COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.

COVID‐19 (Corona Virus Disease 2019) has attacked many countries around world and caused profound impacts on public life. The outbreak of pandemic and other relevant factors are considered to cause emotion responses of residents. And the emotion responses of individuals are crucial for the execution of the prevention and control measures. By analyzing the linguistic features of posts on social media, this study aims to explore the change of public emotion responses during COVID‐19 in China. We sampled 22,423 Weibo users and collected their Weibo posts by provincial area each day from January 1st, 2020 to April 18th, 2020. Next, we extracted linguistic features from posts according to the emotion‐related dictionary. Based on important news and information released by the national and international organizations of public health, we divided the period from January 1st, 2020 to April 18th, 2020 into four stages (the initial period, the outbreak period, the stable period, and the prevention and control period). Then we gathered linguistic features by stage. After that, ANOVA was performed to examine the differences among these four stages. The results showed that the frequencies of 11 word categories showed significant differences among four stages, including fear, disappointment, guilt, missing, anger, panic, blessing, faith, love, praise, and surprise. The uses of several negative emotion words, such as fear, disappointment, guilt, and anger, increased saliently in the outbreak period compared with the initial period. Besides, panic words decreased significantly in the prevention and control period compared with the outbreak period. However, missing words were used more in the prevention and control period than other three periods. Moreover, people expressed more faith words and less love words in the outbreak period than the initial periods. Besides, people used more blessing words in the outbreak period compared with the stable period and prevention and control period. And praise words were used more in the outbreak period and the stable period compared with the initial period. The frequency of surprise words was significantly low only in the initial period. This study contributed to the understanding of public emotion responses during COVID‐19, and had implications for the evidence‐based execution of prevention and control measures.

Background: During the COVID-19 pandemic, residential lockdowns were implemented in numerous cities in China to contain the rapid spread of the disease. Although these stringent regulations effectively slowed the spread of COVID-19, they may have posed challenges to the well-being of residents. Objective: This study aims to explore the effects of residential lockdown on the subjective well-being (SWB) of individuals in China during the COVID-19 pandemic. Methods: The sample consisted of 1790 Sina Weibo users who were residents of cities that imposed residential lockdowns, of which 1310 users (73.18%) were female, and 3580 users who were residents of cities that were not locked down (gender-matched with the 1790 lockdown residents). In both the lockdown and nonlockdown groups, we calculated SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown using individuals’ original posts on Sina Weibo. SWB was calculated via online ecological recognition, which is based on established machine learning predictive models. Results: The interactions of time (before the residential lockdown or after the residential lockdown) × area (lockdown or nonlockdown) in the integral analysis (N=5370) showed that after the residential lockdown, compared with the nonlockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F1,5368=13.593, P<.001) and paranoid ideation (F1,5368=14.333, P<.001). The interactions of time (before the residential lockdown or after the residential lockdown) × area (developed or underdeveloped) in the comparison of residential lockdown areas with different levels of economic development (N=1790) indicated that the SWB of residents in underdeveloped areas showed no significant change after the residential lockdown (P>.05), while that of residents in developed areas changed. Conclusions: These findings increase our understanding of the psychological impact and cost of residential lockdown during an epidemic. The more negative changes in the SWB of residents in developed areas imply a greater need for psychological intervention under residential lockdown in such areas.

Vaccine uptake is considered as one of the most effective methods of defending against COVID-19 (coronavirus disease 2019). However, many young adults are hesitant regarding COVID-19 vaccines, and they actually play an important role in virus transmission. Based on a multi-theory model, this study aims to explore the influencing factors related to COVID-19 vaccine willingness among young adults in China. Using semi-structured interviews, this study explored the factors that would motivate young adults with vaccine hesitancy to get the COVID-19 vaccine. Thematic analysis was used to analyze the interview data with topic modeling as a complementarity method. After comparing the differences and similarities of results generated by thematic analysis and topic modeling, this study ultimately identified ten key factors related to COVID-19 vaccination intention, including the effectiveness and safety of vaccines, application range of vaccine, etc. This study combined thematic analysis with machine learning and provided a comprehensive and nuanced picture of facilitating factors for COVID-19 vaccine uptake among Chinese young adults. Results may be taken as potential themes for authorities and public health workers in vaccination campaigns.