The framing of initial COVID‐19 communication: Using unsupervised machine learning on press releases

Abstract

AbstractThe COVID‐19 pandemic was a global health crisis that required US residents to understand the phenomenon, interpret the cues, and make sense within their environment. Therefore, how the communication of COVID‐19 was framed to stakeholders during the early stages of the pandemic became important to guide them through specific actions in their state and subsequently with the sensemaking process. The present study examines which frames were emphasized in the states' press releases on policies and other COVID information to influence stakeholders on what to focus on to help with sensemaking during the crisis. We conducted content analysis on 602 press releases from 50 US states using an unsupervised machine learning approach called Latent Dirichlet Allocation (LDA). The results show that health communication using press releases to help the public make sense of the crisis were framed to include health frames as well as economic frames. Health communication messages are typically framed with health and safety measures; however, this study shows that economic frames were emphasized more than public health frames in the government's health communication for COVID‐19, which forced both large and small businesses to engage in specific socially responsible activities that were previously voluntary to support public health safety.