Nostalgia, Social Media, and Subjective Wellbeing: The Dualistic and Conditional Effects of Nostalgia During the COVID-19 Pandemic.

Drastic lifestyle changes due to the COVID-19 pandemic have caused many people to undergo nostalgic longing for the past. Drawing on the regulatory model of nostalgia, we built a research model to examine the dualistic effects of nostalgia on subjective wellbeing, using self-continuity as a mediator and social media use as a moderator. The findings from an online survey (N = 373) indicated that when nostalgia is associated with an enhanced sense of self-continuity, it has a positive indirect effect on subjective wellbeing. In contrast, when not mediated by such a restorative function, nostalgia has a direct negative impact on subjective wellbeing. Both of these (positive) indirect and (negative) direct effects were moderated by social media usage, suggesting that social media use is a crucial communication-related boundary condition that reinforces or mitigates the dualistic effects of nostalgia. This study offers contributions to the literature by uncovering distinct pathways through which nostalgia carries differing implications for subjective wellbeing in times of crisis, as well as by identifying social media use as a boundary condition under which such dualistic roles of nostalgia manifest.

Using social media data to assess the impact of COVID-19 on mental health in China.

BACKGROUND: The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) not only caused an adverse impact on physical health, but also brought about mental health problems among the public. METHODS: To assess the causal impact of COVID-19 on psychological changes in China, we constructed a city-level panel data set based on the expressed sentiment in the contents of 13 million geotagged tweets on Sina Weibo, the Chinese largest microblog platform. RESULTS: Applying a difference-in-differences approach, we found a significant deterioration in mental health status after the occurrence of COVID-19. We also observed that this psychological effect faded out over time during our study period and was more pronounced among women, teenagers and older adults. The mental health impact was more likely to be observed in cities with low levels of initial mental health status, economic development, medical resources and social security. CONCLUSIONS: Our findings may assist in the understanding of mental health impact of COVID-19 and yield useful insights into how to make effective psychological interventions in this kind of sudden public health event.

Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.

BACKGROUND: Dementia is a global public health priority due to rapid growth of the aging population. As China has the world's largest population with dementia, this debilitating disease has created tremendous challenges for older adults, family caregivers, and health care systems on the mainland nationwide. However, public awareness and knowledge of the disease remain limited in Chinese society. OBJECTIVE: This study examines online public discourse and sentiment toward dementia among the Chinese public on a leading Chinese social media platform Weibo. Specifically, this study aims to (1) assess and examine public discourse and sentiment toward dementia among the Chinese public, (2) determine the extent to which dementia-related discourse and sentiment vary among different user groups (ie, government, journalists/news media, scientists/experts, and the general public), and (3) characterize temporal trends in public discourse and sentiment toward dementia among different user groups in China over the past decade. METHODS: In total, 983,039 original dementia-related posts published by 347,599 unique users between 2010 and 2021, together with their user information, were analyzed. Machine learning analytical techniques, including topic modeling, sentiment analysis, and semantic network analyses, were used to identify salient themes/topics and their variations across different user groups (ie, government, journalists/news media, scientists/experts, and the general public). RESULTS: Topic modeling results revealed that symptoms, prevention, and social support are the most prevalent dementia-related themes on Weibo. Posts about dementia policy/advocacy have been increasing in volume since 2018. Raising awareness is the least discussed topic over time. Sentiment analysis indicated that Weibo users generally attach negative attitudes/emotions to dementia, with the general public holding a more negative attitude than other user groups. CONCLUSIONS: Overall, dementia has received greater public attention on social media since 2018. In particular, discussions related to dementia advocacy and policy are gaining momentum in China. However, disparaging language is still used to describe dementia in China; therefore, a nationwide initiative is needed to alter the public discourse on dementia. The results contribute to previous research by providing a macrolevel understanding of the Chinese public's discourse and attitudes toward dementia, which is essential for building national education and policy initiatives to create a dementia-friendly society. Our findings indicate that dementia is associated with negative sentiments, and symptoms and prevention dominate public discourse. The development of strategies to address unfavorable perceptions of dementia requires policy and public health attention. The results further reveal that an urgent need exists to increase public knowledge about dementia. Social media platforms potentially could be leveraged for future dementia education interventions to increase dementia awareness and promote positive attitudes.

Multiplicity and dynamics of social representations of the COVID-19 pandemic on Chinese social media from 2019 to 2020.

Documenting the emergent social representations of COVID-19 in public communication is necessary for critically reflecting on pandemic responses and providing guidance for global pandemic recovery policies and practices. This study documents the dynamics of changing social representations of the COVID-19 pandemic on one of the largest Chinese social media, Weibo, from December 2019 to April 2020. We draw on the social representation theory (SRT) and conceptualize topics and topic networks as a form of social representation. We analyzed a dataset of 40 million COVID-19 related posts from 9.7 million users (including the general public, opinion leaders, and organizations) using machine learning methods. We identified 12 topics and found an expansion in social representations of COVID-19 from a clinical and epidemiological perspective to a broader perspective that integrated personal illness experiences with economic and sociopolitical discourses. Discussions about COVID-19 science did not take a prominent position in the representations, suggesting a lack of effective science and risk communication. Further, we found the strongest association of social representations existed between the public and opinion leaders and the organizations' representations did not align much with the other two groups, suggesting a lack of organizations' influence in public representations of COVID-19 on social media in China.

Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study.

BACKGROUND: Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. OBJECTIVE: The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. METHODS: We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19-related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," in which users report their own or other people's symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. RESULTS: We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. CONCLUSIONS: Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance.

Chinese scientists' mediated participation in public outreach: Multiple direct and personal norm-mediated predictors.

The rise of new media technologies has reshaped the landscape of science communication. There is little research on scientists' outreach participation and its possible predictors in different media contexts. Based on a national survey of 8,533 scientists in China, this study examined multiple direct and personal norm-mediated predictors of scientists' intentions to participate in public outreach via legacy media versus social media. Our findings revealed two consistent direct predictors (past outreach participation and personal norms) and two inconsistent direct predictors (descriptive norms and intrinsic rewards) that are significant only for participating via social media in the Chinese context. Moreover, our findings suggest a significant mediation effect of personal norms on the influence of various social norms (descriptive and subjective) and rewards (intrinsic and extrinsic) on Chinese scientists' intentions to participate in public outreach via media. The theoretical and practical implications of these findings are discussed.

Social media and anti-immigrant prejudice: a multi-method analysis of the role of social media use, threat perceptions, and cognitive ability.

Introduction: The discourse on immigration and immigrants is central to contemporary political and public discussions. Analyzing online conversations about immigrants provides valuable insights into public opinion, complemented by data from questionnaires on how attitudes are formed. Methods: The research includes two studies examining the expressive and informational use of social media. Study 1 conducted a computational text analysis of comments on Singaporean Facebook pages and forums, focusing on how social media is used to discuss immigrants. Study 2 utilized survey data to examine the use of social media at the individual level, testing the relationships between cognitive ability, perceptions of threat, negative emotions towards immigrants, and social media usage within the Integrated Threat Theory framework. Results: Study 1 found that discussions about immigrants on social media often involved negative emotions and concerns about economic impact, such as competition for jobs and crime. Complementing these findings about perceived economic threats, Study 2 showed that individuals with higher social media usage and greater perceptions of threat were more likely to have negative emotions towards immigrants. These relationships were mediated by perceptions of threat and were stronger in individuals with lower cognitive abilities. Discussion: The findings from both studies demonstrate the role of social media in shaping public attitudes towards immigrants, highlighting how perceived threats influence these attitudes. This research suggests the importance of considering how digital platforms contribute to public opinion on immigration, with implications for understanding the dynamics of attitude formation in the digital age.

How did Chinese public health authorities promote COVID-19 vaccination on social media? A content analysis of the vaccination promotion posts.

Objective: Drawing upon the health belief model, this study aims to analyze the message characteristics of coronavirus disease 2019 (COVID-19) vaccination promotion messages posted by influential Chinese public health institutions and how those characteristics affect audiences' participative engagement on Weibo, which is a popular social media site in China. Methods: Two Chinese phrases for the COVID-19 vaccine were adopted as search terms to retrieve qualified posts on Weibo from 1 December 2019 to 18 March 2023. A total of 2546 posts by the top nine most impactful public health institutions were retained for quantitative content analysis. Message characteristics derived from the health belief model and participative engagement indicators were coded by the authors. Results: Among health belief model constructs, the collective-oriented constructs (i.e., benefits, cues to action, and susceptibility) appeared in almost half of the posts, while the individual-oriented constructs (i.e., barriers, self-efficacy, and severity) were mentioned less. Moreover, negative binomial regression models revealed that collective-oriented constructs and self-efficacy facilitated engagement, while other constructs played impeding roles. Conclusions: Appearances and functions of the health belief model's constructs in the COVID-19 vaccination promotion context are closely associated with China's collectivistic culture. Furthermore, constructs conforming to people's psychological traits are likely to promote public engagement and may facilitate vaccination behavior.

The bright and dark sides of social media use during COVID-19 lockdown: Contrasting social media effects through social liability vs. social support.

There exist ongoing discussions regarding whether, when, or why heightened reliance on social media becomes benefits or drawbacks, especially in times of crisis. Using the concepts of social liability, social support, and cognitive appraisal theory, this study examines distinct theoretical pathways through which the relational use of social media has contrasting impacts on cognitive appraisals of and emotional responses to the COVID-19 lockdown. We collected online survey data from 494 social media users in the U.S. during the COVID-19 lockdown. The results based on structural equation modeling (SEM) showed double-edged social media effects. When social media use results in perceived social support, it has a favorable impact on coping appraisals of the COVID-19 lockdown. This, in turn, is associated with lower levels of negative affective responses, such as anger, anxiety, and loneliness. In contrast, when social media use results in increased social liability (i.e., obligation to provide support to others), it negatively impacts cognitive appraisals and affective responses. The study makes significant contributions by unpacking two distinct theoretical mechanisms underlying social media effects: particularly social liability which has been underexplored but was found to be an essential concept to explain the dualistic impact of social media.

What makes an online help-seeking message go far during the COVID-19 crisis in mainland China? A multilevel regression analysis.

Various studies have explored the underlying mechanisms that enhance the overall reach of a support-seeking message on social media networks. However, little attention has been paid to an under-examined structural feature of help-seeking message diffusion, information diffusion depth, and how support-seeking messages can traverse vertically into social media networks to reach other users who are not directly connected to the help-seeker. Using the multilevel regression to analyze 705 help-seeking posts regarding COVID-19 on Sina Weibo, we examined sender, content, and environmental factors to investigate what makes help-seeking messages traverse deeply into social media networks. Results suggested that bandwagon cues, anger, instrumental appeal, and intermediate self-disclosure facilitate the diffusion depth of help-seeking messages. However, the effects of these factors were moderated by the epidemic severity. Implications of the findings on support-seeking behavior and narrative strategies on social media were also discussed.

Excavating the social representations and perceived barriers of organ donation in China over the past decade: A hybrid text analysis approach.

Background: Organ donation has been claimed as a prosocial behavior to prolong the recipient's life and deliver great love. However, the supply-demand ratio of organs in China is highly unbalanced. Being entangled with multiple factors derived from individual and supra-individual levels, organ donation in China is important but sensitive. Previous scholars usually depended on obtrusive approaches to explore the facilitators and hindrances of organ donation, which is hard to discover genuine perceptions toward organ donation. Besides, relatively limited scholarly attention has been paid to what hampers organ donation in China. Objective: We intended to excavate the diversified social representations and perceived barriers to organ donation in China over the past decade. Method: Two kinds of text analysis methods-semantic network analysis and conventional content analysis, were applied to 120,172 posts from ordinary users on the Sina Weibo platform to address the research questions. Results: Regarding social representations, the "hope, understanding, and acceptance" of organ donation was the most pronounced one (34% of the whole semantic network), followed by "family story" (26%), "the procedure of organ donation in NGOs" (15%), "the practical value of organ donation" (14%), and "organ donation in the medical context" (11%). Regarding perceived barriers, a four-layer framework was constructed, including (1) the individual level, mainly about the fear of death and postmortem autopsy; (2) the familial level, which refers to the opposition from family members; (3) the societal level, which alludes to distrust toward medical institutions and the general society; (4) the cultural level, which covers religious-cultural concerns about fatalism. Conclusion: In concordance with prior works on social representations regarding organ donation, the current study also uncovered the coexistence of antithetical representations about organ donation-the longing for survival and the fear of death. This representation pair serves as the foundation of Chinese people's ambivalence. Besides, family-related narratives were dispersed over various representations, demonstrating the critical position of family support in organ donation. Moreover, the four-layer framework concerning donation barriers affords a reference for future empirical studies. The practical implications of this work are further discussed.

Before and after the Chinese gene-edited human babies: Multiple discourses of gene editing on social media.

This study examines discourses in Chinese online discussions of gene editing by multiple social actors on Weibo before and after a significant scientific crisis, the 2018 scandal of Chinese gene-edited human babies. A content analysis of 2074 posts was done to identify frames, emotions, and metaphors. Findings reveal that Chinese social media have opened up new spaces for multiple social actors to generate multiple discourses. This has resulted in a more participatory public engagement with science and technology on Chinese social media, potentially influencing the online agenda and policy decisions on science and technology. Finally, findings indicate that a scientific crisis can serve as a trigger for significant changes in public attitudes and opinions regarding gene editing.

Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China.

The development and uptake of the COVID-19 (coronavirus disease 2019) vaccine is a top priority in stifling the COVID-19 pandemic. How the public perceives the COVID-19 vaccine is directly associated with vaccine compliance and vaccination coverage. This study takes a cultural sensitivity perspective and adopts two well-known social media platforms in the United States (Twitter) and China (Weibo) to conduct a public perception comparison around the COVID-19 vaccine. By implementing semantic network analysis, results demonstrate that the two countries' social media users overlapped in themes concerning domestic vaccination policies, priority groups, challenges from COVID-19 variants, and the global pandemic situation. However, Twitter users were prone to disclose individual vaccination experiences, express anti-vaccine attitudes. In comparison, Weibo users manifested evident deference to authorities and exhibited more positive feelings toward the COVID-19 vaccine. Those disparities were explained by the cultural characteristics' differences between the two countries. The findings provide insights into comprehending public health issues in cross-cultural contexts and illustrate the potential of utilizing social media to conduct health informatics studies and investigate public perceptions during public health crisis time.

Conditional transparency: Differentiated news framings of COVID-19 severity in the pre-crisis stage in China.

Transparency of Chinese media coverage became an international controversy when the COVID-19 outbreak initially emerged in Wuhan, the eventual crisis epicenter in China. Unlike studies characterizing mass media in authoritarian contexts as government mouthpieces during a crisis, this study aims to disaggregate Chinese media practices to uncover differences in when, where, and how the severity of COVID-19 was reported. We examine differences in how media institutions reported the severity of the COVID-19 epidemic in China during the pre-crisis period from 1 January 2020 to 20 January 2020 in terms of both the "vertical" or hierarchical positions of media institutions in the Chinese media ecosystem and the "horizontal" positions of media institutions' social proximity to Wuhan in terms of geographical human traffic flows. We find that the coverage of crisis severity is negatively associated with the media's social proximity to Wuhan, but the effect varies depending on the positional prominence of a news article and situation severity. Implications of the institutions' differentiated reporting strategies on future public health reporting in an authoritarian context are also discussed.

Misinformation During the COVID-19 Outbreak in China: Cultural, Social and Political Entanglements.

Not only did COVID-19 give rise to a global pandemic, but also it resulted in an infodemic comprising misinformation, rumor, and propaganda. The consequences of this infodemic can erode public trust, impede the containment of the virus, and outlive the pandemic itself. The evolving and fragmented media landscape, particularly the extensive use of social media, is a crucial driver of the spread of misinformation. Focusing on the Chinese social media Weibo, we collected four million tweets, from December 9, 2019, to April 4, 2020, examining misinformation identified by the fact-checking platform Tencent-a leading Chinese tech giant. Our results show that the evolution of misinformation follows an issue-attention cycle pertaining to topics such as city lockdown, cures and preventive measures, school reopening, and foreign countries. Sensational and emotionally reassuring misinformation characterizes the whole issue-attention cycle, with misinformation on cures and prevention flooding social media. We also study the evolution of sentiment and observe that positive sentiment dominated over the course of Covid, which may be due to the unique characteristic of "positive energy" on Chinese social media. Lastly, we study the media landscape during Covid via a case study on a controversial unproven cure known as Shuanghuanglian, which testifies to the importance of scientific communication in a plague. Our findings shed light on the distinct characteristics of misinformation and its cultural, social, and political implications, during the COVID-19 pandemic. The study also offers insights into combating misinformation in China and across the world at large.

What triggers online help-seeking retransmission during the COVID-19 period? Empirical evidence from Chinese social media.

The past nine months witnessed COVID-19's fast-spreading at the global level. Limited by medical resources shortage and uneven facilities distribution, online help-seeking becomes an essential approach to cope with public health emergencies for many ordinaries. This study explores the driving forces behind the retransmission of online help-seeking posts. We built an analytical framework that emphasized content characteristics, including information completeness, proximity, support seeking type, disease severity, and emotion of help-seeking messages. A quantitative content analysis was conducted with a probability sample consisting of 727 posts. The results illustrate the importance of individual information completeness, high proximity, instrumental support seeking. This study also demonstrates slight inconformity with the severity principle but stresses the power of anger in help-seeking messages dissemination. As one of the first online help-seeking diffusion analyses in the COVID-19 period, our research provides a reference for constructing compelling and effective help-seeking posts during a particular period. It also reveals further possibilities for harnessing social media's power to promote reciprocal and cooperative actions as a response to this deepening global concern.