The 2nd International Workshop on Social Science Meets Web Data: Reproducible and Reusable Computational Approaches (R2CASS)
The R2CASS workshop advances computational reproducibility in social science, often relying on digital behavioral data from social media platforms. It makes the workshop relevant to computer scientists, social scientists, behavioral analysts, and digital policy makers to unite, discuss, and suggest improvements for making computational social science research more credible, reproducible, and impactful. The 2nd edition of the workshop builds on the 1st edition of R2CASS1 in ICWSM2 in Denmark, assessing the available resources as guidelines and practices, checklists, and templates along with the services that facilitate their adoption. The workshop presents Methods Hub3 as a case for realizing these efforts by utilizing the available resources and integrating with the aligned services. Acknowledging the contributions of the 1st R2CASS edition in shaping and improving Methods Hub as a community-driven platform, we plan to continue with the brainstorming and discussion-based format for the workshop. The participants will engage in a collaborative hands-on session to perform a computational reproducibility task on social science use cases on the Methods Hub platform. It will set the stage to gather feedback and discuss the pains and gains of lowering the computational reproducibility barriers, enhancing scalability and transparency in offering easy-to-adopt solutions. By linking the methodological rigor with substantive inquiry, the workshop invites research contributions related to computational reproducibility and social science.
The R2CASS objectives are;
To evaluate and critically assess the current computational reproducibility practices in social science research.
To analyze the availability, accessibility, and consistency of digital behavioral data with the associated security and privacy concerns.
To overcome technical barriers hindering computational reproducibility for higher adaptability.
Building resources, e.g., guidelines, frameworks, checklists, and tools that specifically address the computational reproducibility challenges in social science research.
Practically demonstrating the efficiency and viability of the available resources through a computational reproducibility platform such as Methods Hub.
To engage the community in discussing, assessing, and cocreating the computational reproducibility resources.
To build a dynamic interdisciplinary community integrating meta-science, social science, and computer science researchers, promoting broader adoption of reproducibility standards.
Fakhri Momeni (GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany)
Arnim Bleier (GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany)
Danilo Dessi (University of Sharjah, Sharjah, United Arab Emirates)
Muhammad Taimoor Khan (GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany)