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

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

Caveat emptor, computational social science: Large-scale missing data in a widely-published Reddit corpus

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

Gaps are not evenly distributed across communities.

<p>The total historical counts of comments per community comments are mildly correlated with the number of dangling references, while submissions are not very correlated with the number of dangling references.</p

Varied measures of missing submissions per month.

<p>Medium blue circles denote the percent of submissions missing for each month of data, bright blue squares denote the average percent of missing submissions to date, and dark blue stars denote the cumulative total percent of missing submissions to date.</p

Totals for missing data in the Baumgartner dataset.

<p>Totals for missing data in the Baumgartner dataset.</p