Temporal and Emotional Variations in People’s Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data (Preprint)
Authors: Dai Jing , Lyu Fang , Yu Lin , He Yunyu 
Published: November 2023

BACKGROUND

The COVID-19 pandemic has had profound impacts on society, including public health, the economy, daily life, and social interactions. Social distancing measures, travel restrictions, and the influx of pandemic-related information on social media have all led to a significant shift in how individuals perceive and respond to health crises. In this context, there is a growing awareness of the role that social media platforms such as Weibo, among the largest and most influential social media sites in China, play in shaping public sentiment and influencing people’s behavior during public health emergencies.

OBJECTIVE

This study aims to gain a comprehensive understanding of the sociospatial impact of mass epidemic infectious disease by analyzing the spatiotemporal variations and emotional orientations of the public after the COVID-19 pandemic. We use the outbreak of influenza A after the COVID-19 pandemic as a case study. Through temporal and spatial analyses, we aim to uncover specific variations in the attention and emotional orientations of people living in different provinces in China regarding influenza A. We sought to understand the societal impact of large-scale infectious diseases and the public’s stance after the COVID-19 pandemic to improve public health policies and communication strategies.

METHODS

We selected Weibo as the data source and collected all influenza A–related Weibo posts from November 1, 2022, to March 31, 2023. These data included user names, geographic locations, posting times, content, repost counts, comments, likes, user types, and more. Subsequently, we used latent Dirichlet allocation topic modeling to analyze the public’s focus as well as the bidirectional long short-term memory model to conduct emotional analysis. We further classified the focus areas and emotional orientations of different regions.

RESULTS

The research findings indicate that, compared with China’s western provinces, the eastern provinces exhibited a higher volume of Weibo posts, demonstrating a greater interest in influenza A. Moreover, inland provinces displayed elevated levels of concern compared with coastal regions. In addition, female users of Weibo exhibited a higher level of engagement than male users, with regular users comprising the majority of user types. The public’s focus was categorized into 23 main themes, with the overall emotional sentiment predominantly leaning toward negativity (making up 7562 out of 9111 [83%] sentiments).

CONCLUSIONS

The results of this study underscore the profound societal impact of the COVID-19 pandemic. People tend to be pessimistic toward new large-scale infectious diseases, and disparities exist in the levels of concern and emotional sentiments across different regions. This reflects diverse societal responses to health crises. By gaining an in-depth understanding of the public’s attitudes and focal points regarding these infectious diseases, governments and decision makers can better formulate policies and action plans to cater to the specific needs of different regions and enhance public health awareness.

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