Leader Emotional (in)consistency in a Time of Crisis: A Preliminary Text Mining Study based on the Daily COVID-19 Press Briefings

Abstract

By utilizing daily COVID-19 press briefing transcripts of two political leaders, the current research-report-style study applied a mixed-method approach based on text mining techniques to measure, analyze, and compare their emotional (in)consistency. Specifically, this study examines two political leaders’ (56th Governor of New York State, Andrew M. Cuomo, and 45th President of the United States, Donald J. Trump) displayed emotional states and emotional (in)consistency over time while they are handling novel coronavirus (2019-nCoV) crisis. Based on the proposed framework called: area- and shape-based emotional states (in)consistency comparison, the results of the current preliminary study showed that Governor Cuomo’s emotional states tend to be more consistent/stable than President Trump when conducting a series of COVID-19 daily press. In an era of the proliferation of digital data (i.e., big data), insights from the current proposed methodological/ statistical approach in conjunction with examining emotional states of decision makers could contribute to the advancement of theory building and testing in the domains of organizational science and leadership.