Call for Papers: CCR Special Issue on Media Use and Effects in the Computational Era
Call for Papers: Special Issue of Computational Communication Research
From Normalization to Next Frontiers: Media Use and Effects in the Computational Era
Guest Editors
Frank Mangold, GESIS – Leibniz-Institute for the Social Sciences, Cologne, Germany
Christina Viehmann, GESIS – Leibniz-Institute for the Social Sciences, Cologne, Germany
Chung-hong Chan, GESIS – Leibniz-Institute for the Social Sciences, Cologne, Germany
Ahrabhi Kathirgamalingam, GESIS – Leibniz-Institute for the Social Sciences, Cologne, Germany
Lukas Otto, GESIS – Leibniz-Institute for the Social Sciences, Cologne, Germany
Mareike Wieland, GESIS – Leibniz-Institute for the Social Sciences, Cologne, Germany
Julia Niemann-Lenz, Deutsches Zentrum für Hochschul- und Wissenschaftsforschung, Hannover, Germany
Pablo Jost, Johannes Gutenberg-Universität Mainz, Germany
In recent years, digital behavioral data and computational methods have moved from novelty to routine in communication research. Scholars increasingly draw on web tracking, app usage, sensor data, data donations, and other forms of digital behavioral data – often processed through automated pipelines – to complement, combine with, or, in some cases, substitute for traditional methods such as surveys and experiments in studying media use, exposure, and effects. This normalization has generated major advances – but also exposed persistent challenges in theory, measurement, and ethics.
This special issue takes stock of these developments and looks ahead: How have computational approaches changed the way we conceptualize and measure media use and effects? Where have they delivered on their promise, and where have limitations persisted? What new methodological, theoretical, and infrastructural frontiers emerge as the field matures?
At the same time, computational approaches invite a fresh look at classical theories of media use and effects. Frameworks such as selective exposure, cultivation, or uses and gratifications offer enduring insights into how media exposure shapes and is shaped by individual and collective orientations, attitudes, behaviors, and norms. How might such theories be adapted in an era of cross-platform fragmentation, algorithmic curation, and large-scale behavioral data? Conversely, what new models are needed to capture dynamics that fall outside older paradigms?
Building on earlier work in computational communication research, which has often concentrated on specific platforms, content features, or short-term outcomes, this collection adopts a broader perspective. It emphasizes the measurement and infrastructural foundations of media use and effects across platforms and over time, and encourages contributions that combine conceptual renewal with methodological rigor.
Thematic Areas
We invite submissions that critically engage with these questions through conceptual refinement, methodological innovation, or empirical application. Rather than portraying computational approaches as novel or unprecedented, this special issue treats them as integral to the field’s repertoire and asks what the next generation of media use and effects research should look like. Submissions may engage with, but are not limited to, the following four areas:
1. Integrating and Interpreting Digital Behavioral Data
Forms of digital behavioral data such as web tracking, app usage, sensor data, data donations, and platform APIs (where accessible) have increasingly been adopted as standard tools. Yet challenges remain in integrating them with surveys, experiments, or physiological measures, in assessing their validity and feasibility, and in developing automated pipelines that make such integration scalable and reproducible. We seek contributions that evaluate lessons learned from combining data sources and explore new ways to link exposure to its individual and societal drivers and effects.
2. Revisiting and Rethinking Theories of Media Use and Effects
Computational approaches allow us to observe exposure and engagement in granular detail – but what does this mean for theory? Classical frameworks such as selective exposure, cultivation, uses and gratifications, agenda-setting, or the third-person effect offer enduring insights but require adaptation to fragmented, algorithmically curated, and cross-platform environments. We invite work that reexamines these frameworks in light of computational evidence or proposes new models to capture dynamics that escape older paradigms. Submissions may demonstrate how computational data refine established theories, advance novel conceptual approaches, and show how new forms of measurement shape what theory can and cannot explain about media use and its consequences.
3. Representation, Validity, and Bias in Computational Measurement
Studies based on voluntary participation and the installation of research software (e.g., browser plugins, apps) often suffer from selection effects, while platform data provide only partial and selective access to media use and exposure. As a result, computational studies of media use and effects are prone to challenges of representation and validity. We invite contributions that address these issues, including statistical and causal inference techniques to mitigate bias, benchmark comparisons, and design strategies that enhance inclusivity and robustness. Submissions may also explore how infrastructures, standards, or ethical safeguards affect what can be measured, and how such approaches improve the validity, reliability, and generalizability of insights into media use and effects.
4. New Frontiers in Computational Media Research
As computational approaches continue to reshape the study of media use and effects, new frontiers are emerging. Capturing complex, multimodal environments that span text, images, video, and interaction remains a key challenge, as does linking such computational measures to established psychological constructs in valid and reliable ways. Equally pressing are methodological questions of how to model temporal dynamics, feedback loops, and causal mechanisms in increasingly complex systems. At the same time, the field must address issues of robustness and generalizability, developing strategies for replication, benchmarking, and cross-context comparison. We invite contributions that critically examine these challenges, propose novel methodological solutions, or advance conceptual frameworks that help situate computational evidence within broader theories of media use and effects.
Submission Information
Please submit your abstract (800-1,200 words, including references, figures/tables, and title page) to the submission portal, before 16 October 2026. When submitting, please select the section: “Special Issue: Media use and effects”
We welcome empirical studies, methodological innovations, theoretical contributions, simulations, and critical reflections that engage with computational approaches to media use and effects. Submissions should combine computational rigor with conceptual clarity and address methodological, theoretical, or ethical dimensions of studying media use and its consequences in digital environments.
While computational communication research spans many domains, this special issue specifically centers on advances in the study of media use and effects. Authors should clearly indicate how their contributions speak to this core area. To ensure a coherent focus, submissions that do not directly engage with these topics will not be considered.
Timeline
- Abstract submission deadline: 16 October 2026
- Abstract selection announcement: 13 November 2026
- Full Manuscript submission: 1 February 2027
- SI publication: Summer 2027 (individual papers will be published online-first upon acceptance)
Expressions of interest or preliminary inquiries are welcome and may be directed to the guest editors (refer to Frank Mangold, Frank.Mangold@gesis.org). For questions relating to the CCR account creation or submission process, please reach out to ccreditorialteam@gmail.com.
