Negative Campaigning in German elections: Measurement, Dynamics, and Determinants

• Applicants: Professor Dr. Jürgen Maier; Professorin Dr. Corinna Oschatz; Professor Dr. Sebastian Stier
• Subject Area: Political Science ; Communication Sciences
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 441574527

Research on negative campaigning (NC) - i.e. verbal attacks on political opponents - has gained considerable attention in recent years. Studies suggest that NC can have dysfunctional consequences for democracy (e.g. polarization, declining trust in politics). This applies in particular to negative campaign strategies that attack an opponent personally or in an uncivilized manner. However, the causes and effects of NC have rarely been analyzed outside of the United States. This project investigates the determinants of the use of NC in German election campaigns at the federal and state level. The aim is to test an integrated model of negative campaign communication (IMNCC) that includes explanatory factors at the micro level (politicians who use NC) and macro factors (structural conditions during election campaigns). In addition to examining the classical assumption that NC is the result of rational cost-benefit considerations, the IMNCC also examines the viability of other explanations (e.g. the role of values, attitudes towards NC, personality, image management). For this purpose, we first use self-reports from candidate surveys to investigate the use and evaluation of NC by candidates. We draw on the candidate studies conducted as part of the German Longitudinal Election Study (GLES) for the 2013, 2017 and 2021 federal elections. In addition, testing our comprehensive theoretical model requires the operationalization of new variables. Therefore, we will interview all candidates of the relevant political parties who will take part in the 2021 Landtag elections in Baden-Württemberg, Mecklenburg-Western Pomerania, Rhineland-Palatinate and Saxony-Anhalt (N~2,400) in a postal and an online survey. In order to measure the actual use of NC, we collect the entire Twitter communication of the candidates in the above-mentioned elections. We hand code a high-quality training dataset that serves as the input for machine learning models for the detection of NC at a large scale. In contrast to established sources such as party manifestos, Twitter data is available at a more fine-grained level (of the individual candidate) and in larger numbers (hundreds of thousands of posts during an election campaign, in standardized form across parties). This makes it possible to examine various theoretical explanations at the micro and macro levels simultaneously in integrated models. Both data sources will be linked at the candidate level and analyzed using multiple regression analysis. We will revise the integrated model of negative campaign communication based on the empirical findings. The project thus makes an essential theoretical and empirical contribution towards a better understanding of the determinants of NC (1) over time, (2) at different federal levels and (3) depending on different individual characteristics of politicians.

• DFG Programme: Research Grants
• International Connection: Netherlands