Who are they talking about? Detecting mentions of social groups in political texts with supervised learning

Politicians appeal to social groups to court their electoral support. However, quantifying which groups politicians refer to, claim to represent, or address in their public communication presents researchers with challenges. We propose a novel supervised learning approach for extracting group mentions in political texts. We first collect human annotations to determine the exact text passages that refer to social groups. We then fine-tune a Transformer language model for contextualized supervised classification at the word level. Applied to unlabeled texts, our approach enables researchers to automatically detect and extract word spans that contain group mentions. We illustrate our approach in three applications, generating new empirical insights how British parties use social groups in their rhetoric. Our methodological innovation allows to detect and extract mentions of social groups from various sources of texts, creating new possibilities for empirical research in political science.