Sentiment Impact of Public Health Agency communication Strategies on TikTok under COVID-19 Normalization: Deep Learning Exploration.

Abstract

Aim: The accessibility of social media data has allowed researchers to measure official-public interactions during COVID-19. However, previous work analyzing official posts or public comments has failed to explore the link between the two. Therefore, this study investigates the relationship between the communication strategies of public health agencies (PHAs) on TikTok and public emotional/sentiment tendencies in COVID-19 normalization.

Subject and methods: This study uses the 2022 Shanghai city closure event as a public health communication case study in the context of COVID-19 normalization, using TikTok as a data source. We first analyze the communication strategies adopted by the PHA based on the Crisis and Emergency Risk Communication (CERC) model. Then, we classify the sentiment of public comments using the Large-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation (ERNIE) pre-training model. Finally, we explore the connection between PHA communication strategies and public sentiment tendencies.

Results: First, the public's sentiment tendencies differ at different stages. Therefore, appropriate communication strategies should be developed stage-by-stage. Second, the public's emotional disposition to different communication strategies varies: government statements, vaccines, and prevention and control programs are more likely to produce a friendly comment environment, while policy and new cases per day are more likely to produce unfavorable comment content. However, this does not mean that policy and new cases per day should be avoided; the judicious use of these two strategies can help PHAs understand the current issues causing public dissatisfaction. Third, videos with celebrity appearances can significantly increase positive public sentiment and, thereby, public participation.

Conclusion: We propose an improved CERC guideline for China based on the Shanghai lockdown case.