Weibo Data Research Articles

Weibo Criticism of Fang Fang’s Wuhan Lockdown Diary as an ‘Enemy Within’: Rhetorical Tactics and Discourse Practices of Voluntary Propaganda in China During the COVID-19 Pandemic

ABSTRACT This paper examines the discourse practices of voluntary propaganda on social media in response to Fang Fang’s Wuhan Lockdown Diary during the COVID-19 pandemic in China. Analysis of Weibo data reveals the use of negative labeling and grouping tactics to discredit Fang Fang as a ‘public intellectual’ and a traitor in the anti-‘public intellectual’ discourse. Additionally, the positive mobilization tactic was employed through moral teaching in the ‘positive energy’ discourse to portray her as having negative energy, in contrast to the prototype of a good citizen. The analysis highlights the adept use of rhetorical tools by voluntary propagandists in adapting their discourses to specific events. However, it also exposes loopholes in voluntary propaganda, where ambiguous discourses may legitimize criticism of the regime and uphold the liberal sphere in cyberspace. This study sheds light on the discourse practices of voluntary propaganda on social media and their implications for political communication in contemporary China.

Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics

PurposeThe emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.Design/methodology/approachThis study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.FindingsThe results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.Originality/valueThrough the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.

Multivariate opinion evolution analysis on individual opinion differences: An agent-based model.

The spiral of silence is a classic theory originating from the age of mass media, which posits that individuals may suppress dissenting opinions to avoid social exclusion. In the digital era, despite the persistence of this phenomenon, the rise of social media has catalyzed the emergence of an antispiral of silence-a counterphenomenon fueled by growing self-awareness and online anonymity. In this paper, we focus on an interesting phenomenon: how these two spirals compete with each other to promote pluralism in social networks. To this end, we propose the spiral competition model (SCM) to quantify the two spirals and explore the impact of this phenomenon on the evolution of public opinion. In the SCM, agents can generate pseudo-opinions as well as antiopinions. In addition, we propose a time-varying network topology updating algorithm based on triangle closure, which simulates the fluid nature of social connections. Furthermore, we propose a community-based opinion initialization method with Gaussian distribution for enhancing simulation fidelity. Finally, we applied complex network theory to information dissemination, and the model was then simulated and analyzed in Erdős-Rényirandom networks. And the rationality of SCM was verified using Sina Weibo data. The experimental results show that while the spiral of silence initially holds sway, the antispiral of silence can precipitate significant opinion divergence during critical junctures, challenging the status quo. And if the antispiral of silence fails to prevail, the trend of public opinion will be difficult to reverse. This research helps us recognize the influence that can be brought about by the awakening of individual consciousness, and provides an important reference for public opinion prediction.

Length of Stay and Destination Image: Insights from Social Media Content of Day Trippers and Tourists

ABSTRACT Social media platforms have a significant role in determining destination image. This study examines the impact of length of stay on destination image as reflected on visitors’ posts on social media through a comparative analysis of day trippers and tourists. Two text mining methods (Term Frequency-Inverse Document Frequency and Latent Dirichlet Allocation) and an image classification model (ResNet) were applied to Weibo data to identify day trippers’ and tourists’ online destination image. Differences were found between day trippers and tourists when examining keywords, topics, and visual scenes. The keyword-based image was predominantly utilitarian for day trippers and more recreational-oriented for tourists. Moreover, tourists shared a greater variety of topic-based image attributes (e.g. accommodation, catering, activity) than day trippers, and form an abundant holistic image. Both groups were highly consistent in the visual category of common images and top-10 visual scenes of unique features, though significant differences were recorded when considering gender.

Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters.

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems.

Shifting sentiments: analyzing public reaction to COVID-19 containment policies in Wuhan and Shanghai through Weibo data

This study examines the dynamic relationship between China’s COVID-19 containment policies and public sentiment, focusing on the significant lockdowns in Wuhan and Shanghai. We employed natural language processing (NLP) on Weibo text data to uncover how people’s emotions towards these containment measures changed over time and space. Our analysis reveals a critical evolution in public sentiment, transitioning from initial support to growing dissatisfaction, highlighting the impact of ‘pandemic fatigue’ and the socio-economic factors influencing these shifts. This study contributes to understanding the complex interplay between public health strategies and societal reactions, providing practical insights into the spatial variations of sentiment across different demographic and socio-economic groups. By elucidating the causal effects of containment policies on public sentiment and the subsequent rise in public skepticism, our research offers valuable lessons for policymakers in tailoring communication and interventions to mitigate negative public perceptions and foster compliance during health crises.

Unraveling the propagation dynamics of passenger congestion in public transportation systems using big smart card data

We propose a comprehensive framework to explore propagation of passenger crowdedness in public transportation systems using 74.16 million trip chains. A deep learning method HSTGCNT is adopted which firstly models normal spatio-temporal dependencies in time series data, and assigns an anomaly score to new data based on its prediction error. The derived discrete anomaly events are further aggregated into groups based on their spatio-temporal proximity by using a clustering algorithm that binds shared nearest neighbors together based on pairwise correlations. The clustering results uncover several key stages located in a propagation chain, such as a core event, adjacent events with spatio-temporal proximity, or a broadcasting group. Weibo data serves as a third-party dataset to verify the above anomaly clusters. Results show that the framework is effective to detect outlier clusters, with recall value being 0.86. Findings may assist public safety departments in taking pro-active actions to interrupt chaos from broadcasting.

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.

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.

Analysis of Public Opinion and Sentiments Towards Gap Year in China Based on Weibo Data Mining

Analysis of Public Opinion and Sentiments Towards Gap Year in China Based on Weibo Data Mining

The effect of theme on the number of celebrity active fans under China Weibo data

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

Effectiveness of Emergency Response to Typhoons: Lessons Learned from Historical Typhoon Disasters in Zhuhai, China

Typhoons significantly impact socio-economics, varying in impact due to unique characteristics. However, limited studies explore why similar intensity typhoons affect regions differently. This study compares the emergency response measures of Zhuhai, Guangdong Province, China, in response to typhoon Hato (2017), Mangkhut (2018), and Higos (2020). These three similar typhoons offer an empirical opportunity to assess the effectiveness of emergency management. This research builds a timeline of Zhuhai's emergency response measures to the typhoons, collects Weibo data 72 hours before and after the typhoon landfall, performs keyword frequency analysis on contents and analyzes in detail the content of weibo postings, and changes in sentiment. Results show that timely and systematic emergency response measures may reduce disaster losses, which means emergency management is crucial in mitigating typhoon impacts, offering valuable lessons for other coastal cities.

A Multi-Feature Fusion Method for Urban Functional Regions Identification: A Case Study of Xi’an, China

Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions.

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.

Detecting multiple coexisting emotions from public emergency opinions

To detect multiple coexisting emotions from public emergency opinions, this article proposes a novel two-stage multiple coexisting emotion-detection model. First, the text semantic feature extracted through bidirectional encoder representation from transformers (BERT) and the emotion lexicon feature extracted through the emotion dictionary are fused. Then, the emotion subjectivity judgement and multiple coexisting emotion detection are performed in two separate stages. In the first stage, we introduce synthetic minority oversampling technique (SMOTE) to enhance the balance of data distribution and select the optimal classifier to recognise opinion texts with emotion. In the second stage, the label powerset (LP)-SMOTE is proposed to increase the number of the minority category samples, and multichannel emotion classifiers and the decision mechanism are employed to recognise different types of emotions and determine the final coexisting emotion labels. Finally, the Weibo data about coronavirus disease 2019 (COVID-19) are collected to verify the effectiveness of the proposed model. Experiment results indicate that the proposed model outperforms state-of-the-art models, with the F1_macro of 0.8532, the F1_micro of 0.8333, and the hamming loss of 0.0476. The emotion detection results are conducive to decision-making for public emergency departments.

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.

The Failure of Social Media Politics? Unpacking Interlocking Discourses in Contemporary China’s Online Anti-Surrogacy Sentiments

ABSTRACT This paper combines automated scraping of Weibo data and critical discourse analysis to examine the online anti-surrogacy sentiments in China, and finds three nodal points in the interlocking anti-surrogacy discourses. Firstly, through a heterosexual male persona, anti-surrogacy sentiments argue that the legalization of surrogacy would reduce the number of women of marriageable age. Secondly, radical feminist voices argued that surrogacy would make women suffer more in the current gender relations. Finally, gay men were singled out and blamed for exploiting women’s body. Through a discussion of these three voices, we argue that despite their apparent diversity, they are fundamentally conditioned and restricted by the state-corporate power axis in China. As a result, they perpetuate current structures of gender and sexuality inequality, instead of engendering meaningful discussions and social change.

An insightful metric for evaluating perceived benefits from water quality enhancement in waterscape parks: A behavioral analysis approach

The rational evaluation of pollution control measures is crucial for advancing water pollution management, ensuring water quality, and creating high-standard urban environments. Currently the evaluation heavily relies on water quality indicators with little regard to the sense of gain of the people. In this paper, we proposed an objective indicator PVH (Park Visiting Hours) to represent the behavioral response, as a materialized sense of humans, to water quality. We demonstrated the application of PVH in four typical waterscape parks in Beijing with multi-source data, and the results showed that: (1) The Mann-Kendall Test and an independent correlation test utilizing Sina Weibo data (Chinese Twitter) indicated that PVH was significantly negatively correlated with the Satellite image based Sensory Pollution Index (SPI), as corroborated the competence of PVH to appraise the sense of gain from water quality improvement. (2) Multivariate regressions showed that an increase in SPI of 0.1 corresponds to an average 8.8 min reduction in PVH. (3) The relationship between PVH and SPI was consistent across various visitor demographics, and whether visitors were in groups or alone. This study introduced PVH as a novel but concise indicator to represent water quality-related behavior of park visitors and would inspire the use of big multisource data for measuring human perceptions of ecosystem services.