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

德国选举中的负面竞选活动：衡量、动态和决定因素

• 批准号: 441574527
• 负责人: Professor Dr. Jürgen Maier
• 金额: --
• 依托单位: Abteilung Politikwissenschaft
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/441574527?language=en
• 关键词: Negative; Campaigning; German; elections; Measurement

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.

近年来，对负面竞选（NC）（即对政治对手的口头攻击）的研究引起了相当大的关注。研究表明，NC 可能会给民主带来功能失调的后果（例如两极分化、对政治的信任度下降）。这尤其适用于对对手进行人身攻击或以不文明方式攻击的消极竞选策略。然而，美国以外地区很少对 NC 的原因和影响进行分析。该项目调查了德国联邦和州一级竞选活动中使用 NC 的决定因素。目的是测试负面竞选沟通的综合模型 (IMNCC)，其中包括微观层面的解释因素（使用 NC 的政客）和宏观因素（竞选期间的结构条件）。除了检验 NC 是理性成本效益考虑的结果这一经典假设外，IMNCC 还检验其他解释的可行性（例如价值观的作用、对 NC 的态度、个性、形象管理）。为此，我们首先使用候选人调查中的自我报告来调查候选人对 NC 的使用和评价。我们借鉴了 2013 年、2017 年和 2021 年联邦选举德国纵向选举研究 (GLES) 的一部分进行的候选人研究。此外，测试我们的综合理论模型需要新变量的操作化。因此，我们将通过邮寄和在线调查的方式采访所有参加 2021 年巴登-符腾堡州、梅克伦堡-前波美拉尼亚州、莱茵兰-普法尔茨州和萨克森-安哈尔特州议会选举的相关政党候选人（N~2,400）。为了衡量 NC 的实际使用情况，我们收集了上述选举中候选人的整个 Twitter 交流。我们手工编写了一个高质量的训练数据集，作为大规模检测 NC 的机器学习模型的输入。与政党宣言等既定来源相比，推特数据可以在更细粒度的层面上（单个候选人）获得，并且数量更大（竞选期间的数十万个帖子，以跨政党的标准化形式）。这使得在集成模型中同时检验微观和宏观层面的各种理论解释成为可能。两个数据源将在候选人级别链接并使用多元回归分析进行分析。我们将根据实证结果修改负面竞选传播的综合模型。因此，该项目为更好地理解 NC 的决定因素做出了重要的理论和实证贡献：(1) 随着时间的推移，(2) 在不同的联邦层面，(3) 取决于政客的不同个人特征。