Social Big-Data Analysis of Particulate Matter, Health, and Society

Spatial Disparity of Visitors Changes during Particulate Matter Warning Using Big Data Focused on Seoul, Korea

This study examined the changes in the number of visitors to regions during periods of high particulate matter (PM) concentrations in Seoul and analyzed the regional differences of these changes. Further, it examined the spatial characteristics that affect these regional differences. This study mapped the regional differences by conducting a spatial cluster analysis using GIS and examined factors affecting the regional differences using logistic regression analysis. The visiting population data used in this study were obtained from the Big Data on the de facto population measured every hour at mobile base stations, and all analyses were conducted in terms of weekdays and weekends. The results indicated that the visiting population decreases significantly on weekdays when there are high PM concentrations; however, visits increase on weekends, even during periods of high PM concentrations. Moreover, there was a huge regional gap in visiting population changes. Regions with more commercial use, higher bus accessibility, and better pedestrian environment (pedestrian paths, Walk Score) were more likely to be hotspots, whereas regions with high residential and industrial use were more likely to be cold spots. These results can be used as the basic data for PM policies based on regional characteristics.

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

The main purpose of this study is to investigate what topic indicators correlate with public sentiment during “coronavirus disease 2019 (COVID-19) epidemic” and which indicators control the complex networks of the topic indicators. We obtained 68,098 Weibo, categorized them into 11 topic indicators, and grouped these indicators into three dimensions. Then, we constructed the complex networks model of Weibo's topics and examined the key indicators affecting the public's sentiment during the major public emergency. The results showed that “positive emotion” is positively correlated with “recordings of epidemic” and “foreign comparisons,” while “negative emotion” is negatively correlated with “government image,” “recordings of epidemic,” and “asking for help online.” In addition, the two vertexes of “recordings of epidemic” and “foreign comparisons” are the most important “bridges” which connect the government and the public. The “recordings of epidemic” is the main connection “hub” between the government and the media. In other words, the “recordings of epidemic” is the central topic indicator that controls the entire topic network. In conclusion, the government should publish the advance of the events through official media on time and transparent way and create a platform where everyone can speak directly to the government for advice and assistance during a major public emergency in the future.

Influencing Factors of Public Satisfaction with COVID-19 Prevention Services Based on Structural Equation Modeling (SEM): A Study of Nanjing, China

Service satisfaction with public policies is an important component of public service quality management, which is of great significance to the improvement of public service quality. Based on an online questionnaire survey and in combination with the characteristics of public policies and services, in this study the influencing factors of residents' satisfaction with COVID-19 pandemic prevention services were analyzed with structural equation modeling. The results reveal that the data fit the model well, and all the hypotheses formulated in this study were supported. Among the factors that were found to directly affect residents' satisfaction with pandemic prevention services, perceived quality (PQ) has the greatest impact on satisfaction, followed by the disaster situation (DS) and policy expectation (PE). The observed variables that have significant impacts on the latent variables were also explored. Regarding the main findings, the residents who were seriously affected by the pandemic tended to have lower satisfaction with the policies and services provided by the government. Moreover, the improvement of PQ was found to significantly increase pandemic prevention service satisfaction (SS). Finally, the residents with a good psychological status during the pandemic were found to have higher satisfaction. According to the results, implications for the prevention and control practices of similar public health emergencies are proposed.

The Effects of Risk Perceptions Related to Particulate Matter on Outdoor Activity Satisfaction in South Korea

In recent years, the Korean public has become aware of the form of air pollution known as particulate matter, with a consequent growth of public anxiety causing a negative risk perception about outdoor activity. This study aims at determining the causal relationship between risk perceptions about particulate matter and outdoor activity satisfaction in South Korea. An Internet survey was conducted with 412 people, and a structural equation model was used to perform confirmatory factor analysis. The statistically significant results show that the perceived risk of particulate matter is higher when people do not show interest in or trust public opinion or policy on the subject. This increases people’s perceptions of health risks, which in turn lowers their satisfaction with outdoor activity. Although trust levels in public opinion or policy had a positive impact on outdoor activity satisfaction, this was not statistically significant. These results are expected to contribute to risk communication guidelines in public opinion reporting and to the direction of environmental health policies in developing countries with high levels of air pollution, such as particulate matter.