Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data

Cognitively motivated or emotionally charged? Issue-switching mechanism of second screening-facilitated political participation during the 2022 COVID-19 waves in China

This study examines the effects of mass media and mobile media on civil behaviors in China during COVID-19 outbreaks. It explores the media practices of the Chinese public and the psychological mechanisms that led to their protests and boycotts against the government's COVID policies. Chinese official media has a reputation for depoliticizing natural disasters and censoring the online mobilization of collective actions. Based on previous research and theories regarding media multitasking and political communication, this study demonstrates the psychological mechanism by which a special type of media multitasking-second screening-contributed to political participation in a politically restrictive environment. This study contributes to existing research on media multitasking by proposing a unique theoretical framework that entails an issue-based typology of second screening. The framework expands knowledge by highlighting the importance of issue-switching and investigating its psychological influence on political participation. Previous studies have primarily focused on device-switching and its effects, so this study offers fresh insights in this field. Drawing on a nationwide online survey (N = 1180) conducted during the late-2022 wave of COVID-19 in China, this study reveals that second screening on official televised COVID-19 news can elicit simultaneous issue-switching among Chinese audiences: from the de-politicalized COVID-19 propaganda toward politics. Further, second screening on different issues (i.e., politics vs. health sciences) may instigate political participation through cognitive and emotion channels differently. That is, both political efficacy (cognitive) and negative emotions (emotion) facilitate the process, with the former making a greater contribution.

Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study

BACKGROUND

Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC's social media communications and the actual epidemic metrics to improve public health agencies' communication strategies during health emergencies.

OBJECTIVE

This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC's digital health communication strategies for future health emergencies.

METHODS

Two types of data were collected: (1) a total of 17,524 COVID-19-related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19-related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data.

RESULTS

Four major topics from the CDC's COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC's topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data.

CONCLUSIONS

Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively.

Analysis of the evolving factors of social media users' emotions and behaviors: a longitudinal study from China's COVID-19 opening policy period

The outbreak of the COVID-19 pandemic has triggered citizen panic and social crises worldwide. The Chinese government was the first to implement strict prevention and control policies. However, in December 2022, the Chinese government suddenly changed its prevention and control policies and completely opened up. This led to a large-scale infection of the epidemic in a short period of time, which will cause unknown social impacts. This study collected 500+ epidemic-related hotspots and 200,000+ data from November 1, 2022, to March 1, 2023. Using a sentiment classification method based on pre-trained neural network models, we conducted inductive analysis and a summary of high-frequency words of various emotions. This study focuses on the inflection point of the emotional evolution of social media users and the evolution of "hot topic searches" events and emotional behavioral factors after the sudden open policy. Our research results show that, first of all, the positive emotions of social media users are divided into 4 inflection points and 5 time periods, and the negative emotions are divided into 3 inflection points and 4 time periods. Behavioral factors are different at each stage of each emotion. And the evolution patterns of positive emotions and negative emotions are also different. Secondly, the evolution of behavioral elements deserves more attention. Continue to pay attention: The treatment of diseases, the recovery of personal health, the promotion of festive atmosphere, and the reduction of publicity on the harm of "new crown sequelae and second infections" are the behavioral concerns that affect users' emotional changes. Finally, it is necessary to change the "hot topic searches" event by guiding the user's behavioral focus to control the inflection point of the user's emotion. This study helps governments and institutions understand the dynamic impact of epidemic policy changes on social media users, thereby promoting policy formulation and better coping with social crises.

"Help Us!": a content analysis of COVID-19 help-seeking posts on Weibo during the first lockdown

BACKGROUND

Social media is playing an increasingly important role in public emergencies for help-seekers, especially during the global COVID-19 pandemic. Wuhan, China, firstly official reported COVID-19 cases and implemented lockdown measures to prevent the spread of the virus. People during the first lockdown were restricted from seeking help face-to-face. Social media is more prominent as an online tool for people seeking help, especially for patients, than in other stages of the COVID-19 pandemic.

OBJECTIVE

This study aimed to explore the urgent needs presented in help-seeking posts in Wuhan during the first COVID-19 lockdown, the content features of these posts, and how they influenced online user engagement.

METHODS

This study collected posts from Weibo posted with specific help tags during the first COVID-19 lockdown in Wuhan: from 23 January 2020 to 24 March 2020, and eventually received 2055 data, including textual content, comments, retweets, and publishing location. Content analysis was conducted, and manual coding was performed on help-seeking typology, narrative mode, narrative subject, and emotional valence.

RESULTS

The result showed that help-seeking posts primarily were seeking medical (97.7%). Features of these posts were mainly adopting a mixed narrative mode (46.4%), released by relatives of patients (61.7%), and expressing negative emotions (93.2%). Chi-square tests suggested that help-seeking posts with mixed narrative modes released by relatives express more frequent negative emotions. Results of negative binomial regression indicated posts of seeking information (B = 0.52, p < .001, e^0.52 = 1.68), with mixed narrative mode (B = 0.63, p < .001, e^0.63 = 1.86), released by themselves (as referential groups) and with neutral emotions increased comments. Posts of seeking medical (B = 0.57, p < .01, e^0.57 = 1.77), with mixed narrative mode (B = 1.88, p < .001, e^1.88 = 6.53), released by people of unrelated patients (B = 0.47, p < .001, e^0.47 = 1.60) and with neutral emotions increased retweets.

CONCLUSIONS

This study provides evidence of what actual public demands are to be considered and addressed by governments and public administrators before implementing closure and lockdown policies to limit the spread of the virus. Meanwhile, our findings offer strategies for people help-seeking on social media in similar public health emergencies.

Social divisions and risk perception drive divergent epidemics and large later waves

During infectious disease outbreaks, individuals may adopt protective measures like vaccination and physical distancing in response to awareness of disease burden. Prior work showed how feedbacks between epidemic intensity and awareness-based behaviour shape disease dynamics. These models often overlook social divisions, where population subgroups may be disproportionately impacted by a disease and more responsive to the effects of disease within their group. We develop a compartmental model of disease transmission and awareness-based protective behaviour in a population split into two groups to explore the impacts of awareness separation (relatively greater in- vs. out-group awareness of epidemic severity) and mixing separation (relatively greater in- vs. out-group contact rates). Using simulations, we show that groups that are more separated in awareness have smaller differences in mortality. Fatigue (i.e. abandonment of protective measures over time) can drive additional infection waves that can even exceed the size of the initial wave, particularly if uniform awareness drives early protection in one group, leaving that group largely susceptible to future infection. Counterintuitively, vaccine or infection-acquired immunity that is more protective against transmission and mortality may indirectly lead to more infections by reducing perceived risk of infection and therefore vaccine uptake. Awareness-based protective behaviour, including awareness separation, can fundamentally alter disease dynamics. Social media summary: Depending on group division, behaviour based on perceived risk can change epidemic dynamics & produce large later waves.

Community pandemic prevention and control measures and their influence on citizen satisfaction during the COVID-19 pandemic in China

At the forefront of the fight against the pandemic, the community' s measures and services would have a greater impact than ever before on citizen satisfaction. However, the influence of citizen satisfaction on community pandemic prevention and control measures (CPPCM) during the pandemic is poorly understood. This study aims to investigate the allocation of CPPCM and its impact on CS. The Chinese national data was analyzed for the outcome. (1) Pandemic prevention propaganda (PPP), disinfection (DT), and body temperature tests (BTTs) were the primary measures taken by the Chinese community. (2) The CS for pandemic prevention and control is high, and urban and central Chinese communities express greater satisfaction. (3) The impact of disinfection, body temperature tests, free supplies, and assistance purchasing supplies on CS was greater in rural areas than in urban areas. (4) Regional variations exist in the impact of CS on CPPCM. (5) The number of measures has an inverted U-shaped relationship with citizen satisfaction. This study also suggests that the government should disseminate information about pandemic prevention in a timely manner, provide basic health and medical services, and evaluate the measures taken to avoid the discount effect.

China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo

The prevention and control of the coronavirus disease 2019 (COVID-19) epidemic in China has entered a phase of normalization. The basis for evaluating and improving public health strategies is understanding the emotions and concerns of the public. This study establishes a fine-grained emotion-classification model to annotate the emotions of 32,698 Sina Weibo posts related to COVID-19 prevention and control from July 2022 to August 2022. The Dalian University of Technology (DLUT) emotion-classification system was adjusted to form four pairs (eight categories) of bidirectional emotions: good-disgust, joy-sadness, anger-fear, and surprise-anticipation. A lexicon-based method was proposed to classify the emotions of Weibo posts. Based on the selected Weibo posts, the present study analyzed the Chinese public's sentiments and emotions. The results showed that positive sentiment accounted for 51%, negative sentiment accounted for 24%, and neutral sentiment accounted for 25%. Positive sentiments were dominated by good and joy emotions, and negative sentiments were dominated by fear and disgust emotions. The proportion of positive sentiments on official Weibo (accounts belonging to government departments and official media) is significantly higher than that on personal Weibo. Official Weibo users displayed a weak guiding effect on personal users in terms of positive sentiment and the two groups of users were almost completely synchronized in terms of negative sentiment. The linear discriminant analysis (LDA) was performed on the two negative emotions of fear and disgust in the personal posts. The present study found that the emotion of fear was mainly related to COVID-19 infection and death, control of people with positive nucleic acid tests, and the outbreak of local epidemic, while the emotion of disgust was mainly related to the long-term existence of the epidemic, the cost of nucleic acid tests, non-implementation of prevention and control measures, and the occurrence of foreign epidemics. These findings suggest that Chinese attitudes toward epidemic prevention and control are positive and optimistic; however, there is also a notable proportion of fear and disgust. It is expected that this study will help public health administrators to evaluate the effectiveness of possible countermeasures and work toward precise prevention and control of the COVID-19 epidemic.

Using geospatial social media data for infectious disease studies: a systematic review

Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

The Association of Social Emotions, Perceived Efficiency, Transparency of the Government, Concerns about COVID-19, and Confidence in Fighting the Pandemic under the Week-Long Lockdown in Shenzhen, China

The outbreak and spread of the COVID-19 pandemic have had a far-reaching impact. The present study investigated the factors primarily affecting the execution of the control measures, including social emotions, concerns about the pandemic, perceived efficiency, transparency of the government in publishing the pandemic-related information, and confidence in fighting the pandemic. Specifically, we examined the differences in these factors across four areas (i.e., lockdown area, control area, prevention area, and safe area) according to different COVID control measures under the week-long lockdown in Shenzhen. We found that social emotions, concerns about the pandemic, perceived efficiency of the government, and confidence in fighting the pandemic were more negative in the lockdown area than that in other areas. More importantly, after controlling for areas and education level of participants, the emotion of optimism, concerns about the COVID-19 pandemic, perceived efficiency, and perceived transparency of the government in releasing COVID-19 relevant information positively predicted confidence in fighting the pandemic, while anger negatively predicted confidence in fighting the pandemic. Therefore, the government and communities could make efforts at effective communication and find innovative approaches to make individuals (especially in the lockdown area) maintain social connections, reduce negative emotions, and enhance confidence in combating the pandemic.

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.

Capturing the Trajectory of Psychological Status and Analyzing Online Public Reactions During the Coronavirus Disease 2019 Pandemic Through Weibo Posts in China

When a major, sudden infectious disease occurs, people tend to react emotionally and display reactions such as tension, anxiety, fear, depression, and somatization symptoms. Social media played a substantial awareness role in developing countries during the outbreak of coronavirus disease 2019 (COVID-19). This study aimed to analyze public opinion regarding COVID-19 and to explore the trajectory of psychological status and online public reactions to the COVID-19 pandemic by examining online content from Weibo in China. This study consisted of three steps: first, Weibo posts created during the pandemic were collected and preprocessed on a large scale; second, public sentiment orientation was classified as "optimistic/pessimistic/neutral" orientation via natural language processing and manual determination procedures; and third, qualitative and quantitative analyses were conducted to reveal the trajectory of public psychological status and online public reactions during the COVID-19 pandemic. Public psychological status differed in different periods of the pandemic (from December 2019 to May 2020). The newly confirmed cases had an almost 1-month lagged effect on public psychological status. Among the 15 events with high impact indexes or related to government decisions, there were 10 optimism orientation > pessimism orientation (OP) events (2/3) and 5 pessimism orientation > optimism orientation (PO) events (1/3). Among the top two OP events, the high-frequency words were "race against time" and "support," while in the top two PO events, the high-frequency words were "irrationally purchase" and "pass away." We proposed a hypothesis that people developed negative self-perception when they received PO events, but their cognition was developed by how these external stimuli were processed and evaluated. These results offer implications for public health policymakers on understanding public psychological status from social media. This study demonstrates the benefits of promoting psychological healthcare and hygiene activity in the early period and improving risk perception for the public based on public opinion and the coping abilities of people. Health managers should focus on disseminating socially oriented strategies to improve the policy literacy of Internet users, thereby facilitating the disease prevention work for the COVID-19 pandemic and other major public events.

An Assessment of Social Distancing Obedience Behavior during the COVID-19 Post-Epidemic Period in China: A Cross-Sectional Survey

Social distancing plays a critical role in reducing the disease diffusion risk during the COVID-19 pandemic and post-pandemic period. In order to explore the social distancing obedience behavior, a comprehensive survey was conducted in this study by collecting data from 1064 Chinese residents in January 2021 by means of a questionnaire. Structural equation modeling (SEM) and hierarchical linear regression (HLR) analyses were employed to investigate the research hypotheses considered, testing the three influencing factors of social distancing obedience behavior: public guidance, risk perception, and regulation punishment. The reliability and validity of the measurements are demonstrated. The outcomes from the conducted analyses show that the public guidance significantly affects risk perception of individuals, while risk perception imposes a positive impact on social distancing obedience behavior. Moreover, risk perception serves a mediating role in the relationship between the public guidance and social distancing obedience behavior. In addition, regulation punishment positively predicts social distancing obedience behavior and could even have a greater effect by enhancing risk perception. Hence, this study suggests that the relevant authorities and agencies implement strong social distancing policies during the COVID-19 post-pandemic period from the perspective of promoting the public guidance, risk perception, and regulation punishment.

COVID-19 Information Dissemination Using the WeChat Communication Index: Retrospective Analysis Study

Background

The COVID-19 outbreak has tremendously impacted the world. The number of confirmed cases has continued to increase, causing damage to society and the economy worldwide. The public pays close attention to information on the pandemic and learns about the disease through various media outlets. The dissemination of comprehensive and accurate COVID-19 information that the public needs helps to educate people so they can take preventive measures.

Objective

This study aimed to examine the dissemination of COVID-19 information by analyzing the information released by the official WeChat account of the People’s Daily during the pandemic. The most-read COVID-19 information in China was summarized, and the factors that influence information dissemination were studied to understand the characteristics that affect its dissemination. Moreover, this was conducted in order to identify how to effectively disseminate COVID-19 information and to provide suggestions on how to manage public opinion and information governance during a pandemic.

Methods

This was a retrospective study based on a WeChat official account. We collected all COVID-19–related information, starting with the first report about COVID-19 from the People’s Daily and ending with the last piece of information about lifting the first-level emergency response in 34 Chinese provinces. A descriptive analysis was then conducted on this information, as well as on Qingbo Big Data’s dissemination index. Multiple linear regression was utilized to study the factors that affected information dissemination based on various characteristics and the dissemination index.

Results

From January 19 to May 2, 2020, the People’s Daily released 1984 pieces of information; 1621 were related to COVID-19, which mainly included headline news items, items with emotional content, and issues related to the pandemic’s development. By analyzing the dissemination index, seven information dissemination peaks were discerned. Among the three dimensions of COVID-19 information—media salience, content, and format—eight factors affected the spread of COVID-19 information.

Conclusions

Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance.