Sina Weibo Research Articles

A Sentiment Analysis Model Based on Attention Map Convolutional Network

Sina Weibo has evolved into a daily social tool for people, yet effectively leveraging its data for sentiment analysis remains a challenging task due to the presence of information beyond text, such as emojis or images. In this paper, we propose an attention graph convolutional network (AGCN) for fine-grained sentiment classification of Weibo posts. Utilizing an attention network based on cosine similarity, the rich emotional information embedded in emoji features interacts with the textual content, effectively enhancing the capability to represent emotions in the text. Leveraging the characteristics of attention networks to construct a graph structure effectively enables graph convolutional networks to capture higher-order relationships between words in textual features. This approach addresses the challenge of extracting sentiment tendencies from Weibo comments. Experimental results on public data sets demonstrate the effectiveness of AGCNs.

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study.

Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigorous external validation. By analyzing a large-scale social media dataset (Sina Weibo), this paper aims to fully cover varied forms of negative attitudes and develop a classification model for predicting negative attitudes as a whole, and then to externally validate its performance on population and individual levels. 938,866 Weibo posts with relevant keywords were downloaded, including 737,849 posts updated between 2009 and 2014 (2009-2014 dataset), and 201,017 posts updated between 2015 and 2020 (2015-2020 dataset). (1) For model development, based on 10,000 randomly selected posts from 2009 to 2014 dataset, a human-based content analysis was performed to manually determine labels of each post (non-negative or negative attitudes). Then, a computer-based content analysis was conducted to automatically extract psycholinguistic features from each of the same 10,000 posts. Finally, a classification model for predicting negative attitudes was developed on selected features. (2) For model validation, on the population level, the developed model was implemented on remaining 727,849 posts from 2009 to 2014 dataset, and was externally validated by comparing proportions of negative attitudes between predicted and human-coded results. Besides, on the individual level, similar analyses were performed on 300 randomly selected posts from 2015 to 2020 dataset, and the developed model was externally validated by comparing labels of each post between predicted and actual results. For model development, the F1 and area under ROC curve (AUC) values reached 0.93 and 0.97. For model validation, on the population level, significant differences but very small effect sizes were observed for the whole sample (χ 2 1 = 32.35, p < 0.001; Cramer's V = 0.007, p < 0.001), men (χ 2 1 = 9.48, p = 0.002; Cramer's V = 0.005, p = 0.002), and women (χ 2 1 = 25.34, p < 0.001; Cramer's V = 0.009, p < 0.001). Besides, on the individual level, the F1 and AUC values reached 0.76 and 0.74. This study demonstrates the efficiency and necessity of machine learning prediction of negative attitudes as a whole, and confirms that external validation is essential before implementing prediction models into practice.

The Relationship Between Social Media Information Sharing Characteristics and Problem Behaviors Among Chinese College Students Under Recommendation Algorithms.

With the development of information technology and various social media, recommendation algorithms have increasingly more influence on users' social media usage. To date, there has been limited research focused on analyzing the impact of recommendation algorithms on social media use and their corresponding role in the development of problematic behaviors. The present study analyzes the impact of recommendation algorithms on college students' information sharing and internalizing, externalizing problem behaviors to address the aforementioned shortcomings. An online questionnaire survey was conducted among 34,752 college students in China. A latent profile analysis was conducted to explore the various behavioral patterns of Chinese college students' information sharing across the three social media platforms identified for this study. The Bolck-Croon-Hagenaars (BCH) method Regression Mixture Modeling was then used to analyze the differences in internalizing and externalizing problem behaviors among the different subgroups of Chinese college students. The level of information sharing by college students across different social media platforms could be divided into "WeChat Moments low-frequency information sharing", "middle-frequency comprehensive information sharing", "TikTok high-frequency information sharing", and "Sina Weibo high-frequency information sharing". Significant differences were observed regarding internalizing and externalizing problem behaviors among college students in different information-sharing subgroups. This study identified four subgroups with different information-sharing characteristics using latent profile analysis. Among them, college students who are in subgroup of social media information sharing influenced by recommendation algorithms exhibit higher frequency of information sharing and higher level of internalizing and externalizing problematic behaviors. These results expand our understanding of college students' social media usage and problem behaviors from a technological perspective. In future, the negative impacts of recommendation algorithms on college students can be reduced by improving their awareness of these algorithms and optimizing the algorithms themselves.

Social Media in Shaping Public Opinion Roles and Impact: A Systematic Review

This systematic literature review examines the impact of social media on public opinion and its implications for policy-making. Utilising the PRISMA framework, the study analysed 19 articles from Scopus and Web of Science databases published between 2013-2023. The review identified five main categories of social media platforms discussed; Twitter/X, Meta (Facebook, Instagram, WhatsApp), YouTube, Chinese apps (Sina Weibo, WeChat, QQ), and several unspecified platforms. Key themes emerged across these categories, including the role of social media in knowledge dissemination, creation of filter bubbles and echo chambers, amplification of diverse voices, and spread of misinformation. The findings highlighted social media potential for real-time public opinion monitoring, facilitating engagement between policymakers and citizens, and early identification of emerging issues. However, challenges such as information credibility and algorithmic curation of content were also noted. The review suggests that strategic use of social media can raise awareness and mobilise support for global initiatives like the Sustainable Development Goals. It emphasises the need for policymakers to understand and leverage social media's influence on public sentiment while addressing associated risks. The study contributes to a nuanced understanding of how different social media platforms shape public discourse and influence policy decisions in the digital age. Future research directions are proposed to further explore the complex dynamics between social media, public opinion, and governance in an evolving technological landscape. Keywords: Social media impact, public opinion formation, digital policymaking, online political discourse, civic engagement.

Exploring the Chinese public's affective attitudes towards digital transformation in agriculture: A social media-based analysis.

This study utilizes natural language processing techniques and panel vector autoregression methodology, to delve into the perceived attitudes of social media users towards the digital transformation of agriculture, and to assess its impact on total agricultural output and agricultural science and technology inputs. Data related to agricultural digital transformation were collected from Sina Weibo using web crawlers. The SnowNLP model was employed to infer users' attitudes, encompassing both positive and negative aspects. Furthermore, the study delves into the specific themes capturing users' positive attitudes and explores regional variations in focus. The findings reveal a sustained increase in users' interest in agricultural digital transformation since 2013. Positive attitudes primarily center around green development, agricultural intelligence, and global cooperation and innovation. Moreover, the study establishes a significant positive impact of users' positive attitudes on both total agricultural output value and agricultural science and technology investment, highlighting the constructive influence of user support on the agricultural industry's development.

Attention and sentiment of Chinese public toward rural landscape based on Sina Weibo

Rural landscapes, as products of the interaction between humans and nature, not only reflect the history and culture of rural areas but also symbolize economic and social progress. This study proposes a deep learning-based model for Weibo data analysis aimed at exploring the development direction of rural landscapes from the perspective of the Chinese public. The research reveals that the Chinese public’s attention to rural landscapes has significantly increased with the evolution of government governance concepts. Most people express a high level of satisfaction and happiness with the existing rural landscapes, while a minority harbor negative emotions towards unreasonable new rural construction. Through the analysis of public opinion regarding rural landscapes, this study will assist decision-makers in understanding the mechanisms of public discourse on social media. It will also aid relevant scholars and designers in providing targeted solutions, which hold significant importance for policy formulation and the exploration of specific development patterns.

How can urban green space be planned for a ‘happy city’? Evidence from overhead- to eye-level green exposure metrics

The benefits of urban green spaces (UGSs) for human health have been extensively documented. Nevertheless, few studies have incorporated multidimensional UGS exposure indicators, and little is known about the effectiveness of different metrics that should be prioritized as nature-based solutions for improving mental well-being. This study aimed to investigate the associations between various UGS exposure metrics and residents’ expressed happiness (EH) as well as to determine the prioritization of metrics in Nanjing, China, a megacity with 9.5 million inhabitants. The study region was divided into 500 m × 500 m grids, and 330,000 geotagged posts from social media (Sina Weibo) were retrieved for sentiment analysis using the Natural Language Processing (NLP). We developed a systematic UGS exposure framework using satellite, land-cover, and street view-derived data, encompassing 17 indicators of composition and configuration at overhead level as well as street green space visibility and perceived quality at eye level. A regression model and Likelihood Ratio Test were used to examine the associations between various UGS indicators and EH and determine the prioritization of indicators. The results indicated that UGS size had the greatest potential for promoting residents’ EH, followed by overall greenness in the surrounding area, aggregated UGS, perceived quality, and visibility of street green spaces. This study also found that overhead-level metrics may be more effective than eye-level metrics in enhancing residents’ EH, although both perspectives showed significant associations with EH. These findings provide valuable insights into health-oriented landscapes and urban planning to promote the development of a “happy city,” particularly in low-green resource settings in low- and middle-income countries.

Assessing the Interplay Between Public Attention and Government Responsiveness With Digital Trace Data: Navigating Leadership and Followership in China’s COVID-19 Vaccination Campaign

China’s digital campaign to “vaccinate all who can be vaccinated,” officially launched in December 2020, carries global implications. Grounded in the agenda-setting framework to reveal how the responses of different groups are shaped and their agendas interact, this study analyzed two years of Sina Weibo (the largest Chinese microblogging service) and Baidu (the leading search engine in China) search index data to investigate the interrelationships among different groups’ issue foci and the effects of sentiment, rationality, and moral motivation in the agenda-setting process. Large-scale computational analyses were conducted to determine the extent to which the Chinese government followed the public’s issue preferences and identify which segment of the public had a stronger ability to set the agenda. The results indicated that as the central government transitioned from leading to following the public, regional governments had a greater impact on the public agenda compared to the central government or media. The government, public, and media differed in their usage of sentiment and moral motivation on social media during the vaccination campaign, and this varied depending on the campaign’s stage. Notably, all stakeholders emphasized individual-centered values over community-centered values when addressing vaccination. The findings shed light on effective strategies for social mobilization through targeted public health messaging.

The Impact of Short Video Addiction on Self-identity : Mediating Roles of Self-esteem and Appearance Anxiety

Purpose: This study aims to evaluate the attributes of female self-identity, and investigate the correlation between addiction to short videos, self-esteem, objectification of female content, internalization of beauty ideals, and self-identity. The goal is to develop a psychological framework that enhances our comprehension of the self-identity of contemporary Chinese women.&lt;br /&gt; Methodology: This study employed an online survey with a convenience sampling approach to gather responses from 400 female short video users from four most popular Chinese social media platforms: Sina Weibo, Xiaohongshu, Douban and WeChat, the Chinese equivalents of Twitter, Instagram, Reddit and WhatsApp respectively.&lt;br /&gt; Results: The results suggest that (1) self-esteem played a significant role in mediating the relationship between short video addiction and self-identity; (2) the chain mediating effect of internalisation of beauty ideals and self-esteem was not statistically significant, and (3) the chain mediating effect of objectification of female content and self-esteem was statistically significant. Therefore, the greater the short video addiction, the lower self-esteem, the deeper the appearance anxiety, and thus the higher the likelihood that the individual would not identify with themselves. The results provide a theoretical foundation for gaining a deeper understanding of the factors that influence females’ self-identity. Simultaneously, it offers new ideas for enhancing women’s selfidentity. In addition, previous studies on women’s appearance anxiety have given less attention to self-esteem and self-identity. This study, however, looks more extensively into both aspects, so broadening the investigation of the factors impacting women’s self-identity.

Digital data and personality: A systematic review and meta-analysis of human perception and computer prediction.

In recent years, our increasing use of technology has resulted in the production of vast amounts of data. Consequently, many researchers have analyzed digital data in attempt to understand its relationship with individuals' personalities. Such endeavors have inspired efforts from divergent fields, resulting in widely dispersed findings that are seldom synthesized. In this two-part study, we draw from two distinct areas of personality prediction across psychology and computer science to explore the convergent validity of self-reports with human perception and machine learning algorithms, the identifiability of the Big Five traits, and the predictability of different types of data. In Study 1, five meta-analyses of human perception studies integrating findings from 24,124 individuals rated across 30 independent samples demonstrated moderate convergent validity across all traits (ranging from ρ = 0.38 for Neuroticism, to ρ = 0.57 for Openness). In Study 2, a multilevel meta-analysis of computer prediction studies reporting 534 effect sizes across 42 studies also demonstrated moderate convergent validity (ρ = 0.30). Multivariate analyses of the significant moderators highlighted that X, Facebook, Sina Weibo, videos, and smartphones had a negative impact on the variance identified. Finally, in synthesizing the extant literature, we discuss the measures used to assess personality and the analytical approaches adopted. We identify the strengths and limitations across each field and explain how interdisciplinary methodologies could advance the testing and development of psychological theory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Gendered Manifestations of Para-social Interactions between Female Fans and Male Idols in China

This study aims to reveal the gendered manifestations of para-social interactions between female fans and male idols in social media platforms in China, to clarify in which aspects they serve to reinforce the traditional gender order and in which aspects they deviate from the traditional gender norms, with a view to contributing positively to the optimisation of the relationship between female fans and male idols. Digital ethnography approach is used in this work, based on the Sina Weibo platform, to conduct a 15-day online observational study of seven Chinese male idols, namely Ma Jiaqi, Liu Yaowen, Song Yaxuan, Deng Jiaxin, Tang Yuzhe, Zhu Yilong, and Yang Yang, as well as their female fans. The blind consumption and unpaid labour of Chinese female fans for their male idols, as well as the polygamous "courtly love" relationship between the two, reaffirm and reinforce the traditional gender order. At the same time, the transformation of Chinese female fans from erotic objects to erotic subjects, their love for multiple male idols, their concern for ambiguous relationships between male idols, and their adherence to self-principle and subjectivity have rebelled against and even subverted traditional gender norms. However, the power to counteract the traditional gender order is not strong enough, and is still limited by factors such as exclusivity within the fan community, heterosexual hegemony, and idolatry that is as crazy and irrational as religious beliefs.

Protection or commodification of women? Discursive construction of bridewealth on Chinese social media

ABSTRACT Bridewealth, the long-standing practice of groom-to-bride monetary transfer, is now facing growing backlash in China. Researchers mostly approach bridewealth from the perspectives of anthropology and economics, highlighting its social impact yet leaving public opinions on it less heeded. To fill this niche, this study collected the most “liked” 5,000 posts from Sina Weibo, identified dominant themes of bridewealth supporters and detractors, and analyzed how these themes are (de)legitimatized. Drawing on feminist critical discourse analysis and van Leeuwen’s analytical toolkit, content analysis reveals that supporters mainly see bridewealth as a protection of women, an investment in the new families, and a worthy tradition to follow, whereas opponents associate it with the commodification of women, financial challenges to men’s natal families, and long-gone feudal shackles. These themes are enacted with strategies like rationalization, moralization, victimization, and authorization. This study stands out for situating bridewealth in media studies and illuminating the contested public discourse on it. More importantly, given that most supporters are women and they bear the brunt of the family-work balance, the study also gives voice to the much-silenced women’s marriage-related concerns in institutional media so that their welfare and self-actualization can be accommodated in future policy-making.

Public Attitudes and Sentiments toward Common Prosperity in China: A Text Mining Analysis Based on Social Media

Since 2021, China’s promotion of common prosperity has captured global attention and sparked considerable debate. Yet, scholarly examination of the Chinese public’s attitudes toward this policy, which is crucial for guiding China’s strategic directions, remains limited. To address this gap, this paper collects 256,233 Sina Weibo posts from 2021 to 2023 and utilizes text mining methods such as temporal and trend analysis, keyword analysis, topic analysis, and sentiment analysis to investigate the attitudes and emotions of the Chinese people towards common prosperity. The posts holding negative sentiments are also analyzed, so as to uncover the underlying reasons for the dissatisfaction among Chinese citizens regarding common prosperity. Our analysis reveals that China’s strategy for promoting common prosperity is primarily focused on economic development rather than wealth redistribution. Emphasis is placed on enhancing education, achieving regional balance, implementing market-oriented reforms, and improving livelihoods. Notably, there is increasing public dissatisfaction, particularly with issues such as irregularities in financial and real estate markets, growing wealth inequality, exploitation by capital, generation of illicit income, and regional development imbalances. These challenges necessitate urgent and effective policy interventions.

Analyzing the impact of unemployment on mental health among Chinese university graduates: a study of emotional and linguistic patterns on Weibo.

This 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. The 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. The 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. The 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.

Epidemiological characteristics and behaviors of online broadcast suicidality in China: implications for targeted prevention strategies.

Suicide is a global health concern, exacerbated by stigma around mental illnesses. Online platforms like Twitter and Sina Weibo have seen a rise in "online broadcast suicide," where individuals share suicidal thoughts and actions. However, there is limited understanding of the epidemiological characteristics, particularly in China. This study aims to analyze the demographics and behaviors of individuals engaging in online broadcast suicide in China to inform targeted prevention strategies. A total of 525 incidents were identified through systematic retrieval of relevant news reports from online sources. Subsequently, a content analysis was performed on these reports to extract detailed information on the characteristics of each individual incident. Among the incidents analyzed, the male-to-female ratio was 1:1.6, with an average age of 23.1 ± 5.9 years. Approximately 71.9% took place in Southern China. Unemployment was reported in 15.0% of incidents. Relationship breakup (62.3%) was cited as the leading cause of suicide. Wrist cutting (58.2%) emerged as the predominant suicide method, and home (36.2%) was the most common location for these tragic events. Instant messaging apps were the primary platforms (54.7%) for conveying suicidal thoughts and actions. Additionally, among the 525 incidents examined, 12.0% disclosed having a mental disorder, and 7.6% had a history of prior suicide attempts. Significant variations were observed across age, gender, region, and occupation categories. This study emphasizes the importance of developing suicide prevention programs for internet users. Besides, interventions should be customized to meet the specific needs of various populations.

Does transparency facilitate the fundraising capacity of public welfare crowdfunding? An empirical examination on Weibo

PurposeInformation opacity in donation crowdfunding activities has constrained the healthy development of China’s public welfare activities. Addressing the trust crisis and enhancing public engagement warrants further investigation. This study aims to uncover the moderating effect of activity transparency by utilizing data from 1,029 donation crowdfunding projects on the Sina Weibo Public Welfare Social Platform. In this way, we seek to elucidate the impact of donation crowdfunding events on fundraising ability.Design/methodology/approachThis study selects text complexity, number of supporters, creator experience, and social capital as explanatory variables; innovatively selects the number of updates of online crowdfunding activities and total reading volume as moderating variables; selects the number of shares of crowdfunding activities as a mediating variable; and constructs a moderated mediation multiple regression model for fundraising ability.FindingsOur findings indicate that independent variables, such as text complexity, number of supporters, and social capital, can significantly affect the dependent variable, fundraising ability. However, creator experience does not influence fundraising ability. Furthermore, social interaction has a mediating effect, whereas activity transparency has a reverse moderating effect. These results indicate that social interaction can enhance the fundraising ability of donation crowdfunding events. However, with an increase in information transparency, the fundraising ability of social media decreases.Originality/valueThe originality of this research is in clarifying the internal factors affecting fundraising ability through induction, making bold assumptions, and focusing on how social media’s effective interaction and activity transparency will affect public welfare crowdfunding fundraising ability.

Self‐media information disclosure and firm productivity

AbstractUsing Sina Weibo data obtained through crawler technologies from 2013 to 2021, this study investigates the relationship between self‐media information disclosure and firm productivity. We find that self‐media information disclosure promotes firm productivity, a conclusion that withstands various robustness and endogeneity tests. Particularly, original microblogs exhibit a more significant impact. Further analysis reveals that the promoting effect is more pronounced in listed companies with lower annual retweets, small companies, and those with lower turnover. Finally, a mechanism analysis indicates that self‐media information disclosure enhances enterprise productivity by alleviating financing constraints, enhancing corporate innovation, and strengthening corporate governance.

Rally-around-the-destination? Changes in host-guest emotional solidarity after crises

Utilizing the political concept of “rally-around-the-flag”, this study takes Jiuzhai Valley as a case to construct a framework of host-guest emotional solidarity and examines the dynamics of such solidarity after an earthquake and the COVID-19 pandemic. Thematic and sentiment analyses are applied to interpret Sina Weibo data and the narratives derived from residents’ interviews as a form of triangulation. Results show that under crisis: (1) host-guest emotional solidarity includes shared beliefs, shared emotions, and interactive behaviors; (2) the short-term rally effect is emotion-driven and depends on “we-ness” relationships, emotional empathy, and place identity; (3) the long-term rally effect is value-driven and emerges when host-guest emotional empathy develops into value resonance through resource integration and relationship reconstruction. The results have implications for emotional solidarity theory and destination recovery in times of crisis.

Formation of professional social networks via physicians’ online engagement: Evidence from Sina Weibo and Sina Health

The professional social networks of physicians are an essential source of health-related user-generated content and online social capital. Yet limited research has examined how social networks of physicians emerge at the level of relationships between physicians. To help fill this gap, this study investigates the formation of followship between physicians (i.e., when one physician follows another) from the perspectives of network structures, actor attributes, and external contextual factors. Using exponential random graph models and strategic network formation models, this study analyzes the effects of reciprocity and transitive closure, physicians’ social media and online healthcare community (OHC) engagement, and physicians’ interaction networks. The results reveal that physicians’ professional social networks have a reciprocated and open structure. Physicians’ social media connectivity and content, online service performance, professional capital, and social identity homophily facilitate the formation of professional social networks. In addition, the interaction networks have an entrainment effect on professional social networks. This study reveals the mechanism of social processes, including reciprocity, preferential attachment, and homophily, in the formation of social networks. The results also extend social cognitive theory and signaling theory into the context of physicians’ professional social networks.

Exploring the Relationship between the Sentiments of Young People and Urban Green Space by Using a Check-In Microblog

Green spaces have a positive impact on the mood of urban residents. However, previous studies have focused primarily on parks or residential areas, neglecting the influence of green spaces in different socioeconomic locations on public sentiment. This oversight fails to acknowledge that most young individuals are exposed to places beyond their homes and parks throughout the day. Using web crawlers, we collected 105,214 Sina Weibo posts from 14,651 geographical check-in points in Hangzhou, Zhejiang Province. We developed a mixed ordered logistic regression model to quantify the relationship between public sentiment (negative/neutral/positive) and the surrounding green space. The findings are as follows: (1) the correlation between GVI and public sentiment is stronger than that between public sentiment and NDVI; (2) among different socioeconomic regions, residential areas are associated with lower levels of public sentiment, while parks are associated with higher levels; and (3) at a scale of 1000 m, an increase of 1% in GVI significantly improves public sentiment regarding transportation hubs, with a regression coefficient of 0.0333. The relationship between green space and public sentiment is intricate and nuanced, and it is influenced by both public activities and spatiotemporal contexts. Urban green space planners should consider additional factors to enhance the effectiveness of green space in improving public sentiment.