We welcome submissions on any topic in the field of computational social science, including
- work that advances methods and approaches for computational social science,
- data-driven work that describes and discovers social and cultural phenomena or explains and estimates relations between them and other things,
- theoretical work that generates new insights, connections and frameworks for computational social science research.
Researchers across disciplines, faculty, graduate students, industry researchers, policy makers, and nonprofit workers are all encouraged to submit computational data- driven research and innovative computational methodological or theoretical contributions on social phenomena for consideration.
Topics include but are not limited to:
- Network analysis of social systems
- Large-scale social experiments
- Empirically calibrated simulation models
- Large language models for social research
- Text analysis and natural language processing (NLP) of social phenomena
- Analysis of meaning through computational analysis of text, images, audio, video, etc.
- Computational methods to map and study cultural patterns and dynamics
- Agent-based or other simulation of social phenomena
- Methods and issues of social data collection
- Images as social data
- Causal inference and machine learning
- Methods and analyses of biased, selective, or incomplete observational social data
- Integration and triangulation of multi-modal social and cultural data
- Methods and analyses for social information / digital communication dynamics
- Neural network methods for social analysis and policy exploration
- Reproducibility in computational social science research
- Theoretical discussions/concepts in computational social science
- Ethics of computational research on human behavior
- Issues of inclusivity in computational social science
- Methods and analyses of algorithmic accountability and trustworthiness
- Novel digital data and/or computational analyses for addressing societal challenges
- Social news curation and collaborative filtering
- Building and evaluating socio-technical systems
- Methods and analyses of integrated human-machine decision-making
- Science and technology studies approaches to computational science work
- Infrastructure to facilitate industry/academic cooperation in computational social science
- Computational social science research in industry, government, and philanthropy
- Practical problems in computational social science