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.

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.

Pandemic Nationalism: Use of Government Social Media for Political Information and Belief in COVID-19 Conspiracy Theories in China

The COVID-19 pandemic unleashed a torrent of conspiracy theories across different social media platforms. Parallel to this conspiracy wave was a heightened sense of nationalism, which manifested through both in-group solidarity and perceived out-group threats. In this study, we examine how individuals' use of government social media to gather political information correlated with nation-related conspiracy beliefs during the pandemic. Data were collected from 745 subjects in China and analyzed through path analyses, which allowed us to examine the direct association with political information consumption from government social media and the indirect association with nationalism on conspiracy beliefs. The results indicated that the use of government social media to gather political information was associated with greater beliefs in nation-variant COVID-19 conspiracies, both directly and through different mediations of nationalism. Our findings highlight the importance of examining government social media use and how nationalism can have differentiated mediation effects on beliefs in conspiracy theories.

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.

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.

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.

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.

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.

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.

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.