Sina Weibo Research Articles

Chinese young adults' perception of fertility: Emotional contagion in fertility policy discussions on Sina Weibo

We investigated the reactions and preferences of the Chinese populace, particularly young adults, toward China's three-child policy, a recent strategy aimed at addressing the nation's aging population that allows couples to have up to three children. By monitoring Sina Weibo discussions related to fertility policies, we gauged public sentiment and potential emotional contagion. We collected 711 main posts and 26,515 responses and used the sentiment analysis method to identify the emotional tendencies of each post, thus determining if emotional contagion occurred. Results showed there were 631 posts with negative emotional contagion effects and 554 posts with positive emotional contagion effects. Further textual analysis indicated that the Chinese public maintains a pessimistic outlook on the three-child policy and expresses concern about the detrimental impact of social circumstances and the difficulties women encounter concerning fertility.

What methods are used to examine representation of mental ill-health on social media? A systematic review

There has been an increasing number of papers which explore the representation of mental health on social media using various social media platforms and methodologies. It is timely to review methodologies employed in this growing body of research in order to understand their strengths and weaknesses. This systematic literature review provides a comprehensive overview and evaluation of the methods used to investigate the representation of mental ill-health on social media, shedding light on the current state of this field. Seven databases were searched with keywords related to social media, mental health, and aspects of representation (e.g., trivialisation or stigma). Of the 36 studies which met inclusion criteria, the most frequently selected social media platforms for data collection were Twitter (n = 22, 61.1%), Sina Weibo (n = 5, 13.9%) and YouTube (n = 4, 11.1%). The vast majority of studies analysed social media data using manual content analysis (n = 24, 66.7%), with limited studies employing more contemporary data analysis techniques, such as machine learning (n = 5, 13.9%). Few studies analysed visual data (n = 7, 19.4%). To enable a more complete understanding of mental ill-health representation on social media, further research is needed focussing on popular and influential image and video-based platforms, moving beyond text-based data like Twitter. Future research in this field should also employ a combination of both manual and computer-assisted approaches for analysis.

Does heterogeneous media sentiment matter the ‘green premium’? An empirical evidence from the Chinese bond market

This paper selects 346 green bonds issued in China from 2016 to 2021 as the sample, and the Propensity Score Matching (PSM) method is employed to confirm the existence of ‘green premium’ in the Chinese bond market. On this basis, data on internet media sentiment and print media sentiment are collected from ‘Sina Weibo’ and ‘China Important Newspaper Full Text Database’ by both Web Crawler Technology and Textual Analysis Methods to explore the impact and the mechanism of heterogeneous media sentiments on the ‘green premium’. The results show that both the optimism of internet media and print media can significantly promote the ‘green premium’ of green bonds, and the influence of print media sentiment on the ‘green premium’ is greater than that of internet media sentiment. In addition, the Bootstrap method verifies the mediating effect of print media sentiment in the influence of internet media sentiment on ‘green premium’, indicating that print media sentiment is an important transmission path. Moreover, the results of the heterogeneity test show that the more optimistic the media is, the more significant the ‘green premium’ effect is in the regions with higher institutional environments and financial subsidies. The ‘green premium’ of green bonds is most pronounced for higher levels of institutional environment and green bond preferential policies.

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.

A Virtual Battlefield for Embassies: Longitudinal Network Analysis of Competing Mediated Public Diplomacy on Social Media

ABSTRACT Mediated public diplomacy is a critical way of strategic communication for governments to influence global public opinion, especially during international conflicts. Based on the theories of mediatization and digitalization as well as the layered model of communicative space, this study investigates how the United States and Iranian embassies competed on the Chinese social media platform Sina Weibo over 13 months. Following a network ethnography approach, this study conducts longitudinal network analysis and textual analysis. The findings suggest that the macro-layer function (i.e. hashtag) facilitated horizontal dissemination of news about the US-Iran conflict from mainly China’s state-media accounts, increasing public awareness of the issue within a short time. The meso-layer (repost and follower network) and micro-layer (mention and comment) functions helped establish vertical communication facilitated by opinion leaders and crowd-enabled leaders that perpetuated public attention on relevant issues. The two embassies engaged with the Chinese public in different communicative layers. The US embassy interacted with the Chinese publics through reposts and replies, while the Iranian embassy constructed their posts in the form of replies. The Iranian embassy leveraged China’s state media agendas as narrative agency to conduct diplomatic communication by incorporating relevant hashtags in their posts. A layered model of mediated public diplomacy is proposed. The findings deepen our understanding of competing mediated public diplomacy, and contribute to the field of international communication of international conflicts.

Research on 5G User Perception Detection and Experience Improvement Optimization Based on Capsule Network

COVID-19 caused a global public disaster as well as an economic crisis, and other challenges. The fifth-generation network, or 5G, connects practically every machine, person, and thing on the planet. We can analyse the public's opinions and sentiments connected to COVID-19 from 5G user-generated content on social media, which will eventually aid in promoting health intervention strategies and designing successful projects based on public perceptions. The BERT language model is first used to preprocess data that has been obtained from Sina Weibo. Following that, the features of the preprocessed data are chosen using a class-wise information technique. Finally, a capsule network (CapsNet) approach is used to identify the 5G user perception and experience optimization. Dynamic routing algorithm is used for optimizing the capsule network. By comparing the suggested framework's performance with certain existing approaches, its effectiveness is evaluated. Simulation results show that the proposed method is more accurate than previous approaches at identifying 5G user experiences.

Research on the death psychology among Chinese during and after the COVID-19 pandemic.

Under the threat of the novel coronavirus, people are compelled to contemplate some ultimate existential questions, such as life and death. This study collected texts related to the death psychology from Sina Weibo, and after data cleaning, a total of 3868 Weibo texts were included. Study 1 employed grounded theory from qualitative research to explore the core categories and evolutionary mechanisms of people's psychology when facing death threats in the context of the pandemic. Study 2 utilized big data mining techniques such as topic mining and semantic network analysis to validate the effectiveness of the death psychology theory developed in qualitative research. The findings demonstrate that within the "Emotion-Cognition-Behavior-Value" framework, the implications of death threats manifest in four aspects: death anxiety, death cognition, coping efficacy, and sense of meaning. As time progresses, the study of death psychology can be segmented into four distinct phases: the tranquil phase prior to lifting pandemic restrictions, the threat phase at lifting pandemic restrictions onset, the coping phase mid-lifting pandemic restrictions, and the reformative phase post-lifting pandemic restrictions. The calculated outcomes of topic mining and semantic network analysis corroborate the coding results and theories derived from the grounded theory. This reaffirms that data mining technology can be a potent tool for validating grounded theory.

Development of a Chrono‐Urbanism Status Composite Index under the 5/10/15‐Minute City Concept Using Social Media Big Data

AbstractDeveloping walkable urban environments is crucial for enhancing urban liveability and sustainability. To evaluate the chrono‐urbanism status, this study combines conventional census and mapping data with social media check‐in big data. A composite index is proposed, which assesses the accessibility of essential urban functions under the 5/10/15‐minute chrono‐thresholds. This index incorporates five categories of urban functions (public transit, living, education, health care and entertainment) in Macau. Utilising check‐in information from Sina Weibo, the level of attraction for different urban functions is integrated into the calculation. The findings highlight significant disparities in chrono‐urbanism status, with regions inhabited by lower socio‐economic status residents being more vulnerable than the affluent institutional and commercial centre of Macau. Leveraging social media big data, this study provides valuable insights to urban planners and policy‐makers, enabling them to understand the overall chrono‐urbanism status and target interventions for developing a more sustainable and liveable urban environment in Macau.

Public Relations Management After COVID-19 Lockdown: A Case Study of Wuhan, China

The aim of the research study was to understand the effectiveness of the public relations strategies of crisis communication and how impactful they were for saving the image of Wuhan city after the pandemic. Wuhan was hailed as the epicentre of Corona virus infections as the initial infections have spread from this place. Thus, the image of this prominent city was maligned not only towards the local and national audience, but the international audience as well. Thus, the Wuhan government took up most of the necessary public relations strategies to restore the image of the city among the national and international audience. This paper has intrinsically dealt with the opinions from the different sample population on the effectiveness of the crisis communication done by the PR strategists of the Wuhan government. From most of the respondents it has been found out that Wuhan government and its public relations strategies were wholly operated through the digital medium and this has helped in restoring the image of the image to greater and effective extent. Through the disposition of information through different social media networking sites like Facebook and Sina Weibo, the PR strategists were able to provide information on the improvements in the city condition and the reduction of infection in Wuhan along with other positive news articles. However from the respondents it has been also found that, including lesser opinion from the public, decrease in the participation of the common mass in the information dissemination process by the Wuhan government has decreased the effectiveness of the image restoration PR strategy in the Wuhan city. The research study concludes that while the PR crisis communication and its strategies have been helpful towards restoring the image of the city, there are still some loop holes that needs to be fixed.

From panic to banter: How do routine government releases and clarifications cause unexpected public opinion crisis—An analysis of public opinion toward a release by Chinese Ministry of Commerce encouraging the storage of necessities

AbstractThis research explored why a routine government notice caused a series of unexpected public opinion crises. We used K‐means clustering and Baidu Application Programming Interface Gateway to analyse 12,478 comments from Sina Weibo on Chinese Ministry of Commerce's release to encourage people to stock up necessities. Results show that even government's routine notice releases are regular events with relatively low objective risk, but in practice, how the government releases information and makes clarification would significantly infect public risk perception and emotion. First, ambiguous government releases magnify existing risks, especially during sensitive times, and resulting in heightened uncertainty, public risk perception and spread of negative emotions, therefore, lead to heated public discussion. Second, timely and effective clarifications can lower public risk perception as well as lead to positive emotions, although public banter may come along and generate another round of online public opinion within a short time. Our work answers why regular events would cause unexpected public opinion from the perspective of government behaviour, illustrates the dynamic interaction between government and online public opinion and can inform public policy aimed at more efficient communication with the public.

The Roles of Social Media and Traditional News Media in Promoting Government Responsiveness in Social Incidents

Social media is considered to play an important role in promoting government responsiveness. However, it is impossible for the government to respond to all issues in the actual situation. Especially when encountering controversial social incidents, the local government usually evades responsibility due to damage to reputation or interests. This paper argues that when a controversial social incident occurs in China, the traditional news media will work with social media to encourage the local government to respond. Three cases are selected according to the hot discussion index of Sina Weibo, China's largest social media platform for multiple-case studies. 21 participants were interviewed to explore the function of social media and traditional news media during the process of government responsiveness. The results showed that traditional news media played the role of professionalism, supervision, and broker to develop social incidents. And social media took the information aggregation part in most situations. Social media and traditional news media work together to promote the local government to make government responsiveness active.

Heart or mind? The impact of congruence on the persuasiveness of cognitive versus affective appeals in debunking messages on social media during public health crises

Designing persuasive debunking messages poses significant challenges in addressing rumors during public health crises, especially when confronting diverse informational backgrounds. However, the underlying mechanisms through which the congruence between debunking messages and background information influences their persuasiveness remains unclear. Therefore, drawing upon the elaboration likelihood model (ELM) of persuasion theory, this study develops a theoretical model to explore how topic congruence and reputation congruence moderate the relationships between cognitive and affective appeals and debunking message persuasiveness. We collected 18,825 COVID-19 debunking posts and associated data from Sina Weibo. Employing linguistic psychoanalysis, machine learning algorithms, and econometric analysis, we operationalize key variables and subject our model to rigorous testing. The results indicated that cognitive appeals, including argument adequacy (β = 0.040, p < 0.01), evidence quality (β = 0.163, p < 0.01), expression certainty (β = 0.048, p < 0.01), media richness (β = 0.264, p < 0.01), and source credibility (β = 0.138, p < 0.01), as well as affective appeals, encompassing emotional pleasure (β = -0.020, p < 0.01), emotional arousal (β = 0.033, p < 0.01), and emotional dominance (β = 0.015, p < 0.01) significantly influenced debunking message persuasiveness through the central and peripheral cues of ELM, respectively. Moreover, topic congruence and reputation congruence exhibited differing moderating effects. Specifically, topic congruence amplified the impacts of cognitive appeals while weakening that of affective appeals on debunking message persuasiveness. Conversely, reputation congruence yielded inverse effects on the two types of appeals. These findings furnish invaluable insights for government authorities and social media platforms in comprehending the intricate mechanisms underlying debunking message persuasiveness, thereby facilitating the development of more persuasive strategies to counter rumors in public health emergencies.

Information cascade prediction of complex networks based on physics-informed graph convolutional network

Cascade prediction aims to estimate the popularity of information diffusion in complex networks, which is beneficial to many applications from identifying viral marketing to fake news propagation in social media, estimating the scientific impact (citations) of a new publication, and so on. How to effectively predict cascade growth size has become a significant problem. Most previous methods based on deep learning have achieved remarkable results, while concentrating on mining structural and temporal features from diffusion networks and propagation paths. Whereas, the ignorance of spread dynamic information restricts the improvement of prediction performance. In this paper, we propose a novel framework called Physics-informed graph convolutional network (PiGCN) for cascade prediction, which combines explicit features (structural and temporal features) and propagation dynamic status in learning diffusion ability of cascades. Specifically, PiGCN is an end-to-end predictor, firstly splitting a given cascade into sub-cascade graph sequence and learning local structures of each sub-cascade via graph convolutional network , then adopting multi-layer perceptron to predict the cascade growth size. Moreover, our dynamic neural network, combining PDE-like equations and a deep learning method, is designed to extract potential dynamics of cascade diffusion, which captures dynamic evolution rate both on structural and temporal changes. To evaluate the performance of our proposed PiGCN model, we have conducted extensive experiment on two well-known large-scale datasets from Sina Weibo and ArXIv subject listing HEP-PH to verify the effectiveness of our model. The results of our proposed model outperform the mainstream model, and show that dynamic features have great significance for cascade size prediction.

A Temporal Variable-Scale Clustering Method on Feature Identification for Policy Public-Opinion Management

The development of various digital social network platforms has caused public opinion to play an increasingly important role in the policy making process. However, due to the fact that public opinion hotspots usually change rapidly (such as the phenomenon of public opinion inversion), both the behaviour feature and demand feature of netizens included in the public opinion often vary over time. Therefore, this paper focuses on the feature identification problem of public opinion simultaneously considering the multiple observation time intervals and key time points, in order to support the management of policy-focused online public opinion. According to the variable-scale data analysis theory, the temporal scale space model is established to describe candidate temporal observation scales, which are organized following the time points of relevant policy promulgation (policy time points). After proposing the multi-scale temporal data model, a temporal variable-scale clustering method (T-VSC) is put forward. Compared to the traditional numerical variable-scale clustering method, the proposed T-VSC enables to combine the subjective attention of decision-makers and objective timeliness of public opinion data together during the scale transformation process. The case study collects 48552 raw public opinion data on the double-reduction education policy from Sina Weibo platform during Jan 2023 to Nov 2023. Experimental results indicate that the proposed T-VSC method could divide netizens that participate in the dissemination of policy-focused public opinion into clusters with low behavioural granularity deviation on the satisfied observation time scales, and identify the differentiated demand feature of each netizen cluster at policy time points, which could be applied to build the timely and efficient digital public dialogue mechanism.

A Study of Changes in College English Online Phrases Supported by Clustering Algorithm

Abstract The hyperlinks on the Sina Weibo platform are tracked by a web crawler so as to obtain the textual resources of university English online phrases on the platform. To process microblog text data, this paper proposes the use of multiple linguistic expressions of translation machines to enhance the text feature representation and apply it to clustering to enhance the clustering results. Based on commonly used algorithms such as K-means, DBSCAN, and hierarchical clustering, we analyze the clustering effect of each algorithm according to internal and external evaluation indexes and finally determine the clustering algorithms to be used for the change of university English network terms. Using the English network word “hand” as an example, this paper observes a fluctuating trend in the heat of this word from January to June 2023, with peaks in heat generation in January and February, respectively, and a peak heat of about 10,500 in February. In terms of spatial variation, the heat level of the English word “hand” is high in the east and south regions and low in the west and north regions, but in February, due to the return of the population to their hometowns for the Lunar New Year, the north region exceeds the east and south regions with a heat level of 11086.77.

Extracting the diffusion dynamics of crisis information on online social networks: Model and application

Disaster mitigation greatly benefits from the diffusion of crisis information. To learn about the diffusion dynamics of crisis information on online social networks, this paper puts forward a crisis information diffusion model based on population dynamics and further analyses its parameters, such as ordinary users’ average number of followers, the number of potential audience members who might spread the crisis information, the forwarding probability for each audience member and the attenuation speed of the information based on real data from Sina Weibo. In addition, a method for the prediction of crisis information diffusion is proposed and verified on the basis of this model. The results show that this model could describe and predict well the diffusion process of crisis information on online social networks, enabling relevant departments to develop appropriate strategies for crisis information diffusion.

MODELING AND ANALYZING DYNAMIC INFORMATION PROPAGATION ON SINA WEIBO IN A SEMI-DIRECTED NETWORK

In reality, the dissemination of information about COVID-19 vaccines typically involves a combination of opinion leaders and self-organizing networks, with each node being exposed to information in varying ways. However, conventional models often assume homogeneity in networks, treating all nodes as equal in terms of propagation probabilities within a fixed timeframe, thereby neglecting the inherent heterogeneity of social networks in information dissemination. To address this limitation, we propose a novel semi-directed network model, referred to as the susceptible-forwarding-immune model, which incorporates the complex structure of actual social networks and classifies nodes based on their mode of contact and the number of users they reach within a specific period. We calibrated and validated our model using real data on COVID-19 vaccine information from the Chinese Sina microblog, and our sensitivity analysis yielded insights into optimal strategies for disseminating such information.

Modeling and Analyzing Information Propagation Evolution Integrating Internal and External Influences

AbstractOnline social networks have revolutionized communication, providing individuals with platforms to express their personal opinions on diverse topics. Researchers have independently explored information propagation and opinion evolution within complex networks. However, these phenomena exhibit interconnectedness, where information dissemination influences opinion evolution and vice versa. To address challenges in complex network modeling and opinion‐information coupling, internal and external factors are considered in public opinion scenarios by incorporating the crowd effect, enhancement effect, and evolutionary game theory. The susceptible‐latent‐forwarding‐immune‐Jager‐Amblard (SLFI‐JA) model is presented by modifying the SLFI propagation dynamics model and the JA opinion dynamics model, enabling the integration of information propagation and opinion evolution at the microlevel. Through analyzing real‐world social hotspots on Sina Weibo, case studies and comparative analyses are conducted to validate the rationality and effectiveness of the proposed model. Furthermore, the findings identify key factors influencing public opinion dissemination and group opinion evolution, offering valuable insights to relevant departments in public opinion response and management. The study aims to mitigate the harmful effects of negative public opinions, prevent extreme adverse online events, and foster a healthier online environment.

Transmission mechanism of public concern in waste-sorting policy: Evidence from text mining

An escalating amount of urban waste poses a significant threat to the sustainable growth of cities. Therefore, a waste-sorting policy is crucial for the advancement of humankind. The implementation of a waste-sorting policy relies heavily on public participation to ensure effective governance. This study examines public participation in urban waste-sorting by mining more than 580,000 microblog texts related to waste-sorting from 2012 to 2022, using data from Sina Weibo, China's leading social media platform. My findings indicate that (1) residents’ attention to and support for mandatory waste-sorting policies varies by region; (2) widespread public interest stimulates publicity, education, and commercial entertainment related to waste-sorting policies and investment in thematically related sectors; (3) residents in regions with high education and income levels are more likely to generate waste and pay greater attention to waste-sorting policies; and (4) the promotion of waste-sorting policies affects knowledge dissemination. This study combines textual analysis and econometric techniques to offer a fresh perspective on the significance of public participation in promoting waste-sorting policies. It serves as an invaluable resource for governments to implement waste-sorting policies.

Uncovering the heterogeneous effects of depression on suicide risk conditioned by linguistic features: A double machine learning approach

Depression has been identified as a risk factor for suicide, yet limited evidence has elucidated the underlying pathways linking depression to subsequent suicide risk. Therefore, we aimed to examine the psychological mechanisms that connect depression to suicide risk via linguistic characteristics on Weibo. We sampled 487,251 posts from 3196 users who belong to the depression super-topic community (DSTC) on Sina Weibo as the depression group, and 357,939 posts from 5167 active users as the control group. We employed the double machine learning method (DML) to estimate the impact of depression on suicide risk, and interpreted the pathways from depression to suicide risk using SHapley Additive exPlanations (SHAP) values and tree interpreters. The results indicated an 18% higher likelihood of suicide risk in the depression group compared to people without depression. The SHAP values further revealed that Exclusive (M = 0.029) was the most critical linguistic feature. Meanwhile, the three-depth tree interpreter illustrated that the high suicide risk subgroup of the depression group (N = 1196, CATE = 0.32 ± 0.04, 95％CI [0.20, 0.43]) was predicted by higher usage of Exclusive (>0.59) and Health (>-0.10). DML revealed pathways linking depression to suicide risk. The visualized tree interpreter showed cognitive complexity and physical distress might be positively associated with suicide risk in depressed populations. These findings have invigorated further investigation to elucidate the relationship between depression and suicide risk. Understanding the underlying mechanisms serves as a basis for future research on suicide prevention and treatment for individuals with depression.