# Computational Reproducibility in Computational Social Science

arXiv: https://arxiv.org/abs/2307.01918 · HTML: https://mlsanity.com/paper/2307.01918/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2023.

## Built on by (most cited first, 4 of 4)

- [Automating Computational Reproducibility in Social Science: Comparing Prompt-Based and Agent-Based Approaches](https://mlsanity.com/paper/2602.08561/) (2026)
- [The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance](https://mlsanity.com/paper/2602.02100/) (2026)
- [Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models](https://mlsanity.com/paper/2505.02858/) (2025)
- [Mapping Political-Elite Networks in Europe with a Multilingual Joint Entity-Relation Extraction Pipeline](https://mlsanity.com/paper/2606.27347/) (2026)

## Abstract (arXiv)

Replication crises have shaken the scientific landscape during the last decade. As potential solutions, open science practices were heavily discussed and have been implemented with varying success in different disciplines. We argue that computational-x disciplines such as computational social science, are also susceptible for the symptoms of the crises, but in terms of reproducibility. We expand the binary definition of reproducibility into a tier system which allows increasing levels of reproducibility based on external verfiability to counteract the practice of open-washing. We provide solutions for barriers in Computational Social Science that hinder researchers from obtaining the highest level of reproducibility, including the use of alternate data sources and considering reproducibility proactively.