| Abstrakt: |
Government information activities are often thought to be motivated by a classic calculus of blame minimization and credit maximization. However, the precise interactions of "blame" and "credit" communication activities in government are not well understood, and questions abound about how they are deployed in practice. This paper uses Natural Language Processing (NLP) machine-learning sentiment analysis of a unique dataset composed of several thousand tweets of high-level political leaders in four OECD countries--namely the Prime Ministers of the United Kingdom, Ireland, Australia, and Canada--during 2020-2022 to examine the relationships existing between "blame" and "credit" communication strategies and their relation to the changing severity of the COVID-19 pandemic, both in an objective and subjective sense. In general, the study suggests that during this high-impact, long-lasting, and waxing and waning crisis, political leaders acted in accordance with theoretical expectations when it came to communicating credit seeking messages during the periods when the COVID situation was thought to be improving, but they did not exclusively rely upon communicating blame or scapegoating when the situation was considered to be deteriorating. The consequences of this finding for blame and credit-based theories of government communication are then discussed. [ABSTRACT FROM AUTHOR] |
