Cognitive Research in Political Studies

Cognitive Research in Political Studies

Algorithmic Society and the Reconfiguration of Political Cognition in the Islamic Republic of Iran: A Conceptual Model

Document Type : Original Article

Author
Assistant Professor of Political Science, Imam Hossein University
Abstract
The expansion of artificial intelligence and data-driven platforms has transformed the mechanisms of political cognition and information. Despite extensive research, a comprehensive model explaining this transformation in Iran remains lacking. Accordingly, this study aims to develop a model of political cognitive reconfiguration in the Islamic Republic of Iran. The study is applied-developmental in purpose and qualitative in approach, employing a systematic literature review based on the PRISMA framework and conceptual network analysis. The research population comprised credible domestic and international scholarly studies and documents addressing the algorithmic society, artificial intelligence, data governance, algorithmic power, and political cognition; sources meeting the inclusion criteria were selected and analyzed as the research sample. Data were analyzed using MAXQDA, VOSviewer, and Gephi. The findings indicate that the algorithmic society, algorithmic power, the attention economy and platform-based media, and cognitive biases constitute the principal dimensions of political cognitive reconfiguration and interact within a multilevel conceptual network. Based on these findings, the proposed conceptual model demonstrates that political cognition in the age of artificial intelligence emerges from the dynamic interaction among humans, algorithms, data, and power structures. In the Islamic Republic of Iran, this process is shaped simultaneously by governance structures, cyberspace regulation, infrastructural conditions, and patterns of digital political participation.
Keywords

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Volume 3, Issue 2 - Serial Number 9
Summer Quarterly
Summer 2026
Pages 1-35

  • Receive Date 10 July 2026
  • Revise Date 12 August 2026
  • Accept Date 12 September 2026