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.

中文翻译：

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政府政策沟通中的回避和信用主张动态：来自 2020-2022 年 COVID-19 大流行期间四个 OECD 国家领导层推文的证据

政府信息活动通常被认为是受到指责最小化和信用最大化的经典计算的推动。然而，政府中“责备”和“信用”沟通活动的精确相互作用尚不清楚，并且在实践中如何部署它们的问题也很多。本文使用自然语言处理（NLP）机器学习情绪分析一个独特的数据集，该数据集由四个经合组织国家（即英国、爱尔兰、澳大利亚和加拿大的总理）高级政治领导人的数千条推文组成： 2020-2022 年期间，从客观和主观意义上研究“责备”和“信用”沟通策略之间存在的关系，以及它们与 COVID-19 大流行不断变化的严重程度的关系。总体而言，该研究表明，在这场影响深远、持久且起伏不定的危机中，政治领导人在新冠疫情被认为是“危急时刻”期间传达信贷寻求信息时，按照理论预期行事。情况有所改善，但当情况被认为正在恶化时，他们并不完全依赖于相互指责或寻找替罪羊。然后讨论了这一发现对基于责任和信用的政府沟通理论的影响。