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

近年来，对负面竞选活动（NC）-即对政治对手的口头攻击-的研究得到了相当大的关注。研究表明，NC可能对民主产生功能失调的后果（例如两极分化，对政治的信任下降）。这尤其适用于对对手进行人身攻击或以不文明方式进行攻击的负面竞选策略。然而，NC的原因和影响很少在美国以外进行分析。本项目调查的决定因素使用NC在德国的竞选活动在联邦和州一级。其目的是测试一个综合模型的负面竞选传播（IMNCC），其中包括在微观层面的解释因素（政治家谁使用NC）和宏观因素（结构条件在竞选活动）。除了研究NC是理性成本效益考虑的结果这一经典假设外，IMNCC还研究了其他解释的可行性（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的自我报告来调查候选人对NC的使用和评价。我们借鉴了作为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可操作性。因此，我们将通过邮寄和在线调查的方式，采访所有将参加2021年巴登-符滕贝格、梅克伦堡-西波美拉尼亚、莱茵兰-腭骨的和萨克森-安哈尔特州（N~2,400）州议会选举的相关政党候选人。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通信。我们手工编写了一个高质量的训练数据集，作为机器学习模型的输入，用于大规模检测NC。与政党宣言等既定来源相比，Twitter数据的粒度更细（单个候选人），数量更大（竞选期间数十万个帖子，以标准化的形式跨越政党）。这使得有可能在综合模型中同时在微观和宏观层面上审查各种理论解释。这两个数据来源将在候选人一级联系起来，并使用多元回归分析进行分析。本研究将根据实证结果修正负面竞选传播的整合模式。因此，该项目为更好地理解NC的决定因素（1）随着时间的推移，（2）在不同的联邦级别和（3）取决于政治家的不同个人特征做出了重要的理论和经验贡献。

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.