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Nie, Z. Meta-Communication in Digital Media. Encyclopedia. Available online: (accessed on 21 June 2024).
Nie Z. Meta-Communication in Digital Media. Encyclopedia. Available at: Accessed June 21, 2024.
Nie, Zhou. "Meta-Communication in Digital Media" Encyclopedia, (accessed June 21, 2024).
Nie, Z. (2023, April 26). Meta-Communication in Digital Media. In Encyclopedia.
Nie, Zhou. "Meta-Communication in Digital Media." Encyclopedia. Web. 26 April, 2023.
Meta-Communication in Digital Media

Meta-communication is formed in various social contexts, resulting in varying communication patterns among different groups. Empirical research on the social processes that form meta-communication in digital media is scarce due to the challenges in quantifying meta-communication. 

exponential random graph models for social networks meta-communication digital media

1. Introduction

Digital media, defined as any form of media that utilizes digital technology for communication, has become a widely used tool for exchanging information (Jensen 2011). Its diverse functions allow for the exchange of both verbal and nonverbal information, which are both crucial natures of human communication. Research by Bateson (1973) has shown that face-to-face communication often includes nonverbal cues about the context in which the communication is taking place. These nonverbal cues, such as clothing, dialect, social identity, and body language, provide important information about the social environment and cultural norms that shape communication patterns (Cenni et al. 2020; Ekti 2022). This flow of information regarding the context of communication is referred to as meta-communication. Bateson (1973) has identified two ways in which meta-communication influences the exchange of information, including the codification of interaction and the influence of social relationships.
The coding of information, through means such as repetition, illustration, and rephrasing, determines the meaning of the information within specific contexts. For instance, Bateson (1973) discovered that individuals could assign the meaning of “tiger” to the word “cat” while playing a specific game. The use of coding to explain interactions within various contexts can facilitate comprehension and reduce communication costs.
On the aspect of social relationships, the nature and form of information conveyed between communicators are influenced by the social identities of the communicators and the type of relationship they share. For instance, the statement “don’t be rude” may carry varying connotations and impacts depending on whether the communicators are co-workers, family members, friends, or business partners.
Meta-communication encompasses these two interdependent aspects that permeate the entire communication process. Social relationships play a role in shaping the ways in which information is codified, while the underlying principles of codification imply social relationships. The interplay between codification and social relationships is an ongoing and reciprocal relationship, shaped by cultural context. Different cultural contexts give rise to distinct forms of codification and social relationships, leading to varied communication patterns of different social groups (Huston and Burgess 1979).
The significance of meta-communication in shaping information is increasingly significant in the context of digital media. The global communication facilitated by digital media allows for the comparison of communication patterns between different social groups worldwide (Saka and Garoma 2019; Meier and Reinecke 2021). Studies (Ajzen 2011; Madden et al. 1992) have shown that people primarily use digital media to maintain existing social relationships, rather than creating new ones, indicating that meta-communication including codification and social relationships is mostly shaped by local experiences rather than the digital media itself (Ortega et al. 2020). However, the mechanisms by which different forms of meta-communication constitute information in digital media remain to be fully understood.
To study meta-communication in digital media, it is first necessary to define its components. Jensen (2011) proposed a preliminary typology of four main forms of meta-communication in digital media, rooted in Bateson’s (1973) concept of meta-communication as a cybernetic mechanism for exchanging information. Jensen viewed meta-communication in digital media as encompassing the control of digital information bases, as well as the control of information content and timing, similar to information codification. In addition to Bateson’s logic, Jensen also drew on Roland Barthes’ (1973) distinction between connotation and denotation in language signs to define relationships between communication systems and users based on the control forms of digital information. These relationships can result in four main types of meta-communication in digital media: third-party communication, iterative communication, processed communication, and recommended communication.
Having defined the components of meta-communication in digital media, further research on its practices is necessary. Despite being introduced in the 1970s, meta-communication in digital media has not received sufficient attention in the field of digital media studies despite its growing importance. One of the reasons for this neglect is the challenge of quantifying meta-communication (Jensen 2012). The formation of meta-communication occurs in dynamic and localized social processes, and uncovering the underlying regularities requires extensive and potentially multidisciplinary efforts. As a result, meta-communication in digital media may be viewed as a hypothesis for some time.
Despite the difficulties in precisely defining meta-communication, a social network analysis method can estimate the distribution of various types of ties formed by meta-communication within a given social network. The formation of social ties between individuals is similar to the formation of meta-communication, as they are both established through interactions with information and relationships between actors (Marsden and Friedkin 1993). As a result, the structural features of meta-communication can also be studied through the examination of social ties. One method is to use exponential random graph models for social networks to simulate the structure of meta-communication (Yon et al. 2021). This modeling approach allows for the investigation of which types of meta-communication are more likely to occur in a particular social network based on statistical data from the network (Yamane 1973; Zhang and Pentina 2012; Caimo and Gollini 2020).
Furthermore, this modeling approach prioritizes the formation of diverse structures of interaction that can be represented in social networks. These structures are observable, therefore minimizing the impact of unobserved details and other social processes on the estimation of probability distributions. The accuracy of these estimations improves with an increase in observed data and parameters incorporated into the model (Stivala et al. 2020). This modeling approach can be adapted to various research requirements, enabling more empirical investigation into meta-communication structures within specific social networks.

2. The Four Types of Meta-Communication

Meta-communication was first introduced as a concept in communication studies by Bateson (1973). Based on his observations of face-to-face conversations, Bateson (1973) recognized the existence of multiple levels of abstraction in human verbal communication, a phenomenon known as polysemy. For instance, when an angry person states they are not angry, they are sending a message that contradicts reality. However, the recipient of the message can respond in one of three ways: by directly challenging the statement, by complying with it, or by changing the subject of the conversation. The choice of how to interpret the message often depends on the relationship between the two individuals and the codification of interactions in specific contexts. This highlights the idea that human communication is a layered system, where information exchange is at the surface level but also has an underlying system including codification and social relationships that determines the interpretation of the information. Thus, in human communication, people not only exchange information but also the underlying system, which Bateson referred to as meta-communication (Bateson 1973; Jensen 2011; Hjelmslev 1963).
The meta-communication is shaped by local experiences and is internalized through daily interactions with family, friends, neighbors, coworkers, and in social groups and communities (Pettegrew and Day 2015). The more exposure to real-life practices, the more likely communication patterns will become ingrained habits, providing a foundation for understanding communication in similar contexts without the need for clarifying every message (Bateson 1973; Jensen 2011). Scholars such as Castells (2007), McQuail (2010), and Hofstede (2006) have noted that even in the age of globalization and digitalization, communication experiences remain rooted in local practices. People exhibit different behaviors in various online spaces, and these localized patterns persist because they are cultivated through daily interactions in local contexts. As a result, the integration of local experiences into global experiences is of great significance in digital media, particularly the presentation of unobserved social processes just as meta-communication.
Meta-communication is a complex phenomenon that encompasses both conscious and unconscious social processes, as well as both verbal and nonverbal communication. It can encode multiple levels of information, such as words, tones, gestures, and other signs, to enhance the communication process. In real-life situations, these signs are interwoven in the context of face-to-face communication, where they serve as carriers of information. Over time, the advancement of communication systems has expanded the capacity for multi-layered communication, starting from interpersonal communication to mass communication, and finally to networked communication in digital media. In digital media, the various affordances of previous communication methods have been combined to support multi-layered communication. However, it is important to note that the layered structure of communication is not a result of digital media but rather a fundamental aspect of human communication (Jensen 2011; Park and Pooley 2008).
Digital media is equipped with the most advanced communication technology, which supports multi-layered communication. According to scholars such as Jensen (2011), Berry (2012), and Hofstede (2006), the potential of meta-communication in digital media has yet to be fully realized. Meta-communication can facilitate the understanding of local and global communication patterns and the interaction between them in digital media. Bateson (1973) defined meta-communication as the control processes of digital information, including the codification of information and the relationships between communicators. Jensen (2012) further expanded on this concept by identifying four prototypes of meta-communication, including the codification and relationships of interaction patterns between systems and users in digital media regarding two types of digital information control: information base control and items and time of information control. The four concrete types of meta-communication are as follows:
Third-party communication, as defined in this form, refers to information that is completely controlled by the system. This information can be shared with various entities, such as marketers, advertisers, or government authorities. In Table 1, the intersection of third-party communication is the second type of meta-communication of iterative communication. Unlike third-party communication, iterative communication is user-controlled and encompasses various forms of interactive patterns between users, including synchronous and asynchronous interactions in the form of comments, messages, re-sends, likes, video conversations, and other user-generated interfaces using tools from collaborative open sources.
Table 1. Jensen’s four prototypes of meta-communication in digital media.
  Control of Information Base
Control of time and items selected System User
System Third-party communication Processed communication
User Recommended communication Iterative communication
From “How to do things with data: Meta-data, meta-media, and meta-communication,” by K. B. Jensen (2012), First Monday, 18(10), (accessed on 10 February 2022, https://doi:10.5210/fm.v18i10.4870).
The remaining two types of meta-communication are processed communication and recommended communication, which are opposite in nature. Processed communication involves the documentation and analysis of an individual user’s patterns of information utilization for the purpose of market analysis and billing. On the other hand, recommended communication focuses on grouping users with similar interests and customizing information recommendations to these targeted user segments.
Jensen (2011) uses these four prototypes to illustrate the range of communication practices in digital media. Although this typology is preliminary (Jensen 2011), it provides a structural perspective to analyze the concept of meta-communication. Defining these four types would be the first step to quantitatively analyzing the concept of meta-communication in digital media; the operationalization of these four types would be the next step.


  1. Jensen, Klaus Bruhn. 2011. Meta-media and meta-communication-Revisiting the concept of genre in the digital media environment. Medie Kultur: Journal of Media and Communication Research 27: 14.
  2. Bateson, Gregory. 1973. Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution and Epistemology. London: Granada.
  3. Cenni, Irene, Patrick Goethals, and Camilla Vásquez. 2020. A cross-linguistic study of metacommunication in online hotel reviews. Intercultural Pragmatics 17: 445–70.
  4. Ekti, Meltem. 2022. The use of meta language in the humor. Rumeli DE Dil ve Edebiyat Araştırmaları Dergisi 29: 660–72.
  5. Huston, Ted L., and Robers L. Burgess. 1979. Social exchange in developing relationships: An overview. Social Exchange in Developing Relationships 3: 28.
  6. Saka, Abebe Lemessa, and Eba Teresa Garoma. 2019. Comparative Analysis of Amharic and Afaan Oromoo Proverbs: A meta-communication perspective. Macrolinguistics 7: 72–91.
  7. Meier, Adrian, and Leonard Reinecke. 2021. Computer-mediated communication, social media, and mental health: A conceptual and empirical meta-review. Communication Research 48: 1182–209.
  8. Ajzen, Icek. 2011. The theory of planned behavior: Reactions and reflections. Psychology & Health 26: 1113–27.
  9. Madden, Thomas J., Pamela Scholder Ellen, and Icek Ajzen. 1992. A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin 18: 3–9.
  10. Ortega, Lorena, Zsófia Boda, Ian Thompson, and Harry Daniels. 2020. Understanding the structure of school staff advice relations: An inferential social network perspective. International Journal of Educational Research 99: 101517.
  11. Barthes, Roland. 1973. Mythologies. Selected and translated by Annette Lavers. London: Paladin Books.
  12. Jensen, Klaus Bruhn, ed. 2012. A Handbook of Media and Communication Research: Qualitative and Quantitative Methodologies, 2nd ed. New York: Routledge.
  13. Marsden, Peter V., and Noah E. Friedkin. 1993. Network studies of social influence. Sociological Methods & Research 22: 127–51.
  14. Yon, George G. Vega, Andrew Slaughter, and Kayla de la Haye. 2021. Exponential random graph models for little networks. Social Networks 64: 225–38.
  15. Yamane, Taro. 1973. Statistics: An Introductory Analysis. London: John Weather Hill.
  16. Zhang, Lixuan, and Iryna Pentina. 2012. Motivations and usage patterns of Weibo. Cyberpsychology, Behavior, and Social Networking 15: 312–17.
  17. Caimo, Alberto, and Isabella Gollini. 2020. A multilayer exponential random graph modelling approach for weighted networks. Computational Statistics & Data Analysis 142: 106825.
  18. Stivala, Alex, Garry Robins, and Alessandro Lomi. 2020. Exponential random graph model parameter estimation for very large directed networks. PLoS ONE 15: e0227804.
  19. Hjelmslev, Louis. 1963. Prolegomena to a Theory of Language, rev. ed. Translated by Francis J. Whitfield. Madison: University of Wisconsin Press.
  20. Pettegrew, Loyd S., and Carolyn Day. 2015. Smart phones and mediated relationships: The changing face of relational communication. Review of Communication 15: 122–39.
  21. Castells, Manuel. 2007. Communication, power and counter-power in the network society. International Journal of Communication 1: 29.
  22. McQuail, Denis. 2010. McQuail’s Mass Communication Theory, 6th ed. London: Sage.
  23. Hofstede, Geert. 2006. What did GLOBE really measure? Researchers’ minds versus respondents’ minds. Journal of International Business Studies 37: 882–96.
  24. Park, David W., and Jefferson Pooley. 2008. The History of Media and Communication Research: Contested Memories. New Youk: Peter Lang.
  25. Berry, David M., ed. 2012. Understanding Digital Humanities. New York: Palgrave Macmillan.
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