Weibo Users Research Articles

The discursive construction of tradition and modernity in counter-homophobic discourses in China

ABSTRACT This study investigates the counter-homophobic discursive construction of tradition and modernity in China, focusing on Sina Weibo users’ comments on anti-same-sex marriage arguments. The study reveals how proponents of same-sex marriage in China actively imagine and project a China that is inclusive of gay rights by (re)constructing China’s tradition as tolerant of homosexuality and positioning China’s pursuit of development and modernity as entailing gay rights development. Yet, analysis shows that the discursive construction of tradition may potentially romanticize China’s tradition as if it were devoid of homophobia, and the discursive construction of modernity, by representing China’s gay rights development as merely playing catch-up with the purportedly already modernized West, sometimes implicitly produces a homonationalist binary of modernism versus traditionalism. However, from a postcolonial transnational perspective, the paper argues that contrary to the homocolonialist representation of China as essentially homophobic, these discursive practices may work partially to confront and contest Western exceptionalism.

Predicting social media users' indirect aggression through pre-trained models.

Indirect aggression has become a prevalent phenomenon that erodes the social media environment. Due to the expense and the difficulty in determining objectively what constitutes indirect aggression, the traditional self-reporting questionnaire is hard to be employed in the current cyber area. In this study, we present a model for predicting indirect aggression online based on pre-trained models. Building on Weibo users' social media activities, we constructed basic, dynamic, and content features and classified indirect aggression into three subtypes: social exclusion, malicious humour, and guilt induction. We then built the prediction model by combining it with large-scale pre-trained models. The empirical evidence shows that this prediction model (ERNIE) outperforms the pre-trained models and predicts indirect aggression online much better than the models without extra pre-trained information. This study offers a practical model to predict users' indirect aggression. Furthermore, this work contributes to a better understanding of indirect aggression behaviors and can support social media platforms' organization and management.

Developing a machine learning-based instrument for subjective well-being assessment on Weibo and its psychological significance: An evaluative and interpretive research.

Demystifying machine learning (ML) approaches through the synergy of psychology and artificial intelligence can achieve a balance between predictive and explanatory power in model development while enhancing rigor in validation and reporting standards. Accordingly, this study aimed to bridge this research gap by developing a subjective well-being (SWB) prediction model on Weibo, serving as a psychological assessment instrument and explaining the model construction based on psychological knowledge. The model establishment involved the collection of SWB scores and posts from 1,427 valid Weibo users. Multiple machine learning algorithms were employed to train the model and fine-tune its parameters. The optimal model was selected by comparing its criterion validity and split-half reliability performance. Furthermore, SHAP values were calculated to rank the importance of features, which were then used for model interpretation. The criterion validity for the three dimensions of SWB ranged from 0.50 to 0.52 (P< 0.001), and the split-half reliability ranged from 0.94 to 0.96 (P< 0.001). The identified relevant features were related to four main aspects: cultural values, emotions, morality, and time and space. This study expands the application scope of SWB-related psychological theories from a data-driven perspective and provides a theoretical reference for further well-being prediction.

Public perceptions of international genetic information sharing for biomedical research in China: a case study of the social media debate on the article “A Pangenome Reference of 36 Chinese Populations” published in Nature

BackgroundThe international disclosure of Chinese human genetic data continues to be a contentious issue in China, generating public debates in both traditional and social media channels. Concerns have intensified after Chinese scientists’ research on pangenome data was published in the prestigious journal Nature.MethodsThis study scrutinized microblogs posted on Weibo, a popular Chinese social media site, in the two months immediately following the publication (June 14, 2023–August 21, 2023). Content analysis was conducted to assess the nature of public responses, justifications for positive or negative attitudes, and the users’ overall knowledge of how Chinese human genetic information is regulated and managed in China.ResultsWeibo users displayed contrasting attitudes towards the article’s public disclose of pangenome research data, with 18% positive, 64% negative, and 18% neutral. Positive attitudes came primarily from verified government and media accounts, which praised the publication. In contrast, negative attitudes originated from individual users who were concerned about national security and health risks and often believed that the researchers have betrayed China. The benefits of data sharing highlighted in the commentaries included advancements in disease research and scientific progress. Approximately 16% of the microblogs indicated that Weibo users had misunderstood existing regulations and laws governing data sharing and stewardship.ConclusionsBased on the predominantly negative public attitudes toward scientific data sharing established by our study, we recommend enhanced outreach by scientists and scientific institutions to increase the public understanding of developments in genetic research, international data sharing, and associated regulations. Additionally, governmental agencies can alleviate public fears and concerns by being more transparent about their security reviews of international collaborative research involving Chinese human genetic data and its cross-border transfer.

Social Media Attention to Geopolitical Conflicts - An Analysis of Weibo Users' Comments on the Israeli-Palestinian Conflict

In the globalized information age, social media has become a primary channel for news and information dissemination, particularly during geopolitical conflicts. This study investigates public sentiment and discourse on the Israeli-Palestinian conflict on the Chinese social media platform Weibo. Utilizing content analysis, the researcher conducted sentiment statistics and word frequency analysis on Weibo comments to understand Chinese public attitudes toward this conflict. The research reveals a significant increase in negative emotions from 33.33% in October 2023 to 100% in April 2024, indicating growing public discontent and concern as the conflict intensified. Concurrently, positive emotions sharply declined from 47.62% to 0%, reflecting diminished hopes for a peaceful resolution. Neutral sentiments also fluctuated, initially at 19.05%, dropping to 0% by April 2024. Additionally, the study identifies a shift in keyword usage from "world peace" and "hope" to specific entities like "Israel" and "Hamas," and terms like "disaster." This highlights a change in public and media focus from peace initiatives to the humanitarian impact of the conflict. Understanding these dynamics is crucial for promoting a more inclusive gaming environment, challenging existing gender stereotypes, and fostering social stability. This research contributes to a deeper understanding of Chinese public opinion on international geopolitical issues and underscores the importance of social media in shaping public discourse.

Attitudes of Chinese public towards the autism community: Evidence from a decade of Weibo data

Autism is a highly stigmatized developmental disorder in Chinese society, with the public harboring many prejudices and misunderstandings towards individuals with autism.Grounded in ABC attitude theory, explores the status, trends, and characteristics of Weibo users' attitudes towards the autism community. Utilizing natural language processing and machine learning techniques, the study analyzes 1,113,014 Weibo posts concerning autism, spanning from January 1, 2013, to December 31, 2022. Findings indicate that Weibo users generally hold a positive and progressively improving attitude towards the autism community, particularly in affective and behavioral dimensions. However, the cognitive dimension of these attitudes remains relatively underdeveloped. Notable variations in attitudes and their components are evident across demographic variables such as gender, age, educational level, and verification status. These insights offer valuable guidance for policy-making by relevant authorities and contribute to enhancing public acceptance of individuals with autism.

Changes in online public opinions associated with various three-child supportive policies in China: Observational study using social media data over time.

According to the Seventh National Census, China's fertility rate is less than 1.5, marking a significant national issue with potential risks. To counter this low birth rate, the Chinese government has relaxed family planning policies and introduced supportive measures. Changes in birth policy have attracted considerable attention from the people of China. This article aims to study the public's response to the three-child support policy using Weibo as a window. The goal is to provide a more balanced evaluation of current perspectives, enabling policymakers to formulate better fertility information, particularly when anticipating a poor public response to controversial policies. This research uses a crawler to gather data from Sina Weibo. Through opinion mining of Weibo posts on the three-child policy, Weibo users' online opinions on the three-child policy are analyzed from two perspectives: their attention content and sentiment tendency. Using an interrupted time series, it examines changes in online views on the policy, matching policy documents to the time nodes of Weibo posts. The public has shown great interest in and provided short-term positive feedback on policies related to improving maternity insurance, birth rewards, and housing subsidies. In contrast, there has been a continuous negative response to policies such as extending maternity leave, which has particularly sparked concerns among women regarding future employment and marital rights protection. On social media, the public's attention to the three-child birth policy has focused mainly on the protection of women's rights, especially legal rights after childbirth, and issues related to physical and mental health. Child-rearing support and economic pressure are also hot topics, involving the daily expenses of multichild families, childcare services, and housing pressure. However, this study also revealed that infertile or single women express a strong desire to have children, but due to limitations in the personal medical insurance system, this desire has not been fully satisfied. Our study demonstrates the feasibility of a rapid and flexible method for evaluating the public response to various three-child supportive policies in China using near real-time social media data. This information can help policy makers anticipate public responses to future pandemic three-child policies and ensure that adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed but controversial, restrictions.

Discovering public attitudes and emotions toward educational robots through online reviews: a comparative analysis of Weibo and Twitter

PurposePublic reviews on educational robots are of great importance for the design, development and management of the most advanced robots with an educational purpose. This study explores the public attitudes and emotions toward educational robots through online reviews on Weibo and Twitter by using text mining methods.Design/methodology/approachOur study applied topic modeling to reveal latent topics about educational robots through online reviews on Weibo and Twitter. The similarities and differences in preferences for educational robots among public on different platforms were analyzed. An enhanced sentiment classification model based on three-way decision was designed to evaluate the public emotions about educational robots.FindingsFor Weibo users, positive topics tend to the characteristics, functions and globalization of educational robots. In contrast, negative topics are professional quality, social crisis and emotion experience. For Twitter users, positive topics are education curricula, social interaction and education supporting. The negative topics are teaching ability, humanistic care and emotion experience. The proposed sentiment classification model combines the advantages of deep learning and traditional machine learning, which improves the classification performance with the help of the three-way decision. The experiments show that the performance of the proposed sentiment classification model is better than other six well-known models.Originality/valueDifferent from previous studies about attitudes analysis of educational robots, our study enriched this research field in the perspective of data-driven. Our findings also provide reliable insights and tools for the design, development and management of educational robots, which is of great significance for facilitating artificial intelligence in education.

Research on Influencing Factors of Weibo Users' Intention to Use "Visitor Record" Function Based on UTAUT Model

The purpose of this paper is to explore the influencing factors of users' use of "visitor record" function from the two dimensions of technology itself and users' social media use psychology. Based on the integrated information technology acceptance and use Model (UTAUT), and based on the perspective of users' social media use, two factors of "self-disclosure degree" and "missing anxiety" were introduced, and the Weibo user group was taken as the research object through questionnaire survey method. Based on the 313 questionnaires obtained by screening, the analysis was conducted by hypothesis testing and multiple linear regression methods. The effects of integrated technology acceptance model factors and two newly introduced factors on user intention and behavior were explored. Performance expectancy, effort expectancy, social influence and facilitating factors have significant positive effects on users' intention to use the "visitor record" function of Weibo. The facilitating factors have a negative influence on the user's use behavior of the "visitor record" function of microblog. The introduction of new perspectives, including fear of missing out and degree of self-disclosure, provides new ideas for further understanding of social media user behavior and subsequent related research.

Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art

The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.

A Quantitative Exploration: Weibo Users Perception of Trolling

A Quantitative Exploration: Weibo Users Perception of Trolling

"Russian-Ukrainian conflict" through the prism of users of the Chinese social network "Weibo"

The subject of this article is the views of Chinese netizens on the "Russian-Ukrainian conflict". The object of the study is posts about this topic on the Chinese social network "Weibo". The authors analyze the importance, functions of the Weibo in the production of public opinion and its main opinion leaders. Then the article examines the number of posts in month (15.02-15.03) compared to other international events and makes a content analysis of posts to find out the main topics and the attitude of users on the social network "Weibo" to the "Russian-Ukrainian conflict". At the end of the article, authors learn the relationship of the research results with stereotypes about the image of Russia in China. The results of the study show that Chinese Weibo users began to care about the event from the beginning of a special military operation, but interest faded with the first round of negotiations between Russia and Ukraine. Chinese Weibo users pay attention to the role of the United States in the conflict and Western sanctions against Russia. They mainly reflect support for the Russian economy and opposition to Western sanctions, which also affects the Chinese government's statement on this event. The article considers that such views are closely connected with stereotypes about the image of Russia in China. The results of the study show that in future international events, the peoples of Russia and China have more opportunities to reach agreement.

Evaluating the American-Chinese trade war on Chinese social media: discourses of nationalism and rectifying a humiliating past

ABSTRACT The US and China have both benefited greatly from their trading relationship. However, motivated by a US concern that their partner was becoming more of a rival, then-president Donald Trump began a ‘trade war’ in 2018. In US news outlets and, of particular interest here, on American social media platforms, China was represented as a global menace, with extreme xenophobia against Chinese people. Yet less is known about how Chinese people responded on social media to the same situation. This paper explores the discourses carried on the Chinese social media platform Weibo, focusing on the 2021 Alaska talks between the US and China, where negotiations took place. As with their US counterparts, Weibo users comment little on actual trade benefits or damages caused by the conflict. We find a strong sense of nationalism, but this is not based on xenophobia; rather on a discourse comparing the talks to similar events early in the twentieth century, where the ‘Century of Humiliation’ began, and where territories such as Hong Kong, Taiwan and Shaoshan were taken by colonial powers. Foregrounded is China’s ability now to end this humiliation.

A Multi-Information Dissemination Model Based on Cellular Automata

Significant public opinion events often trigger pronounced fluctuations in online discourse. While existing models have been extensively employed to analyze the propagation of public opinion, they frequently overlook the intricacies of information dissemination among heterogeneous users. To comprehensively address the implications of public opinion outbreaks, it is crucial to accurately predict the evolutionary trajectories of such events, considering the dynamic interplay of multiple information streams. In this study, we propose a SEInR model based on cellular automata to simulate the propagation dynamics of multi-information. By delineating information dissemination rules that govern the diverse modes of information propagation within the network, we achieve precise forecasts of public opinion trends. Through the concurrent simulation and prediction of multi-information game and evolution processes, employing Weibo users as nodes to construct a public opinion cellular automaton, our experimental analysis reveals a significant similarity exceeding 98% between the proposed model and the actual user participation curve observed on the Weibo platform.

Overview of big data information privacy protection technology and challenges

With the rapid development of advanced technologies like the mobile internet, cloud computing, and the Internet of Things, the era of big data is gradually upon us. In this era, big data has been widely utilized in various fields like business, finance, social networking, and healthcare, and the value of data has become increasingly prominent. Data has become a new factor of production, the digital economy has become a new engine of development, and an important driving force for promoting social progress and economic development. However, with the advancement of big data technology, the risks of data leakage and misuse have increased. Personal data information leakage or misuse can lead to personal privacy infringement and fraud, such as the Sina Weibo data leak in March 2020 where attackers breached part of the Sina Weibo database, obtaining sensitive data including real names, website usernames, genders, locations, and phone numbers, impacting 538 million Weibo users. Therefore, there is an increasing demand for data information privacy protection. This paper will explore the core technologies and application scenarios in data information privacy protection and investigate data privacy protection in real-life settings.

Impact of the COVID-19 epidemic on collectivism and individualism in China: A study of Weibo users

The global COVID-19 pandemic affected numerous aspects of individual lives, and researchers are now turning to study the potential impacts of the pandemic on culture and values. Previous research has shown that infectious disease outbreaks tend to make groups more collectivist. The present study drew on micro-blog big data from Weibo to verify whether COVID-19 and related factors changed collectivism and individualism in China, while explaining abnormal data. Time-series models verified that COVID-19 significantly affected collectivism and individualism among the Chinese people, and the severity of the epidemic, epidemic-related policies, and discussion about the epidemic among Weibo users significantly explained variation in these levels. Analysis of Weibo text one day before and one day after the dates of statistical outliers in collectivism and individualism suggested that the changes in these levels were associated with significant events in the epidemic.

“Doing What’s Best for Me”: A Cultural Values Comparison of Social Media Responses to Kyrie Irving’s COVID-19 Vaccination Status

In October 2021, National Basketball Association player Kyrie Irving was banned from competing for the Brooklyn Nets because of his resistance to being vaccinated for COVID-19. Two months later, the Nets softened that stance and allowed Irving to participate in road games. This study examines two prongs of the social media response to Irving’s vaccination status. A total of 12,000 posts were collected from the U.S.-based Twitter (now known as X; 6,000) and the Chinese-based Weibo (6,000), covering the first week of coverage of Irving and the Nets’ announcement. Results showed that Hofstede’s cultural dimensions help explain the theme differences, with Twitter’s comments reflecting more individualistic notions and lower power distance than Weibo. Moreover, Twitter users focused more on opinion-based expression themes, while Weibo users avoided commenting on the COVID-19 policy. These findings contribute to the literature by identifying the role culture plays in people’s response to a social issue.

Fake News Sharing Among Weibo Users in China

In an era where social media's impact on public discourse is increasingly pronounced, this study probes into the spread of fake news among Weibo users in China, a significant issue given the platform's massive user base and China's unique media environment. Adopting a quantitative research approach, the study primarily investigates how situational motivation and information-seeking behaviours influence the sharing of fake news. Utilising regression analysis, a method pivotal for understanding the relationship between various independent variables and the sharing of misinformation, the research sheds light on the nuanced interplay of factors that drive users to disseminate false information. Key findings reveal that situational factors, such as the need for social recognition or the urge to disseminate urgent news, considerably elevate the likelihood of sharing fake news. Additionally, the study uncovers the complex nature of information-seeking behaviour: while it generally leads to better-informed decisions, it can sometimes result in the sharing of unverified news under certain circumstances. These outcomes are crucial for understanding the behavioural patterns behind fake news sharing on social media. They offer valuable insights for policymakers, social media platforms, and educators in formulating effective countermeasures against misinformation, aiming to foster a more discerning and informed online community. This study, therefore, contributes significantly to the existing body of knowledge on digital media, misinformation, and public opinion, highlighting the critical need for strategies to combat the spread of fake news in the digital age. Keywords: Fake news sharing, information seeking, situational motivation, agenda-setting theory, China.

A comparative study of self-praise on English and Chinese social media: strategies, themes, and motivations.

The act of self-praise, a novel and prevalent discourse pattern on social media across different cultures, is still understudied. As such, this study delves into self-praise from linguistic and psychological perspectives by examining and comparing the strategies, themes, and motivations of self-praise on English and Chinese social media. A discourse-based analysis of the data comprising 200 posts from Twitter (English) users and 200 posts from Weibo (Chinese) users revealed that Twitter users favored more unmodified explicit self-praise strategies, while Weibo users opted for more implicit self-praise strategies. Both groups employed explicit self-praise equally, but with different sub-strategies. The most prevalent themes were appearance, competence/performance, skills, virtues, and self-encouragement. Appearance was more dominant among Twitter users, whereas skills and self-encouragement were more popular among Weibo users. Both groups valued competence/performance and virtues, but with different virtues. Twitter users valued cherishing friendship, bravery, and maturity, whereas Weibo users stressed kindness, cherishing kinship, and thoughtfulness. Two semi-structured interviews conducted with six Twitter participants and six Weibo participants indicated that the motivations for online self-praise in both groups were psychological needs such as self-flattery, self-presentation, and self-encouragement, while commercial profit was a motivation only for Weibo participants. This study also discusses the social and cultural factors that may account for these differences and provides implications for future research on online self-praise.

Analyzing Public Sentiments: A Study of Weibo Users Comments on Peoples Daily Coverage of the Japan Nuclear-Contaminated Water Discharge Incident

Analyzing Public Sentiments: A Study of Weibo Users Comments on Peoples Daily Coverage of the Japan Nuclear-Contaminated Water Discharge Incident