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Rhetoric, or the art of persuasion, is a key element of politics. Politicians seek to persuade the electorate that they are best suited for an office, to rally populations around causes for military intervention, or to make citizens follow measures imposed by government in times of crisis. As numerous studies show, the persuasiveness of political communication not only derives from what is said, or the substance of political communication, but also from the manner in which it is articulated, that is, the rhetorical strategies employed by political actors. It is through these strategies that political actors tailor their communication to the characteristics of different audiences and different political contexts.
Existing studies of rhetorical strategies in political communication predominantly use qualitative approaches and mostly focus on individual or small numbers of cases. Although this research has generated many important insights, the use of qualitative research designs makes it hard to compare rhetorical strategies across countries and, in particular, to identify causal factors that influence the choice of rhetorical strategies. Research employing quantitative approaches, on the other hand, has so far only focused on individual elements or individual rhetorical appeals, which leads to incomplete accounts of rhetorical strategies. Overcoming these limitations, this paper introduces a quantitative, three-dimensional measure of three rhetorical appeals of ethos (authority), logos (reason), and pathos (emotion). The added value of this measure is that enables comparisons of rhetorical strategies in political communication across different types of political actors, across states and across different points in time.
To illustrate this added value for the analysis of rhetorical strategies, our paper uses the case of political crisis communication during the first phase of the COVID-19 pandemic. It draws on a data set of 1109 press conferences with 433 speakers in 17 OECD countries and three US-American states and employs automated methods of text analysis.