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An agent-based model of collective emotions in online communities

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  • F. Schweitzer

  • D. Garcia

Abstract

We develop an agent-based framework to model the emergence of collective emotions, which is applied to online communities. Agent’s individual emotions are described by their valence and arousal. Using the concept of Brownian agents, these variables change according to a stochastic dynamics, which also considers the feedback from online communication. Agents generate emotional information, which is stored and distributed in a field modeling the online medium. This field affects the emotional states of agents in a non-linear manner. We derive conditions for the emergence of collective emotions, observable in a bimodal valence distribution. Dependent on a saturated or a superlinear feedback between the information field and the agent’s arousal, we further identify scenarios where collective emotions only appear once or in a repeated manner. The analytical results are illustrated by agent-based computer simulations. Our framework provides testable hypotheses about the emergence of collective emotions, which can be verified by data from online communities. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Suggested Citation

  • F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
    • Handle: RePEc:spr:eurphb:v:77:y:2010:i:4:p:533-545
    DOI: 10.1140/epjb/e2010-00292-1
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    Cited by:

    1. Fan, Rui & Xu, Ke & Zhao, Jichang, 2018. "An agent-based model for emotion contagion and competition in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 245-259.
    2. Chmiel, Anna & Sobkowicz, Pawel & Sienkiewicz, Julian & Paltoglou, Georgios & Buckley, Kevan & Thelwall, Mike & Hołyst, Janusz A., 2011. "Negative emotions boost user activity at BBC forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2936-2944.
    3. Zhai, Xueting & Zhong, Dixi & Luo, Qiuju, 2019. "Turn it around in crisis communication: An ABM approach," Annals of Tourism Research, Elsevier, vol. 79(C).
    4. Ignacio Tamarit & Angel Sánchez, 2016. "Emotions and Strategic Behaviour: The Case of the Ultimatum Game," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-12, July.
    5. F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
    6. Mitrović, Marija & Tadić, Bosiljka, 2012. "Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5264-5278.
    7. Claudio J. Tessone & Angel Sanchez & Frank Schweitzer, "undated". "Diversity-induced resonance in the response to social norms," Working Papers ETH-RC-12-017, ETH Zurich, Chair of Systems Design.
    8. Wen Zheng & Ailin Yu & Ping Fang & Kaiping Peng, 2020. "Exploring collective emotion transmission in face-to-face interactions," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-11, August.
    9. Xu, Yuxin & Gao, Fei, 2024. "A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    10. Anna Chmiel & Julian Sienkiewicz & Mike Thelwall & Georgios Paltoglou & Kevan Buckley & Arvid Kappas & Janusz A Hołyst, 2011. "Collective Emotions Online and Their Influence on Community Life," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    11. Marie Lisa Kapeller & Georg Jäger, 2020. "Threat and Anxiety in the Climate Debate—An Agent-Based Model to Investigate Climate Scepticism and Pro-Environmental Behaviour," Sustainability, MDPI, vol. 12(5), pages 1-25, February.

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