IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v192y2025ics0960077924014875.html
   My bibliography  Save this article

An evidence-accumulating drift–diffusion model of competing information spread on networks

Author

Listed:
  • Corsin, Julien
  • Zino, Lorenzo
  • Ye, Mengbin

Abstract

In this paper, we propose an agent-based model of information spread, grounded on psychological insights on the formation and spread of beliefs. In our model, we consider a network of individuals who share two opposing types of information on a specific topic (e.g., pro- vs. anti-vaccine stances), and the accumulation of evidence supporting either type of information is modelled by means of a drift–diffusion process. After formalising the model, we put forward a campaign of Monte Carlo simulations to identify population-wide behaviours emerging from agents’ exposure to different sources of information, investigating the impact of the number and persistence of such sources, and the role of the network structure through which the individuals interact. We find similar emergent behaviours for all network structures considered. When there is a single type of information, the main observed emergent behaviour is consensus. When there are opposing information sources, both consensus or polarisation can result; the latter occurs if the number and persistence of the sources exceeds a threshold value identified in the simulations. Importantly, we find the emergent behaviour is mainly influenced by how long the information sources are present for, as opposed to how many sources there are.

Suggested Citation

  • Corsin, Julien & Zino, Lorenzo & Ye, Mengbin, 2025. "An evidence-accumulating drift–diffusion model of competing information spread on networks," Chaos, Solitons & Fractals, Elsevier, vol. 192(C).
  • Handle: RePEc:eee:chsofr:v:192:y:2025:i:c:s0960077924014875
    DOI: 10.1016/j.chaos.2024.115935
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924014875
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2024.115935?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:plo:pone00:0175799 is not listed on IDEAS
    2. Zhang, Wei & Brandes, Ulrik, 2023. "Conformity versus credibility: A coupled rumor-belief model," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Lu, Peng & Yang, Hou & Li, Mengdi & Zhang, Zhuo, 2021. "The sandpile model and empire dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    4. Mengbin Ye & Lorenzo Zino & Žan Mlakar & Jan Willem Bolderdijk & Hans Risselada & Bob M. Fennis & Ming Cao, 2021. "Collective patterns of social diffusion are shaped by individual inertia and trend-seeking," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. Bulai, Iulia Martina & Sensi, Mattia & Sottile, Sara, 2024. "A geometric analysis of the SIRS compartmental model with fast information and misinformation spreading," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    6. Nekovee, M. & Moreno, Y. & Bianconi, G. & Marsili, M., 2007. "Theory of rumour spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 457-470.
    7. Mohammadi, Sohameh & Nadimi-Shahraki, Mohammad H. & Beheshti, Zahra & Zamanifar, Kamran, 2024. "Improved information diffusion models based on a new two-sided sign-aware matching framework in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    8. Daniel A Sprague & Thomas House, 2017. "Evidence for complex contagion models of social contagion from observational data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-12, July.
    9. Acemoglu, Daron & Ozdaglar, Asuman & ParandehGheibi, Ali, 2010. "Spread of (mis)information in social networks," Games and Economic Behavior, Elsevier, vol. 70(2), pages 194-227, November.
    10. Bjarke Mønsted & Piotr Sapieżyński & Emilio Ferrara & Sune Lehmann, 2017. "Evidence of complex contagion of information in social media: An experiment using Twitter bots," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-12, September.
    11. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ding, Fei & Liu, Yun, 2009. "A decision theoretical approach for diffusion promotion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3572-3580.
    2. Azzimonti, Marina & Fernandes, Marcos, 2023. "Social media networks, fake news, and polarization," European Journal of Political Economy, Elsevier, vol. 76(C).
    3. Matjaž Steinbacher & Mitja Steinbacher, 2019. "Opinion Formation with Imperfect Agents as an Evolutionary Process," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 479-505, February.
    4. Borges, Henrique M. & Vasconcelos, Vítor V. & Pinheiro, Flávio L., 2024. "How social rewiring preferences bridge polarized communities," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    5. Mitja Steinbacher & Matjaž Steinbacher & Clemens Knoppe, 2024. "Opinion Dynamics with Preference Matching: How the Desire to Meet Facilitates Opinion Exchange," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 735-768, August.
    6. Huo, Liang’an & Chen, Sijing, 2020. "Rumor propagation model with consideration of scientific knowledge level and social reinforcement in heterogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    7. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    8. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    9. Hosni, Adil Imad Eddine & Li, Kan & Ahmad, Sadique, 2020. "Analysis of the impact of online social networks addiction on the propagation of rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    10. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    11. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    12. Paul Fesenfeld, Lukas & Maier, Maiken & Brazzola, Nicoletta & Stolz, Niklas & Sun, Yixian & Kachi, Aya, 2023. "How information, social norms, and experience with novel meat substitutes can create positive political feedback and demand-side policy change," Food Policy, Elsevier, vol. 117(C).
    13. Silvio Vismara, 2018. "Information Cascades among Investors in Equity Crowdfunding," Entrepreneurship Theory and Practice, , vol. 42(3), pages 467-497, May.
    14. Amir, Gideon & Arieli, Itai & Ashkenazi-Golan, Galit & Peretz, Ron, 2025. "Granular DeGroot dynamics – A model for robust naive learning in social networks," Journal of Economic Theory, Elsevier, vol. 223(C).
    15. Inyoung Chae & Andrew T. Stephen & Yakov Bart & Dai Yao, 2017. "Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns," Marketing Science, INFORMS, vol. 36(1), pages 89-104, January.
    16. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    17. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    18. Jia, Pingqi & Wang, Chao & Zhang, Gaoyu & Ma, Jianfeng, 2019. "A rumor spreading model based on two propagation channels in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 342-353.
    19. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Yi, Dongyun, 2018. "Effectively identifying multiple influential spreaders in term of the backward–forward propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 404-413.
    20. Vincent Labatut & Jean-Michel Balasque, 2010. "Business-oriented Analysis of a Social Network of University Students," Post-Print hal-00633643, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:192:y:2025:i:c:s0960077924014875. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.