Improving Reproducibility in Computational Social Science with the Methods Hub - Bleier, Arnim; Kiesel, Johannes; Viehmann, Christina; Münch, Felix Victor; Chan, Chung-hong; Costa da Silva, Raniere; Kathirgamalingam, Ahrabhi; Chang, Po-Chun; Khan, Muhammad Taimoor; Linzbach, Stephan; et al. (Proceedings of the International AAAI Conference on Web and Social Media, 2026)
Computational methods have become central to contemporary social science research, ranging from classical statistical modeling and simulation to large-scale computational social science enabled by organic data. While these developments rely on increasingly complex and compute-intensive analysis pipelines, reproducibility remains a major challenge. The Methods Hub is a community-driven service developed at GESIS to address this gap by enabling reproducible, executable, and well-documented computational methods and tutorials tailored to the needs of social scientists. It combines curated content, persistent identifiers, and seamless integration with interactive execution environments, lowering technical barriers while promoting best practices in open and reproducible science. In our ICWSM 2026 demonstration, we will show how to submit computational methods to the Methods Hub (methodshub.gesis.org) and how the editorial workflow helps make contributors’ work more reusable, executable, and accessible to a wider audience.