ABSTRACT
This study focuses on news content related to China and COVID-19 during the COVID-19 pandemic and investigates how media frame, affected the emergence of anti-China sentiments through a case study of Japanese online news discourse. We collected large-scale digital trace data including online news and comments during the COVID-19 pandemic. By employing deep learning-based sentiment classifications, we were able to measure the extent of anti-China sentiments expressed through comments during the pandemic's different phases and on different types of news content. Our results provide empirical evidence that the news media's negative depictions of China and coverage related to political and international relations issues increased as the prevalence of COVID-19 in Japan increased. Importantly, since this coverage can prompt the expression of anti-China sentiment, we argue that the framing used by the media can provide discursive contexts that escalate COVID-19 issues into a broader expression of anti-China sentiment. This study not only identifies the impact of media frames on the expression of anti-China sentiment but also contributes to the development of methods for detecting public opinion and measuring the framing effect with big data and advanced computational tools.
ABSTRACT
BACKGROUND: The availability of large-scale and fine-grained aggregated mobility data has allowed researchers to observe the dynamics of social distancing behaviors at high spatial and temporal resolutions. Despite the increasing attention paid to this research agenda, limited studies have focused on the demographic factors related to mobility, and the dynamics of social distancing behaviors have not been fully investigated. OBJECTIVE: This study aims to assist in designing and implementing public health policies by exploring how social distancing behaviors varied among various demographic groups over time. METHODS: We combined several data sources, including mobile tracking mobility data and geographical statistics, to estimate the visiting population of entertainment venues across demographic groups, which can be considered the proxy of social distancing behaviors. Next, we used time series analysis methods to investigate how voluntary and policy-induced social distancing behaviors shifted over time across demographic groups. RESULTS: Our findings demonstrate distinct patterns of social distancing behaviors and their dynamics across age groups. On the one hand, although entertainment venues' population comprises mainly individuals aged 20-40 years, a more significant proportion of the youth has adopted social distancing behaviors and complied with policy implementations compared to older age groups. From this perspective, the increasing contribution to infections by the youth should be more likely to be attributed to their number rather than their violation of social distancing behaviors. On the other hand, although risk perception and self-restriction recommendations can induce social distancing behaviors, their impact and effectiveness appear to be largely weakened during Japan's second state of emergency. CONCLUSIONS: This study provides a timely reference for policymakers about the current situation on how different demographic groups adopt social distancing behaviors over time. On the one hand, the age-dependent disparity requires more nuanced and targeted mitigation strategies to increase the intention of elderly individuals to adopt mobility restriction behaviors. On the other hand, considering that the effectiveness of policy implementations requesting social distancing behaviors appears to decline over time, in extreme cases, the government should consider imposing stricter social distancing interventions, as they are necessary to promote social distancing behaviors and mitigate the transmission of COVID-19.
ABSTRACT
Previous studies have revealed medical, democratic, and political factors altering responses to unexpected infectious diseases. However, few studies have attempted to explore the factors affecting disease infection from a social perspective. Here, we argue that trust, which plays an important role in shaping people' s risk perception toward hazards, can also affect risk perception toward infections from a social perspective. Drawing on the indication that risk perception of diseases helps prevent people from being infected by promoting responsible behaviors, it can be further asserted that trust may alter the infection rate of diseases as a result of risk perception toward infectious diseases. This is an essential point for preventing the spread of infectious diseases and should be demonstrated. To empirically test this prediction, this study uses the COVID-19 outbreak in China as an example and applies an original dataset combining real-time big data, official data, and social survey data from 317 cities in 31 Chinese provinces to demonstrate whether trust influences the infection rate of diseases. Multilevel regression analyses reveal three main results: (1) trust in local government and media helps to reduce the infection rate of diseases; (2) generalized trust promotes a higher rather than lower infection rate; and (3) the effects of different types of trust are either completely or partly mediated by risk perception toward diseases. The theoretical and practical implications of this study provide suggestions for improving the public health system in response to possible infectious diseases.