Call for Papers:

Algorithms, AI, and Computational Communication: Frontiers in Agent-Based Research
Special issue of the journal Communication and Change
https://link.springer.com/journal/44382/updates/27853888

Deadline for submission of abstracts: September 1, 2026

Editors:
Jinhui Li, Professor at the School of Journalism and Communication, Jinan University, China
Wen Shi, Associate Professor at the School of Journalism and Communication, Jinan University, China
Mario Haim, Professor and Chair of Communication Studies at the Department of Media and Communication at LMU Munich, Germany

THEME:

As communication environments have become increasingly algorithmically mediated, personalized, and opaque, traditional research approaches face growing challenges in capturing the dynamics of contemporary media systems. Platform algorithms now shape information exposure, news visibility, public discourse, and social interaction through continuously adaptive recommendation and ranking systems. Yet many of these processes remain difficult to observe directly, reproduce experimentally, or explain theoretically.

Against this backdrop, agent-based approaches have emerged as an important frontier in journalism and communication research. By constructing simulated entities (i.e., agents) capable of interacting within digital environments, scholars are now able to investigate algorithmic systems in ways previously unavailable through conventional surveys, experiments, or content analyses (Shi et al., 2026). These approaches commonly include both Agent-Based Testing (ABT) — which deploys simulated users to audit and measure real-world algorithmic systems (e.g., Haim, 2020; Shi & Li, 2025) — and Agent-Based Modeling (ABM) — which simulates communication dynamics within virtual environments to explore emergent social phenomena (e.g., Sohn, 2022; Waldherr & Wettstein, 2019; Zhong et al., 2023).

These developments also invite broader reflections on the epistemological position of agent-based approaches within social science research. Questions remain regarding how ABT and ABM relate to established research paradigms (Schwabl et al., 2024), whether they constitute new forms of computational experimentation, and how they may be integrated with existing qualitative, quantitative, and mixed-method approaches. Such discussions are particularly important in journalism and communication research, as the field increasingly aims to integrate computational methods with theoretically grounded analyses of media, technology, and society.

Recent advances in generative AI and large language models (LLMs) further expand the possibilities of agent-based research (e.g., Huang & Zhang, 2026). AI-driven agents are increasingly capable of adaptive reasoning, multimodal interpretation, strategic interaction, and socially situated behavior. Such developments create new opportunities for communication scholars to examine how algorithms, users, platforms, and AI systems co-evolve within complex digital ecosystems. At the same time, they raise critical methodological, theoretical, and ethical questions regarding explainability, validity, bias, transparency, governance, and the future of computational communication research.

This special issue seeks to advance empirical, methodological, and theoretical discussions surrounding agent-based approaches in journalism and communication research. We welcome empirical studies that apply ABT, ABM, and combinations of generative AI and agent-based approaches to substantive communication problems across diverse domains, including journalism studies, health communication, science communication, platform studies, or digital media research. We also welcome contributions that develop, reflect upon, or critically evaluate agent-based methods.

Possible topics include, but are not limited to:

Algorithmic systems and digital media environments

• Human–algorithm interaction and feedback loops in digital environments
• AI-mediated journalism, automated news production, and synthetic media ecosystems
• Algorithmic bias, digital inequalities, and platform governance

Communication processes and social dynamics

• Communication processes, public discourse, and social interaction in digital environments
• Information diffusion and networked communication dynamics
• Revisiting relevant communication theories in the context of emerging media technologies

Methodological developments and reflection

• Methodological developments in Agent-Based Testing (ABT) and Agent-Based Modeling (ABM)
• Integration of ABT, ABM, and generative AI in communication research
• Ethical, epistemological, and Open Science challenges in agent-based communication research

This special issue particularly welcomes empirical studies, while also inviting methodological and theoretical review papers. Interdisciplinary and cross-cultural perspectives are especially encouraged. We strongly support open science practices, including transparency in data, code, simulation design, and research materials.

SUBMISSION INSTRUCTIONS:

Please submit an abstract (1,500 words) to cac@fudan.edu.cn by September 1, 2026. Authors of accepted abstracts will be notified by October 1, 2026, with full papers (6,000 – 10,000 words) submitted via the journal’s online submission system by January 15, 2027.

The special issue is expected to be published online by the end of 2027.

For any inquiries about the special issue, please contact the special issue editor, Jinhui Li (lijinhuihust@gmail.com).

REFERENCES:

Haim, M. (2020). Agent-based testing: An automated approach toward artificial reactions to human behavior. Journalism Studies, 21(7), 895-911.

Huang, M., & Zhang, Z. K. (2026). Influence in Motion: Tracing Persuasive Dynamics via Multi‐Agent Networks. Computational Communication Research, 8(2), 1.

Schwabl, P., Haim, M., & Unkel, J. (2024). Aligning agent-based testing (ABT) with the experimental research paradigm: A literature review and best practices. Journal of Computational Social Science, 7(2), 1625-1644.

Shi, W., & Li, J. (2025). News diversity under algorithms: the effects of pre-selected and self-selected personalization on Chinese TikTok (Douyin). Digital Journalism, 13(7), 1190-1208.

Shi, W., Zhao, H., Huang, Y., & Li, J. (2026). Agent-based approaches in studying algorithm-mediated communication: a methodological review. Communication and Change, 2(1), 2.

Sohn, D. (2022). Spiral of silence in the social media era: A simulation approach to the interplay between social networks and mass media. Communication Research, 49(1), 139-166.

Waldherr, A., & Wettstein, M. (2019). Bridging the gaps: Using agent-based modeling to reconcile data and theory in computational communication science. International Journal of Communication, 13, 3976-3999.

Zhong, Q., Hilbert, M., & Frey, S. (2023). Breaking the structural reinforcement: An agent-based model on cultural consumption and social relations. Social Science Computer Review, 41(3), 848-870.