The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era

In sociological research based on online public opinion, scholars often overlook the delay and combinatory nature of online responses to real-world events. This study aims to explore the delayed and combinatory responses of online public opinion to the intensity of the COVID-19 pandemic. Specifically, we ask: (a) Is there a temporal delay in the response of online public opinion to the intensity of the pandemic? (b) Does this delay exhibit general characteristics of social networks, such as combinatory effects and higher-order interactions? We employ natural language processing techniques to extract online public opinion data and utilize statistical and machine learning-based causal inference methods for analysis. The findings indicate that online public opinion's response to the intensity of COVID-19 is not immediate but exhibits a long-term lag. Identical COVID-19 intensity data can trigger multiple delayed public opinion responses, while a single delayed public opinion datum may be influenced by multiple preceding COVID-19 intensity data points. This delayed response and its higher-order network characteristics result in a waveform structure of real-world impacts influenced by online public opinion. We also use machine learning causal inference to investigate sensitivity differences in online public opinion responses to COVID-19 during various time periods.