Differential Racism in the News: Using Semi-Supervised Machine Learning to Distinguish Explicit and Implicit Stigmatization of Ethnic and Religious Groups in Journalistic Discourse

Abstract

ABSTRACT News coverage plays a crucial role in the formation of attitudes toward ethnic and religious minority groups. On the attitudinal level, it is an established notion that individuals’ explicit and implicit judgments of the same groups can vary. Yet, less is known about the prevalence of implicit group judgments in news coverage. Focusing on a large variety of ethnic and religious minority groups in Germany, the present study sets out to fill this gap. We use semi-supervised machine learning to distinguish explicit and implicit stigmatization of ethnic and religious groups in German journalistic coverage (n = 697,913 articles). Findings suggest that groups that are associated with less wealthy countries, and with culturally more distant countries, face more stigmatization, both explicitly and implicitly. Yet, the data also show that groups associated with Islam and groups with large refugee populations living in the country of study are implicitly, but not explicitly stigmatized in news coverage. We discuss these and other resulting patterns against the backdrop of sociological and psychological intergroup theories and reflect upon their implications for journalism.