Search Results (1,609)

Background/Objectives: Adolescence is a developmental period characterised by intensive use of social media and an increased prevalence of emotional problems such as depression and anxiety. Scientific evidence indicates that the modality of social media use (active or passive) can significantly predict these problems, with active use being linked to a higher likelihood of cyberbullying victimisation. As victimisation is associated with more severe emotional problems, social media represents an important context for understanding adolescent mental health. Following this, the main aim of this study was to examine how the modality of social media use (SMU) is related to emotional problems, and whether cyberbullying victimisation mediates this relationship. Methods: This study was conducted on a convenient sample of N = 1822 students (49.0% boys, 51.0% girls; M_age = 13.22 years, SD_age = 0.629) from a total of 64 elementary schools throughout Croatia. A modified Croatian version of the Active and Passive Use of Social Networks Scale, the Anxiety and Depression subscales of the Depression, Anxiety, Stress Scale—Youth Version, and the Cyber-Victimisation subscale of the European Cyberbullying Intervention Project Questionnaire were used. Results: The results indicate that passive SMU among boys is directly related only to anxiety, while that among girls contributes only to the explanation of depression. Regarding cyberbullying victimisation as a mediator, full mediation in the association between active SMU and emotional problems was found for both girls and boys. Conclusions: This represents a significant theoretical contribution, as well as a contribution to the development of psychosocial interventions. Full article

Numerous valuable information is available on the Internet, and many individuals rely on mass media as their primary source of information. Various views, comments, expressions, and opinions on social networks have been a tremendous source of information. Harvesting free, resourceful information through social media makes text mining a powerful tool for analyzing public opinions on various issues across diverse social networks. Various research projects have implemented text sentiment analysis through machine and deep learning approaches. Social media text often expresses sentiment through complex syntax and negation (e.g., implicit and double negation and nested clauses), which many classifiers mishandle. We propose hybrid negation, a clause-aware approach that combines (i) explicit/implicit/double-negation rules, (ii) dependency-based scope detection, (iii) a TextBlob back-off for phrase polarity, and (iv) an MLP-learned clause-weighting module that aggregates clause-level scores. Across 156,539 tweets (three-class sentiment), we evaluate six negation strategies and 228 model configurations with and without SMOTE (applied strictly within training folds). Hybrid Negation achieves 98.582% accuracy, 98.196% precision, 98.189% recall, and 98.193% F1 with BERT, outperforming rule-only and antonym/synonym baselines. Ablations show each component contributes to the model’s performance, with dependency scope and double negations offering the largest gains. Per-class results, confidence intervals, and paired tests with multiple-comparison control confirm statistically significant improvements. We release code and preprocessing scripts to support reproducibility. Full article

Understanding how investors maintain positions during adverse market conditions, investment grip, is increasingly important as retail participation rises and information environments diversify. While prior research identifies demographic, psychological, and economic determinants of investment grip, little is known about how information sources influence investors’ tolerance for losses. This study examines the relationship between investment information channels and investment grip among Japanese retail investors using a large-scale dataset of 161,677 respondents from the 2025 Survey on Life and Money. Investment grip is measured through a hypothetical loss scenario, and ordered probit and probit models are used to analyze associations between loss tolerance, information sources, and investor characteristics. Results show that reliance on professional information sources such as outsourced independent financial advisors, one’s own securities company, other securities firms, and external financial experts is negatively associated with investment grip. Free information sources, including mass media and personal networks, are also linked to lower loss tolerance. In contrast, reliance on social media is consistently associated with higher investment grip. Financial literacy, wealth, and age increase investment grip, whereas risk aversion, short-term outlooks, and family responsibilities reduce it. These results have implications for policy design, advisory practices, and digital and AI-enhanced investment platforms. Full article

Background: Men who have sex with men (MSM) using HIV pre-exposure prophylaxis (PrEP) experience a high burden of human papillomavirus (HPV) infection and related diseases, yet data on HPV vaccination among this group in Brazil remain limited. Aims: The aims of this study were to estimate the prevalence of complete HPV vaccination and to identify factors associated with vaccination completion among MSM using PrEP in Brazil. Methods: We conducted a cross-sectional online survey between May and September 2025 among MSM aged ≥18 years, residing in Brazil and currently using oral PrEP. Participants were recruited through virtual snowball sampling and targeted advertisements on social media and a gay geosocial networking application. Data were collected using a structured, self-administered questionnaire hosted on REDCap^®. Complete HPV vaccination was defined as self-reported receipt of all doses recommended according to the participant’s age and clinical condition. Sociodemographic characteristics, relationship patterns, sexual behaviors, lubricant use during sexual activity, and history of sexually transmitted infections (STIs) were assessed. Adjusted prevalence ratios (aPRs) and 95% confidence intervals (95% CIs) were estimated using Poisson regression with robust (sandwich) variance. Results: A total of 872 MSM using PrEP were included, of whom 59.4% reported complete HPV vaccination. In adjusted analyses, complete vaccination was more frequent among participants reporting both steady and casual partners (aPR = 1.90; 95% CI: 1.36–2.65) or only casual partners (aPR = 1.72; 95% CI: 1.24–2.39), those reporting lubricant use during sexual activity (aPR = 1.41; 95% CI: 1.23–1.61), and those with a diagnosis of chlamydia and/or gonorrhea in the previous 12 months (aPR = 1.22; 95% CI: 1.08–1.36). Conclusions: Although HPV vaccination coverage among MSM using PrEP in Brazil is higher than that reported for MSM in general, it remains incomplete in a population with regular contact with specialized health services. Integrating systematic assessment and delivery of HPV vaccination into PrEP care may help increase vaccination completion and reduce missed opportunities for prevention. Full article

Research often emphasises dysfunctional Social Networking Site (SNS) usage. In contrast, the current research examined a more positive element of human functioning, specifically how motivations to use SNSs may be associated with meaning in life, which can help give purpose and direction to people’s lives. A sample of 384 undergraduate students (aged 18 to 50; M = 20.95; SD = 4.95; 81.5% females) completed questionnaire-based measures of motivations to use SNSs, self-reported time spent on SNSs, and meaning in life (coherence, purpose, and mattering). Multiple regressions showed that models for coherence, purpose, and mattering explained 5.8–8.8% of the variance (R² = 0.058–0.088). Self-expression was positively associated with coherence (β = 0.128), purpose (β = 0.16), and mattering (β = 0.137). Following/monitoring others predicted higher coherence (β = 0.158), and using SNSs to find information predicted higher purpose (β = 0.12). Academic purposes were positively related to mattering (β = 0.12). By contrast, using SNSs for new friendships predicted lower coherence (β = −0.197) and mattering (β = −0.154), entertainment predicted lower coherence (β = −0.178), and greater time on SNSs predicted lower purpose (β = −0.186). Overall, different motivations for using SNSs are associated with different facets of meaning in life. Full article

Targeted multimodal sentiment classification is frequently impeded by the semantic sparsity of social media content, where text is brief and context is implicit. Traditional methods that rely on direct concatenation of textual and visual features often fail to resolve the ambiguity of specific targets due to a lack of alignment between modalities. In this paper, we propose the Complementary Description Network (CDNet) to bridge this informational gap. CDNet incorporates automatically generated image descriptions as an additional semantic bridge, in contrast to methods that handle text and images as distinct streams. The framework enhances the input representation by directly translating visual content into text, allowing for more accurate interactions between the opinion target and the visual narrative. We further introduce a complementary reconstruction module that functions as a regularizer, forcing the model to retain deep semantic cues during fusion. Empirical results on the Twitter-2015 and Twitter-2017 benchmarks confirm that CDNet outperforms existing baselines. The findings suggest that visual-to-text augmentation is an effective strategy for compensating for the limited context inherent in short texts. Full article

The containment of misinformation diffusion on social media is a critical challenge in computational social science. However, prevailing intervention strategies predominantly rely on static topological metrics or time-agnostic learning models, thereby overlooking the profound impact of temporal–demographic heterogeneity. This oversight frequently results in a “spatio-temporal mismatch”, where limited intervention resources are misallocated to structurally central but temporarily inactive nodes, particularly during non-stationary propagation bursts driven by exogenous triggers. To bridge this gap, we propose a Spatio-Temporal Deep Reinforcement Learning (ST-DRL) framework for proactive misinformation defense. By seamlessly integrating continuous trigonometric time encoding with demographic-aware Graph Attention Networks, our model explicitly captures the coupling dynamics between group-specific circadian rhythms and event-driven transmission surges. Extensive simulations on heterogeneous networks demonstrate that ST-DRL achieves a Peak Prevalence Reduction of 93.2%, significantly outperforming static heuristics and approaching the theoretical upper bound of oracle-assisted baselines. Crucially, interpretability analysis reveals that the agent autonomously evolves a “Preemptive Strike” strategy—prioritizing the sanitization of high-risk bridge nodes, such as bots, prior to event onsets—thus establishing a new paradigm for predictive rather than reactive network governance. Full article

With the ubiquitous connectivity and exposure of social network service (SNS), the stressors it causes have received extensive attention in the academic community. Unlike previous studies, this research focuses on the cross-cultural dimension and explores the different effects of multiple SNS-generated stressors on user behavior outcomes. Based on the “Stressors-Strain-Outcome” (SSO) theoretical framework, we constructed a “technical stressors—exhaustion—reduced SNS usage intention” pathway to systematically investigate five types of technical stressors. These were perceived information overload, perceived social overload, perceived compulsive use, perceived privacy concern, and perceived role conflict. We introduce “fear of missing out” (FOMO) as a moderating variable to explore its moderating role in SNS exhaustion and reduced SNS usage intention. In this study, we took SNS users from China and the United States as the research subjects (338 samples from China and 346 samples from the United States), and conducted empirical tests using structural equation models and multiple comparative analyses. The results show that there are significant cultural differences between Chinese and American users in terms of the perceived intensity of technostress, the path of stress transmission, and the moderating effect of FOMO. Against the background of collectivist culture in China, perceived information overload, privacy concerns, and role conflicts have a significant positive impact on SNS exhaustion, and SNS exhaustion further positively drives the intention to reduce usage of SNS. However, the direct impacts of perceived social overload and perceived compulsive usage are not significant, and FOMO does not play a significant moderating role. In the context of the individualistic culture found in the United States, only perceived information overload and perceived social overload have a significant positive impact on SNS exhaustion, and FOMO significantly negatively moderates the relationship between exhaustion and reduced SNS usage intention, as high FOMO levels will strengthen the driving effect of exhaustion on reduced usage intention. The innovation this study exhibits lies in verifying the applicability of the SSO model in social media behavior research from a cross-cultural perspective, revealing the cultural boundaries of the FOMO moderating effect, and enriching the cross-cultural research system of reduced usage intention of SNS. The research results not only provide empirical support for a deep understanding of the psychological mechanisms of users’ SNS usage behaviors in different cultural backgrounds, but also offer important references that SNS enterprises can use to formulate differentiated operation strategies and optimize cross-cultural user experiences. Full article

The excessive and inappropriate use of the internet by children and young people increases their exposure to risky situations, especially since the COVID-19 pandemic. This study analyzes risky situations on social media among children and adolescents. The objective of this work was to identify the risks associated with the use of social media. A comparative analysis of five clustering algorithms was applied to a dataset developed by eBiz Latin America in collaboration with La Salle University of Arequipa and the Institute of Christian Schools of the De La Salle Brothers of the Bolivia-Peru district. Among the results, it was shown that children around 11 years old display a high prevalence of digital risk behaviors such as adding strangers, followed by pretending to be someone else; adults around 43 years old exhibit a tendency to follow strangers and, even more so, to take photographs without permission; adolescents with an average age of 11 show a heavy use of YouTube, TikTok, and Instagram. It is concluded that among digital risks in children and adults, the clusters highlight shared vulnerabilities, such as the addition of strangers and exposure to requests for personal data, which persist throughout the life stages but intensify in early adulthood. These findings emphasize the urgency of preventive policies addressing generational differences in social network use to promote proactive responses to digital harassment. Full article

Despite the increasing number of social media users and the advantages linked to agility in other areas, the implementation of agility within a social media framework remains unexamined. This study aims to examine how perceived social media agility influences customer-based brand equity through the mediating roles of customer engagement and customer motivation. A quantitative method was utilized. Data was collected from 420 Social Networking Site users in Turkey using a questionnaire. The study utilised convenience sampling method to gather the data. Structural equation modelling was used to analyse the data, employing SmartPLS 4. The results show that perceived social media agility has a positive impact on customer-based brand equity, customer engagement, and customer motivation. Customer engagement and customer motivation were found to impact customer-based brand equity significantly. Furthermore, customer motivation has no significant impact on customer engagement. Change-seeking has a positive influence on customer engagement and customer motivation. Customer engagement and customer motivation were found to significantly mediate the link between perceived social media agility and customer-based brand equity. The study contributes to the literature by integrating social media agility into established frameworks of brand equity and consumer behaviour. Practically, the results suggest that firms should develop agile and responsive social media strategies to enhance customer engagement and strengthen brand equity. Full article

The occurrence of filter bubbles and echo chambers in social media recommendation systems poses a significant threat to information diversity and democratic discourse. Although graph neural networks (GNNs) achieve leading accuracy in user recommendation, their optimization for engagement metrics inadvertently reinforces homophily, creating isolated information ecosystems. This research developed community-aware two-stage diversification with GNNs (CATD-GNN), a method that leverages the inherent community structure of social networks to promote diversity without sacrificing recommendation quality. CATD-GNN integrates community detection with GNN learning through a two-stage diversification process. The proposed method employs the Louvain method to identify community structures as pseudo-categories, then applies submodular neighbor selection and community-based loss reweighting during GNN training (Stage 1), followed by coverage and redundancy-aware reranking (Stage 2). Twitter data capturing Black Lives Matter discourse and Reddit political discussion networks were used to evaluate the method. CATD-GNN achieves improvements in diversity metrics while maintaining competitive accuracy. The two-stage architecture demonstrates a synergistic effect: the combination of diversity-aware training and coverage-based reranking produces greater improvements than either component alone. The proposed method successfully identifies and recommends users from different communities while preserving recommendation relevance. Full article

Fine-grained sentiment analysis requires a deep understanding of emotional intensity in the text to distinguish subtle shifts in polarity, such as moving from positive to more positive or from negative to more negative, and to clearly separate emotionally neutral statements from polarized expressions, especially in short or contextually sparse texts such as social media posts. While recent advances combine deep semantic encoding with context-aware architectures, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs), many models still struggle to detect nuanced emotional cues, particularly in short texts, due to the limited contextual information, subtle polarity shifts, and overlapping affective expressions, which ultimately hinder performance and reduce a model’s ability to make fine-grained sentiment distinctions. To address this challenge, we propose an Emotion- Aware Bidirectional Gating Network (Electra-BiG-Emo) that improves sentiment classification and subtle sentiment differentiation by learning contextual emotion representations and refining them with auxiliary emotional signals. Our model employs an asymmetric gating mechanism within a BiLSTM to dynamically capture both early and late contextual semantics. The gates are temperature-controlled, enabling adaptive modulation of emotion priors, derived from Reddit post datasets to enhance context-aware emotion representation. These soft emotional signals are reweighted based on context, enabling the model to amplify or suppress emotions in the presence of an ambiguous context. This approach advances fine-grained sentiment understanding by embedding emotional awareness directly into the learning process. Ablation studies confirm the complementary roles of semantic encoding, context modeling, and emotion modulation. Further our approach achieves competitive performance on Sem- Val 2017 Task 4c, Twitter US Airline, and SST5 datasets compared with state-of-the-art methods, particularly excelling in detecting subtle emotional variations and classifying short, semantically sparse texts. Gating and modulation analyses reveal that emotion-aware gating enhances interpretability and reinforces the value of explicit emotion modeling in fine-grained sentiment tasks. Full article

This article examines whether, and under what conditions, there is room for new mosques in Belgian cities by analyzing how media controversies around mosque projects are assembled. We study a corpus of press articles (2014–2024) using a two-step approach: First, keyword mapping identifies dominant discursive patterns across six themes (mobility, legality, size and visibility, social cohesion and integration, security and extremism, financing). Second, argument coding links lexical signals to public modes of judgment through actor–network theory (ANT) and controversy registers. Applied to five case studies across Flanders, Wallonia, and the Brussels-Capital Region, this framework offers comparative depth. The results show that identity and security controversies frequently outweigh strict urban planning controversies; neutral planning criteria (e.g., traffic congestion, permit compliance) are often recoded as symbolic markers of alterity. Regional contrasts provide nuance to this pattern: in Flanders, politicization through security/identity is salient; in Wallonia, debates emphasize size, form, and spatial integration; in Brussels-Capital, technico-legal compliance intertwines with aesthetic visibility. Media operate as boundary objects that hierarchize registers and amplify controversies. We conclude that mosques are treated less as ordinary urban infrastructure than as contested symbols of belonging and visibility. Moving toward negotiated pluralism requires institutional mechanisms that ensure transparency, equal treatment, local anchoring, and symbolic requalification. Full article

The high accessibility and interconnectedness of social media platforms have led to their increasing exploitation as tools for criminal activity. A notable example of such digital sexual offenses is the “Nth Room” case, in which sexually exploitative content and illegal recordings were unlawfully distributed on platforms such as X, Telegram, and Discord. Despite amendments to legislations, including the Sexual Violence Punishment Act and Youth Protection Act, aimed at preventing the recurrence of incidents, these crimes continue to persist. Perpetrators employ tactics such as the repeated creation and deletion of accounts, which complicate efforts to track and apprehend them. Consequently, there is an urgent need to develop advanced cyber investigation technologies capable of effectively monitoring sexual crimes posted on social media. This study aimed to propose a novel cyber investigation technology designed to trace criminal organizations by collecting tweets related to sexual crimes from X, which is the most frequently used social media platform for such content in Korea, and subsequently constructing a knowledge graph. Slang terms commonly associated with sexual crimes on X were employed as search keywords to collect relevant tweets. The knowledge graph is then generated based on three key elements extracted from the tweets: hashtags, words, and URL/invite codes. This graph serves as a tool for tracking the criminal networks involved in the distribution of sexually exploitative content and unauthorized recordings. Furthermore, to enhance tracking efficiency, an optimization model was developed to generate knowledge graphs from various analytical perspectives. In this study, to evaluate the performance of the proposed technology, a dataset of 3387 tweets was collected using an X crawler. Knowledge graphs were generated and optimized through both single and combined analyses of the three key elements, demonstrating the effectiveness of the proposed technology in tracking criminal organizations engaged in sexual crimes. Full article

Background: Smartphones and social media (SPSM) use has become an integral part of life around the globe, including the United Arab Emirates (UAE). This study investigated the association and strength of association between SPSM screen time and dietary and physical activity behaviours among adults in the UAE. Methods: A cross-sectional household-based study was conducted in the Emirate of Abu Dhabi, UAE, between June 2024 and March 2025. A total of 5152 adults aged 18 years and above were selected through a multistage stratified random sampling method. Data were obtained from structured interviews that collected information about demographics, SPSM use time, dietary habits and physical activity levels. The statistical analyses conducted using SPSS software. Results: The mean SPSM usage time was 277.6 ± 165.6 and 234.6 ± 170.8 min per day for smartphone and social media, respectively. Social networking was the main purpose for smartphone (71.7%) and social media (74.8%) usage. Higher SPSM use was significantly associated with more frequent consumption of unhealthy foods, including sugar-sweetened beverages, fast food, and energy drinks (p < 0.001). The associations between SPSM and dietary behaviour and physical activity were generally weak. Only social media usage time was significantly associated with physical activity (p = 0.012). Conclusion: SPSM use time is associated with dietary patterns and physical activity among adults in the UAE. Higher smartphone and social media screen time was associated with increased consumption of unhealthy foods and lower physical activity; however, the relationships were weak to moderate. Full article