Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19.

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events. Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19. Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic. Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant "rebound effect" by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003). Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks.

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook-involving more than 500,000 respondents from 64 countries-showing that there is a "one-to-one" relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease-sharing epidemiological features with COVID-19-that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.

Social innovation based on collaboration between government and non-governmental organizations in COVID-19 crisis: evidence from Iran.

BACKGROUND: One of the effective ways to attract social collaboration to provide effective, prompt, and coordinated interventions in emergencies is through social innovation. The present study seeks to identify the factors affecting the implementation of the social innovation plan based on the collaboration between government and non-governmental organizations (NGOs) for saving people's lives in crises. The initial idea of this research was obtained from the best practice "Every Home Is a Health Base" which was implemented in Iran. METHODS: The Grounded Theory strategy has been used in this study. The statistical population of the study is health experts from the Ministry of Health and Medical Education of Iran. The study time span is during the first half of 2020. Exploratory analysis was used to identify the factors of social innovation. By selecting and reviewing 68 research in-depth, the initial framework was prepared. Then, through a semi-structured interview with experts, the framework was adapted and reviewed. Based on the analysis of the collected data, 39 open codes were extracted and the factors affecting the implementation of the social innovation were identified. RESULTS: The eight axis codes as the factors affecting the implementation of the social innovation plan based on the collaboration between government and NGOs are as follows: Paying attention to the components of the NGOs collaboration effectiveness, investment to attract NGOs collaboration, the ability to manage the implementation, the ability of networking, the ability of policymaking, providing the necessary cultural and educational infrastructure; Existence of capable legal organizations to solve the executive problems of the plan and facilitate coordination, and controlling, containing and reducing the effects of the crisis, as consequences. CONCLUSIONS: Lessons learned from the COVID-19 pandemic have shown the world that the current governmental and social structures are not efficient enough to respond quickly to the emergence of global challenges. Social innovation is a solution to this problem. The findings of this study also confirm this and identify the factors affecting the implementation of the social innovation plan based on collaboration between governments and NGOs in crises. The results of this research give governments and policymakers an efficient solution by involving NGOs, especially in times of widespread crises. Also, they can be used in planning for social development.

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China.

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases' degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

Who Is Suffering from the "Corona Blues"? An Analysis of the Impacts of the COVID-19 Pandemic on Depression and Its Implications for Health Policy.

COVID-19 is tremendously affecting not only social structures but also people's psychological states. In particular, COVID-19 is negatively affecting psychological health, in particular, the depression. When individuals are experiencing the depression, there is increase in the suicide rate and occurrence of serious social problems. This study therefore examines factors affecting depression by using hypothesis testing. Previous studies have limitations in that they focus only on demographic variables or other specific variables. In contrast, this study focuses on the influences of four non-pandemic and seven pandemic-related variables on people's depression. We analyze data from a social survey (N = 1525) in Korea which adopted the stratified quota sampling method. Results show that, first, among the demographic variables, young people experience depression to a greater extent than older people do. Second, among the non-pandemic variables, individuals with more social support, good health, optimism, and self-efficacy exhibit lower levels of depression. Third, among the factors related to COVID-19, fear of infection, financial instability, personal lifestyle changes, and poor health status increase depression.

COVID-19 vulnerability and perceived norm violations predict loss of social trust: A pre-post study.

While pandemic containment measures benefit public health, they may jeopardize the social structure of society. We hypothesize that lockdowns and prolonged social distancing measures hinder social support and invite norm violations, eroding social trust. We conducted a pre-registered pre-post study on a representative sample of the Dutch population (n = 2377; participation rate = 88.8%), measuring social trust reported by the same individuals before and after the first wave of the COVID-19 pandemic. Results show that social trust in the Netherlands suddenly dropped from its historically stable level, reaching one of its lowest points on record. The decline was stronger among residents belonging to official high-risk categories, especially if they perceived themselves as likely to become infected. Individuals who more strongly agreed with self-isolation norms or did not perceive a widespread compliance or agreement with such norms also reported a greater loss of trust.