Weibo

Sijia Li, Yilin Wang, Jia Xue, Nan Zhao, T. Zhu

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.

Lifang Li, Qingpeng Zhang, Xiao Wang, J. Zhang, Tao Wang, Tian-Lu Gao, Wei Duan, K. Tsoi, Fei-yue Wang

During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.

Qingyue Sun, Runze Ding

Through a qualitative analysis of feminist posts shared by Chinese beauty influencers, this study explores the political potential and limitations of their engagements in digital feminism on Weibo. Chinese beauty influencers show the potential to shape everyday feminist discourses and promote female solidarity on Weibo. However, beauty influencers’ feminist practices represent a form of contested activism, situated at the intersection of state governance, platform power, and entrepreneurial demands, subtly reconfiguring digital feminism in China. By converting feminist sentiments into brand assets through self-branding and promotional activities, influencers’ feminist practices feed into the platform’s profit motives and their own career advancements. Moreover, beauty influencers adopted what we call “state-aligned, soft activism,” inadvertently perpetuating the non-emancipatory gender discourses endorsed by state governance. Their engagement with digital feminism exemplifies the ambivalent feminist politics within the Chinese digital space dominated by state control and commercial logic, reflecting inherent contradictions and broader tensions in conducting meaningful feminist activism within a constrained environment.

S. Javed, M. Rashidin, Wang Jian

The present study enhances our understanding of followers’ perceptions of the information credibility of Chinese social media influencers. Employing the heuristic-systematic model, we examined the influence of source credibility and argument quality on the content credibility of micro-influencers and their relational impact on followers’ attitudes and behavioural decisions, with involvement as a moderator. Chinese respondents who follow beauty influencers on Sina Weibo were targeted. The respondents were contacted by posting a web link on WeChat to the survey created on Sojump. Structural equation modelling was used to examine the relationship between variables. The results revealed that argument quality (i.e. systematic cue) and source credibility (i.e. heuristic cue) are significantly affect the information credibility perceived by consumers. The findings also indicate that perceived information credibility has a significant impact on brand/video attitude and purchase intention. There are notable theoretical extensions to the literature on information processing, attitude and consumer behaviour.

Zhenwen Zhang, Jianghong Zhu, Zhihua Guo, Yu Zhang, Zepeng Li, Bin Hu

Abstract Background Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for depression continue to face significant barriers to timely diagnosis and effective treatment, thereby exacerbating a burgeoning social health crisis. Objective This study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo. Methods First, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical transformer network for learning user-level semantic representations, which consists of 3 primary components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder learns semantic embeddings from individual posts, while the post-level encoder explores features in user post sequences. The semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation that can be classified as depressed or nondepressed. Next, a classifier is employed to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the post content from individuals with and without depression using the Chinese Linguistic Inquiry and Word Count. Results We divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each validation and test set comprised 600 users, with 300 individuals from both cohorts (depression and nondepression). Our method achieved an accuracy of 84.62%, precision of 84.43%, recall of 84.50%, and F1-score of 84.32% on the test set without employing sampling techniques. However, by applying our proposed retrieval-based sampling strategy, we observed significant improvements in performance: an accuracy of 95.46%, precision of 95.30%, recall of 95.70%, and F1-score of 95.43%. These outstanding results clearly demonstrate the effectiveness and superiority of our proposed depression risk detection model and retrieval-based sampling technique. This breakthrough provides new insights for large-scale depression detection through social media. Through language behavior analysis, we discovered that individuals with depression are more likely to use negation words (the value of “swear” is 0.001253). This may indicate the presence of negative emotions, rejection, doubt, disagreement, or aversion in individuals with depression. Additionally, our analysis revealed that individuals with depression tend to use negative emotional vocabulary in their expressions (“NegEmo”: 0.022306; “Anx”: 0.003829; “Anger”: 0.004327; “Sad”: 0.005740), which may reflect their internal negative emotions and psychological state. This frequent use of negative vocabulary could be a way for individuals with depression to express negative feelings toward life, themselves, or their surrounding environment. Conclusions The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and noninvasive prediction of depression among online social media users.