Search Results (1,264)

Over the last few years, TikTok has become one of the most notable social networking apps, allowing users to upload videos with the possibility of gaining popularity. This has given rise to the emergence of influencers native to the platform. While Global North–focused TikTok studies have empirically examined the media economics of this platform in various capacities, emerging markets have experienced a research dearth in this regard. To understand the strategies they employ to monetize, as well as the challenges they face in generating revenue, this study interviewed 23 Nigerian TikTok influencers between the ages of 19 and 26. We find that Nigerian TikTok influencers rely on a mixture of brand partnerships, sponsored promotions, referral systems, cross-platform visibility, collaborative growth strategies, and account-region bypass to generate income within a platform environment that offers limited direct monetization opportunities to creators. The findings further show that influencer labour in the Nigerian context is shaped by unstable payment systems, algorithmic dependence, brand distrust, and the constant pressure to remain visible and relevant. Beyond economic concerns, the study reveals that creators experience emotional strain arising from online harassment, body shaming, and the psychological demands of continuous self-presentation. Full article

This original empirical study examines how online discourse on fair tourism in Korea was structured and transformed during the post-pandemic tourism transition. Using unstructured online text data collected from Korean digital platforms, the study compares two periods: the pandemic continuation and early tourism recovery period (1 June 2020–31 May 2023) and the post-endemic tourism restructuring period (1 June 2023–31 May 2026). Fair tourism is conceptualized as a practical and policy-oriented discourse that connects local participation, benefit distribution, market fairness, destination governance, and tourism justice, rather than as a simple synonym for sustainable or responsible tourism. After text cleaning, morphological analysis, and keyword refinement, the top 50 core keywords for each period were analyzed using frequency analysis, degree and closeness centrality analysis, semantic network visualization, and CONCOR analysis. The results show that first-period discourse centered on regions, public policy, support projects, fair ecotourism, the social economy, resident participation, and local recovery. In contrast, second-period discourse was more strongly associated with foreign tourists, international tourism recovery, accommodation and service use, price fairness, overcharging and unfairness controversies, and sustainable destination governance. This study extends fair tourism research by revealing how fair tourism is constructed and rearticulated through digitally mediated public discourse. Full article

Identifying the spatio-temporal evolutionary patterns and driving mechanisms of rural tourism network attention is essential for predicting the development trends of the rural tourism industry and delivering refined industrial governance. Taking 356 prefecture-level cities in China from 2015 to 2024 as basic research units, this paper constructs a comprehensive evaluation system for rural tourism network attention based on multi-source data. Furthermore, its spatio-temporal evolution characteristics and internal influencing factors are systematically investigated by means of spatial autocorrelation analysis and geographically weighted regression. The results indicate that the overall level of rural tourism network attention in China shows an obvious fluctuating growth trend, which can be divided into three successive stages, namely steady growth (from 0.8530 in 2015 to 1.2028 in 2019), explosive growth (from 1.9563 in 2020 to 3.7471 in 2021) and high-level fluctuation (maintained in the high range of 2.4–3.4). In addition, with the continuous iteration of internet communication media, the guiding influence of traditional search platforms has gradually weakened, while emerging social media and short-video platforms have become the core carriers of online tourism traffic. Correspondingly, media innovation persistently reshapes the spatial distribution pattern of rural tourism network attention. In terms of spatial characteristics, rural tourism network attention has undergone a significant transformation from geographical gradient polarization to overall regional equilibrium. Specifically, from 2015 to 2024, the overall Moran’s I index remained positive, with values ranging from 0.0116 to 0.1358, indicating an overall trend of gradual decline. High-attention areas are predominantly concentrated in economically developed urban agglomerations, whereas remote and economically underdeveloped regions exhibit contiguous low-value aggregation characteristics, which reveals a remarkable trend of balanced development nationwide. In view of driving mechanisms, highway network density, tourism income, rural tourism resource and enrollment of university students are identified as the core driving factors dominating the spatio-temporal evolution of rural tourism network attention. Moreover, the intensity of the influence of each factor presents distinct spatial heterogeneity. This study further reveals that the spatial heterogeneity of rural tourism network attention calculated using multi-source fused data shows a remarkable convergent characteristic, which can reflect the actual distribution of the rural tourism market more objectively and accurately. Meanwhile, rural tourism network attention is typically characterized by scale-dependent with the spatial distribution at the macro-scale being more balanced than that at the meso- and micro-scales. Full article

Background/Objective: Social media platforms have become increasingly integrated into nursing students’ academic and daily lives, influencing how health-related information is accessed, evaluated, and shared. At the same time, eHealth literacy has emerged as an important competency for nursing students within contemporary digital healthcare environments. This study aimed to examine the relationship between multidimensional social networking engagement and perceived eHealth literacy among undergraduate nursing students. Methods: A cross-sectional descriptive correlational design was used among undergraduate nursing students (N = 146). Data were collected using an electronic survey that included demographic characteristics, the Social Networking Usage Questionnaire, and the Revised eHealth Literacy Scale. Descriptive statistics, Pearson’s correlation, and linear regression analyses were performed using SPSS version 26. Results: Participants reported moderate-to-high social networking engagement and generally favorable perceived eHealth literacy. A strong positive correlation was identified between SNUQ and eHEALS-R scores (r = 0.75, p < 0.001), with academic use showing the strongest dimension-level association with perceived eHealth literacy. In the adjusted regression model, social networking engagement remained independently associated with perceived eHealth literacy, whereas demographic variables and daily social media use showed no significant associations. Conclusions: The findings suggest that how nursing students engage with digital platforms is associated with their confidence in accessing, evaluating, and applying online health information. Strengthening purposeful and critical digital engagement within nursing education may help prepare students for contemporary digital healthcare environments. Full article

The COVID-19 pandemic transformed online and distance learning from a supplementary educational option into a central component of education systems, bringing anxiety, stress, and psychological well-being to the forefront of digital learning research. This study traces the two-decade evolution of research on anxiety and stress in online and distance learning between 2005 and February 2026 and examines the thematic reorientation associated with the COVID-19 period. Bibliometric mapping of 243 Web of Science (WoS) records was used to identify publication trends, intellectual structures, international collaboration patterns, and thematic developments. Piecewise analysis indicated a significant post-2020 break in annual scientific output, with the growth rate increasing nearly eightfold (p = 0.0015). Thematic evolution showed that pre-pandemic studies mainly focused on technology acceptance, computer anxiety, and learner adaptation, whereas pandemic and post-pandemic research shifted toward mental health, psychological well-being, social isolation, technostress, and resilience. China and the United States dominated scientific production, while Australia and Saudi Arabia showed notable mobility in collaboration networks. Overall, the findings suggest that the COVID-19 period was associated with a field-level reorientation toward an interdisciplinary psychosocial agenda for future online education. Full article

This study develops a computational framework for quantifying how technological disruptions are associated with shifts in media–public discourse alignment. Using the public release of ChatGPT (30 November 2022) as a temporal breakpoint, we examine whether the broad public availability of generative AI was associated with structural changes in the relationship between media agendas and online public discourse in South Korea. The framework combines PPMI-weighted semantic network construction with QAP correlation, MRQAP regression and Cohen’s q effect-size analysis, applied to 181,081 Korean-language texts encompassing media agendas (national newspapers, economic dailies, regional newspapers, and broadcast news) and public agendas (online communities) over six years (2019–2025). Results reveal that national newspapers lost their dominant agenda-setting position, with public alignment declining sharply (r: 0.678 → 0.430, Cohen’s q = 0.369, large effect), while economic papers rose from lowest to second-highest alignment (r: 0.352 → 0.567) by addressing market and industry dimensions that direct AI experience could not supply. Broadcasting emerged as the dominant structural anchor of public discourse (unique coefficient β: 0.263 → 0.569; its removal alone lowers model fit from R² = 0.517 to 0.347), while national newspapers’ unique contribution reversed in sign. Collective media explanatory power itself remained essentially stable (R² = 0.537 → 0.517), indicating a structural reconfiguration of which media align with public discourse rather than a wholesale weakening of media–public alignment—consistent with agenda-melding, in which publics integrate media coverage with firsthand technological experience. A residual analysis further shows that the variance media agendas leave unexplained is not noise but a structured, public-specific layer of discourse organized around the hands-on use of generative-AI tools (e.g., ChatGPT, image generation)—direct evidence of a bounded public autonomy in which public discourse is distinct from, and not reducible to, media agendas. These findings demonstrate the framework’s utility for detecting how publicly accessible AI adoption—exemplified by ChatGPT—is associated with shifts in media–public structural dynamics within discourse ecosystems and carry implications for computational social science, technology communication, and applied network analysis. Full article

Framing the study within the Unified Theory of Acceptance and Use of Technology (UTAUT) and the extended online privacy concern model, this study investigates the effects of effort expectancy, social influence, privacy concerns, and perceived risk on e-commerce consumer behavior, while positioning trust in AI as a key mediating factor. The data were collected from 250 active e-commerce users in Bangladesh, and the analysis was done with a hybrid approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) analysis. The SEM results indicate that effort expectancy and social influence have significant positive effects on consumer behavior. Social influence also shows a significant positive effect on trust in AI. Trust in AI exhibits a significant positive effect on consumer behavior. However, perceived risk does not show a significant effect on either trust in AI or consumer behavior. Privacy concerns demonstrate a significant positive relationship with both trust in AI and consumer behavior, contrary to the hypothesized negative relationships. The mediation analysis shows that trust in AI significantly mediates the relationship between social influence and consumer behavior, while no significant mediation effects are observed for privacy concerns or perceived risk. The ANN results further confirm the dominance of social influence as the most important predictor of both trust in AI and consumer behavior, followed by effort expectancy and trust in AI, while perceived risk shows minimal predictive relevance. Overall, the findings suggest that consumer adoption of AI-enabled e-commerce is primarily driven by benefit-oriented factors rather than risk-based considerations in the present context. The study contributes to the literature by extending UTAUT and privacy calculus theory to AI-mediated commerce and by demonstrating the value of combining SEM and ANN to capture both explanatory relationships and predictive importance. From a managerial perspective, the results highlight the importance of strengthening social influence mechanisms, improving system usability, and building trust in AI systems to enhance consumer engagement in AI-driven e-commerce environments. Full article

Background/Objectives: Social media is an increasingly important source of pediatric health information for parents. In particular, the growing presence of pediatricians on social networks opens up new scenarios for adapting to the communication models of the younger generations, supporting information, prevention and parental support, but also with critical issues related to integration with the traditional clinical relationship. This study aimed to evaluate the use of social media for pediatric health information, the perceived usefulness of content shared by pediatricians, its impact on parental behavior and the degree of integration of this information into dialogue with the treating pediatrician. Methods: A cross-sectional observational study was conducted using an online questionnaire aimed at 453 parents of minors. The questionnaire investigated socio-demographic characteristics, frequency of following pediatricians on social media, perceived usefulness of online health information, impact on parenting behaviors, discussion of content with the treating physician, and perception of risks and the need for regulation. Associations between categorical variables were evaluated using Fisher’s exact test and multivariable logistic regression. Results: Most participants follow pediatricians on social media (74.2%) and rate online health information as very useful (81.7%). Almost two-thirds reported changing at least one health-related behavior following exposure to pediatric content on social media (65%). Greater frequency of following pediatricians was significantly associated with both higher perceived usefulness and behavioral change (p < 0.0001). Perceived usefulness emerged as the strongest independent predictor of behavioral change (OR 9.967; p < 0.001). Conclusions: Pediatric communication on social media appears to be widely used, perceived as useful and associated with parental behavior. However, such content is poorly integrated into clinical dialogue, mainly taking the form of a one-way flow of information. The results highlight the need to promote more participatory communication models and to recognize pediatricians active on social media as a distinct professional category of digital health communicators. Full article

In online innovation communities, idea diffusion constitutes a core mechanism through which user-generated ideas are transformed into collective innovation outcomes. However, existing research remains fragmented and lacks a process-oriented explanation of how ideas become actionable and trigger diffusion. This study draws on affordance theory to explain how ideas become actionable and drive diffusion in online innovation communities. Cognitive affordance is operationalized as operability and detail richness, entry affordance as actionability signaling and linguistic hedging, and collaborative affordance as mediating structural position and interaction depth. Using text mining and social network analysis on idea-level data and employing a negative binomial regression model, we find that the effects of affordances are inherently nonlinear. While some affordances exhibit positive linear effects, others follow U-shaped relationships, suggesting threshold-dependent activation mechanisms. The results suggest that AI may influence the activation of affordances and alter certain affordance-diffusion relationships under specific conditions. Theoretically, this study advances affordance theory by introducing an activation-based perspective that captures the nonlinear transformation from perceived possibilities to realized actions. It also integrates content and structural dimensions into a unified framework for understanding idea diffusion. Practically, the findings offer insights for platform governance, particularly in designing content structures, participation mechanisms, and AI-assisted tools that effectively facilitate innovation diffusion. Full article

Background: In contemporary society, marked by rapid transformations in the sphere of communication and social interaction, social networks have gone beyond the role of simple communication platforms, becoming places where identities are formed, cultural norms are negotiated and self-perceptions are shaped. This reality has generated a growing interest in studying the relationship between the digital environment and health behaviors, especially among young people, a social category extremely receptive to visual and normative influences promoted online. Methods: A cross-sectional study was conducted among students enrolled in different kinds of faculties and specialties. An online questionnaire was distributed to gather socio-demographic, academic and medical data along with lifestyle and health-related behaviors. Several psychometric instruments were used: Health Behaviour Scale (HBS) to measure various dimensions of health-related actions, Bergen Social Media Addiction Scale (BSMAS) to screen for addictive or problematic social media use, Social Media Disorder Scale (SMDS) to measure the problematic social media use among adolescents, Body Appreciation Scale–2 (BAS-2) to evaluate measure of one’s acceptance, favorable opinions and respect of their own body, and Dutch Eating Behavior Questionnaire (DEBQ) to identify the psychological motives behind overeating. Results: More than 70% of students declared that they had their first smartphone before the age of 12 and 65% of students had screentime higher than 3 h per day during the weekdays, with a small increase during the weekends. Women scored higher than men in emotional eating (food consumption in response to negative emotions), and external eating (response to food stimuli in the environment, independent of hunger). Respondents from rural areas showed a significantly lower level of respect and acceptance of their own body and higher risk for Social Media Disorder compared to participants from urban areas. Important statistical correlation has been identified among the variables of the research. Social media addiction was associated with higher emotional eating both directly and indirectly, via lower body appreciation. The analysis also indicated that it does not show a direct relationship with restrictive eating behaviors; rather, its association with restrained eating is fully mediated by the individual’s body appreciation. Conclusions: The use of social platforms is a challenging process, with a great impact on psychological and emotional balance of young people. Even if the study identified a normative use among young people with a high education level, the risks factors should be taken into consideration when dealing with screentime, psychological and mental health and the risk for addiction. Full article

Opinion polarization, echo chambers, and the rapid formation of opinion clusters have become defining features of debates on contemporary online social platforms. To explain these phenomena from a control-theoretic perspective, this paper investigates opinion dynamics in social networks with edge-heterogeneous confidence bounds, focusing on clustering and polarization behaviors driven by pair-dependent trust and asymmetric influence. Two discrete-time models are proposed, including an unsigned bounded-confidence model and a more general signed model that incorporates both supportive and oppositional interactions. The interaction structures are described by time-varying unsigned and signed digraphs, respectively, in which heterogeneous interpersonal influence is characterized by edge-dependent confidence bounds that naturally encode platform-mediated trust. For the proposed models, rigorous sufficient conditions are established for invariant cluster consensus and structurally balanced polarization. Numerical simulations, including a case study on the Slashdot Zoo signed social network with 50 controversial users, illustrate the theoretical results and demonstrate their relevance for understanding opinion evolution on internet-scale platforms. Full article

Every day, people are exposed to social stressors and environmental stimuli, both online and offline, that may contribute to psychological distress (PD), a phenomenon that may be further affected by the pervasive diffusion of the Internet and Information and Communication Technologies (ICTs). In keeping with this, the aim of the present study was to investigate the role of mattering and anti-mattering in both offline and online environments, as well as Social Media Capital on Distress Tolerance (DT). Data were collected through the administration of an online and anonymous survey among 252 participants (32.1% cisgender males; 63.1% cisgender females; 4.8% people belonging to the LGBTQIA+ community) aged 18 to 85 years (mean age: 40.5, SD = 17.0253). In line with the objective of the present study, correlation, multiple linear regression, and network analyses (NA) were performed. Overall, the results pointed out that offline mattering and anti-mattering and social media capital were associated with DT. Moreover, the NA suggested that offline relational experiences, particularly offline mattering and anti-mattering, were more consistently connected with DT within the overall network structure than online relational indicators. In conclusion, the study deepened the investigation of DT in relation to potential social antecedents (both offline and online), laying the groundwork for the development of further studies in this area. Full article

With the rapid development of social media and online interactive platforms, online public opinion has become a vital information source for public emotional expression, social risk perception, and decision support. However, public opinion texts are typically characterized by short length, obscure semantics, complex emotional expressions, and strong context dependence, making it difficult for traditional lexicon-based or shallow neural network methods to achieve stable and robust performance in sentiment discrimination tasks. To address these issues, this paper proposes BERT-BiLSTM-MHSA-Capsule (BBMC), hereafter referred to as BBMC, an online public opinion sentiment analysis model based on multi-level semantic feature fusion. The model first utilizes the pretrained language model BERT to extract dynamic semantic representations with context-aware capabilities; subsequently, a Bidirectional Long Short-Term Memory (BiLSTM) network is employed to model the bidirectional temporal dependencies within the texts, while a Multi-Head Self-Attention (MHSA) mechanism is introduced to achieve adaptive focusing on key emotional information. Building upon this, a three-layer cascaded capsule network is constructed to achieve structured modeling of high-order emotional attributes through vector neurons and dynamic routing mechanisms, effectively mitigating the loss of spatial feature information caused by traditional pooling and fully connected structures. Experimental results on a manually annotated online public opinion dataset show that BBMC achieves better performance than the evaluated baseline models in terms of accuracy, recall, and F1-score. These results indicate the empirical effectiveness of the proposed task-oriented feature-integration strategy and capsule-based classification head for online public opinion sentiment analysis. Full article

Background: Malocclusion has important functional, esthetic, and psychosocial consequences; however, access to orthodontic treatment remains uneven and strongly influenced by socio-economic factors. While these disparities are well documented internationally, evidence from Romania remains limited. This study aimed to evaluate the influence of socio-economic factors on orthodontic treatment initiation within the Romanian population. Methods: A cross-sectional questionnaire-based study was conducted in 2025 using an online survey distributed through social media and community networks. A total of 285 adults were included. Data were analyzed using descriptive statistics, chi-square tests, and multivariable logistic regression. Results: Overall, 56.5% of respondents reported having undergone orthodontic treatment. Age and self-perceived information level were significantly associated with treatment initiation in the multivariable model. Participants older than 30 years were significantly less likely to have undergone orthodontic treatment compared with those aged 18–30 years (OR = 0.28, 95% CI: 0.12–0.62, p = 0.002). Higher levels of self-perceived information were associated with a greater likelihood of having undergone orthodontic treatment (OR = 0.75, 95% CI: 0.59–0.96, p = 0.020). Income and area of residence were not significantly associated with treatment initiation. However, respondents with lower income levels were significantly more likely to perceive treatment cost as a barrier to orthodontic care. Conclusions: Within this surveyed sample, age and self-perceived information level were independently associated with orthodontic treatment initiation. Although income was not associated with treatment uptake, financial cost remained an important perceived barrier, particularly among lower-income respondents. Given the convenience sampling strategy and limited representativeness of the sample, the findings should be interpreted as exploratory and require confirmation in larger population-based studies. Full article

Sentiment analysis has become a critical task in natural language processing for extracting subjective insights from large-scale textual data across domains such as social media, e-commerce, and online reviews. However, existing methods often fail to simultaneously capture contextual semantics and structural relationships, particularly in cross-domain settings. This study proposes a hybrid RoBERTa–graph attention network (GAT) framework that integrates transformer-based contextual embeddings with graph-based relational learning. The methodology involves encoding text using RoBERTa, constructing token-level dependency graphs, and applying multi-head graph attention to model inter-token relationships. The model is evaluated on multiple benchmark datasets, including Twitter, Amazon, and IMDB reviews. The cross-domain results refer only to binary-harmonized positive/negative sentiment transfer and should not be interpreted as full three-class sentiment transfer, including the neutral class. The experimental results show that the proposed approach achieves consistent improvements over the selected baseline models in terms of accuracy, F1-score, MCC, and AUC. Statistical robustness analysis further supports the stability of these improvements across repeated runs. The findings highlight the effectiveness of combining semantic and structural learning, making the proposed framework suitable for robust cross-domain sentiment analysis applications. Full article