Search Results (1,120)

This study focuses on cultural and urban development policies under China’s 14th Five-Year Plan, exploring the content and semantic structure of discussions on the “East Asian Cultural Capital” project on the Weibo platform. It analyzes how national cultural development policies are reflected in the discourse system related to the “East Asian Cultural Capital” on social media and emphasizes the guiding role of policies in the dissemination of online culture. When China announced the 14th Five-Year Plan in 2021, the strategic direction and policy framework for cultural development over the five-year period from 2021 to 2025 were clearly outlined. This study employs text mining and semantic network analysis methods to analyze user-generated content on Weibo from 2023 to 2024, aiming to understand public perception and discourse trends. Word frequency and TF-IDF analyses identify key terms and issues, while centrality and CONCOR clustering analyses reveal the semantic structure and discourse communities. MR-QAP regression is employed to compare network changes across the two years. Findings highlight that urban cultural development, heritage preservation, and regional exchange are central themes, with digital media, cultural branding, trilateral cooperation, and cultural–economic integration emerging as key factors in regional collaboration. Full article

With the popularization of social media, information related to public health events has seen explosive growth online, making it essential to accurately identify informative tweets with decision-making and management value for public health emergency response and risk monitoring. However, existing methods often suffer performance degradation during cross-event transfer due to differences in data distribution, and research specifically targeting public health events remains limited. To address this, we propose the Label Confusion Domain Adversarial Network (LCDAN), which innovatively integrates label confusion with domain adaptation to enhance the detection of informative tweets across different public health events. First, LCDAN employs an adversarial domain adaptation model to learn cross-domain feature representation. Second, it dynamically evaluates the importance of different source domain samples to the target domain through label confusion to optimize the migration effect. Experiments were conducted on datasets related to COVID-19, Ebola disease, and Middle East Respiratory Syndrome public health events. The results demonstrate that LCDAN significantly outperforms existing methods across all tasks. This research provides an effective tool for information detection during public health emergencies, with substantial theoretical and practical implications. Full article

This study investigates how young Chinese consumers across generational lines engage with wine, addressing three key research questions: What motivates their wine purchases? What sensory preferences do they exhibit? And through which channels do they prefer to buy wine? Based on a qualitative design combining focus groups and semi-structured interviews, the study identifies significant generational differences between millennials and post-millennials. Millennials treat wine as a social tool for networking and status, while post-millennials view wine as a medium of personal identity shaped by digital culture. Similarly, millennials prefer a balance of traditional and digital retail, whereas post-millennials favour online platforms. Experiential consumption follows the same pattern, from formal tourism to virtual tastings. By linking these findings to institutional and cultural theories of consumer behaviour, the study contributes to a nuanced understanding of wine consumption in an emerging market. It provides practical implications for wine marketers aiming to localize their strategies for younger Chinese segments. Full article

The purpose of the present research is to analyse the negative outcomes associated with the excessive Instagram dependency of those users that access the application through their smartphones. An empirical study was conducted through online interviews using structured questionnaires, resulting in 342 valid responses, with the target population being young users over 18 years old who access Instagram daily. Research shows that dependency on Instagram is primarily driven by individuals’ need for orientation and understanding, with entertainment being a secondary motivation. The results indicate that dependency on the social network is positively associated with excessive use, addiction, and Instastress. Furthermore, excessive use contributes to personal and social problems and increases both stress levels and mindfulness related to the platform. In turn, this excessive use intensifies addiction, which functions as a mediating variable between overuse and Instastress, mindfulness, and emotional exhaustion. This study offers valuable insights for academics, mental health professionals, and marketers by emphasizing the importance of fostering healthier digital habits and developing targeted interventions. Full article

This paper explores emotional analysis in Hebrew texts, focusing on improving machine learning techniques for depression detection by integrating psychological feature lexicons. Hebrew’s complex morphology makes emotional analysis challenging, and this study seeks to address that by combining traditional machine learning methods with sentiment lexicons. The dataset consists of over 350,000 posts from 25,000 users on the health-focused social network “Camoni” from 2010 to 2021. Various machine learning models—SVM, Random Forest, Logistic Regression, and Multi-Layer Perceptron—were used, alongside ensemble techniques like Bagging, Boosting, and Stacking. TF-IDF was applied for feature selection, with word and character n-grams, and pre-processing steps like punctuation removal, stop word elimination, and lemmatization were performed to handle Hebrew’s linguistic complexity. The models were enriched with sentiment lexicons curated by professional psychologists. The study demonstrates that integrating sentiment lexicons significantly improves classification accuracy. Specific lexicons—such as those for negative and positive emojis, hostile words, anxiety words, and no-trust words—were particularly effective in enhancing model performance. Our best model classified depression with an accuracy of 84.1%. These findings offer insights into depression detection, suggesting that practitioners in mental health and social work can improve their machine learning models for detecting depression in online discourse by incorporating emotion-based lexicons. The societal impact of this work lies in its potential to improve the detection of depression in online Hebrew discourse, offering more accurate and efficient methods for mental health interventions in online communities. Full article

Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit social media network is proposed in this study, and posts are classified into four levels: minimum, mild, moderate, and severe. We take a dual approach using classical machine learning (ML) algorithms and recent Transformer-based architectures. For the ML track, we build ten classifiers, including Logistic Regression, SVM, Naive Bayes, Random Forest, XGBoost, Gradient Boosting, K-NN, Decision Tree, AdaBoost, and Extra Trees, with two recently proposed embedding methods, Word2Vec and GloVe embeddings, and we fine-tune them for mental health text classification. Of these, XGBoost yields the highest F1-score of 94.01 using GloVe embeddings. For the deep learning track, we fine-tune ten Transformer models, covering BERT, RoBERTa, XLM-RoBERTa, MentalBERT, BioBERT, RoBERTa-large, DistilBERT, DeBERTa, Longformer, and ALBERT. The highest performance was achieved by the MentalBERT model, with an F1-score of 97.31, followed by RoBERTa (96.27) and RoBERTa-large (96.14). Our results demonstrate that, to the best of the authors’ knowledge, domain-transferred Transformers outperform non-Transformer-based ML methods in capturing subtle linguistic cues indicative of different levels of depression, thereby highlighting their potential for fine-grained mental health monitoring in online settings. Full article

This article investigates how digital competences contribute to the production of social capital and professional recognition through a systematic review of international literature. Drawing on 62 peer-reviewed articles indexed in Web of Science, Scopus, and ERIC, the review identifies the most frequently mobilized theoretical frameworks, the predominant types and sources of recognition, and the associated dimensions of social capital. The findings reveal a growing emphasis on communicative and network-based digital competences—particularly digital communication, information management, and virtual collaboration—as key assets in professional contexts. Recognition is shown to take predominantly non-material, extrinsic, and visibility-oriented forms, with social media platforms emerging as central sites for the performance and circulation of digital competences. The results indicate that social media proficiency has become a central determinant of social recognition, favoring individuals who possess not only digital fluency but also the ability to strategically develop and mobilize their networks. This dynamic reframes signal theory in light of today’s platformed ecosystems: recognition no longer depends increasingly on one’s capacity to render competences legible, visible, and endorsed within algorithmically mediated environments. Those who master the codes of visibility and reputation-building online are best positioned to convert recognition into social capital and professional opportunity. Full article

With the rapid evolution of social computing, online sentiments have become valuable information for analyzing the latent structure of social networks. Sentiment leaders in social networks are those who bring in new information, ideas, and innovations, disseminate them to the masses, and thus influence the opinions and sentiment of others. Identifying sentiment leaders can help businesses predict marketing campaigns, adjust marketing strategies, maintain their partnerships, and improve their products’ reputations. However, capturing the complex sentiment dynamics from multi-hop interactions and trust/distrust relationships, as well as identifying leaders within sentiment-aligned communities while maximizing sentiment spread efficiently through both direct and indirect paths, is a significant challenge to be addressed. This paper pioneers a challenging and important problem of sentiment leader identification in social networks. To this end, we propose an original solution framework called “SentiRank” and develop the associated algorithms to identify sentiment leaders. SentiRank contains three key technical steps: (1) constructing a sentiment graph from a social network; (2) detecting sentiment communities; (3) ranking the nodes on the selected sentiment communities to identify sentiment leaders. Extensive experimental results based on the real-world datasets demonstrate that the proposed framework and algorithms outperform the existing algorithms in terms of both one-step sentiment coverage and all-path sentiment coverage. Furthermore, the proposed algorithm performs around 6.5 times better than the random approach in terms of sentiment coverage maximization. Full article

Emotional intelligence is a crucial determinant of socioemotional adaptation, psychological well-being and healthy habits in a population, although it has been barely studied in Generation Z. Therefore, the following research objectives were established: (1) to measure the levels of attention, clarity and emotional repair of Spanish university students in teaching undergraduates and (2) to design predictive models of emotional intelligence considering sex, anthropometric measurements, physical activity and the use of social networks as factors. A cross-sectional study was conducted with the involvement of Spanish teaching undergraduates. An online questionnaire integrating sociodemographic questions, the International Physical Activity Questionnaire Short Form, Trait Meta-State Mood Scale TMMS-24 and Social Network Addiction Scale SNAddS-6S were administered. University students exhibited higher levels of emotional attention (30.32 ± 6.08) than those of emotional clarity (28.18 ± 6.34) and emotional repair (28.51 ± 6.02). Most students use X, Pinterest, TikTok, Instagram, YouTube and WhatsApp most days of the week. There are positive relationships between attention and emotional clarity (r = 0.33; p ≤ 0.001), attention and emotional repair (r = 0.18; p ≤ 0.001) and clarity and emotional repair (r = 0.44; p ≤ 0.001). In conclusion, males have higher levels of emotional clarity and emotional repair, but females show higher levels of emotional attention. The model with the highest explanatory power is the one obtained for men’s emotional attention. Full article

This study aims to explore the interrelationships among key factors influencing learning burnout, such as motivation and negative emotions (depression, anxiety, and stress) along with other factors influencing including problematic mobile phone use, nomophobia, and interactive learning, as well as whether their pathways of influence on learning burnout differ between online and offline learning contexts. Using the convenience sampling method, data from 293 college students were collected. Measurements were carried out using the Nomophobia Scale, the Problematic Mobile Phone Use Scale, the Depression Anxiety Stress Scale (DASS), the Interactive Learning Scale, the Learning Burnout Scale, and the Scale of Motivation for Activity Participation. By applying network analysis and network comparison methods, and based on the Social Comparison Theory and the Affective Socialization Heuristics Model, it was found that under the online learning condition the motivation to pursue value directly affects learning burnout. In contrast, under the offline learning condition learning motivation indirectly affects learning burnout through negative emotions. This study posits that this difference is caused by peer comparison. In a collective learning atmosphere, students’ comparison with their peers triggers negative emotions such as anxiety and stress. These negative emotions weaken the learning motivation to pursue value, ultimately resulting in an elevated level of learning burnout. Full article

As an emerging e-commerce model, live streaming e-commerce integrates instant interaction, content marketing, and online sales to bring consumers a new shopping experience. However, there are many risks in the process of live e-commerce transactions. Identifying key risk factors and implementing targeted control measures are crucial for promoting the sustainable and healthy development of live streaming e-commerce. This paper firstly constructs a business model of live streaming e-commerce transactions according to the transaction scenario and summarizes 24 risk factors from the three dimensions of live streaming e-commerce platforms, merchants, and anchors based on relevant national standards and other relevant literature. Secondly, the Delphi method is employed to modify and optimize the initial risk factors. On this basis, the social network model of risk factors is constructed to determine the influence relationship among risk factors. By calculating the degree centrality, factor types are segmented, and key risk factors as well as influence paths are identified. Finally, corresponding countermeasures and suggestions are proposed. The results indicate that Credit Evaluation System Perfection, Service Evaluation System Perfection, Qualification Audit Mechanism Perfection, Dispute Complaint Handling Channels Perfection, Risk Identification Mechanism Perfection, Platform Qualification, Merchant Qualification, and Merchant Credit are the critical risk factors affecting live streaming e-commerce transactions. Full article

This study examines the use of digitalized educational solutions among students in higher education institutions (HEIs). Drawing upon theories of technology, digitalization, and education, we analyze the suitability of different digitalization solutions for students in HEIs. Educational organizations that apply different digitalized technologies provide customizable platforms for authoring and disseminating multimedia-rich e-education and smart education. However, pedagogical practices indicate several gaps between the level of HEI digitalization achieved and its suitability for HEI participants. Thus, we analyze the state of various digitalized technologies in HEIs and their suitability for meeting students’ expectations. The results of our research show that students most highly rate modern educational methods such as practical learning supported by access to digitized materials via websites, social networks, and smartphones while assigning a lower rating to the use of classic education, supported by digital textbooks and traditional technologies such as Skype, Zoom, podcasts, and online videos. This study has several theoretical implications, among which is the need to further develop highly digitized materials and purpose-designed digitized solutions for individual areas and specific educational purposes. The practical implications indicate the need to expand the use of website networks, smartphones, and smart table solutions in modern educational practices in HEIs. Full article

This study investigates the mechanisms underlying the diffusion of risk information about genetically modified organisms (GMOs) on the Chinese social media platform Weibo. Drawing upon social contagion theory, we examine how endogenous and exogenous mechanisms shape users’ information-sharing behaviors. An analysis of 388,722 reposts from 2444 original GMO risk-related texts enabled the construction of a comprehensive sharing network, with computational text-mining techniques employed to detect users’ attitudes toward GMOs. To bridge the gap between descriptive and inferential network analysis, we employ a Shannon entropy-based approach to quantify the uncertainty and concentration of attitudinal differences and similarities among sharing and non-sharing dyads, providing an information-theoretic foundation for understanding positional and differential homophily. The entropy-based analysis reveals that information-sharing ties are characterized by lower entropy in attitude differences, indicating greater attitudinal alignment among sharing users, especially among GMO opponents. Building on these findings, the Exponential Random Graph Model (ERGM) further demonstrates that both endogenous network mechanisms (reciprocity, preferential attachment, and triadic closure) and positional homophily influence GMO risk information sharing and dissemination. A key finding is the presence of a differential homophily effect, where GMO opponents exhibit stronger homophilic tendencies than non-opponents. Despite the prevalence of homophily, this paper uncovers substantial cross-attitude interactions, challenging simplistic notions of echo chambers in GMO risk communication. By integrating entropy and ERGM analyses, this study advances a more nuanced, information-theoretic understanding of how digital platforms mediate public perceptions and debates surrounding controversial socio-scientific issues, offering valuable implications for developing effective risk communication strategies in increasingly polarized online spaces. Full article

For the last decade, social networking services (SNS), such as X, Facebook, and Instagram, have become mainstream media for advertising and marketing. In SNS marketing, word-of-mouth among users can spread posted advertising information, which is known as viral marketing. In this study, we first analyzed the time series of user reactions to Instagram posts to clarify the characteristics of user behavior. Second, we modeled these variations using statistical distributions to predict the information diffusion of future posts and to provide some insights into the factors that affect users’ reactions on Instagram using the estimated parameters of the modeling. Our results demonstrate that user reactions have a peak value immediately after posting and decrease drastically and exponentially as time elapses. In addition, modeling with the Weibull distribution is the most suitable for user reactions, and the estimated parameters help identify key factors that influence user reactions. Full article

Background: Human papillomavirus (HPV) vaccination is crucial for preventing HPV-related cancers, yet vaccination rates remain suboptimal, particularly in Florida. Social influence, including family and peer support, may shape parental decisions to vaccinate their children. In this study, we examined the role of social networks (online and offline) in parental intention to vaccinate their 11- to 12-year-old children against HPV. Methods: We conducted a cross-sectional survey among 746 parents in Florida as part of the Text & Talk trial (2022–2023). Among other questions, parents reported on their intention to vaccinate, perceived social norms, and support received from up to three reported confidants. We performed logistic regression and multivariable analyses to assess the relationship between network support, social norms, and vaccination intent. Results: Seventy percent of parents intended to vaccinate their children. Greater support from the first reported confidant was significantly associated with higher vaccination intention (OR = 1.30, p < 0.0001). Perceived norms among friends (p = 0.01) and higher overall network support (p < 0.0001) were also predictive of intent. The higher the percentage of reported family members, the higher the support received for the vaccine (p = 0.04). Conclusions: Social support, particularly from close confidants and peers, plays a critical role in shaping parental HPV vaccination decisions while accounting for perceived social norms. Public health interventions can leverage peer networks alongside family support to enhance HPV vaccine uptake. Full article