  Natural language processing tools have become frequently used in social
sciences such as economics, political science, and sociology. Many publications
apply topic modeling to elicit latent topics in text corpora and their
development over time. Here, most publications rely on visual inspections and
draw inference on changes, structural breaks, and developments over time. We
suggest using univariate time series econometrics to introduce more
quantitative rigor that can strengthen the analyses. In particular, we discuss
the econometric topics of non-stationarity as well as structural breaks. This
paper serves as a comprehensive practitioners guide to provide researchers in
the social and life sciences as well as the humanities with concise advice on
how to implement econometric time series methods to thoroughly investigate
topic prevalences over time. We provide coding advice for the statistical
software R throughout the paper. The application of the discussed tools to a
sample dataset completes the analysis.
