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

Author

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  • 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
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    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.
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