Title: Comparing the relevance of topics in economic journals
Authors: Peter Winker - University of Giessen (Germany) [presenting]
David Lenz - Justus-Liebig University Giessen (Germany)
Abstract: The comparison of information content of different sources is relevant. We present an approach for comparing text corpora, specifically articles in economic journals. The focus is on the development of the relevance of topics over time and its correlation across journals. Similar questions arise in many fields and, if at all, are mostly answered qualitatively. We present a quantitative framework for comparing text corpora using text mining techniques. Paragraph Vector Topic Modeling is applied to identify latent topics in text corpora and time information is utilized to track the evolution of these topics. This allows the comparison of corpus compositions over time. Three comparison methods are evaluated: Treat both text corpora as a single corpus, train a model on one corpus and evaluate the other corpus based on this model and vice versa, and train a model for each corpus and use a matching approach for pairing corresponding topics. For the empirical application, we exploit the corpus of articles published in the Journal of Economics and Statistics and the corpus of articles published in the Review of World Economics, both from 1913 to 1940. We present topic dynamics for both corpora and information on how strong the correlation of these dynamics. Furthermore, the analysis indicates which of the methods presented above is most promising for this type of analysis. We find that the matching approach and the combined corpus approach produce very reasonable results.