Search Results (531)

Mycotoxins contaminate a wide range of foods and may contribute to both acute toxicity and chronic dietary exposure. Because online video increasingly shapes public interpretation of food-safety hazards, we evaluated the quality of English-language YouTube™ content on foodborne mycotoxin risks. Of 166 records screened for eligibility, 53 were excluded and 113 videos were analyzed. Two food-safety experts independently evaluated each video with an investigator-developed Video Content Quality (VCQ) checklist, the Global Quality Scale (GQS), and a five-item modified DISCERN instrument. The final sample comprised 77 company/commercial and 36 academic/noncommercial videos. After Holm adjustment across five source-group outcomes, company/commercial videos had higher VCQ and modified DISCERN scores (both adjusted p < 0.001), whereas academic/noncommercial videos had a higher interaction rate (adjusted p = 0.024). The GQS comparison did not meet the adjusted significance threshold (adjusted p = 0.066) and should not be interpreted as proof of equivalence. Video age was associated with cumulative views and likes but not with any quality score after correction. Item-level VCQ results showed that authoritative-source citation and health-effects coverage were among the least frequently awarded full-credit domains. Although the three quality measures were strongly correlated, neither interaction rate nor viewing rate was significantly associated with them. Platform engagement therefore did not serve as a reliable marker of scientific quality in this sample. Source- and region-based comparisons remain exploratory because the single-query design, broad uploader categories, and uneven source composition may have shaped the sampled videos. Full article

This study aimed to investigate visitors’ perceptions of ecotourism landscapes in Thailand’s protected areas by integrating spatial, semantic, and affective information derived from social-sensing textual data. To this end, it examined the influence of ecosystem characteristics on thematic expressions and emotional tones across five national parks. Methodologically, a spatio-semantic, natural language processing (NLP) social-sensing framework was developed using geotagged Flickr tags and YouTube comments from international (English) and domestic (Thai) tourists. Textual data were preprocessed (cleaned, normalized, and tokenized) and analyzed using latent Dirichlet allocation (LDA), sentiment analysis, and diversity metrics. Concurrently, YouTube comments were similarly processed using LDA and rule-based sentiment analysis. Subsequently, a Diversity × Topic × Season matrix integrated Flickr-derived indicators with YouTube-derived sentiment and topic dominance. Binary logistic regression was applied to examine cross-platform relationships. The analysis identified three dominant themes, namely Nature & Landscapes, Travel & Activities, and Feelings & Experiences. Forest-mountain parks showed high semantic diversity and strong positive sentiment, whereas marine parks exhibited narrower but predominantly positive activity-driven discourses. Moreover, seasonal variation was evident, with summer and winter yielding the highest diversity and positivity. Building on these results, this study devised a spatio-semantic framework for analyzing variation in visitor expressions across parks and seasons. It captures eco-awareness, perceptions, and preferred activities in protected landscapes. From a practical perspective, semantic diversity and sentiment indicators can support ecotourism management through improved visitor monitoring and communication strategies. Full article

Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory to systematically elucidate the thematic configurations, framework dynamics, and ecological activation mechanisms underlying the international image of Chinese EVs. By integrating unsupervised BERTopic modeling, Large Language Model (LLM) semantic mapping, the Entropy Weight Method (EWM), and Social Network Analysis (SNA), this inquiry operationalizes a comprehensive computational communication framework to mine large-scale behavioral and textual data from YouTube. The empirical findings unveil that: (1) international audience perceptions have broken through the traditional “low-cost manufacturing” stereotype, spontaneously giving rise to a multidimensional, composite cognitive schema centered on smart ecosystems and design experiences; (2) driven by the interplay of rational and irrational user feedback loops, the ecological activation efficiencies across diverse discursive dimensions exhibit pronounced nonlinear variances, characterized by a “strong activation of intelligent ecosystems versus a long-tail stagnation of cost-effectiveness salience”; and (3) positive technological frameworks and negative geopolitical or regulatory risks engage in fierce, adversarial contestation and structural hybridization within a highly volatile network topology, culminating in a unique “dual-core” configuration. Theoretically, this study enriches the scholarly understanding of country-of-origin and corporate brand images through a complex systems lens; methodologically and practically, it offers a high-fidelity, actionable quantitative paradigm for global brand empowerment and targeted cross-border public opinion governance. Full article

Existing platform-monitoring methods for sport communities commonly rely on isolated descriptive text-mining outputs or general sentiment scores; they rarely integrate interpretable ESG issue coding with class-sensitive risk detection and provide limited support for auditable, privacy-conscious analysis of safety, ethics, and institutional trust. These limitations motivate a multi-stage framework that converts heterogeneous platform discourse into complementary structural and evaluative signals. Conceptually, digital trust is treated as the focal governance outcome; ethics and safety are substantive domains of concern; ESG provides the bounded classification and response ontology; and early warning denotes a prototype, human-reviewed weak-signal triage concept rather than incident prediction or a deployed security-monitoring system. Using 377,700 cleaned Korean-language comments on judo and Brazilian Jiu-Jitsu (BJJ) collected from Naver News and YouTube between 2010 and 2025, the framework combines n-gram analysis, LDA topic modeling, CONCOR network analysis, bounded ESG discourse classification, and three-class sentiment prediction. The individual analytical algorithms are established; the methodological contribution lies in their governance-oriented orchestration through a bounded ESG/non-ESG coding gate, a study-specific index layer, and a human-reviewed pathway from aggregate discourse signals to proportionate review. The analytical workflow identifies issue salience, relational topic structures, ESG dimensions, sentiment risk, legitimacy balance, and platform-specific risk concentration while excluding personally identifiable information. Empirically, social and governance concerns dominate the corpus, and governance-related negative sentiment consistently exceeds social-risk sentiment, highlighting rule transparency, coach ethics, misinformation, platform reputation, and institutional response as central trust-risk domains. Cell-weighted sensitivity checks preserved the governance-over-social and YouTube-over-Naver risk ordering, although the pooled salience estimate remained sensitive to the rapid expansion of BJJ discourse on YouTube. The fine-tuned KLUE-BERT model achieved a Macro-F1 of 0.838 and a negative-class F1 of 0.862, outperforming the strongest baseline, Text-CNN (Macro-F1 = 0.791), by 0.047 absolute Macro-F1 points (approximately 6.0% relative improvement). These findings support the feasibility of a batch-oriented, human-reviewed prototype for prioritizing aggregate discourse patterns. They do not establish the effectiveness of a real-time security-monitoring, incident-detection, or operational early-warning system. Full article

This article explores the relationship between music and religion through a case study of Harmony of the Spheres by the Dutch composer Joep Franssens. Situated within the context of religious change in the Netherlands, where institutional religion has declined while spiritual and sacred repertoires remain culturally significant, this article asks how musical experience can generate, mediate, or articulate forms of religiosity. Combining musicological analysis, theories of sound and religion, and a digital ethnographic reading of YouTube listener responses, this article argues that Franssens’ music opens a space for non-institutional, sonically mediated religion. The composition’s slow harmonic movement, repetition, tonal affirmation, textual unintelligibility, spatial sonority, and evocation of timelessness invite immersive listening and self-transcendence. Listener responses confirm that these musical techniques are frequently interpreted through religious and spiritual language, including references to God, angels, heaven, the sublime, peace, and mystical experience. The article argues that Harmony of the Spheres should not be understood as explicitly confessional music, but as a form of “religion in sound”: an aesthetic and performative practice through which listeners encounter transcendence, community, and an unbound spirituality beyond traditional religious institutions. Full article

This paper examines how the emergence of over-parameterized artificial intelligence models and the phenomenon of double descent challenge the classical assumption that simpler models generalize better. Traditional predictive analytics relied on parsimonious models grounded in the bias-variance trade-off, where increasing complexity was expected to produce overfitting. However, recent advances in deep learning demonstrate that highly over-parameterized models can achieve superior generalization after surpassing the interpolation threshold. This paradigm shift has enabled systems such as AlphaFold, Aurora, Delphi-2M, and recommenders to model complex, high-dimensional relationships through contextual attention rather than global feature selection. The paper argues that higher education analytics remains largely reductionist, relying on limited variables such as GPA, demographics, and course completion rates to identify “at-risk” students. While interpretable, these approaches often fail to capture the dynamic and multidimensional nature of student success. In response, this study proposes a transition toward over-parameterized personalization, where students’ academic and behavioral histories are modeled as longitudinal high-dimensional sequences. Drawing parallels to commercial recommendation systems such as Amazon, Netflix, and YouTube, the paper explores how higher education can move from generalized early-warning systems toward adaptive “n-of-1” interventions. Importantly, the paper is conceptual rather than empirical: it develops a research agenda and a set of testable propositions, and it identifies the evaluation designs—temporally valid prediction protocols and causal intervention studies—by which the promise of over-parameterized personalization in higher education should be assessed before any claim of superiority can be made. Full article

This study investigates how artificial intelligence (AI) is represented within Korean-language recommendation algorithm discourse on TikTok and YouTube. To examine the structural characteristics and discourse tendencies of AI-related discussions, the study applies text mining and keyword network analysis methods, including TF, TF-IDF analysis, centrality analysis, CONCOR clustering, and sentiment analysis. The findings indicate that AI occupies a central position within recommendation algorithm discourse and is strongly associated with algorithms, data, content recommendation, and technological systems across both platforms. The analysis further reveals notable differences between the two platforms: TikTok discourse demonstrates a stronger emphasis on automation and technological mechanisms, whereas YouTube discourse is more closely associated with content production, commercialization, and educational contexts. In addition, public discourse surrounding AI-driven recommendation systems reflects both positive-oriented and concern-related perspectives regarding technological innovation, platform influence, and social implications. This study contributes to a broader understanding of how AI is socially interpreted and represented within contemporary digital platform discourse. Full article

This study investigates how YouTube content producers incorporate scientific evidence into their videos. The purpose is to understand which types of knowledge sources most influence science-related video production and how alternative communication channels shape the visibility of research. A dataset encompassing 81,302 scientific papers in biotechnology serves as the empirical foundation for this analysis. Through log-linear regression modeling, the results reveal a divergence. While a research paper’s integration into digital video creation scales positively with its prominence in public-facing platforms (news and Wikipedia), it correlates negatively with traditional scholarly benchmarks, namely peer-reviewed citations, policy document references, and patent applications. Video creators, it appears, prioritize public visibility over conventional academic influence. This asymmetry underscores a systemic gap in how scholarly outputs filter into digital communication spaces. Full article

Synchrony refers to similarity in movement between different actors such as dancing, clapping, or singing together. Prior research demonstrates that synchrony often elicits emotions that may be characterized as aesthetic or self-transcendent (e.g., awe, connectedness). Our research situated the concept of synchrony in media contexts, examining viewers’ affective responses to videos featuring synchronous movement. Study 1 employed an experiment, showing that synchronized videos elicited greater awe, with awe associated with a host of prosocial outcomes reflecting connectedness and motivations to do good. Study 2 employed content analytic procedures to examine how the synchrony present in a large sample of YouTube videos was associated with user comments reflecting self-transcendent emotions, and how these emotions were associated with the salience of moral foundations. The results showed that synchrony was associated with greater feelings of awe, admiration, and elevation. Further, comments reflecting self-transcendence were associated with the salience of moral foundations, and particularly the foundation of care. Full article

This research aims to analyze audiovisual productions related to the development of tourism at the Armero ruins and the visitor experience in the area. The methodology used is qualitative and was carried out in two phases. This research began in August 2023 with interviews with visitors to Armero and a content analysis of YouTube videos that recount the Armero tragedy. The impact on collective memory and the sense of belonging among visitors is highlighted. The visitors’ personal productions show that the experience in Armero becomes an emotional journey, where history is tangled with the hope of rebuilding the social fabric and honoring the memory of the thousands who lost their lives. This research reveals a range of emotions: from awe at the natural beauty to respect and sadness when remembering the tragedy that buried this prosperous Colombian city in 1985. In conclusion, the importance of preserving historical memory is evident, so that tragedies like Armero’s are not forgotten and can promote reflection on natural risk management and community resilience. Full article

Social media studies often rely on one platform while drawing conclusions about online publics more generally. This study tests that inferential move through an event-centered comparison of El Niño discourse across X/Twitter, YouTube, Facebook, Reddit, TikTok, and LinkedIn. The observation window ran from 9 May through 17 May 2026, several days before and after the May 14 El Niño Watch issued by the National Oceanic and Atmospheric Administration (NOAA), which reported an 82 percent probability of El Niño emerging during May to July 2026 and a 96 percent probability of continuation through the 2026 to 2027 Northern Hemisphere winter. The corpus contains 8145 items classified as highly or moderately related to El Niño after platform-specific collection and common annotation. X/Twitter supplies 7075 items, YouTube 864, Facebook 66, Reddit 59, TikTok 50, and LinkedIn 31. Texts were annotated with a shared structured schema covering relevance, sentiment, emotion, topic, stance, likely misinformation, personal experience, humor, calls to action, language, engagement, and length. The results show that platform choice changes the empirical object. X/Twitter appears multilingual, fast-moving, and weather-heavy. YouTube is more negative, humorous, and personally experiential. Facebook is long-form and media/news oriented, with the highest model-flagged likely misinformation rate. Reddit is concentrated around weather concern. TikTok is short, playful, and personal. LinkedIn is small, professional, and mostly informational. These differences caution against generalizing from one platform to social media as a whole unless a study explicitly defines its scope, accounts for platform and genre differences, and recognizes that visible discourse may include organizational, algorithmically amplified, automated, or otherwise inauthentic activity alongside genuine human expression. Full article

Background and objectives: Social media use has become pervasive among the general population, with growing concern regarding its potential effects on mental health and sleep. While existing studies report associations between social media engagement and psychological outcomes, limited attention has been given to users’ self-perceived impact. To assess the self-perceived impact of social media use on mental health and sleep-related outcomes among healthy adolescents and adults aged 16–50 years old, and to identify associated demographic and behavioral factors. Methods: A cross-sectional survey was conducted among residents of Jeddah, Saudi Arabia, aged 16–50 years without a history of psychiatric or chronic sleep disorders, using a structured online questionnaire. Perceived mental health impact was assessed using a six-item study-specific questionnaire evaluating participants’ subjective perceptions regarding emotional and psychological responses to social media exposure. Higher perceived impact was defined as a composite score of 12–24 points on the study-specific scale. Data included sociodemographic characteristics, patterns of social media use, perceived mental health impact assessed through a 6-item Likert scale, and sleep-related outcomes. Associations were evaluated using chi-square tests and logistic regression analysis. Results: Most participants reported daily social media use exceeding 3 h, with 44.9% engaging in late-night use and 87.6% using devices within 30 min before sleep. Overall, 18.6% exhibited higher perceived mental health impact. Higher odds were observed among younger participants, students, and single individuals. Snapchat and YouTube use, and late-night engagement were independently associated with increased perceived impact. Approximately one-third reported insomnia after social media use, and 44.3% perceived improved sleep with reduced usage. Conclusions: Social media use is widely prevalent and commonly perceived to negatively affect mental well-being and sleep, particularly with intensive and late-night use. Self-awareness of these effects may represent a valuable leverage point for prevention, supporting the need for targeted digital wellness strategies and public health interventions. Full article

The relocation of the National Capital City (IKN) has instigated significant polarization of public discourse on YouTube, presenting substantial challenges for sentiment analysis due to the high variability of non-standard linguistic patterns. Existing scholarship, how-ever, has frequently overlooked the bias inherent in accuracy metrics within imbalanced datasets, while also neglecting the critical alignment between feature characteristics and algorithmic geometry. To address these methodological limitations, this study conducts a comparative analysis of TF-IDF, Word2Vec, and FastText feature extraction techniques applied to Naïve Bayes, Support Vector Machine (SVM), and Random Forest algorithms, utilizing a dataset of 3441 comments. Empirical results demonstrate that the synergy be-tween SVM and FastText yields the most robust performance, achieving an accuracy of 85.2% and outperforming other model combinations. To mitigate the bias in accuracy metrics due to class imbalance, this study further incorporates Precision, Recall, and F1-Score as additional evaluation metrics, with SVM + FastText achieving a Precision(+) of 89.7%, Recall(+) of 83.2%, and F1(+) of 86.3%. These findings underscore the efficacy of margin maximization on semantic vectors over conventional probabilistic approaches in processing informal text, thereby offering precise insights for policymakers regarding the social legitimacy of the IKN development. Full article

Climate hazards increasingly unfold as information crises alongside physical impacts, producing rapid shifts in what people search for and discuss online. This case study demonstrates how AI-supported analysis of online data can complement conventional disaster intelligence by providing a scalable social sensing layer for climate hazard resilience in Galicia. It integrates Google Trends as a proxy for changing public attention and information demand, and YouTube videos and comment threads to capture public sensemaking and resilience-relevant signals. Monthly Google Trends series were used for eight hazards, with floods showing the highest mean interest, followed by wildfires and heatwaves. For the three highest-salience hazards, the study analyzed YouTube comments using gpt-5-mini to extract sentiment, emotions, topics, institutional trust cues, collective efficacy cues, calls to action, impacts, vulnerable groups, and coping actions. The corpus included 184 heatwave comments, 20,427 wildfire comments, and 4882 flood comments. Across hazards, discourse is predominantly negative but differs in structure. Heatwave threads skew toward mockery and normalization, wildfire threads center on anger, governance and low institutional trust, and flood threads combine solidarity with demands for localized warnings and guidance. The study translates comment-level signals into traceable policy recommendations emphasizing actionable risk communication, early warning and response capacity, and trust-building practices. The study concludes with an operational pipeline concept for continuous monitoring and dashboard-based decision support, while emphasizing limitations related to Google Trends sampling and normalization, platform and API biases, and model-mediated uncertainty. Full article

Presently, video communities such as YouTube, bilibili and TikTok have emerged as core fields for information dissemination and public opinion generation. Their embedded user dynamic interaction data support research on public cognitive behavior and content dissemination laws. This study used web crawling technology to construct a complete dataset including 367 video metadata and 2.39 million comment records from Luo Xiang Speaks on Criminal Law—a prominent legal popularization account on the bilibili platform—and systematically explored the temporal evolution patterns of comment interactions in video communities. By establishing a four-dimensional feature system alongside the k-means++ clustering algorithm, this study successfully identified three distinct comment growth patterns (p < 0.001): the burst–decay, the multi-wave oscillation, and the delayed peak. The results of non-parametric tests showed that these three patterns have significant differences in core features (e.g., peak delay time, skewness) and are systematically related to user grade structure, content interaction depth, and release timing. In addition, the user interaction networks of different videos demonstrate significant structural heterogeneity and disassortative mixing, characterized by a highly active minority dominating the discourse, while peripheral nodes gravitate toward high-profile hubs. These findings offer researchers deeper insights into the micro-mechanisms of information dissemination. Full article