How does media coverage of electoral campaigns distinguish parties and candidates in emerging democracies? To answer, we present a multi-step procedure that we apply in South Africa. First, we develop a theoretically informed classification of election coverage as either “narrow” or “broad” from within the entire corpus of news coverage during an electoral campaign. Second, to deploy our classification scheme, we use a supervised machine learning approach to classify news as “broad,” “narrow,” or “not election-related.” Finally, we combine our supervised classification with a topic modeling algorithm (BERTTopic) that is based on Bidirectional Encoder Representations from Transformers (BERT), in addition to other statistical and machine learning methods. The combination of our classification scheme, BERTTopic, and associated methods allows us to identify the main election-related themes among broad and narrow election-related coverage, and how different candidates and parties are associated with these themes. We provide an in-depth discussion of our method for interested users in the social sciences. We then apply our proposed techniques on text from nearly 100,000 news articles during South Africa’s 2014 campaign and test our empirical predictions about candidate and party coverage of corruption, the economy, health, public infrastructure, and security. The application of our method highlights a nuanced campaign environment in South Africa; candidates and parties frequently receive distinct and substantive coverage on key campaign themes.

中文翻译：

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报道竞选活动：衡量候选人和政党新闻报道差异的计算工具及其应用于新兴民主国家

媒体对竞选活动的报道如何区分新兴民主国家的政党和候选人？为了回答这个问题，我们提出了一个在南非应用的多步骤程序。首先，我们从竞选期间的整个新闻报道范围内对选举报道进行“狭义”或“广泛”分类。其次，为了部署我们的分类方案，我们使用监督机器学习方法将新闻分类为“广泛”、“狭义”或“与选举无关”。最后，我们将监督分类与基于 Transformers 双向编码器表示 (BERT) 的主题建模算法 (BERTTopic) 以及其他统计和机器学习方法相结合。我们的分类方案、BERTTopic 和相关方法的结合使我们能够在广泛和狭义的选举相关报道中识别主要的选举相关主题，以及不同的候选人和政党如何与这些主题相关联。我们为社会科学感兴趣的用户提供对我们方法的深入讨论。然后，我们将我们提出的技术应用于南非 2014 年竞选期间近 100,000 篇新闻文章的文本，并测试我们对候选人和政党对腐败、经济、健康、公共基础设施和安全的报道的实证预测。我们方法的应用凸显了南非微妙的竞选环境；候选人和政党经常收到关于关键竞选主题的独特和实质性报道。