Replication Data for \"Measuring and understanding parties' anti-elite strategies\"

This repository contains code and data to replicate the analysis in the JOP article \"Measuring and understanding parties' anti-elite strategies\" Paper Abstract: This article presents a new measure and analysis of parties' anti-elite appeals. In order to measure parties' anti-elite appeals we apply crowd-sourced coding, supervised machine learning, and novel cross-lingual transfer learning techniques to parties' Twitter posts. Our dataset records quarterly estimates of parties' anti-elite strategies for 20 countries between 2008 and 2021. Based on these indicators, we analyze whether parties' anti-elite rhetoric reflects the potential costs and benefits of this electoral strategy. We find that mainstream parties use anti-elite rhetoric less frequently when they are more likely to be included in the next governing coalition. When challenger parties do well in the polls they become more anti-elitist. Our article not only contributes to the literature on democratic competition by introducing and applying a new measure of anti-elite strategies, but also outlines a novel, modular and scalable procedure to measure party appeals using social media posts.

本代码仓库包含用于复现发表于《政治学杂志》(Journal of Politics, JOP) 题为《衡量与理解政党反精英战略》的论文中的分析所需的代码与数据。论文摘要如下：
本论文提出了一种全新的衡量方法，并对政党的反精英诉求展开分析。为量化政党的反精英诉求，本研究将众包编码、监督式机器学习以及全新的跨语言迁移学习技术应用于政党的Twitter（推特）发文数据。
本数据集收录了2008年至2021年间20个国家的政党反精英战略的季度估算数据。基于上述指标，本研究探讨政党的反精英言论是否匹配该选举策略的潜在成本与收益。
研究结果显示，主流政党若更有可能加入下一届执政联盟，则其使用反精英言论的频率会更低；而挑战者政党若在民调中表现优异，则会愈发倾向于反精英立场。
本研究不仅通过提出并应用全新的反精英战略衡量方法，拓展了民主竞争领域的相关研究文献，同时还构建了一套新颖、模块化且可扩展的分析流程，可基于社交媒体发文量化政党诉求。