Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus

TL;DR

This paper examines significant data gaps in a widely-used Reddit dataset, highlighting potential biases and risks to research validity in computational social science studies.

Contribution

It documents the dataset's missing data issues and analyzes their impact on research validity, providing critical insights for future studies using this data.

Findings

Substantial missing observations in the dataset.

Risks to research involving user histories and network analysis.

Moderate risks to participation count comparisons.

Abstract

As researchers use computational methods to study complex social behaviors at scale, the validity of this computational social science depends on the integrity of the data. On July 2, 2015, Jason Baumgartner published a dataset advertised to include ``every publicly available Reddit comment'' which was quickly shared on Bittorrent and the Internet Archive. This data quickly became the basis of many academic papers on topics including machine learning, social behavior, politics, breaking news, and hate speech. We have discovered substantial gaps and limitations in this dataset which may contribute to bias in the findings of that research. In this paper, we document the dataset, substantial missing observations in the dataset, and the risks to research validity from those gaps. In summary, we identify strong risks to research that considers user histories or network analysis, moderate risks to research that compares counts of participation, and lesser risk to machine learning research that avoids making representative claims about behavior and participation on Reddit.