GIS technology is increasingly being introduced into analytical activities due to a special focus on the relationship of observation objects in space. In addition, the growth of interest in geoinformation methods is facilitated by emergence of geographically structured data sets, including big social data, which, in addition to obvious advantages, are characterized by possible errors and incomplete facts, including address information. The aim of the work is to study the influence of the applied data geocoding algorithms and the weight matrices formed on their basis on the results of assessing the spatial autocorrelation of prices. The study was conducted on the prices of potato for 1952 settlements of the Russian Federation, in daily detail for the period from 01/01/2021 to 12/31/2024. The computational experiment is aimed at comparing alternative estimates of the global Moran`s index with testing various geocoding sources (Openstreetmap and Rosreestr) and approaches to constructing spatial weight matrices. The research showed that, in general, the geocoding source does not greatly affect the conclusion on presence/absence of spatial dependence of prices, but in the case of OSM API geocoding, higher index estimates were derived, with a corresponding proportion of statistically significant estimates and less variation in the values obtained. The choice of the weight matrix can influence the final estimates

The health problems caused by heat waves have attracted widespread global attention. High temperature is characterized by human perception before triggering discomfort. Understanding the public perception of high temperature based on social big data can help the relevant authorities to take appropriate countermeasures and reduce the risk of morbidity. Heat-related posts from Sina Microblog platform during the summer of 2023 are collected, and daily maximum temperature data of all meteorological stations across China from 1991 to 2023 is downloaded in the NCEI website. The relationship between public perception and temperature is investigated using the Latent Dirichlet Allocation (LDA) topic model, the heat attention index, and the heat perception model. The results reveal that 1) Heat-related microblogs are primarily concentrated in the southeastern regions of China. “Complaints About High Temperatures” is the most prominent topic. The most densely distributed regions for the four heat-related topics are Guangdong and Beijing. This distribution is closely related to the climate conditions, economic development levels, and internet penetration. 2) Heat attention index and daily maximum temperature of each province have a similar spatial distribution, while several provinces show discrepancies between the distribution of heat attention index and daily maximum temperature. 3) The daily maximum temperature corresponding to the lowest value of heat attention index in Guangdong is higher than that in other provinces. 4) There are obvious regional differences in the public's ability to heat tolerance and sensitivity. Regions with higher heat tolerance are mainly distributed in the southeastern part of China. People with higher heat sensitivity are mainly concentrated in the central and eastern regions of China. High temperature in Summer brings public discomfort and negative emotions. It is crucial to understand these regional perception differences and take timely measures to prevent heat-related health issues.

YouTube is considered as one of the most widely used video-sharing platforms in the world. Users can express their reactions to videos through comments, which often convey emotions that can be automatically identified using computational techniques. Emotion detection on YouTube data presents a challenging task due to the heterogeneity, unstructured nature, and large scale of user generated contents. In this study, we develop an emotion detection framework implemented on Apache Spark, an open-source platform for distributed Big Data processing. The proposed system integrates Machine Learning algorithms with Natural Language Processing (NLP) techniques and leverages Spark’s MLlib library to classify emotions expressed in YouTube comments. To efficiently deal with the complexity and noise inherent in largescale multimedia data, several preprocessing and feature extraction steps are introduced. The K-Means clustering algorithm is used after data preparation for the corpus automatic annotation, the resulting labeled Dataset is labeled according to the Ekman emotional model with six basic emotions. The selected classifiers are trained using the resulting labeled Dataset. Experimental results demonstrate that the proposed approach improves both scalability and accuracy, making it suitable for leveraging the emotion detection in social Big Data environments.

Exploring expressions of adolescent delinquency in South Korea with social big data and topic modeling

The remediation of contaminated sites necessitates robust and objective sustainability assessment frameworks to guide decision-making, yet prevailing methods often rely on qualitative or semi-quantitative metrics susceptible to subjectivity. This study establishes a comprehensive, fully quantitative evaluation system integrating environmental, economic, and social dimensions, comprising 13 objective indicators derived from Life Cycle Assessment (LCA), economic documentation, and publicly accessible social data—including nighttime light intensity, Point of Interest (POI) data, and social media sentiment analysis. The system employs the Analytic Hierarchy Process (AHP) for weight assignment, ensuring methodological rigor and expert consensus. Validated through three case studies of remediated contaminated sites in Shandong Province, China, the framework reveals distinct sustainability profiles: Site 1 achieved the highest composite score (0.1030), demonstrating balanced performance across all dimensions, whereas Sites 2 and 3 yielded negative scores (−0.2490 and −0.1069, respectively), reflecting trade-offs between remediation efficiency, secondary environmental impacts, and socio-economic outcomes. The key findings underscore the dominance of environmental health indicators—notably Disability-Adjusted Life Years (DALYs)—in overall weighting and highlight the critical influence of remediation technology selection on lifecycle impacts. The study validates the utility of a quantitative, multi-criteria approach in identifying optimal remediation strategies, facilitating cross-project comparability, and supporting the transition from cost-centric remediation toward value-driven, sustainable redevelopment.

Protected areas face growing challenges in balancing biodiversity conservation with the pressures of nature-based tourism. This study develops a geospatially explicit framework integrating social big data analytics and species distribution modeling (SDM) to assess tourism-induced ecological stress in Shei-Pa National Park, Taiwan. We used datasets on the potential distribution range of 49 protected terrestrial vertebrate species. We selected 16 protected species on the IUCN Red List whose potential distributions overlapped with the park. We analyzed 2914 spatial grids (500 × 500 m) using photo-user-days (PUD) derived from geotagged photographs uploaded to the Flickr platform (2008–2017). The monthly PUD values were strongly correlated with official visitor statistics ( r = 0.818, p = 0.001), validating their reliability for seasonal analysis. Kernel density estimation and regression analyses revealed that infrastructure variables—particularly visitor centers (estimate = 2.0160) and hotels (1.6550)—were the strongest predictors of tourism pressure (Adjusted R 2 = 0.3568), while rivers showed no effect. Spatial overlay analysis revealed that high PUD values strongly overlapped with the predicted habitats of Level 1 protected species (e.g., Passer rutilans, Prionailurus bengalensis chinensis ), especially in the eastern region of the park, including a Generally Protected Area and a Conservation Area. Seasonal circular statistics revealed a bimodal tourism pattern, peaking in February and in November–December (Rayleigh Z = 5.72, p < 0.01), with minimal visitation in May. The PUD index ranged from 0 (minimum) to 35.2 (maximum), with a median value of approximately 0.16. This seasonal trend highlights the potential value of implementing visitor access controls during off-peak months. Tourism pressure was lowest in remote, high-altitude conservation zones, which often harbor sensitive species. We identified four conservation risk zones-LL, LH, HL, and HH-based on spatial overlays of habitat suitability and tourism pressure. Note that HH zones, indicating high ecological value and high tourism pressure, were concentrated in the eastern region of the park and accounted for approximately 6 % of its total area. These areas represent the most critical zones for biodiversity conflict and inform targeted zoning, seasonal access control, and visitor redistribution strategies. This study operationalizes open-access social media data and ecological modeling to support the adaptive management of biodiversity-rich protected areas. • This study integrates social big data (Flickr-derived Photo-User-Days) with species distribution modeling to assess tourism pressure in Shei-Pa National Park. • Tourism pressure is highest in infrastructure-accessible zones such as visitor centers, hotels, and trailheads. • Overlay analysis reveals that several endangered and nationally protected species experience high spatial overlap with tourism hotspots. • Remote, high-altitude areas show low visitor intensity and function as ecological refugia for conservation-priority species. • The study proposes evidence-based management strategies, including spatial zoning, seasonal access regulation, and real-time monitoring, to mitigate tourism impacts.

This study empirically analyzes perceptions and satisfaction with temple food experiences by utilizing social big data, amidst growing societal interest in temple food. A total of 6,103 online reviews posted on TripAdvisor regarding temple food offerings at 17 temples across South Korea were collected. Text data and star ratings were analyzed using a word embedding algorithm. The analysis revealed that keywords, such as “vegetarian,” “dish,” and “buffet”, were frequently mentioned alongside temple food, indicating that these terms are perceived as semantically similar. Furthermore, multiple regression analysis identified 14 perception-related keywords significantly affectingthat significantly affected satisfaction. Among them, five keywords, including “stability,” “extraordinary,” and “staff” had a positive influence, while nine keywords including “dirty,” “budget,” and “breakfast” had a negative influence on satisfaction. A sense of stability within the temple stay environment and the kindness of the staff were confirmed as key positive factors enhancing satisfaction with temple food. In contrast, dissatisfaction with accommodations and repetitive meal compositions was found to diminish satisfaction. By identifying factors that influence perceptions and satisfaction with temple food experiences, this study offers practical and academic insights for developing temple food as a sustainable cultural tourism resource.

Retraction notice to "Analyzing trends in the spatial-temporal visitation patterns of mainland Chinese tourists in Sabah, Malaysia based on Weibo social big data" [Heliyon 9 (2023) e15526

This study analyzed the milk tea consumption patterns to derive implications for sugar-reduction strategies. Three methods were used: social big data analysis of consumer milk tea consumption patterns, case studies of milk tea products sold in beverage outlets and the retail market, and content analysis of milk tea recipe books. The main results were as follows. First, milk tea served in large volumes at beverage outlets had high sugar contents, underscoring the need for reduction. Second, sugar-reduction strategies should consider the digestive and absorptive metabolic pathways and glycemic control functions of alternative sweeteners and carbohydrates for domestically processed ready-to-drink milk tea products. Third, recipe books described milk tea preparations with relatively high sugar contents, highlighting the necessity for developing home café–oriented recipes as part of the sugar-reduction strategies. In conclusion, to promote sugar reduction in milk tea, the industry (beverage outlets and retail market companies) should devote more concerted efforts to plan and commercialize low-sugar milk tea products using alternative sweeteners. Furthermore, from an academic standpoint, researchers should focus on developing and validating milk tea recipes to help consumers incorporate sugar reduction into their daily routines as part of a balanced and healthy lifestyle.

Based on multi-scale geographically weighted regression (MGWR) and spatial autocorrelation analysis, this paper explores the impact of different urban functional elements on urban vitality and their spatial heterogeneity. The results show that urban vitality exhibits significant spatial clustering within the study area, with both high and low values showing a clustered state. Scenic spots, business residences, and science, education, and culture facilities have a significant negative effect on urban vitality, while the positive effects of public facilities and life services are not significant. This suggests that the spatial layout and complexity of functional land use play an important role in urban vitality. The MGWR model significantly outperforms the traditional OLS model in terms of goodness of fit and explanatory power, indicating that considering spatial heterogeneity is crucial for revealing the formation mechanism of urban vitality. This study provides empirical reference and methodological support for urban spatial planning and functional layout optimization.

Abstract Objective The purpose of this study was to identify latent topics in social big data related to bi‐hon , a Korean neologism referring to the civil status of unmarried individuals. Background Social big data can effectively capture complex and fast‐changing public discourse on the recent bi‐hon trend in South Korea and reveal the divergence in traditional marital formation and families. Method We analyzed 65,205 Korean online posts that mentioned bi‐hon and were posted on one of three online channels (news websites, Twitter, and online communities) from July 2016 to June 2019. Results Employing a topic modeling technique, we identified and labeled 20 latent topics that best represented the data collected from each online channel. Next, we categorized these topics into six overarching themes: (a) the changing landscape of marital formation in South Korea; (b) bi‐hon: a choice, declaration, and lifestyles; (c) gender inequality and feminism movements; (d) childcare burden in a country with the lowest fertility rate; (e) conflict with the Confucian tradition of Korean families; and (f) economic barriers to marriage. Conclusion Our study contributes to the growing scholarship on changes in marital formation and families around the globe by providing a comprehensive picture of bi‐hon and leveraging innovative social big data research. Implications This study calls for diverse policy support for the increasing single population and guides family researchers for future social big data studies.

This study aimed to analyze the keywords associated with black tea beverages found in blogs used by various age groups, based on Social big data, to examine consumption patterns and suggest strategies to revitalize the consumption of black tea products. For this purpose, data was collected from the social big data platform Sometrend, setting the search period from May 1, 2024, to April 30, 2025, resulting in 188,274 blog posts related to black tea beverages. The main results are as follows. First, Korean consumers showed interest in black tea beverage brands from various countries. Second, they perceived black tea beverages as wellness products in everyday life, and the trend of reducing sugar and caffeine appeared in such products. Third, consumers shared their experiences with black tea beverages through blog content. In conclusion, to promote the consumption of black tea beverages, it is necessary to provide consumer education that systematically cultivates knowledge about related products. In addition, the industry should reduce the sugar and caffeine content of black tea beverages and indicate these effects to promote healthier consumption. Furthermore, companies should plan content that aligns with social media and facilitates meaningful consumer black tea beverage experiences.

Mental health disorderssuch as depression and suicidal ideation affect hundreds of millions globally, posing serious public health challenges. With the rise of social media platforms, user-generated content has become a valuable source for detecting psychological distress through computational methods.This study presents a comprehensive roadmap for predicting mental health conditions using social media data, focusing on machine learning and deep learning approaches. We examine key components of the predictive pipeline, including data collection, feature extraction, and classification algorithms. Recent research efforts are reviewed to highlight effective techniques for identifying at-risk individuals based on linguistic, behavioral, and interactional patterns online. The paper also discusses the development of automated detection systems, ethical considerations, and future research directions. These advances support scalable, real-time mental health monitoring and offer potential to augment traditional screening and intervention strategies. Key Words: Mental healthcare, Mental disorder prediction, Social media analysis, Big social data, Machine learning, Deep learning

The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical concern for researchers is the trustworthiness of results when machine learning and natural language processing tools are used to analyse such data. We argue that confidence in the credibility and robustness of results depends on adopting a ’human-in-the-loop’ methodology that is able to provide researchers with control over the analytical process, while retaining the benefits of using machine learning and natural language processing. With this in mind, we propose a novel methodological framework for computational grounded theory that supports the analysis of large qualitative datasets, while maintaining the rigour of established grounded theory methodologies. To illustrate the framework’s value, we present the results of testing it on a dataset collected from Reddit in a study aimed at understanding tutors’ experiences in the gig economy.

Environmental communication strategies in green consumption: spatiotemporal shifts across six domains revealed by social big data

Big social data analysis for quality of MOOC courses: the moderating role of practical examples

Motivation: Social media platforms have emerged as a new data source for social sciences. The data extracted from them, known as big social data, are characterized by their complexity and are described within the ‘V’ big data model. Literature has demonstrated the influence of investor activity, as captured by BSD, on the stock market. Modeling the stock market using Twitter data allows for the capture of real-time market sentiments, potentially enhancing the accuracy of financial forecasts. The value of such models lies in their ability to identify subtle yet significant signals that traditional methods might overlook. Aim: The aim of this study is to explore the predictive capabilities of Twitter sentiment on financial markets, specifically focusing on the application of ARX-GARCH models to analyze the impact of both, negative and positive class of emotions on market volatility. Results: Incorporating sentiment variables into the ARX-GARCH models did not significantly enhance their predictive capabilities. While sentiment variables did not broadly improve model performance, certain variables demonstrated statistical significance at various lag levels. This indicates that some sentiments might have a delayed impact on market returns, though the overall effect size was small. Among the sentiment indicators analyzed, those based on n-gram analysis and the bullish index outperformed others, including the volume of individual emotions like anger, fear, and sadness.

The crowd of smart devices surrounds us all the time. These devices popularize social media platforms (SMP), connecting billions of users. The enhanced functionalities of smart devices generate big data that overutilizes the mainstream network, degrading performance and increasing the overall cost, compromising time-sensitive services. Research indicates that about 75% of connections come from local areas, and their workload does not need to be migrated to remote servers in real-time. Collaboration among mobile edge computing (MEC), regional computing (RC), and cloud computing (CC) can effectively fill these gaps. Therefore, we propose a collaborative structure of mobile, regional, and cloud computing to address the issues arising from social big data (SBD). In this model, it may be easily accessed from the nearest device or server rather than downloading a file from the cloud server. Furthermore, instead of transferring each file to the cloud servers during peak hours, they are initially stored on a regional level and subsequently uploaded to the cloud servers during off-peak hours. The outcomes affirm that this approach significantly reduces the impact of substantial SBD on the performance of mainstream and social network platforms, specifically in terms of delay, response time, and cost.

This study aimed to provide marketing strategies for increasing attendance and revitalizing professional baseball teams by utilizing social big data analysis. To achieve this, text mining, TF-IDF, centrality measures, and semantic network analysis were conducted using Textom and Ucinet6. The study period was limited from January 1, 2015, to June 30, 2024. The analysis results showed that, firstly, in professional baseball, professional baseball teams, Shinhan Bank, and Gocheok Sky Dome were identified. Secondly, in the KT Wiz factors, players such as Kang Baek-ho, Hwang Jae-gyun, and Park Byung-ho, who represent or have represented KT Wiz, were extracted, along with words related to uniforms, sales, attending games, and cheering. Thirdly, in terms of game records and results, words related to game outcomes such as wins, losses, losing streaks, runs allowed, and hits were extracted, as well as terms related to game records such as starting pitchers, pitchers, batters, and appearances. Fourthly, in the media factors, words related to media such as photos, reporters, and media outlets, as well as Manager Lee Kang-chul, were extracted. Finally, in the category of food around the stadium, words like popular restaurants, cafes, information, and site maps were identified. Therefore, it is expected that the results of this study can serve as basic data for revitalizing the KT Wiz team and increasing attendance.

Abstract Yuhua Wang's The Rise and Fall of Imperial China and Yasheng Huang's The Rise and Fall of the EAST (Exams, Autocracy, Stability, and Technology) take on grand history with big social science data. While both analyze China's rise and decline, they fundamentally disagree on what rose and fell, whether the imperial state was weak or strong, and whether the driving forces were social terrains or the emperor's designs. Wang's central puzzle is the “sovereign's dilemma” that presents a negative correlation between the emperor's duration and state strength. He uses elite social networks to explain why the Tang-Song transition brought about longer imperial reigns but weaker state strength. A closer look at Wang's data, however, suggests that state strength as fiscal capacity went up alongside the emperor's longevity in the Song. Huang offers a different “emperor's dilemma”: how the rational autocrat could ensure a competent yet loyal staff. Huang features “trailblazing emperors” who crafted the civil service examinations, called keju, to assert “end-to-end controls over the entire pipeline—nomination, evaluation, and final selections.” Keju also dissolves Wang's “sovereign's dilemma” by simultaneously enhancing ruler survival and state strength. For Huang, what rose and rose were keju and state strength; what rose and fell were societal diversity and inventiveness. Imperial China collapsed when the Qing abandoned keju. Both books inform today's China: if the autocrat's relentless pursuit of power through fragmenting the elites was “the final culprit” for imperial China's fall, readers should be deeply concerned about Xi Jinping's China.