FairGer: Using NLP to Measure Support for Women and Migrants in 155 Years of German Parliamentary Debates
We measure support with women and migrants in German political debates over the last 155 years. To do so, we (1) provide a gold standard of 1205 text snippets in context, annotated for support with our target groups, (2) train a BERT model on our annotated data, with which (3) we infer large-scale trends. These show that support with women is stronger than support with migrants, but both have steadily increased over time. While we hardly find any direct anti-support with women, there is more polarization when it comes to migrants. We also discuss the difficulty of annotation as a result of ambiguity in political discourse and indirectness, i.e., politicians' tendency to relate stances attributed to political opponents. Overall, our results indicate that German society, as measured from its political elite, has become fairer over time.
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