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messages1 Jul 2026
- 2025Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning20 Oct 2025
- 2025When or What? Understanding Consumer Engagement on Digital Platforms12 Oct 2025
- 2025Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior28 Jul 2025
- 2024PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization17 Dec 2024
- 2024Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models1 Nov 2024
- 2024Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories25 Jun 2024
- 2024SoMeR: Multi-View User Representation Learning for Social Media2 May 2024
- 2024Algorithmic Fairness: A Tolerance Perspective26 Apr 2024
- 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era17 Apr 2024
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations24 Aug 2023
- 2023On the Complexity of the Bipartite Polarization Problem: from Neutral to Highly Polarized Discussions21 Jul 2023
12 newest of 23 All 23 (JSON)