For a special issue on “The Datafication of Communication – New Methodological Approaches and Challenges” in our journal “Medien & Kommunikationswissenschaft” (Media & Communication Studies), we invite submissions in German and English. Guest editors are Julia Niemann-Lenz, Tim Schatto-Eckrodt, Emese Domahidi and Merja Mahrt.
The ongoing digitalization of mass and interpersonal communication processes has led to an unprecedented level of datafication—where various aspects of (social) life are transformed into data. Today, virtually all types of media content and user interactions generate data that can be collected and analyzed. This growing availability of digital traces offers new ways to address traditional and contemporary questions in digital communication research. However, the datafication of human behavior is not only reshaping the subject matter of media and communication studies, but also transforming the discipline itself—primarily through new forms of data access and methods. Computational communication science has emerged as a research field that places digital trace data at the center of media and communication research (Domahidi et al. 2019).
Central to this discourse on the datafication of both communication and the field of media and communication studies lies a methodological question: (How) can large datasets be used for reliable and valid measurements of social reality? As a discipline that has been conceptualizing and investigating these processes of datafication since its inception, media and communication studies are well placed to make significant contributions. Innovations and expansions in methods, such as data donation, AI-supported content analysis procedures, and other machine learning techniques, offer new possibilities for analyzing social phenomena. For instance, generative AI holds great potential in creating stimulus material for media effects experiments and in automating the coding of media content. These methodological improvements not only provide deeper insights into the dynamics of a datafied society but also help develop t models that better capture the complexity of social interactions.
However, critics challenge the idea that human behavior can be neutrally and objectively represented through data alone (boyd and Crawford, 2012). Large online platforms’ role in this process has been described as “data colonialism” (Couldry & Mejias, 2019), a theoretical concept that likens the exploitative, extractive practices of historical colonialism to the abstract quantification methods of computer science. Additionally, the ideological basis of datafication is characterized in the literature as “dataism” (van Dijck, 2014), or the misguided belief in the objectively and neutral quantifiability of all human actions through digital systems. The inclusion of new types of data in research or media production raises new legal and ethical concerns (Spirling, 2023).
This special issue of M&K aims to bring together current topics related to datafication, particularly in relation to the methods of media and communication studies, and to foster reflection on how the discipline is evolving as a result of these innovations. The editors welcome methodological and empirical contributions that address questions such as:
- Data Accessibility and Availability: How can researchers gain access to relevant and meaningful data? How can data be collected transparently? How can it be archived and reused in the spirit of open science while respecting copyright and personal rights? What collaborative efforts are necessary or desirable within the discipline?
- New Methods and Research Areas: What new research methods and approaches do the availability of large datasets and advances in the field of machine learning (e.g. large language models) offer for communication research?
- Measuring Change in Communication Processes: How can the effects of datafication and data-processing algorithms on social communication, public opinion, and social interactions be captured?
- Ethics and Accountability: Who collects, processes, enriches, uses, and protects data, and for what purposes? What role do transparency and control play in the use of algorithms in communication?
- Reflection on the Topic: Does the datafication of communication also imply a quantification of the subject? What is the ongoing role of qualitative methods and paradigms?
Contributions in both English and German are welcome.
Scholars wishing to contribute to this special issue are invited to send an extended abstract of their manuscript proposal (max. 6,000 characters including spaces) to the editorial team by November 30, 2024. On the basis of the abstracts, the editorial team, together with the guest editors, will develop a concept for the issue and invite the respective authors to submit a manuscript by the end of March 2025. Decisions on the acceptance of manuscripts will be made according to M&K’s usual review process. The special issue is planned for publication in the 4th quarter of 2025.
Address: Editorial Office Medien & Kommunikationswissenschaft, Christiane Matzen, c.matzen@leibniz-hbi.de
References
boyd, d., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
Couldry, N., & Mejias, U. A. (2019). The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism. Stanford University Press.
Dijck, J. van. (2014). Datafication, Dataism and Dataveillance: Big Data between Scientific Paradigm and Ideology. Surveillance & Society, 12(2), 197–208. https://doi.org/10.24908/ss.v12i2.4776
Domahidi, E., Yang, J., Niemann-Lenz, J., & Reinecke, L. (2019). Computational Communication Science | Outlining the Way Ahead in Computational Communication Science: An Introduction to the IJoC Special Section on “Computational Methods for Communication Science: Toward a Strategic Roadmap”. International Journal of Communication, 13. https://ijoc.org/index.php/ijoc/article/view/10533
Spirling, A. (2023). Why Open-Source Generative AI Models Are an Ethical Way Forward for Science. Nature, 616(7957), 413. https://doi.org/10.1038/d41586-023-01295-4
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