Mood Analysis of COVID-19 Messages: A Systemic Functional Linguistics Approach to Understanding Public Health Communication

Abstract

Purpose: This study examines how Systemic Functional Linguistics (SFL), particularly through mood analysis, can enhance understanding of public health communication related to COVID-19. The aim is to explore the use of mood types in health messages to convey risk, provide instructions, and manage public vulnerability during the pandemic. Methodology: Utilizing a qualitative and quantitative approach, this study analyzed 100 COVID-19 health messages sourced from government websites, social media, and public health announcements. The messages were categorized according to their mood type imperative (commands) and declarative (statements) to assess their functional roles in conveying public health information. Data were tabulated to illustrate the frequency of each mood type and their implications for public health communication. Findings: The analysis revealed that 70% of the messages were in the imperative mood, highlighting the urgency of issuing clear directives to the public. In contrast, 30% of the messages were in the declarative mood, providing essential information and reassurance. This distribution indicates a strategic use of language in public health messaging to promote compliance while fostering public trust. Unique Contribution to Theory, Practice and Policy: This study contributes to the theoretical understanding of SFL mood analysis in public health communication by illustrating how mood types influence public perception and behavior during a crisis. The findings offer practical insights for health authorities to enhance communication strategies, ensuring that messages are both directive and informative. Additionally, this research informs policymakers about effective messaging practices to improve health outcomes during future public health crises.