Using CATA and Machine Learning to Operationalize Old Constructs in New Ways: An Illustration Using U.S. Governors’ COVID-19 Press Briefings
提出一种结合网络爬虫、计算机辅助文本分析和监督机器学习的方法，用标注数据训练模型预测领导风格，并以美国州长COVID-19新闻发布会为例验证其有效性。
Increased computing power and greater access to online data have led to rapid growth in the use of computer-aided text analysis (CATA) and machine learning methods. Using “big data”, researchers have not only advanced new streams of research, but also new research methodologies. Noting this trend and simultaneously recognizing the value of traditional research methods, we lay out a methodology that bridges the gap between old and new approaches to operationalize old constructs in new ways. With a combination of web scraping, CATA, and supervised machine learning, using labeled ground truth data (i.e., data with known inputs and outputs), we train a model to predict CIP (Charismatic-Ideological-Pragmatic) leadership styles from running text. To illustrate this method, we apply the model to classify U.S. state governors’ COVID-19 press briefings according to their CIP leadership style. In addition, we demonstrate content and convergent validity of the method.