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Exploring Two Decades of Parliamentary Speeches on the Use of Narratives

Political scientists are interested in changes in political discourse over time. However, the topics of interest, such as the changes in support for or understanding of certain narratives, are often ill-defined and require deliberation, which prevents most lexical or metadata-based methods of temporal aggregation. To enable a diachronic analysis, we propose to model such settings as a series of binary document classification tasks – which current reasoning LLMs can adequately solve – and aggregate the decisions into a temporal signal. Specifically, we propose to use LLMs to classify if a parliament speech is in support of either of two narratives, and we use the monthly count of positives per narrative to track the support over time. We show that the classification is sufficiently accurate and use it to create detailed time series data showing support for the selected narratives in speeches given in the European Parliament from 2006 to 2023. The method is developed in close collaboration with political scientists and is considered an ideal starting point for diachronic analyses of political decision-making processes by domain experts.