Small data problems in political research: a critical replication study
This is an incremental study that highlights critical data size issues for political researchers and practitioners using machine learning, cautioning against overreliance on small datasets.
The paper tackles the problem of small data in political research by replicating a prior study on tweet classification for organizational reputation, finding that small data causes high model sensitivity to train-test splits and extreme sparsity from preprocessing, undermining the original conclusions.
In an often-cited 2019 paper on the use of machine learning in political research, Anastasopoulos & Whitford (A&W) propose a text classification method for tweets related to organizational reputation. The aim of their paper was to provide a 'guide to practice' for public administration scholars and practitioners on the use of machine learning. In the current paper we follow up on that work with a replication of A&W's experiments and additional analyses on model stability and the effects of preprocessing, both in relation to the small data size. We show that (1) the small data causes the classification model to be highly sensitive to variations in the random train-test split, and that (2) the applied preprocessing causes the data to be extremely sparse, with the majority of items in the data having at most two non-zero lexical features. With additional experiments in which we vary the steps of the preprocessing pipeline, we show that the small data size keeps causing problems, irrespective of the preprocessing choices. Based on our findings, we argue that A&W's conclusions regarding the automated classification of organizational reputation tweets -- either substantive or methodological -- can not be maintained and require a larger data set for training and more careful validation.