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Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks

Author

Listed:
  • Daniel Röchert

    (University of Duisburg-Essen)

  • Manuel Cargnino

    (University of Duisburg-Essen)

  • German Neubaum

    (University of Duisburg-Essen)

Abstract

Opinion leaders (OLs) are becoming increasingly relevant on social networking sites as their visibility can help to shape their followers’ attitudes toward a variety of issues. While earlier research provided initial evidence on the effect of OLs using agent-based modeling, it remains unclear how OLs affect their network environment and, therefore, the opinion climate when: (a) they publicly hold ambivalent attitudes, and (b) they not only express support for their own stance but also discredit or ‘debunk’ the opposing side. This paper presents an agent-based model that determines the influence of OLs in social networks in relation to ambivalence and discreditation. The model draws on theoretical foundations of OLs as well as attitudinal ambivalence and was implemented using two network topologies. Results indicate that OLs have significant influence on the opinion climate and that an unequal number of OLs of different opinion camps lead to an imbalance in the opinion climate only in certain situations. Furthermore, OLs can dominate the opinion climate and turn their stance into a majority opinion more effectively when discrediting the opposing side. Ambivalent OLs, on the other hand, can contribute to greater balance in the opinion climate. These findings provide a more nuanced analysis of OLs in social networks by pointing to potential amplifications as well as boundaries of their influence. Implications are discussed with a focus on human and artificial key actors in online networks and their efficacy therein.

Suggested Citation

  • Daniel Röchert & Manuel Cargnino & German Neubaum, 2022. "Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks," Journal of Computational Social Science, Springer, vol. 5(2), pages 1159-1205, November.
  • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:2:d:10.1007_s42001-022-00161-z
    DOI: 10.1007/s42001-022-00161-z
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    References listed on IDEAS

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    1. Gandica, Yérali & del Castillo-Mussot, Marcelo & Vázquez, Gerardo J. & Rojas, Sergio, 2010. "Continuous opinion model in small-world directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5864-5870.
    2. Nino Boccara, 2008. "Models Of Opinion Formation: Influence Of Opinion Leaders," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 93-109.
    3. Xiaoxuan Liu & Changwei Huang & Haihong Li & Qionglin Dai & Junzhong Yang & Wei Zhou, 2021. "The Combination of Pairwise and Group Interactions Promotes Consensus in Opinion Dynamics," Complexity, Hindawi, vol. 2021, pages 1-8, January.
    4. Cho, Youngsang & Hwang, Junseok & Lee, Daeho, 2012. "Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 97-106.
    5. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    6. Thomas W. Valente & Rebecca L. Davis, 1999. "Accelerating the Diffusion of Innovations Using Opinion Leaders," The ANNALS of the American Academy of Political and Social Science, , vol. 566(1), pages 55-67, November.
    7. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    8. Yu, Hui & Cao, Xi & Liu, Zun & Li, Yongjun, 2017. "Identifying key nodes based on improved structural holes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 318-327.
    9. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    10. Norma L. Abrica-Jacinto & Evguenii Kurmyshev & Héctor A. Juárez, 2017. "Effects of the Interaction Between Ideological Affinity and Psychological Reaction of Agents on the Opinion Dynamics in a Relative Agreement Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-3.
    11. Alessandro Vespignani, 2018. "Twenty years of network science," Nature, Nature, vol. 558(7711), pages 528-529, June.
    12. Gary Mckeown & Noel Sheehy, 2006. "Mass Media and Polarisation Processes in the Bounded Confidence Model of Opinion Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-11.
    13. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
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