Aim & Scope

 

Computational Social Sciences Review (CSSR) is an international, peer-reviewed journal dedicated to advancing data-driven and computational approaches to the study of human behavior, social systems, and digital transformation. As societies become increasingly shaped by digital platforms, algorithmic decision-making, and large-scale digital traces, CSSR provides a scholarly venue for understanding how social realities emerge, evolve, and interact with technology.

Aim

The journal aims to promote high-quality, interdisciplinary research that integrates computational methods with social science theory. CSSR supports methodological innovation, transparent and reproducible analysis, and rigorous empirical studies that contribute to evidence-based understanding of contemporary social phenomena.

Scope

CSSR welcomes theoretical, empirical, methodological, and applied submissions in areas including, but not limited to:

  • Computational Approaches to Social Phenomena: machine learning for social science, NLP-based analysis, social simulation, agent-based modeling, predictive analytics.
  • Social Media and Digital Trace Analysis: online behavior, digital communities, virality, polarization, misinformation, platform dynamics.
  • Network Science and Relational Data: social network analysis, graph modeling, structural patterns, collective dynamics.
  • Digital Society and Algorithmic Governance: digital transformation, public services, platform society, algorithmic fairness, regulation, ethics.
  • Behavioral Data Science: computational psychology, digital well-being, psychoinformatics, technology-mediated behavior.
  • Interdisciplinary Applications: sociology, psychology, political science, communication, public policy, economics, digital humanities, information science, human–technology interaction.

CSSR encourages research that advances theoretical understanding, offers methodological contributions, or provides policy-relevant insights in the digital age. The journal values transparency, open science practices, and the reproducibility of computational research.