IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v551y2020ics0378437120300169.html
   My bibliography  Save this article

Dynamic mechanism of social bots interfering with public opinion in network

Author

Listed:
  • Cheng, Chun
  • Luo, Yun
  • Yu, Changbin

Abstract

Participants in discussions on online social networks tend to become polarized into clusters of users with diametrically opposite opinions. Recent evidence has suggested that social bots are being used on social media networks to manipulate public opinion, but this mechanism has not been adequately investigated. In this paper, using the “spiral of silence” theory of social communication, we establish a multi-agent model based on user interactions on social media, define the behavioral characteristics of social bots and human users at the microscopic level, and reveal the mechanism of manipulation of public opinion by bots. The results of simulations of small-world and scale-free networks show that social bots need only constitute 5%–10% of participants in a given discussion to alter public opinion such that the view being propagated by them eventually becomes the dominant opinion (held by more than 2/3 of the population). The influence of network density, efficiency of clustering, and spatial location on the manipulative effect of bots was analyzed. The results show that social bots can influence the formation of opinions on online social networks.

Suggested Citation

  • Cheng, Chun & Luo, Yun & Yu, Changbin, 2020. "Dynamic mechanism of social bots interfering with public opinion in network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437120300169
    DOI: 10.1016/j.physa.2020.124163
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120300169
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.124163?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. Massimo Stella & Emilio Ferrara & Manlio De Domenico, 2018. "Bots increase exposure to negative and inflammatory content in online social systems," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(49), pages 12435-12440, December.
    2. Galam, Serge, 2010. "Public debates driven by incomplete scientific data: The cases of evolution theory, global warming and H1N1 pandemic influenza," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3619-3631.
    3. Pires, Marcelo A. & Crokidakis, Nuno, 2017. "Dynamics of epidemic spreading with vaccination: Impact of social pressure and engagement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 167-179.
    4. Huang, Gan & Cao, Jinde & Qu, Yuzhong, 2009. "The minority’s success under majority rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3911-3916.
    5. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    6. Javarone, Marco Alberto, 2014. "Social influences in opinion dynamics: The role of conformity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 19-30.
    7. Galam, Serge & Jacobs, Frans, 2007. "The role of inflexible minorities in the breaking of democratic opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 366-376.
    8. Crokidakis, Nuno, 2012. "Effects of mass media on opinion spreading in the Sznajd sociophysics model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1729-1734.
    9. Huang, Gan & Cao, Jinde & Wang, Guanjun & Qu, Yuzhong, 2008. "The strength of the minority," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4665-4672.
    10. Crokidakis, Nuno, 2014. "A three-state kinetic agent-based model to analyze tax evasion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 321-328.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luo, Yun & Li, Yuke & Sun, Chudi & Cheng, Chun, 2022. "Adapted Deffuant–Weisbuch model with implicit and explicit opinions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    2. Dmitrii Gavra & Ksenia Namyatova & Lidia Vitkova, 2021. "Detection of Induced Activity in Social Networks: Model and Methodology," Future Internet, MDPI, vol. 13(11), pages 1-13, November.
    3. Zhang, Yaozeng & Ma, Jing & Fang, Fanshu, 2024. "How social bots can influence public opinion more effectively: Right connection strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    4. Wen Shi & Diyi Liu & Jing Yang & Jing Zhang & Sanmei Wen & Jing Su, 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-18, November.

    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. F. Jacobs & S. Galam, 2019. "Two-Opinions-Dynamics Generated By Inflexibles And Non-Contrarian And Contrarian Floaters," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-30, June.
    2. Pires, Marcelo A. & Crokidakis, Nuno, 2017. "Dynamics of epidemic spreading with vaccination: Impact of social pressure and engagement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 167-179.
    3. Tiwari, Mukesh & Yang, Xiguang & Sen, Surajit, 2021. "Modeling the nonlinear effects of opinion kinematics in elections: A simple Ising model with random field based study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    4. Joshua Uyheng & Kathleen M. Carley, 2020. "Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines," Journal of Computational Social Science, Springer, vol. 3(2), pages 445-468, November.
    5. Hyehyun Hong & Hyun Jee Oh, 2020. "Utilizing Bots for Sustainable News Business: Understanding Users’ Perspectives of News Bots in the Age of Social Media," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    6. Min, Yong & Zhou, Yuying & Liu, Yuhang & Zhang, Jian & Xuan, Qi & Jin, Xiaogang & Cai, He, 2021. "The role of degree correlation in shaping filter bubbles in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    7. Balankin, Alexander S. & Martínez Cruz, Miguel Ángel & Martínez, Alfredo Trejo, 2011. "Effect of initial concentration and spatial heterogeneity of active agent distribution on opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3876-3887.
    8. Ross Schuchard & Andrew Crooks & Anthony Stefanidis & Arie Croitoru, 2019. "Bots fired: examining social bot evidence in online mass shooting conversations," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-12, December.
    9. Kelton Minor & Esteban Moro & Nick Obradovich, 2023. "Adverse weather amplifies social media activity," Papers 2302.08456, arXiv.org.
    10. Wen Shi & Diyi Liu & Jing Yang & Jing Zhang & Sanmei Wen & Jing Su, 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    11. Galam, Serge, 2021. "Will Trump win again in the 2020 election? An answer from a sociophysics model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    12. Ma, Jing & Li, Dandan & Tian, Zihao, 2016. "Rumor spreading in online social networks by considering the bipolar social reinforcement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 108-115.
    13. Delanoë, Alexandre & Galam, Serge, 2014. "Modeling a controversy in the press: The case of abnormal bee deaths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 93-103.
    14. Riccardo Gallotti & Francesco Valle & Nicola Castaldo & Pierluigi Sacco & Manlio De Domenico, 2020. "Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics," Nature Human Behaviour, Nature, vol. 4(12), pages 1285-1293, December.
    15. Zixuan Weng & Aijun Lin, 2022. "Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    16. Li, Tingyu & Zhu, Hengmin, 2020. "Effect of the media on the opinion dynamics in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    17. Malik, Nishtha & Kar, Arpan Kumar & Tripathi, Shalini Nath & Gupta, Shivam, 2023. "Exploring the impact of fairness of social bots on user experience," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    18. Galam, Serge, 2011. "Collective beliefs versus individual inflexibility: The unavoidable biases of a public debate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(17), pages 3036-3054.
    19. Aleksejus Kononovicius & Vygintas Gontis, 2014. "Herding interactions as an opportunity to prevent extreme events in financial markets," Papers 1409.8024, arXiv.org, revised May 2015.
    20. Serge Galam & Marco Alberto Javarone, 2016. "Modeling Radicalization Phenomena in Heterogeneous Populations," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-15, May.

    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:phsmap:v:551:y:2020:i:c:s0378437120300169. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.