  One of the first steps in many text-based social science studies is to
retrieve documents that are relevant for the analysis from large corpora of
otherwise irrelevant documents. The conventional approach in social science to
address this retrieval task is to apply a set of keywords and to consider those
documents to be relevant that contain at least one of the keywords. But the
application of incomplete keyword lists risks drawing biased inferences. More
complex and costly methods such as query expansion techniques, topic
model-based classification rules, and active as well as passive supervised
learning could have the potential to more accurately separate relevant from
irrelevant documents and thereby reduce the potential size of bias. Yet,
whether applying these more expensive approaches increases retrieval
performance compared to keyword lists at all, and if so, by how much, is
unclear as a comparison of these approaches is lacking. This study closes this
gap by comparing these methods across three retrieval tasks associated with a
data set of German tweets (Linder, 2017), the Social Bias Inference Corpus
(SBIC) (Sap et al., 2020), and the Reuters-21578 corpus (Lewis, 1997). Results
show that query expansion techniques and topic model-based classification rules
in most studied settings tend to decrease rather than increase retrieval
performance. Active supervised learning, however, if applied on a not too small
set of labeled training instances (e.g. 1,000 documents), reaches a
substantially higher retrieval performance than keyword lists.
