Replication Material for \"How Populist Are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning\"

One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information of temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the party landscape while reducing the risk of arbitrary classifications. To illustrate the potential contribution of this score, we use it as a proxy for parties’ levels of populism, analysing average trends in six European countries from the early 2000s for nearly two decades.民粹主义比较研究的核心挑战之一，在于如何在大量政党与国家的内部及相互之间，实现民粹主义的时间与空间维度测量。文本分析（textual analysis）已被证实可满足此类研究需求，而自动化方法则能进一步推动该方向的研究进展。本文提出一种方法：借助监督机器学习（supervised machine learning）对各国政党竞选纲领开展文本分析，以此推导各政党的民粹主义程度评分。我们通过实例展示了该方法的优势：它无需依赖耗费大量资源的人工编码流程，即可针对海量政党与国家完成民粹主义测量，并可提供精准且更新及时的民粹主义时间与空间对比信息。此外，本方法可生成连续化的民粹主义评分，这既能实现对政党格局的更精细化分析，又能降低任意分类的风险。为展示该评分的潜在应用价值，我们将其作为政党民粹主义程度的代理变量，分析了21世纪初以来近20年间6个欧洲国家的平均民粹主义发展趋势。