IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v12y2023i4p236-d1124360.html
   My bibliography  Save this article

Using Exponential Random Graph Models for Social Networks to Understand Meta-Communication in Digital Media

Author

Listed:
  • Zhou Nie

    (Faculty of Modern Language and Communication, Universiti Putra Malaysia, Serdang 43400, Malaysia)

Abstract

In recent years; digital media has garnered widespread interest from various domains. Despite advancements in the technology of digital media for globalized communication; disparities persist in user interaction patterns across different regions. These differences can be attributed to the presence of a control system, known as meta-communication, which shapes the coding of information based on social relationships. Meta-communication is formed in various social contexts, resulting in varying communication patterns among different groups. However, empirical research on the social processes that form meta-communication in digital media is scarce due to the challenges in quantifying meta-communication. This study aims to introduce exponential random graph models as a potential tool for analyzing meta-communication in digital media and to provide a preliminary understanding of its formation. The use of such models could prove valuable for researchers seeking to study meta-communication in digital media.

Suggested Citation

  • Zhou Nie, 2023. "Using Exponential Random Graph Models for Social Networks to Understand Meta-Communication in Digital Media," Social Sciences, MDPI, vol. 12(4), pages 1-11, April.
    • Handle: RePEc:gam:jscscx:v:12:y:2023:i:4:p:236-:d:1124360
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/12/4/236/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-0760/12/4/236/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Caimo, Alberto & Gollini, Isabella, 2020. "A multilayer exponential random graph modelling approach for weighted networks," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    2. Leifeld, Philip & Cranmer, Skyler J., 2019. "A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model," Network Science, Cambridge University Press, vol. 7(1), pages 20-51, March.
    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. Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    2. Alexandra Goritz & Nina Kolleck & Helge Jörgens, 2019. "Education for Sustainable Development and Climate Change Education: The Potential of Social Network Analysis Based on Twitter Data," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    3. Zhao, Guimei & Li, Wenxiu & Geng, Yong & Bleischwitz, Raimund, 2023. "Uncovering the features of global antimony resource trade network," Resources Policy, Elsevier, vol. 85(PA).
    4. Park, Jaewoo & Jin, Ick Hoon & Schweinberger, Michael, 2022. "Bayesian model selection for high-dimensional Ising models, with applications to educational data," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    5. Anna Malinovskaya & Philipp Otto, 2021. "Online network monitoring," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1337-1364, December.
    6. Yingjie Lu & Xinwei Wang & Lin Su & Han Zhao, 2023. "Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities," Mathematics, MDPI, vol. 11(21), pages 1-20, October.
    7. Liu, Linqing & Shen, Mengyun & Sun, Da & Yan, Xiaofei & Hu, Shi, 2022. "Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    8. Kei, Yik Lun & Chen, Yanzhen & Madrid Padilla, Oscar Hernan, 2023. "A partially separable model for dynamic valued networks," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    9. Xiaoyi Shi & Xiaoxia Huang & Huifang Liu, 2022. "Research on the Structural Features and Influence Mechanism of the Low-Carbon Technology Cooperation Network Based on Temporal Exponential Random Graph Model," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    10. Polansky, Alan M. & Pramanik, Paramahansa, 2021. "A motif building process for simulating random networks," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    11. Linyan Wang & Haiqing Hu & Xianzhu Wang, 2022. "The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    12. Xu, Yu & Hazée, Simon & So, Kevin Kam Fung & Li, K. Daisy & Malthouse, Edward Carl, 2021. "An evolutionary perspective on the dynamics of service platform ecosystems for the sharing economy," Journal of Business Research, Elsevier, vol. 135(C), pages 127-136.
    13. Ruck, Damian J. & Bentley, R. Alexander & Borycz, Joshua, 2021. "Early warning of vulnerable counties in a pandemic using socio-economic variables," Economics & Human Biology, Elsevier, vol. 41(C).

    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:gam:jscscx:v:12:y:2023:i:4:p:236-:d:1124360. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.