The study examines how framing, psychological uncertainty, and agency type influence campaign effectiveness in promoting coronavirus disease 2019 (COVID-19) vaccines. A 2 (gain vs. loss frame) × 2 (high vs. low uncertainty) × 2 (national vs. local agency) between-subjects experiment was conducted among Houston residents ( N = 382). Findings revealed that a loss frame was more effective among participants primed with high uncertainty through a thought-listing task; however, it was less persuasive under conditions of low uncertainty due to increased psychological reactance. Moreover, there was an interaction effect between uncertainty and agency type on vaccine beliefs. The study contributes to the framing literature by identifying psychological uncertainty as a moderator and provides useful suggestions for vaccine message design.

We compare the social media discourses on COVID-19 vaccines constructed by U.S. politicians, medical experts, and government agencies at the subnational level and investigate how various contextual factors influence the likelihood of government agencies politicizing the issue. Taking the political corpus and the medical corpus as two extremes, we propose a language-based definition of politicization of science and measure it on a continuous scale. By building a machine learning classifier that captures subtle linguistic indicators of politicization and applying it to two years of government agencies' Facebook posting history, we demonstrate that: 1) U.S. politicians heavily politicized COVID-19 vaccines, medical experts conveyed minimal politicization, and government agencies' discourse was a mix of the two, yet more closely resembled medical experts'; 2) increasing COVID-19 infection rates reduced government agencies' politicization tendencies; 3) government agencies in Democratic-leaning states were more likely to politicize COVID-19 vaccines than those in Republican-leaning states; and 4) the degree of politicization did not significantly differ across agencies' jurisdiction levels. We discuss the conceptualization of politicization of science, the incumbency effect, and government communication as an emerging area for political communication research.

The COVID-19 pandemic has posed severe challenges that require collaborative efforts from multi-sector organizations. Guided by an institutional theory framework that considers how both organizational fields and national level contexts affect organizations’ social partnership communication, the current study examines the COVID-19-related social partnership communication network on social media. The cross-national study using semantic network analysis and exponential random graph models (ERGMs) first maps the meaning of COVID-19 social partnership network, and then investigates the role of organizational fields and a country’s political system, economic system, educational system, and cultural system on the formation of interorganizational communication ties surrounding the relief efforts of COVID-19. Results reveal the importance of the political system—such as the presence of populist government, economic disparity, and uncertainty avoidance cultural orientation in shaping the social media-based social partnership communication network. In addition, NGOs from multiple issue areas are actively engaged in the network, whereas corporations from manufacturing and financial industries are active players.

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BACKGROUND The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy for battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies. OBJECTIVE This study examines the content of COVID-19–related tweets posted by public health agencies in Texas and how content characteristics can predict the level of public engagement. METHODS All COVID-19–related tweets (N=7269) posted by Texas public agencies during the first 6 months of 2020 were classified in terms of each tweet’s functions (whether the tweet provides information, promotes action, or builds community), the preventative measures mentioned, and the health beliefs discussed, by using natural language processing. Hierarchical linear regressions were conducted to explore how tweet content predicted public engagement. RESULTS The information function was the most prominent function, followed by the action or community functions. Beliefs regarding susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets that served the information or action functions were more likely to be retweeted, while tweets that served the action and community functions were more likely to be liked. Tweets that provided susceptibility information resulted in the most public engagement in terms of the number of retweets and likes. CONCLUSIONS Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve their strategies for designing social media messages about the benefits of disease prevention behaviors and audiences’ self-efficacy.