Overcoming Language Barriers: Assessing the Potential of Machine Translation and Topic Modeling for the Comparative Analysis of Multilingual Text Corpora

Abstract

This study assesses the potential of topic models coupled with machine translation for comparative communication research across language barriers. From a methodological point of view, the robustness of a combined approach is examined. For this purpose the results of different machine translation services (Google Translate vs. DeepL) as well as methods (full-text vs. term-by-term) are compared. From a substantive point of view, the integratability of the approach into comparative study designs is tested. For this, the online discourses about climate change in Germany, the United Kingdom, and the United States are compared. First, the results show that the approach is relatively robust and second, that integration in comparative study designs is not a problem. It is concluded that this as well as the relatively moderate costs in terms of time and money makes the strategy to couple topic models with machine translation a valuable addition to the toolbox of comparative communication researchers.