Media Effects on Attitudes towards Immigration. A Multilevel, Multimethod, Longitudinal Comparative Study

• Applicant: Professor Dr. Jens Wolling
• Subject Area: Communication Sciences
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 454164884

After decades of internal and external migration, European societies are more ethnically and culturally diverse than ever before in contemporary history. This trend is publicly and politically disputed and has the potential to intensify political polarization and radicalization and thereby threaten liberal democracies. Many people lack appreciable personal experience with migration. For most citizens indirect experiences – mainly transferred by the media – are the most relevant source for attitude formation on the issue. Therefore, the media plays an important role in shaping the public opinion. Recent studies on the subject prove the existence of media influences on beliefs and attitudes towards migration. However, there is still plenty of room for theoretical and empirical improvements. Building on intergroup threat theory and the attribute agenda setting approach, we will provide a methodologically sound investigation on the influence of media in shaping beliefs about and attitudes on immigration in European societies. We do that by exploring how variations in media coverage relate to different dimensions of beliefs about immigration by nationals and how these beliefs influence the attitudes towards different forms of immigration. To achieve this goal, we analyze media effects in a longitudinal (18 years) and international comparative perspective (6 countries), applying a multimethod (3 methods) and multilevel (2 levels, individual and country) design. We will use two publicly available data sources: a) survey data from the European Social Survey (ESS)) and b) exogenous aggregate data from Eurostat, Genesis, and Eurobarometer. Furthermore, we will c) gather media content data by automated content analyses of newspaper articles. Therefore, we will translate articles published in German, Spanish, and French to English by neural machine-translation (NMT) tool, and apply an English codebook on all newspapers from all countries. Supervised machine learning (SML) technique to perform attribute analysis will be used. The results will add to our understanding of the influence of media coverage in a contested social sphere. Moreover, the research will provide an empirical test of macro-level media effects on individuals in the context of varying temporal and national conditions that are hypothesized to influence both the media coverage and individual beliefs and attitudes. From a theoretical perspective, the findings will help to refine the proposed model of contextualized media effects.

• DFG Programme: Research Grants