China Public Psychology Analysis About COVID-19 Under Considering Sina Weibo Data

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.

Anxiety, depression and post-traumatic stress disorder and related factors among Chinese population during the COVID-19 pandemic: A cross-sectional study

The sudden outbreak of coronavirus disease 2019 (COVID-19) has deep and wide negative mental impacts on the public, and studies on the impact of COVID-19 on social and mental well-being are necessary. This study aimed to evaluate mental distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), and its related risk factors in Chinese adults in the early stages of the COVID-19 pandemic. This study used a large-scale cross-sectional design. A total of 2067 adult participants completed the online survey via REDcap from 1st to 15th of March 2020 during the COVID-19 outbreak in China. Anxiety, depression, PTSD, and related risk factors, including self-efficacy, coping style, and social support, were measured using valid and reliable instruments. The data were analyzed using multiple linear regression. We found that 201 (9.7%) participants reported moderate-to-severe anxiety, 669 (33.8%) reported depression, and 368 (17.8%) reported symptoms of PTSD. Self-efficacy, coping style, and social support significantly affected anxiety, depression, and PTSD symptoms. Participants' sociodemographic characteristics, COVID-19 pandemic-related factors, low self-efficacy, low social support, and negative coping were predictors of mental distress during the COVID-19 pandemic. Our study will help healthcare professionals carry out early predictions and identification of high-risk groups and provide appropriate interventions to target groups during public health emergencies that plague the world.

Using search trends to analyze web-based users' behavior profiles connected with COVID-19 in mainland China: infodemiology study based on hot words and Baidu Index

Background

Mainland China, the world's most populous region, experienced a large-scale coronavirus disease 2019 (COVID-19) outbreak in 2020 and 2021, respectively. Existing infodemiology studies have primarily concentrated on the prospective surveillance of confirmed cases or symptoms which met the criterion for investigators; nevertheless, the actual impact regarding COVID-19 on the public and subsequent attitudes of different groups towards the COVID-19 epidemic were neglected.

Methods

This study aimed to examine the public web-based search trends and behavior patterns related to COVID-19 outbreaks in mainland China by using hot words and Baidu Index (BI). The initial hot words (the high-frequency words on the Internet) and the epidemic data (2019/12/01-2021/11/30) were mined from infodemiology platforms. The final hot words table was established by two-rounds of hot words screening and double-level hot words classification. Temporal distribution and demographic portraits of COVID-19 were queried by search trends service supplied from BI to perform the correlation analysis. Further, we used the parameter estimation to quantitatively forecast the geographical distribution of COVID-19 in the future.

Results

The final English-Chinese bilingual table was established including six domains and 32 subordinate hot words. According to the temporal distribution of domains and subordinate hot words in 2020 and 2021, the peaks of searching subordinate hot words and COVID-19 outbreak periods had significant temporal correlation and the subordinate hot words in COVID-19 Related and Territory domains were reliable for COVID-19 surveillance. Gender distribution results showed that Territory domain (the male proportion: 67.69%; standard deviation (SD): 5.88%) and Symptoms/Symptom and Public Health (the female proportion: 57.95%, 56.61%; SD: 0, 9.06%) domains were searched more by male and female groups respectively. The results of age distribution of hot words showed that people aged 20-50 (middle-aged people) had a higher online search intensity, and the group of 20-29, 30-39 years old focused more on Media and Symptoms/Symptom (proportion: 45.43%, 51.66%; SD: 15.37%, 16.59%) domains respectively. Finally, based on frequency rankings of searching hot words and confirmed cases in Mainland China, the epidemic situation of provinces and Chinese administrative divisions were divided into 5 levels of early-warning regions. Central, East and South China regions would be impacted again by the COVID-19 in the future.

Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic

The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation (LDA). To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the open-source natural language processing (NLP) technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment (n = 31,661, 34.44%), fear of COVID-19 (n = 22,597, 24.58%), individual characteristic (n = 17,688, 19.24%), government support (n = 15,482, 16.84%), and community effect (n = 4,505, 4.90%). The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic.

Exploring public attitudes toward live-streaming fitness in China: A sentiment and content analysis of China's social media Weibo

Objectives

Live-streaming fitness is perceived by the Chinese government as an invaluable means to reduce the prevalence of physical inactivity amid the COVID-19 pandemic. This study aims to investigate the public altitudes of the Chinese people toward live-streaming fitness and provide future health communication strategies on the public promotion of live-streaming fitness accordingly.

Methods

This study collected live-streaming fitness-related microblog posts from July 2021 to June 2022 in Weibo, the Chinese equivalent to Twitter. We used the BiLSTM-CNN model to carry out the sentiment analysis, and the structured topic modeling (STM) method to conduct content analysis.

Results

This study extracted 114,397 live-streaming fitness-related Weibo posts. Over 80% of the Weibo posts were positive during the period of the study, and over 85% were positive in half of the period. This study finds 8 topics through content analysis, which are fitness during quarantine; cost reduction; online community; celebrity effect; Industry; fitness injuries; live commerce and Zero Covid strategy.

Conclusions

It is discovered that the public attitudes toward live-streaming fitness were largely positive. Topics related to celebrity effect (5-11%), fitness injuries (8-16%), live commerce (5-9%) and Zero Covid strategy (16-26%) showed upward trends in negative views of the Chinese people. Specific health communication strategy suggestions are given to target each of the negative topics.

Emotional Contagion: Research on the Influencing Factors of Social Media Users' Negative Emotional Communication During the COVID-19 Pandemic

During the epidemic, social media platforms were frequently used by users to express and spread negative emotions. Under emotional contagion, individual emotions gradually generalized into group emotions. At the same time, the public could not regulate their emotions and lacked access to release them rationally. This study explores the factors influencing the negative emotions' communication among social media users during the COVID-19 epidemic from the perspective of emotion contagion theory to discover the psychological mechanisms among the public. The questionnaire was tested for reliability and validity and then distributed online on Chinese social media platforms, and the data collected were statistically analyzed. The findings show that there are significant differences in negative emotional communication in social media among different age groups; the seven dimensions of deindividuation, risk perception, group identity, group efficacy, event stimulation, event publicness, and emotion contagion all have significant positive effects on users' negative emotional communication. This study aims to raise public awareness of negative emotions and promote the reconstruction and recovery of public mental health in the epidemic era.

Public Opinion Spread and Guidance Strategy under COVID-19: A SIS Model Analysis

Both the suddenness and seriousness of COVID-19 have caused a variety of public opinions on social media, which becomes the focus of social attention. This paper aims to analyze the strategies regarding the prevention and guidance of public opinion spread under COVID-19 in social networks from the perspective of the emotional characteristics of user texts. Firstly, a model is established to mine text-based emotional tendency based on the Susceptible-Infectious-Susceptible (SIS) model. In addition, a mathematical and simulation analysis of the model is presented. Finally, an empirical study based on the data of microblog contents regarding COVID-19 public opinion in the Sina Weibo platform from January to March 2020 is conducted to analyze the factors that boost and hinder COVID-19 public opinion. The results show that when positive emotion is higher than 0.8, the spread of negative public opinion can be blocked. When the negative emotion and neutral emotion are both below 0.2, the spread of COVID-19 public opinion would be weakened. To accurately guide public opinion on COVID-19, the government authorities should establish a public opinion risk evaluation and an early warning mechanism. Platforms should strengthen public opinion supervision and users should improve their media literacy. The media organizations should insist on positive reporting, improve social cohesion, and guide the trend of public opinion.

The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study

BACKGROUND

The ongoing COVID-19 pandemic has brought unprecedented challenges to every country worldwide. A call for global vaccination for COVID-19 plays a pivotal role in the fight against this virus. With the development of COVID-19 vaccines, public willingness to get vaccinated has become an important public health concern, considering the vaccine hesitancy observed worldwide. Social media is powerful in monitoring public attitudes and assess the dissemination, which would provide valuable information for policy makers.

OBJECTIVE

This study aimed to investigate the responses of vaccine positivity on social media when major public events (major outbreaks) or major adverse events related to vaccination (COVID-19 or other similar vaccines) were reported.

METHODS

A total of 340,783 vaccine-related posts were captured with the poster's information on Weibo, the largest social platform in China. After data cleaning, 156,223 posts were included in the subsequent analysis. Using pandas and SnowNLP Python libraries, posts were classified into 2 categories, positive and negative. After model training and sentiment analysis, the proportion of positive posts was computed to measure the public positivity toward the COVID-19 vaccine.

RESULTS

The positivity toward COVID-19 vaccines in China tends to fluctuate over time in the range of 45.7% to 77.0% and is intuitively correlated with public health events. In terms of gender, males were more positive (70.0% of the time) than females. In terms of region, when regional epidemics arose, not only the region with the epidemic and surrounding regions but also the whole country showed more positive attitudes to varying degrees. When the epidemic subsided temporarily, positivity decreased with varying degrees in each region.

CONCLUSIONS

In China, public positivity toward COVID-19 vaccines fluctuates over time and a regional epidemic or news on social media may cause significant variations in willingness to accept a vaccine. Furthermore, public attitudes toward COVID-19 vaccination vary from gender and region. It is crucial for policy makers to adjust their policies through the use of positive incentives with prompt responses to pandemic-related news to promote vaccination acceptance.