Using CATA and Machine Learning to Operationalize Old Constructs in New Ways: An Illustration Using U.S. Governors’ COVID-19 Press Briefings

Abstract

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 ground truth data, we train a model to predict CIP (Charismatic-Ideological-Pragmatic) categorical 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.

COVID-19 and communication: A sentiment analysis of US state governors’ official press releases

“…Communications from official leadership during an emergency situation play a significant role in crisis management and successful implementation of rapid responses [ 12 ]. Previous studies investigated the US state governors’ press releases during the COVID-19 pandemic to evaluate the effectiveness of stay-at-home recommendations and communication styles among different governors [ 6 , 13 ]. However, none of these studies has evaluated patterns of governors’ official statements over time during the pandemic.…”