The crisis communication of the COVID-19 pandemic in media discourse: Text mining for infectious disease frames and environmental pollution

Abstract

ABSTRACT This study conducts a text semantic analysis of mainstream media coverage of the 2019 COVID-19 outbreak in China. By examining frequently used keywords and their co-occurrences, researchers infer a semantic network and word collocations. Encoding epidemic-related frames offers insight into cognitive structures used in understanding and communicating issues. Through framing, media and individuals emphasize certain crisis aspects while downplaying others. The study reveals that Chinese mainstream media employed 12 frames during the COVID-19 crisis. Methodologically, the study demonstrates identifying frames in Chinese media news through text mining. Using Multiple Correspondence Analysis (MCA) and Hierarchical Cluster Analysis (HCA), the study elucidates stage-frame connections and frame relationships. Paired statistical analysis examines mainstream media attention to environmental pollution amid the COVID-19 pandemic. Results show frames changed during different pandemic stages, reflecting mainstream media's role in social stability. Applying MCA and HCA techniques, the 12 frames cluster into four groups, highlighting consistent frame usage by Chinese mainstream media. Mainstream media also begins to address COVID-19-related environmental pollution, focusing on virus contamination of goods, medical waste, and wastewater, lacking comprehensive attention to broader environmental pollution. These findings offer insights for public health professionals and environmentalists, aiding crisis communication strategy formulation for future emergencies.