Search Results (120)

High-speed rail (HSR) station areas play a vital role in shaping urban form, stimulating economic activity, and enhancing spatial vitality. Understanding the factors that influence this vitality is key to supporting sustainable urban development and transit-oriented planning. This study investigates 66 HSR station areas in 35 Chinese cities by integrating multi-source data—Sina Weibo check-in records, urban support indicators, station attributes, and built environment variables—within a city–node–place analytical framework. Using Multiple Linear Regression (MLR) and Light Gradient Boosting Machine (LightGBM) models, we identify key drivers of spatial vitality, while SHAP analysis reveals nonlinear and interaction effects. The results show that city population size, urbanization level, commercial land use, transit accessibility, and parking facilities significantly enhance station area vitality. However, diminishing returns are observed when commercial land and bus stop densities exceed certain thresholds. The station location index shows a negative correlation with spatial vitality. The analysis of interaction effects highlights strong synergies between urban development and functional configuration, as well as between accessibility and service infrastructure. Different station types exhibit varied spatial patterns and require differentiated strategies. This study offers empirical insights for aligning transport infrastructure and land use planning, supporting the development of vibrant, accessible, and sustainable HSR station areas. Full article

Streetscapes in old urban areas are not only an important carrier to show regional economies and city style, but also closely correlate to urban residents’ everyday life and the hustle and bustle in which they live. Nevertheless, previous studies have either focused on a few examples with low-throughput surveys or have lacked a specific consideration of spontaneous features in the data-driven explorations. Furthermore, the impact of spontaneous streetscape features on diversified social sensing has rarely been examined. This paper combined the mobile collection of street view images (SVIs) and a machine learning algorithm to calculate eight types of spontaneous streetscape elements and integrated two online platforms (Dianping and Sina Weibo) to map the distribution of economic vitality and social media perception, respectively. Then, through comparing multiple regression models, the impacts of the spontaneous streetscape characteristics on social sensing were revealed. The results include the following two aspects: (1) overall, the spontaneous streetscape features have a certain similarity in the impact on both dimensions of social sensing in Quanzhou, with significant clustering and transitional trends and strong spatial heterogeneity; and (2) specifically, the spontaneous streetscape elements can be divided into three categories, given the differentiated roles of significantly positive, negative, and polarizing impacts on the social sensing results. For example, proper use of open-interface storefronts, ads, and banners is consistent with the common suggestions, while the excessive pursuit of interface diversity and the use of cultural elements may bring an ambiguous effect. This paper provides a transferable analytical framework for mixed and data-driven sensing of streetscape regeneration and can potentially inspire related decisionmakers to adopt a more refined and low-cost approach to enhance urban vitality and sustainability. Full article

Extreme rainfall events are significant manifestations of climate change, causing substantial impacts on urban infrastructure and public life. This study takes the extreme rainfall event in Beijing in 2023 as the background and utilizes data from Sina Weibo. Based on large language models and prompt engineering, disaster information is extracted, and a multi-factor coupled disaster multi-sentiment classification model, Bert-BiLSTM, is designed. A disaster analysis framework focusing on three dimensions of theme, location and sentiment is constructed. The results indicate that during the pre-disaster stage, themes are concentrated on warnings and prevention, shifting to specific events and rescue actions during the disaster, and post-disaster, they express gratitude to rescue personnel and highlight social cohesion. In terms of spatial location, the disaster shows significant clustering, predominantly occurring in Mentougou and Fangshan. There is a clear difference in emotional expression between official media and the public; official media primarily focuses on neutral reporting and fact dissemination, while public sentiment is even richer. At the same time, there are also variations in sentiment expressions across different affected regions. This study provides new perspectives and methods for analyzing extreme rainfall events on social media by revealing the evolution of disaster themes, the spatial distribution of disasters, and the temporal and spatial changes in sentiment. These insights can support risk assessment, resource allocation, and public opinion guidance in disaster emergency management, thereby enhancing the precision and effectiveness of disaster response strategies. Full article

Disasters not only directly cause casualties and property losses but also significantly impact public opinion. In order to identify the evolution characteristics and influencing factors of public opinion during disasters, this paper put forward an analytical framework for analyzing public opinion, which clarified the relationships among key characteristics of public opinion and emphasized the role of official agencies in the processes of information releasing and information feedback. Guided by this framework, this paper collected the public opinion on Sina Weibo during the extraordinary “7.20” rainstorm in Henan Province, China. By analyzing the changes in the discussion regarding both the popularity of and the emotion displayed in Sina Weibo comments over time, it was found that the evolution of public opinion was closely related to disaster development. Novel informational content or innovative forms of information contribute to enhancing the discussion of popularity, while the events or emotions expressed within the information elicit corresponding emotional responses from the public. As popularity increased, the prevalence of negative emotions was observed to diminish concurrently with a rise in the proportion of neutral emotions. Based on these results, some suggestions on the management of public opinion during disasters were put forward. Full article

Background/Aims: The global promotion of HPV vaccines has underscored the importance of vaccination for both males and females in reducing disease transmission and associated complications. Despite robust evidence supporting male HPV vaccination, China has yet to approve it. Public discussions on male HPV vaccination, influenced by policy delays, gender norms, and commercialization, reveal diverse attitudes and significant challenges in achieving equitable health outcomes. This study investigates public perceptions and attitudes toward male HPV vaccination in China by analyzing cognitive frames and the social, cultural, and economic factors shaping these discussions. Methods: This study employs a cross-sectional design to analyze 4997 Sina Weibo posts using the Analysis of Topic Model Networks (ANTMN), identifying five frames: Disease Risk and Prevention, Virus Transmission, Gender Roles and Perceptions, Vaccine Promotion and Acceptance, and Market Dynamics and Consumption. Results: The findings reveal a significant gap between policy implementation and public awareness of male HPV vaccination in China. Despite growing recognition of its benefits, entrenched gender norms and biases hinder equitable health outcomes. Social media, as a pivotal platform for health communication, plays a dual role in facilitating public discourse while also amplifying misinformation. Policy delays and concerns over vaccine commercialization heighten public hesitancy, emphasizing the need for inclusive policies and targeted education. Conclusions: This study highlights the necessity for systemic efforts to address gender biases, enhance public education on male HPV vaccination, and rebuild trust in vaccination programs. A multifaceted approach is required to bridge these gaps, integrating policy reforms, inclusive health communication strategies, and equitable vaccine access. These measures aim to promote awareness and increase vaccination uptake among males in China, ultimately contributing to more comprehensive and equitable public health outcomes. Full article

This research examines the correlation between the COVID-19 pandemic and the desire to engage in compensatory consuming behaviors, specifically emphasizing emotional eating as a psychological coping strategy, particularly with respect to snacks and sweets. Conducting sentiment analysis by using a Natural Language Processing (NLP) method on posts from Sina Weibo, a leading Chinese social media platform, the research identifies three distinct phases of consumer behavior during the pandemic: anxiety, escapism, and compensatory periods. These stages are marked by varying degrees of emotional eating tendencies, illustrating a psychological trajectory from initial shock to seeking comfort through food as a means of regaining a sense of normalcy and control. The analysis reveals a notable increase in posts expressing a desire for compensatory consumption of snacks and sweets in 2020 compared to 2019, indicating a significant shift towards emotional eating amid the pandemic. This shift reflects the broader psychological impacts of the crisis, offering insights into consumer behavior and the role of digital platforms in capturing public sentiment during global crises. The findings have implications for policymakers, health professionals, and the food industry, suggesting the need for strategies to address the psychological and behavioral effects of natural disasters. Full article

Social network analysis (SNA), or the application of network analysis techniques to social media data, is an increasingly prominent approach used in computational public health research. We conducted a systematic review to investigate trends around SNA applied to social media data for public health and epidemiology while outlining existing ethical practices. Following PRISMA guidelines, we reviewed articles from Web of Science and PubMed published between January 2019 and February 2024, leading to a total of 51 papers surveyed. The majority of analyzed research (69%) involved studying Twitter/X, followed by Sina Weibo (16%). The most prominent topics in this timeframe were related to COVID-19, while other papers explored public health topics such as citizen science, public emergencies, behavior change, and various medical conditions. We surveyed the methodological approaches and network characteristics commonly employed in public health SNA studies, finding that most studies applied only basic network metrics and algorithms such as layout, community detection, and standard centrality measures. We highlight the ethical concerns related to the use of social media data, such as privacy and consent, underscoring the potential of integrating ethical SNA with more inclusive, human-centered practices to enhance the effectiveness and community buy-in of emerging computational public health efforts. Full article

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems. Full article

This study analyzed 2421 microblogs posted between the year 2012 to March 2022 reflecting the microbloggers’ attitudes toward different cultures. Results indicated that (1) the number of microblog posts expressing the users’ intercultural attitudes increased distinctly from 2019 to March 2022, with females users in general posting more microblogs than males; (2) females posted more microblogs encompassing positive emotions to show their interest and motivation to learn about foreign cultures, and the tendency to value and appreciate cultural differences, whereas males created more sentimentally neutral posts that revealed their recognition of the existence of cultural differences, and females and males posted a similar number of microblogs containing negative emotions; and (3) more posts involved “small c” culture were posted than those containing themes belonging to the “Big C” culture. Gender gap was further observed regarding the cultural themes concerned by the microbloggers. Implications were discussed in the context of intercultural education. Full article

The rapid development of the Internet has facilitated expression, sharing, and interaction on social networks, but some speech may contain harmful discrimination. Therefore, it is crucial to classify such speech. In this paper, we collected discriminatory data from Sina Weibo and propose the improved Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on Latent Dirichlet Allocation (LDA) to improve data quality and balance. And we propose a new integration method integrating Support Vector Machine (SVM) and Random Forest (RF). The experimental results demonstrate that the integrated model exhibits enhanced precision, recall, and F1 score by 6.0%, 5.4%, and 5.7%, respectively, in comparison with SVM alone. Moreover, it exhibits the best performance in comparison with other machine learning methods. Furthermore, the positive impact of improved SMOTE and this integrated method on model classification is also confirmed in ablation experiments. Full article

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. Full article

This study selected 61 parks in Guizhou province as research points and collected 3282 facial expression photos of park visitors in 2021 on the Sina Weibo platform. FireFACE v1.0 software was used to analyze the facial expressions of the visitors and evaluate their emotional perception of the landscape structure and ecosystem service value (ESV) of different landscape types of blue–green spaces. Research shows that the average ESV of green spaces in parks is USD 6.452 million per year, while the average ESV of blue spaces is USD 3.4816 million per year. The ESV of the blue–green space in the park shows no geographical gradient changes, while the happiness score in facial expressions is negatively correlated with latitude. Compared to blue spaces, green spaces can better awaken positive emotions among visitors. The ESV performance of different types of green spaces is as follows: TheroponcedrymV > GrasslandV > Shrubland V. The landscape structure and ESV of the blue–green space in the park can be perceived by visitors, and GreenV and vegetation height are considered the main driving factors for awakening positive emotions among visitors. In Guizhou, when the park area decreases, people are more likely to experience sadness. Regressions indicated that by increasing the green space area of the park and strengthening the hydrological regulation function of the blue–green space, people can achieve a more peaceful mood. Overall, people perceive more positive sentiments with high ESV in blue–green spaces of Karst parks but low ESV in shrubland. Full article

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. Full article

Salary satisfaction (SS) perception by employees can be affected by psychological impacts from the workplace setting. Landscape attributes of green and blue spaces (GBS) may account for this effect, but relevant evidence is rarely verified. In this study, a total of 56 Chinese industrial parks were chosen as study sites, where employee satisfaction was assessed by rating facial expression scores (happy, sad, and neutral emotions) in photos obtained from social networks (Sina Weibo and Douyin). The structures of the GBSs were characterized remotely by largeness of size, height, and visible ratio of green view (GVI) in a 2 km radius buffer area around the workplace. Street view images from Baidu map were selected for estimating GVI using a pre-trained deep learning model and botanical experts evaluating woody plants’ diversity. The results indicated that SS can be estimated with the maximum likelihood analysis model against the happy score, which ranged within 8.37–18.38 (average: 13.30 ± 2.32) thousand RMB. A regression model indicated SS was lowered by a larger green space area in agreement with a reduced happy score. Further, sad scores in highland areas with tall plants and a strong depression on the happy score was associated with a greater plant diversity. Interesting from this study, the designed apparent size of green space should be considered in green space construction near a workplace to prevent perceptional decline towards SS, while blue space is irrelevant in this relationship. Similarly, the diversity of woody plants should be planned to control its negative impact on the perception of positive emotions, with plant diversity beyond a comfortable level perhaps further decreasing SS. Full article

Ancient canal towns in Jiangnan have become important tourist destinations due to their unique water town scenery and historical value. Creating a unique tourist image boosts these ancient towns’ competitive edge in tourism and contributes significantly to their preservation and growth. The vast amount of data from social media has become an essential source for uncovering tourism perceptions. This study takes two ancient towns in Shanghai, Zhaojialou and Fengjing, as case study areas. In order to explore and compare the destination images of the towns, in the perception of tourists and in official publicity, machine learning approaches like word embedding and K-means clustering are adopted to process the comments on Sina Weibo and publicity articles, and statistical analysis and correspondence analysis are used for comparative study. The results reveal the following: (1) Using k-means clustering, destination perceptions were categorized into 16 groups spanning three dimensions, “space, activity, and sentiment”, with the most keywords in “activity” and the fewest in “sentiment”. (2) The perception of tourists often differs significantly from the official promotional materials. Official promotions place a strong emphasis on shaping the image of ancient towns based on their historical resources, presenting a more general picture. Tourist perception, which is fragmented, highlights emerging elements and the experiential activities, along with the corresponding emotional experiences. (3) Comparing the two towns, Fengjing Ancient Town stands out, with more diverse tourist perceptions and richer emotional experiences. This underscores the effectiveness of tourism activities that use space as a media to evoke emotions, surpassing the impact of the spaces themselves. Full article