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Online social media platforms have emerged as integral channels for facilitating social interactions, with celebrities utilizing these platforms to engage with their fan base and cultivate a substantial following. The group of engaged fans, commonly referred to as “active fans”, represents individuals who actively communicate with celebrities and actively participate in discussions pertaining to the celebrities’ endeavors. For celebrities, the task of retaining and augmenting the count of active fans holds immense significance, as it significantly amplifies their social impact and commercial value. Here, we construct dynamic weighted active fan networks by leveraging data from 2021 on Sina Weibo, which stands as China’s largest social media platform. Through a comparative analysis encompassing the network’s structure, the growth rate and the duration of active fans, we delve into the influence wielded by six distinct thematic categories, endorsement, variety, public welfare, sports, music and national affairs. This analysis covers a cohort of nine celebrities spanning five diverse domains, including actors, singers, online influencers, anchors and athletes. The growth trajectory and life cycle exhibited by celebrity fans exhibit notable variations, both within and across the aforementioned themes. These dynamics are further influenced by the inherent structural attributes of the personal fan network belonging to each celebrity. Employing the K-Shape time series clustering algorithm, we have undertaken an in-depth exploration of outburst growth patterns observed in active fans and determined the optimal value of the number of clusters to be k=4 through comparative analysis. Our findings underscore that the themes of endorsement and public welfare exhibit all four growth patterns, namely Double-Peak, Oscillatory, Single-Peak and Continuous Growth Patterns. In contrast, when considering all themes collectively, they collectively demonstrate a single-peaked decaying growth pattern the insights gleaned from this study not only serve as a valuable reference and guide for celebrities across diverse domains who aspire to bolster and augment their social influence but also contribute to the burgeoning fan economy. Moreover, this research introduces novel perspectives and insights for scrutinizing patterns of fan growth and their corresponding dynamics. [ABSTRACT FROM AUTHOR]

Better understanding of social media uses in crisis situations can help improve disaster management by policy-makers, organizations, businesses, and members of the public. It can also build theoretical understanding of how social life and citizenship incorporate social media usage. This study tracks the evolution of public sentiment in Wuhan, China, during the first 12 weeks after the identification of COVID-19 on the Chinese microblogging platform Sina Weibo. Data consist of 133,079 original Sina Weibo posts dealing with the novel coronavirus. The relative prevalence of eight different emotion groups is traced longitudinally using the ROST Content Mining System and the Emotion Vocabulary of Dalian University of Technology. The study finds a progression from confusion/fear, to disappointment/frustration, to depression/anxiety, then finally to happiness/gratitude. It argues that this progression indexes the changing affective energies of digital medical citizenship, which in turn indicates the context for intervention in future crises. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

PurposeThis study explores the intricate relationship between unemployment rates and emotional responses among Chinese university graduates, analyzing how these factors correlate with specific linguistic features on the popular social media platform Sina Weibo. The goal is to uncover patterns that elucidate the psychological and emotional dimensions of unemployment challenges among this demographic.MethodsThe analysis utilized a dataset of 30,540 Sina Weibo posts containing specific keywords related to unemployment and anxiety, collected from January 2019 to June 2023. The posts were pre-processed to eliminate noise and refine the data quality. Linear regression and textual analyses were employed to identify correlations between unemployment rates for individuals aged 16–24 and the linguistic characteristics of the posts.ResultsThe study found significant fluctuations in urban youth unemployment rates, peaking at 21.3% in June 2023. A corresponding increase in anxiety-related expressions was noted in the social media posts, with peak expressions aligning with high unemployment rates. Linguistic analysis revealed that the category of “Affect” showed a strong positive correlation with unemployment rates, indicating increased emotional expression alongside rising unemployment. Other categories such as “Negative emotion” and “Sadness” also showed significant correlations, highlighting a robust relationship between economic challenges and emotional distress.ConclusionThe findings underscore the profound impact of unemployment on the emotional well-being of university students, suggesting that economic hardships are closely linked to psychological stress and heightened negative emotions. This study contributes to a holistic understanding of the socio-economic challenges faced by young adults, advocating for comprehensive support systems that address both the economic and psychological facets of unemployment.

Sina Weibo, a popular social media platform in China, plays a crucial role in crisis communication. During the 2018 Shouguang flood, the public criticized the government’s delayed, insufficient, and inaccurate information on Weibo, leading to tension between the public and the government. We interviewed 33 Weibo users to understand their experiences in challenging the government’s flood-related information. Key areas of contention included the timing of information release, the portrayal of the crisis, and the government’s role as the primary crisis manager. When the government’s information fails to meet public expectations, it can lead to online discussions that undermine trust. This article delves into theterritory of crisis communication by examining how public online debates evolve in a state-controlled online setting, specifically in China. We propose that diverse interpretations of crisis information serve as a tool to enhance collective crisis response, thereby reducing conflicts among stakeholders.

Social media exhibits an intrinsic capability to broadcast new information. One of its essential characteristics is its ability to transmit and exchange knowledge. With the growth in popularity of social networking sites and their use to spread information, it is more important than ever to spot rumors before they spread. Indeed, the large amount of information on social media makes recognizing the origin of a rumor or conspiracy theory even more challenging. Several deep-learning algorithms, such as recursive neural networks (RvNNs), have been employed to identify rumors based on how they spread. Moreover, machine learning algorithms employed in rumor detection consider patterns of deep information propagation instead of the broad dispersion structures in the data. Therefore, this research introduces a hybrid deep-learning model for information diffusion analysis and rumor detection called the Bidirectional Graph Convolutional Network integrated with Long Short-Term Memory (BiGCN-LSTM), which utilizes two different word embedding approaches: Word2Vec and BERT. Two standard datasets, a Sina Weibo microblog and Twitter16, are used to validate and assess the proposed approach where the main microblog and tweet and their responses are represented in a graph-tree-based structure for obtaining and retrieving important information. Especially the original main microblog and tweet information are integrated into each top-down and bottom-up GCN layer, increasing the effect of the messages delivered by the rumor spreaders. The experimental results showed that the proposed model achieved 92% and 88% accuracy using Word2Vec, and 90% and 95% accuracy using BERT for the Sina Weibo and Twitter16 datasets, respectively. [ABSTRACT FROM AUTHOR]

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Monitoring, analyzing, and managing public sentiment surrounding urban emergencies hold significant importance for city governments in executing effective response strategies and maintaining social stability. In this study, we present a study which was conducted regarding the self-built house collapse incident in Changsha, China, that occurred on 29 April 2022, with a focus on leveraging Sina Weibo (a Twitter-like microblogging system in China) comment data. By employing the Latent Dirichlet Allocation (LDA) topic model, we identified key discussion themes within the comments and explored the emotional and spatio-temporal characteristics of the discourse. Furthermore, utilizing geographic detectors, we investigated the factors influencing the spatial variations in comment data. Our research findings indicate that the comments can be categorized into three main themes: "Rest in Peace for the Deceased", "Wishing for Safety", and "Thorough Investigation of Self-Built Houses". Regarding emotional features, the overall sentiment expressed in the public discourse displayed positivity, albeit with significant fluctuations during different stages of the incident, including the initial occurrence, rescue efforts, and the establishment of accountability and investigative committees. These fluctuations were closely associated with the emotional polarity of the specific topics. In terms of temporal distribution, the peak in the number of comments occurred approximately one hour after the topic was published. Concerning spatial distribution, a positive sentiment prevailed across various provinces. The comment distribution exhibited a stair-like pattern, which correlated with interregional population migration and per capita GDP. Our study provides valuable insights for city governments and relevant departments in conducting sentiment analysis and guiding public opinion trends. [ABSTRACT FROM AUTHOR]

s an important carrier of network public opinion, social media platforms can provide timely and effective information for emergency management of major disaster events such as earthquakes. However, there is still a lack of research on extracting and analyzing the disaster situation with fine-grained topic information contained in social media data. This paper extracts fine-grained seismic topic information contained in social media data based on deep learning algorithms. Take the “Dali Yangbi Ms6.4 earthquake” event on May 21, 2021 as an example, build a framework of topic information extraction process, by optimizing the training of the model, the overall accuracy reaches more than 80%. The newly acquired Qinghai seismic data set was input into the model for verification, and the expected effect was also achieved. The verification results of two earthquakes show that the model is feasible in the extraction of disaster theme information. Finally, combined with the auxiliary public opinion analysis methods such as keyword mining technology, sentiment analysis and kernel density estimation, the earthquake theme was visualized in space and time. It provides new ideas for disaster research and judgment and emergency management. [ABSTRACT FROM AUTHOR]

This article contributes to the understanding of public representations of homosexuality in China by focusing on the case of a homophobic textbook. College student Xixi sued Jinan University Press (JUP) in 2017 for classifying homosexuality as a psychosexual disorder. Three years later, a Chinese court dismissed Xixi's lawsuit against the allegedly homophobic textbook published by JUP. The ruling elicited responses on Chinese social media that demonstrated the polarisation of public opinion regarding homosexuality. This article investigates discursive representations of homosexuality in online space by analysing the public discourse surrounding this problem. Using van Leeuwen's discursive delegitimation strategies (i.e., authorisation, moral evaluation, rationalisation, and mythopoesis), 496 comments posted on Sina Weibo were employed and subjected to discourse analysis. According to our findings, these strategies contribute to public opposition to homosexuality, portrayed as unhealthy, infertile, disruptive, and corrosive. The article concludes by discussing the emerging sociocultural factors on Sina Weibo that influence the anti-homosexuality attitudes of Weibo users. [ABSTRACT FROM AUTHOR]

Studies conducted in Western democratic countries with privately owned press presenting news from different ideological perspectives have found the phenomenon of news overload causes stress in media users (affective load) and this leads to news avoidance behavior. This study, conducting a structural equation modeling using AMOS 27, investigates the relationships between news overload, affective load, and consequent news avoidance behavior in China. The findings suggest, consistent with studies in Western democratic jurisdictions, that news overload and affective load influence users' news avoidance behavior. However, in contrast to studies outside of China, this study found demographic variables of age, gender, education, and occupation had no statistically significant influence on avoidance behavior by Chinese social media news consumers. The results suggest that either social attributes and responses by media users in China are significantly different from those in Western countries or ideology plays an important role in avoidance behavior and the impact of ideology is different across different demographic groups in Western jurisdictions. [ABSTRACT FROM AUTHOR]

China has been implementing garbage classification to improve resource recycling for many years. Since garbage classification is essentially a social activity, it needs the active participation of the public. However, the phenomenon of "high practice, low effect" is widespread in most cities. Therefore, this paper uses the data from Sina Weibo to analyze the reasons for the poor garbage classification effect. First, the key factors affecting residents' willingness to participate in garbage classification are identified based on the text-mining method. Further, this paper analyzes the reasons that promote or hinder the residents' intention of garbage classification. Finally, the resident's attitude towards garbage classification is explored by the score of the text's emotional orientation, and further the reasons for the positive and negative emotional orientation are analyzed, respectively. The main conclusions are as follows: (1) The proportion of residents holding negative sentiment towards garbage classification is as high as 55%. (2) Residents' positive emotions are mainly caused by the public's sense of environmental protection inspired by publicity and education, and the incentive measures taken by the government. (3) The main reasons for negative emotions are imperfect infrastructure and unreasonable garbage sorting arrangements. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Sina Weibo is one of the important social media in the Internet era, and its users play a key role in the development quality of its information services. Analyzing the factors that affect the quality of Sina Weibo information services from the perspective of users will help to improve the information service level, improve the efficiency of information service, and promote the more humanized development of Sina Weibo. [Method/Process] The grounded theory was selected as the research method. Through the process of data collection, three-level coding, saturation test, etc., a model of factors affecting the information service quality of Sina Weibo was constructed. Three factors, namely user base, user experience and user demand, were summarized and their effects on the information service quality were analyzed empirically using statistical methods. The rationality of the model was empirically analyzed by multilevel regression analysis. [Results/Conclusions] The research conclusion shows that user base, user experience and user demand have significant positive effects on Sina Weibo information service quality, among which user base is a guarantee factor, user perception is a key factor, and user demand is a driving factor. To be specific, user base includes user nature, spread scope and interactive experience. A large number of users from a wide range of sources constitute a good online community ecology, which is the fundamental guarantee for the development of Sina Weibo. At the same time, the quality of information service determines the user's experience. Therefore, Sina Weibo should continuously improve the quality of information service to ensure its long-term use. User experience contains user satisfaction and user preference. Specifically, it is reflected in the experience generated by users in the process of using Sina Weibo. Sina Weibo adjusts its information service mode according to users' feelings, so as to improve the quality of information service. Therefore, user experience has a significant positive impact on Sina Weibo information service quality. User demand comprises information authority, information objectivity, information effectiveness, information richness. Specifically, Weibo users tend to pay more attention to public information and have a preliminary judgment on the authority of information according to the identity of the publisher. At the same time, most users hope that the information they publish can be spread widely, which forces the information service quality of Sina Weibo to meet the relevant needs of users. Therefore, user demand has a significant positive impact on Sina Weibo information service quality.

The focus of the paper is to use a single weibo from a user to predict whether the user account is verified, referred to as verified account prediction, on Sina Weibo. To the best of our knowledge, verified account prediction on Sina Weibo has not been studied. For better understanding of the prediction problem, a comprehensive data analysis of weibos related to verified accounts is conducted first. Then, verified account prediction is formulated as a sequence learning problem. Specifically, a weibo from a user is represented as a sequence of feature values by feature hashing and whether the user account is verified is the corresponding label to predict. A deep learning approach is proposed for solving verified account prediction in this formulation. The proposed approach significantly outperforms the shallow learning methods in the comparisons in terms of accuracy and F1 by large margins in the experiments. [ABSTRACT FROM AUTHOR]

While it is increasingly recognised that commercial social media platforms are not neutral tools for online activism, our knowledge of how such platforms concretely intervene in user activities and shape the dynamics of online activism remains limited. This article focuses on analysing the strategies and mechanisms that have been used by Sina Weibo to manage online contention. Based on interviews with Weibo operators and opinion leaders, and analysis of relevant cases and documents, this article finds that in its early days Weibo actively facilitated the production and spread of online contention to establish itself as a key avenue for users to discuss and follow the development of unfolding incidents. Meanwhile, subtle strategies were applied to mitigate the tension between engaging users and conducting censorship. By showing how Weibo managed online contention in order to advance its own commercial interests, this article contributes to a deeper understanding of the role social media platforms play in online activism. [ABSTRACT FROM AUTHOR]

This study examined how different health organizations (i.e., the Chinese CDC, the Korean CDC, the United States CDC, and WHO) communicated about the COVID-19 pandemic on social media, thus providing implications for organizations touse social media effectively in global health crises in the future. Three bilingual researchers conducted a content analysis ofsocial media posts (N = 1,343) of these health organizations on Twitter and Sina Weibo to explore the frames of the COVID-19 pandemic, the purposes, and the strategies to communicate about it. Prevention was the dominant frame of the social media content of these four health organizations. Information update was the major communication purpose for WHO, the United States CDC, and the Korean CDC; however, guidance was the primary communication purpose for the Chinese CDC. The United States CDC, the Chinese CDC, and the Korean CDC heavily relied on multiple social media strategies (i.e., visual, hyperlink, and authority quotation) in their communication to the public about the COVID-19 pandemic, whereas WHO primarily employed quoting authorities. Significantdifferences were revealed across these health organizations in frames, communication purposes, and strategies. Theoretical and practical implications and limitations were discussed. This study examined how different global health organizations communicate about the COVID-19 pandemic on social media. We discussed how and why these global health organizations communicate the COVID-19 pandemic, which would help health-related organizations design messages strategically on global public health issues in the future. [ABSTRACT FROM AUTHOR]

With the advent of the new media era, government social media have become an important paradigm for social governance. We perform a large-sample regression and reveal that the higher the quality of taxation bureaus’ operation of government social media, the lower the degree of local enterprises’ tax avoidance, which works through reducing tax avoidance incentives and increasing the difficulty of committing tax avoidance. Moreover, government social media play a substitution effect on tax enforcement and administration. We also find that government social media should focus on strengthening its official, formal and professional characteristics. Given the significant recent changes in how enterprises handle taxation, the proportion of information that taxation bureaus post on system operation should be appropriately increased.

In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China’s epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the “epidemic data bulletin” and “daily life impact” posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of “sparse east and dense west”, and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people’s lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

It is recognized that a city with a livable environment can bring happiness to residents. In this study, we explored the social media users’ emotional states in their current living spaces and found out the relationship between the social media users’ emotions and urban livability. We adopt six urban livability indicators (including education, medical services, public facilities, leisure places, employment, and transportation) to construct city livable indices. Also, the Analytic Hierarchy Process (AHP) spatial statistic method is applied to identify and analyze the different habitable regions of Wuhan City. In terms of citizen’s emotion analysis, we use Long Short-Term Memory (LSTM) neural network to analyze the Weibo text and obtain the Weibo users’sentiment scores. The correlation analysis of residents’ emotions and city livability results shows a positive correlation between the livable city areas (i.e., the area with higher livable ranking indices) and Weibo users’ sentiment scores (with a Pearson correlation coefficient of 0.881 and P-Value of 0.004). In other words, people who post Weibo in high livability areas of Wuhan express more positive emotional states. Still, emotion distribution varies in different regions, which is mainly caused by people’s distribution and the diversity of the city’s functional areas.

The voluntary frontier settlement hypothesis holds that frontier movements can promote the formation of individualism in the frontier area. The Chuangguandong Movement is one of China's voluntary frontier movements that potentially had a positive impact on the formation of high individualism in the northeastern provinces. Previous studies used independent/ interdependent measures of self-construal scale, symbolic self-inflation, nepotism tasks, and percentage of most common names, to examine the differences in the independence between Heilongjiang and Shandong residents, which may be related to the Chuangguandong Movement. However, these studies were limited by certain factors such as sample size and objectivity of materials acquisition. In this study, we obtained Sina Weibo big data for period 2010-2020 to overcome the limitation of previous work. Using text feature extraction and keyword word frequency calculation methods based on the individualism/collectivism dictionary, we found that the level of individualism in Northeast China was higher than that in Shandong Province, which was consistent with previous research. Through the discussion of the four representative theoretical frameworks of individualism, the voluntary frontier settlement theory was considered as a potential explanation for the high degree of individualism in Northeast China. [ABSTRACT FROM AUTHOR]

Given the benefits and effectiveness of consumer online brand engagement, influencer advertising and marketing have become increasingly popular on social media. Consumer online brand engagement, including consuming, contributing, and creating brand-related content, is closely related to a brand's reputation and image. This study determines how the two aspects related to influencer endorsement—namely, influencer–follower parasocial interaction (PSI) and influencer–brand congruence (congruence)—affect the three levels of consumer online brand-related activities (COBRAs) on Sina Weibo. We also examined the mediation effect of consumers' personal identification (PI) and social identification (SI) with brands in this process. The data were collected from 390 Weibo users in China, and valid data were analyzed using structural equation modeling (SEM). The results show that PSI and congruence have a direct positive effect on consuming brand-related content; PSI has an indirect influence on contributing brand-related content through SI's mediation effect; and both PSI and congruence contributed to creating brand-related content, with the mediating effect of PI and SI. This study extends the literature on the motivation of consumer–brand engagement (engagement) on social media and discusses the roles of PI and SI in influencer marketing, filling a gap in the research and bringing new perspectives for brands and corporates. [ABSTRACT FROM AUTHOR]

Monitoring, analyzing, and managing public sentiment surrounding urban emergencies hold significant importance for city governments in executing effective response strategies and maintaining social stability. In this study, we present a study which was conducted regarding the self-built house collapse incident in Changsha, China, that occurred on 29 April 2022, with a focus on leveraging Sina Weibo (a Twitter-like microblogging system in China) comment data. By employing the Latent Dirichlet Allocation (LDA) topic model, we identified key discussion themes within the comments and explored the emotional and spatio-temporal characteristics of the discourse. Furthermore, utilizing geographic detectors, we investigated the factors influencing the spatial variations in comment data. Our research findings indicate that the comments can be categorized into three main themes: “Rest in Peace for the Deceased”, “Wishing for Safety”, and “Thorough Investigation of Self-Built Houses”. Regarding emotional features, the overall sentiment expressed in the public discourse displayed positivity, albeit with significant fluctuations during different stages of the incident, including the initial occurrence, rescue efforts, and the establishment of accountability and investigative committees. These fluctuations were closely associated with the emotional polarity of the specific topics. In terms of temporal distribution, the peak in the number of comments occurred approximately one hour after the topic was published. Concerning spatial distribution, a positive sentiment prevailed across various provinces. The comment distribution exhibited a stair-like pattern, which correlated with interregional population migration and per capita GDP. Our study provides valuable insights for city governments and relevant departments in conducting sentiment analysis and guiding public opinion trends.

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.

By incorporating social representation theory with science communication and by using a critical milestone scientific crisis (i.e., the scandal of Chinese gene-edited human babies in 2018) as a dividing point, this study adopted a network agenda-setting approach to explore how various actors (i.e., scientists, the media, laypeople, and the government) engaged in the construction of social representations of the controversial issue of gene editing on Chinese social media (i.e., Sina Weibo). Based on large-scale social media data, supervised machine learning was employed to identify attribute categories, and semantic network analysis was used to construct attribute networks. Results reveal that after the 2018 crisis, gene editing received increasing social attention on Chinese social media. Further, two trends emerged in social representations of gene editing on social media: de-scientization and medialization. The following dynamic agenda interactions among various actors were found: On the one hand, the media and laypeople's attribute network agendas converged while scientists and the media's diverged after the scandal. This indicates a scientific crisis can serve as a trigger for agenda convergence and divergence among different actors online. On the other hand, there were constant agenda interactions, such as between the Chinese government and the media. This reveals a feature of Chinese science communication—the media not only mediates between scientists and the public, it also observes the government's agenda closely when representing controversial scientific issues such as gene editing. [ABSTRACT FROM AUTHOR]

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., (© Zhenwen Zhang, Jianghong Zhu, Zhihua Guo, Yu Zhang, Zepeng Li, Bin Hu. Originally published in JMIR Mental Health (https://mental.jmir.org).)

Optimizing the spatial distribution of public service facilities (PSF) is important to promote the quality of life and the equitable distribution of public services. The spatial distribution of PSF is not only related to public service capability but also related to individual preference, so it is essential to integrate residents' preferences and the service capability of PSF for PSF evaluation. This study proposes a new evaluation model for the spatial distribution of PSF by integrating four indicators. We perceive subjective preferences from Sina Weibo data to construct the online popularity indicator and construct three indicators including service area ratio, service population ratio, and service coordination to characterize the service capability of PSF by using land cover data, nighttime light data, road network data, and demographic data. We take Wuhan for example and show the veracity of results and the model's superiority through field investigation and comparative analysis. The results show that the model has high accuracy and timeliness, and it is free from the influence of administrative boundaries. This study is expected to provide the auxiliary basis of PSF evaluation and decision-making for urban planning and relative departments of management. [ABSTRACT FROM AUTHOR]

It is recognized that a city with a livable environment can bring happiness to residents. In this study, we explored the social media users' emotional states in their current living spaces and found out the relationship between the social media users' emotions and urban livability. We adopt six urban livability indicators (including education, medical services, public facilities, leisure places, employment, and transportation) to construct city livable indices. Also, the Analytic Hierarchy Process (AHP) spatial statistic method is applied to identify and analyze the different habitable regions of Wuhan City. In terms of citizen's emotion analysis, we use Long Short-Term Memory (LSTM) neural network to analyze the Weibo text and obtain the Weibo users' sentiment scores. The correlation analysis of residents' emotions and city livability results shows a positive correlation between the livable city areas (i.e., the area with higher livable ranking indices) and Weibo users' sentiment scores (with a Pearson correlation coefficient of 0.881 and P-Value of 0.004). In other words, people who post Weibo in high livability areas of Wuhan express more positive emotional states. Still, emotion distribution varies in different regions, which is mainly caused by people's distribution and the diversity of the city's functional areas. [ABSTRACT FROM AUTHOR]

In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China's epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the "epidemic data bulletin" and "daily life impact" posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of "sparse east and dense west", and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people's lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19. [ABSTRACT FROM AUTHOR]

Situated within the context of online firestorms on Chinese social media platforms, this study combines crisis communication literature and connective action logic, to examine what factors contribute to public engagement in negative word-of-mouth (n-WoM) behaviors in online firestorms. In doing so, this study uses a computational method to leverage large-scale longitudinal data of social media users' digital traces on Sina Weibo and conceptualizes two dimensions of collective legitimacy sources of online firestorms—message and information network legitimacy, revealing their multilayered functional roles in escalating n-WoM engagement. Specifically, our findings suggest that the negativity of social media posts functions as an intensifier of collective message legitimacy escalating publics' n-WoM engagement, while the observation of a greater cumulative number of negative comments functions as a downtoner for n-WoM engagement. In addition, collective legitimacy is contributed by authority and endorsement sources of information networks. Posts from authoritative sources (KOL, media, and corporation) tend to trigger more subsequent public engagement in n-WoM than posts from endorsement sources (ordinary users). Ability-related online firestorms are more likely to have more n-WoM commenting from netizens than social responsibility-related ones. Distinct differences are found between ability- and social responsibility-related firestorms in terms of the impacts of collective legitimacy sources on eliciting n-WoM engagement. • Theorize how two dimensions of collective legitimacy sources contribute to the engagement of negative word-of-mouth (n-WoM). • Employs an unobtrusive computational method of collecting data of digital traces of social media users in online firestorms. • Negativity of online messages intensifies collective legitimacy, boosting publics' n-WoM engagement in online firestorms. • Authority and endorsement sources of information networks contribute to collective legitimacy and trigger n-WoM engagement. • Corporate ability firestorms elicit more n-WoM commenting behaviors than corporate social responsibility firestorms. [ABSTRACT FROM AUTHOR]

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users' posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly. [ABSTRACT FROM AUTHOR]

This study provides a network perspective to segment social-mediated publics in digital activism based on social media users' influence on the information dissemination process. We used social network analysis to segment publics based on their information sharing behaviors in the case of the Chinese #MeToo movement in 2018 on Sina Weibo. Besides, we conducted a content analysis to reveal the characteristics of each type of publics. We have identified three meaningful social-mediated publics in the Chinese #MeToo movement on Sina Weibo: (1) the provoking public, who plays an essential role in initiating the discussion; (2) the bridging public, which contributes to spreading the discussion to various groups of people; and (3) the powerful public, who has the advantages of the first two segments of publics and is the most powerful one in the formation and development of the online discussion. Three publics have different characteristics. The provoking public (e.g., media and internet celebrities) typically has many followers and is interested in various social issues. The bridging public has fewer followers (e.g., grassroots individuals) but tends to have a special interest in the issue and actively uses social media. The powerful public has a large number of followers, a particular interest in this issue, and is active on social media. This public segmentation provides a relatively new perspective to understand publics in digital activism in societies with strict social norms and media censorship. Limitations and future research are discussed. [ABSTRACT FROM AUTHOR]

Authorities in the People's Republic of China communicate with citizens using an estimated 600,000 Sina Weibo microblogs. This study reports on a study of Chinese citizens' adoption of microblogs to interact with the government. Adoption results from trust and peer pressure in smaller-network ties (densely knit, pervasive social networks surrounding individual citizens). Larger-network ties (trust in institutions at large, such as the Chinese Communist Party, executive organizations, the judicial system, the media, etc.) are not associated with the adoption of microblogging. Furthermore, higher levels of anxiety are correlated with lower levels of use intention, and this finding underlines the impact of the Chinese authority's surveillance and control activities on the lives of individual Chinese citizens. Based on these findings, we outline a theory of why citizens use microblogs to interact with the government and suggest avenues for further research into microblogs, state–citizen communication patterns and technology adoption. Points for practitioners: Our research identifies trust in individual civil servants, citizens' anxiety and peer pressure as drivers of Chinese citizens' intentions to use the Weibo microblogging platform to interact with the Chinese government. This insight allows practitioners to better understand citizens' drivers and obstacles in the use of social media in state–citizen relations in China. [ABSTRACT FROM AUTHOR]

The mobile game "Immortal Conquest," created by NetEase Games, caused a dramatic user dissatisfaction event after an introduction of a sudden and uninvited "pay-to-win" update. As a result, many players filed grievances against NetEase in a court. The official game website issued three apologies, with mix results, to mitigate the crisis. The goal of the present study is to understand user feedback content from the perspective of Situational Crisis Communication Theory through semantic network analysis and sentiment analysis to explore how an enterprise's crisis communication strategy affects users' attitudes. First, our results demonstrate that the diminishing crisis communication strategies (excuse and justification) do not change players' negative attitudes. It was not a failure because it successfully alleviated the players' legal complaints and refocused their attention on the game itself. Second, the rebuild (apology & compensation) strategy was effective because it significantly increased the percentage of positive emotions and regenerated expectations for the game. The litigation crisis was identified within gamer communications with respect to Chinese gaming companies for the first time. Nevertheless, this does not indicate an increase in overall legal awareness among the larger Chinese population. It may only reflect greater legal awareness among Chinese online gamers. Fourth, gamers emphasized that they and enterprises should be equally involved when communicating with each other. Finally, in-game paid items should be reasonably priced, otherwise, they will drive users to competitors. [ABSTRACT FROM AUTHOR]

Social media (e.g., Sina Weibo) have the advantage of reflecting traffic information, including the reasons for jams, illegal behaviors, and emergency recourses on roads. However, there remains a challenging issue regarding how to sufficiently mine traffic information. In this paper, we propose a deep learning-based method that uses social media data for traffic jam management. The core ideas of the proposed method are twofold. First, a multichannel network with a Long Short-Term Memory layer (LSTM-layer) and a Convolution layer (Conv-layer) (termed as MC-LSTM-Conv) is proposed. This model consists of two information channels for extracting abstract features from input text. Each channel includes two Conv-layers, and an LSTM-layer is added to one of the four Conv-layers. The MC-LSTM-Conv model is used to extract check-in microblogs reflecting traffic jams from mass Sina Weibo data. Second, a series of matching rules are constructed based on the keywords that are related to traffic-jam scenes. These rules further classify the microblogs extracted by the first step into four classes, and each of the classes reflects a specific road condition (i.e., traffic accidents or large-scale activities, road construction, traffic lights, and the low efficiency of government agencies). Experiments on Sina Weibo data demonstrate that the proposed multichannel network has superior performance in extracting microblogs about traffic jams. The keyword fuzzy matching method can fetch detailed information about traffic jams efficiently. [ABSTRACT FROM AUTHOR]

Most current automatic summarization methods are for English texts. The distinction between words in Chinese text is large, the types of parts of speech are many and complex, and polysemy or ambiguous words appear frequently. Therefore, compared with English text, Chinese text is more difficult to extract useful feature words. Due to the complex syntax of Chinese, there are currently relatively few automatic summarization methods for Chinese text. In the past, only the important sentences in the original text can be selected and simply arranged to obtain a summary with chaotic sentences and insufficient coherence. Meanwhile, because Chinese short text usually contains more redundant information and the sentence structure is not neat, we propose a topic-based automatic summary method for Chinese short text. Firstly, a key sentence selection method is proposed combining topic words and TF-IDF to obtain the score of each text corresponding to the topic in the original text data. Then the sentence with the highest score as the topic sentence of the topic is selected. Considering that the short text of Weibo may contain a lot of irrelevant information and sometimes even lack some important components of topic, three retouching mechanisms are proposed to improve the conciseness, richness and readability of topic sentence extraction results. We validate our approach on natural disaster and social hot event datasets from Sina Weibo. The experimental results show that the polished topic summary not only reflects the exact relationship between topic sentences and natural disasters or social hot events, but also has rich semantic information. More importantly, we can almost grasp the basic elements of natural disaster or social hot event from the topic sentence, so as to help the government guide disaster relief or meet the needs of users for quickly obtaining information of social hot events.