The rise of populism concerns many political scientists and practitioners,
yet the detection of its underlying language remains fragmentary. This paper
aims to provide a reliable, valid, and scalable approach to measure populist
stances. For that purpose, we created an annotated dataset based on
parliamentary speeches of the German Bundestag (2013 to 2021). Following the
ideational definition of populism, we label moralizing references to the
virtuous people or the corrupt elite as core dimensions of populist language.
To identify, in addition, how the thin ideology of populism is thickened, we
annotate how populist statements are attached to left-wing or right-wing host
ideologies. We then train a transformer-based model (PopBERT) as a multilabel
classifier to detect and quantify each dimension. A battery of validation
checks reveals that the model has a strong predictive accuracy, provides high
qualitative face validity, matches party rankings of expert surveys, and
detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses
of how German-speaking politicians and parties use populist language as a
strategic device. Furthermore, the annotator-level data may also be applied in
cross-domain applications or to develop related classifiers.

通过创建一个基于德国联邦议院演讲的标注数据集，训练一种基于变压器的模型（PopBERT）作为多标签分类器，可以可靠、有效且可扩展地测量民粹主义立场及其语言特征，从而实现对德语政治家和政党使用民粹主义语言作为战略工具的动态分析。

PopBERT. 在德国联邦议院中检测民粹主义及其承载意识形态

PopBERT. Detecting populism and its host ideologies in the German  Bundestag

This guide introduces Large Language Models (LLM) as a highly versatile text
analysis method within the social sciences. As LLMs are easy-to-use, cheap,
fast, and applicable on a broad range of text analysis tasks, ranging from text
annotation and classification to sentiment analysis and critical discourse
analysis, many scholars believe that LLMs will transform how we do text
analysis. This how-to guide is aimed at students and researchers with limited
programming experience, and offers a simple introduction to how LLMs can be
used for text analysis in your own research project, as well as advice on best
practices. We will go through each of the steps of analyzing textual data with
LLMs using Python: installing the software, setting up the API, loading the
data, developing an analysis prompt, analyzing the text, and validating the
results. As an illustrative example, we will use the challenging task of
identifying populism in political texts, and show how LLMs move beyond the
existing state-of-the-art.

该文介绍了大型语言模型（LLM）作为社会科学中一种高度多功能的文本分析方法，以及它们对文本注释、分类、情感分析和批判性话语分析等广泛的文本分析任务的应用。通过使用 Python，该文提供了针对具有有限编程经验的学生和研究人员的简单介绍，指导他们如何在自己的研究项目中使用 LLMs 进行文本分析，并提供了最佳实践建议。以政治文本中的民粹主义识别为例子，展示了 LLMs 如何超越现有的最新技术。