计算社会科学：Reddit语料库中的大规模缺失数据（Computational Social Science：Large-Scale Missing Data in a Widely-Published Reddit Corpus）
Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus
摘要：
随着研究人员使用计算方法来大规模研究复杂的社会行为，这种计算社会科学的有效性取决于数据的完整性。2015年7月2日，Jason Baumgartner发布了一个数据集，广告中包括“所有公开的Reddit评论”，该数据集很快在Bittorrent和互联网档案馆上分享。这些数据很快成为许多学术论文的基础，主题包括机器学习、社会行为、政治、突发新闻和仇恨言论。我们在这个数据集中发现了大量的空白和局限性，这可能会导致该研究结果的偏差。在本文中，我们记录了数据集，数据集中大量缺失的观察结果，以及这些差距对研究有效性的风险。总之，我们确定了考虑用户历史或网络分析的研究的高风险，比较参与计数的研究的中等风险，以及避免对Reddit上的行为和参与做出代表性声明的机器学习研究的风险较小。
英文摘要：
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.
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