Call for papers: R2CASS2026 – The 2nd Workshop on Social Science Meets Web Data: Reproducible and Reusable Computational Approaches (R2CASS) co-located with The Web Conference 2026 

Website: https://sites.google.com/view/r2cass/home

The 2nd Workshop on Social Science Meets Web Data: Reproducible and Reusable Computational Approaches (R2CASS2026) will be held in conjunction with "The ACM Web Conference 2026" in Dubai, United Arab Emirates, on April 13-14th, 2026.

Artificial Intelligence (AI) based models have a growing influence in social science research for analyzing behavioral patterns on social media and other digital platforms. Computational reproducibility has been a concern with these models as they deal with living data that is likely to change, contain personal information, and, therefore, raises ethical restrictions on their use. The workshop aims to bring together researchers and practitioners to discuss and exchange ideas towards potential interdisciplinary collaborations from computer science, social science, meta-science, and other related disciplines. The workshop also provides a platform to present and critically analyze the ideas for developing and improving computationally reproducible social science methods and the underlying standards defined as templates, checklists, and guidelines. The participants will focus on the open challenges and propose methodologies in computational reproducibility for social science towards improving research transparency, reproducibility, and reusability.

The workshop calls for theoretical and practical research contributions that employ qualitative, quantitative, and analytical approaches, including full papers (7 - 8 pages), short papers (4 pages), resource papers, position papers, and posters on the topics (but not limited to): 

• Data and software management in machine learning and natural language processing-driven studies
• Computational methods on sentiment analysis, bias analysis, toxicity, and sexism detection 
• Text categorization and topic analysis 
• Digital behavior analysis on social media and other digital platforms 
• Computational reproducibility checklists and workflows in social science 
• FAIR principles in ML research for social science 
• Open Science applications and reproducibility challenges 
• Metadata standards for ML/NLP research 
• Tools for replicating complex social science models using web-based data 
• Integration of open-source ML/NLP tools for web data analysis

The workshop will be held in person, and at least one author of the accepted papers will be required to register and present the paper in the workshop. The papers that are previously published or are presently under review at other venues are not eligible for submission. 

Papers accepted by the workshop can be included in the Companion Proceedings of the Web Conference 2026, subject to meeting the ACM open-access, formatting guidelines, and camera-ready timeline as provided and observed by the ACM Web Conference.  

Submission guidelines

Formatting Requirements. Submissions must be written in English, in double-column format, and must adhere to the ACM template and format (also available in Overleaf). Word users may use the Word Interim Template. The recommended setting for LaTeX is:

\documentclass[sigconf, review]{acmart}

Submission Site. Submissions must be made through the EasyChair system (website link above)

Authorship. Submissions are not anonymous; hence, authors should list their names and affiliations.

Reviewing. Submissions will be peer-reviewed by the program committee of the workshop for originality, significance, quality, and clarity. The review process will be single-blind.

Please visit the ACM Web Conference (https://www2026.thewebconf.org/) for further information on formatting and guidelines.

Timeline

paper submission: December 18, 2025

paper notification: January 13, 2026

paper camera-ready: February 2, 2026 

 Organizers 

Fakhri Momeni, GESIS – Leibniz Institute for the Social Sciences, Germany 

Arnim Bleier, GESIS – Leibniz Institute for the Social Sciences, Germany 

Danilo Dessi, University of Sharjah, United Arab Emirates

Muhammad Taimoor Khan, GESIS – Leibniz Institute for the Social Sciences, Germany