Search Results (49)

With the expansion of the digital economy, platform-based content creation has become a common route through which young people participate in flexible employment. However, the occupational identity of young we-media bloggers remains unstable because platform work combines low entry barriers, ambiguous labour relations, algorithmic governance and uncertain income. This qualitative study examines factors shaping occupational identity among young we-media bloggers in Hangzhou and Shaoxing, China. Data were collected through participatory observation and semi-structured interviews with 20 bloggers active on Xiaohongshu, TikTok and Bilibili. Fifteen interviews were used for open, axial and selective coding, and five additional interviews were used to assess theoretical saturation. The analysis identifies five key factors: perceived occupational competence, occupational development expectations, perceived occupational meaning, external environmental conditions and platform mechanisms. These factors operate through the interaction of internal cognition and external conditions: competence, future expectations and meaning provide internal support for occupational identity, whereas family attitudes, social evaluation, industry conditions, platform feedback and monetisation opportunities shape its stability. The findings suggest that young bloggers’ occupational identity is not a fixed status but a negotiated process formed through content production, platform feedback and social recognition. Full article

As video-based influencer marketing becomes central to digital brand communication, understanding what drives consumer engagement on interactive platforms is increasingly critical. This study examines how content and contextual features influence engagement behavior in influencer-sponsored videos on Bilibili. Drawing on the theory of conversation and the customer-perceived-value framework, we propose a four-part framework—Who (influencer characteristics), What (message content), How (presentation strategy), and When (timin g)—to explain engagement variation. Using a manually coded dataset of 457 sponsored videos, we find that hedonic appeal, comparative messaging, and message sidedness significantly enhance engagement, while signals of brand control, promotional incentives, and technical features (e.g., plug-ins, progress bars) have no significant effect. Notably, perceived expertise and posting during the platform’s “golden period” also do not affect engagement, underscoring the importance of relatability and content salience over authority or timing. By integrating message strategy with interactive design, this study advances marketing communication theory in digital contexts and offers practical insights for optimizing content strategy on social video platforms. Full article

Digital game consumption increasingly unfolds across video platforms, comment sections, and community discussions, where platform visibility, creator-mediated information, interaction metrics, and commercialization signals shape users’ expectations. In platform-mediated digital commerce, visible interaction may indicate information use, cultural resonance, payment concern, or consumption-related complaint rather than uniformly positive engagement. Using self-determination theory as a motivational lens within a platform-mediated consumer-behavior framework, this study examines whether platform content cues, public comment responses, and user perceptions provide convergent evidence on differentiated engagement meanings. The empirical setting is Bilibili content related to the Chinese wuxia role-playing game Where Winds Meet. The analysis combines 1164 public videos, 19,919 hot comments, and a content-exposure-anchored survey of 564 valid respondents. The results show differentiated patterns: functional information cues correspond to saving-oriented engagement and useful responses; cultural-aesthetic cues correspond to supportive interaction and cultural responses; and payment-mechanism and experience-problem cues correspond to payment concerns and complaints. The survey further shows that perceived information value, cultural/experiential connection, perceived monetization fairness, consumer autonomy in spending decisions, and perceived monetization risk are associated with continued engagement intention. These findings suggest that engagement quality should be interpreted through platform-mediated consumer relationships rather than interaction volume alone, while recognizing that hot-comment evidence reflects a platform-visible layer of user response rather than the full distribution of comments or player attitudes. 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

Large-scale infrastructure development disrupts not only the material foundations of agrarian livelihoods but also the social and ecological systems through which households manage uncertainty. This study argues that the livelihood shifts observed among households affected by the construction of the Bili-Bili Reservoir in Lanna Sub-district, Gowa Regency, Indonesia, are best understood as products of contextual rationality operating at the individual level and enacted through household-level strategies. Using a qualitative phenomenological approach with 15 purposively selected informants, each representing a distinct household directly affected by the reservoir’s construction functioned as a structural shock that exceeded the adaptive capacity of the existing agrarian system, triggering differentiated household responses—including reservoir fisheries, small-scale trade, home-based enterprise, and labor migration—whose variation reflects systematic differences in individual skills, asset endowments, and social capital access rather than arbitrary or purely compelled choice. Theoretically, this study advances the sustainable livelihoods framework by specifying the mechanism linking individual rationality to household adaptive outcomes, and by showing how social capital—in its bonding, bridging, and linking dimensions—shapes the option set within which rational calculations are made. These findings suggest that post-displacement livelihood recovery is more effectively supported by policies that strengthen social network structures alongside physical and financial provision. Full article

(1) Background: C-section-related health information is increasingly disseminated through short video platforms such as YouTube and Bilibili, yet the quality and audience engagement of this content remain insufficiently understood. (2) Methods: A cross-sectional study analyzed the top 90 C-section-related videos from each platform (180 total). C-section video characteristics and engagement metrics were collected. Information quality and reliability were assessed using GQS, DISCERN, and JAMA benchmarks. Associations between quality scores and engagement indicators were examined using Spearman’s correlation analysis. (3) Results: YouTube videos were longer and more frequently produced by medical professionals. Although GQS scores were comparable, YouTube content demonstrated higher reliability, with significantly higher DISCERN and JAMA scores (p < 0.001). C-section engagement metrics were strongly intercorrelated but showed weak associations with objective quality measures. (4) Conclusions: Significant cross-platform disparities exist in C-section information quality, with a pronounced dissociation between clinical reliability and audience engagement. This “quality-popularity paradox” underscores a critical mismatch between evidence-based rigor and digital dissemination. Our findings necessitate multi-sectoral interventions, including standardized creator credentialing and algorithm recalibration, to align high-quality obstetric knowledge with public attention. Full article

This research explores how Chinese youth, most of whom lack formal religious beliefs or affiliations, engage with digital non-traditional religious music, such as electronic adaptations of the Great Compassion Mantra chant, on platforms such as Bilibili. A total of 15 interviews and one year of digital ethnography were conducted to examine how various music mediators, such as music, technology, the environment, and the cultural context, shape youth’s affective states, namely their states of tranquility, trance, and transcendence. This study reinserts musicality into the social and cultural studies of religious music and identifies more fluid, contingent, and processual forms of associations and articulations between different mediators, along with the more emergent and ambient affective states brought about by such mediators, their networks, and related mediation processes. In addition, this study reveals Chinese youth’s hybridized and idiosyncratic practices that combine alternative spiritual elements with secular experiences, highlighting the context-specific ways in which Chinese youth navigate spirituality in the post-secular age. Full article

Connectivist learning has emerged as a contemporary theory in technology-enhanced education, emphasising the importance of learners’ metacognitive skills to manage their learning within connected communities. Despite its growing relevance, limited empirical evidence discussing how learners’ metacognitive patterns interact with the development of learning communities. This study took the first step by empirically investigating the interplay between metacognition and social presence through reciprocal interactions on Bilibili, a learning social media platform in China. From a dataset of 4084 comments, 485 interactions were extracted and analysed using k-means clustering, followed by a chi-square test to explore associations with social presence interactions. The findings reveal that learners actively engage in metacognition processes, particularly planning, monitoring, and evaluating their learning, within connectivist environments. Furthermore, the dynamic exchange of ideas fosters continuous knowledge construction, supporting both lifelong and informal learning. Crucially, the interdependence between metacognition engagement and social presence not only underscores their role in achieving deep and sustainable learning but also highlights the evolving identity of online learners as network facilitators on social media platforms. Full article

This study systematically reviewed 38 peer-reviewed articles (2020–2024) on social media and environmental communication in China following the PRISMA 2020 guidelines. It identified dominant research patterns across themes, theories, methods, and platforms. The field is heavily shaped by behavioral models (e.g., TPB, NAT) and survey-based designs, while institutional dynamics, platform governance, and cross-platform processes remain underexamined. Weibo and WeChat dominate as research sites, whereas short-video platforms like TikTok and Bilibili are emerging but undertheorized. Cross-level frameworks are frequently reduced to individual-level predictors, and social media are often treated as neutral delivery tools. The review highlights the need for multi-level approaches linking individual cognition, media architectures, and governance contexts to better capture how environmental publics form and operate in China’s platformed ecology. This study contributes to the realization of the Sustainable Development Goals by revealing how social media infrastructures mediate environmental awareness, engagement, and systemic change. Full article

Social media platforms have emerged as a critical infrastructure for disaster communication and emergency management. However, how public opinion varies across platforms during earthquake events and how such differences can inform resilient disaster strategies remain underexplored. This study analyzes public opinion responses to the 2023 M5.5 Dezhou earthquake across three major Chinese social media platforms—Sina Weibo, Bilibili, and Douyin—based on 28,557 posts. By combining Latent Dirichlet Allocation (LDA), Word2Vec, and Convolutional Neural Networks (CNNs), we examine the temporal, spatial, thematic, and emotional patterns of public discourse. The results show (1) a bimodal public attention pattern within 24 h of the earthquake, with platform-specific response timings; (2) spatial clustering of public concern in the epicenter (Shandong) and historically high-risk regions (Sichuan–Chongqing); (3) differentiated topic preferences reflecting platform functions—emotional expression (Weibo), science popularization (Bilibili), and real-time impact sharing (Douyin); and (4) a predominance of positive/neutral sentiment, influenced by user demographics and algorithmic content curation. This study proposes a resilience-oriented public opinion analysis framework aligned with the disaster lifecycle and offers recommendations for platform-specific risk communication, psychological support, and policy planning. Findings contribute to digital disaster governance and the integration of social media analytics into sustainable emergency management. Full article

Many video platforms (e.g., TikTok and Bilibili) choose to provide bullet screens on their video content. With the different types of bullet screen features, platforms face the challenge of choosing an appropriate bullet screen strategy, especially when consumers have different preferences for bullet screens. To address this challenge, this paper constructs a game-theoretic model to analyze the optimal bullet screen strategy for video platforms with two-sided market characteristics. Although there are some arguments that bullet screens can be detrimental to the platform’s advertising business, our study shows that when the bullet screen feature can attract more consumers, it is beneficial for both the platform and the advertisers. Additionally, we found that as consumers’ attention levels toward bullet screens increase or the proportion of bullet screen preference consumers rises, the video platform will enhance the quality of bullet screens provided to consumers and raise advertising pricing for advertisers. However, the platform’s profits do not necessarily increase accordingly and are also influenced by the platform’s bullet screen cost coefficient. Our comparative analysis of the three bullet screen models reveals that when consumers in the market are ad-fatigued, the model that allows bullet screens to cover ads is the optimal choice for the platform, and when there are differences in the cross-side network effects of advertiser to consumers, the model that maximizes the platform’s profits depends on the size of the cross-side network effects of advertiser to consumers. Our study provides important managerial insights for video platforms, especially on how to provide bullet screens under two-sided market structures. In future research, we will strive to better integrate consumer attention theory with theoretical modeling. Full article

Spotty liver disease (SLD), primarily caused by Campylobacter hepaticus and (less frequently) by Campylobacter bilis, significantly impacts commercial layer hens by causing liver lesions, reducing egg production, and increasing mortality, meaning it can result in serious economic losses for farmers. This study explored the relationship between infection, liver dysfunction, and reproduction, aiming to identify host genetic markers for tracking SLD progression. Hens were orally inoculated with the C. hepaticus strain NSW44L and monitored over a seven-day period. Pathogen colonisation was quantified using qPCR across the liver, bile, caeca, spleen, and ovarian follicles, while liver lesions were scored and hepatic transcriptomes analysed using RNA-seq. C. hepaticus was detected in the liver, caeca, and spleen from one day post-inoculation (dpi) (1.44–1.68 log₁₀ CFU/mL), appeared in bile by the third dpi (3.64 log₁₀ CFU/mL), and reached the follicles by the fourth dpi (3.25 log₁₀ CFU/mL). The highest bacterial loads were found in bile on days six and seven (up to 7.18 CFU/mL). Liver lesions were first observed on the fourth dpi, reaching their peak at the sixth and seventh dpi. Gene expression analysis in liver tissue revealed a notable downregulation of yolk-precursor and metabolic genes, such as prolactin receptor (PRLR), 7-dehydrocholesterol reductase (DHCR7), and malic enzyme 1 (ME1). In contrast, from days three to seven post-infection, there was significant upregulation of avidin (AVD), a biotin-binding protein, and versican (VCAN), which is linked to tissue remodelling and inflammation. These findings correlate with the disease’s progression from initial liver infection to widespread bacterial presence, suggesting value as host biomarkers for effective SLD monitoring and the development of targeted therapies. Full article

To quantify the communication effectiveness of automotive online videos, this study constructs a multimodal deep learning framework. Existing research often overlooks the intrinsic and interactive impact of textual and dynamic visual content. To bridge this gap, our framework conducts an integrated analysis of both the textual (titles) and visual (frames) dimensions of videos. For visual analysis, we introduce FER-MA-YOLO, a novel facial expression recognition model tailored to the demands of computational communication research. Enhanced with a Dense Growth Feature Fusion (DGF) module and a multiscale Dilated Attention Module (MDAM), it enables accurate quantification of on-screen emotional dynamics, which is essential for testing our hypotheses on user engagement. For textual analysis, we employ a BERT model to quantify the sentiment intensity of video titles. Applying this framework to 968 videos from the Bilibili platform, our regression analysis—which modeled four distinct engagement dimensions (reach, support, discussion, and interaction) separately, in addition to a composite effectiveness score—reveals several key insights: emotionally charged titles significantly boost user interaction; visually, the on-screen proportion of human elements positively predicts engagement, while excessively high visual information entropy weakens it. Furthermore, neutral expressions boost view counts, and happy expressions drive interaction. This study offers a multimodal computational framework that integrates textual and visual analysis and provides empirical, data-driven insights for optimizing automotive video content strategies, contributing to the growing application of computational methods in communication research. Full article

This study explores the dissemination of Cantonese Opera on Bilibili, a social media platform popular among younger audiences in China, as traditional cultural forms struggle to captivate younger generations. Platforms like Bilibili present opportunities for revitalizing interest in intangible heritage. This research aims to uncover how user engagement metrics influence the visibility and spread of Cantonese Opera content, providing insights for enhancing heritage promotion. Data were collected from 1916 Cantonese Opera videos using Python-based web scraping, focusing on eight user engagement metrics: Likes, Shares, Coins, Comments, Bullet Comments, Fans, Collects, and Plays. In addition to descriptive statistics, exploratory data analyses—including distributional assessment, correlation analysis, and K-means clustering—were conducted to examine engagement heterogeneity and interaction patterns across videos. The structural equation modeling (SEM) was employed to analyze the relationships among these metrics and their impact on content dissemination. Results show that Coins and Likes significantly predict Comments and Shares, indicating the importance of both monetary and non-monetary support in fostering interaction. The clustering results revealed three distinct video groups—high, moderate, and low engagement—demonstrating a long-tail pattern typical of social media visibility. Collects and Shares notably increase Plays counts, underscoring the role of curation and social diffusion. However, the negative impact of Coins on Plays reveals complex user motivations. These multi-level analyses provide a comprehensive understanding of engagement mechanisms and heterogeneity. They contribute to understanding the promotion of traditional cultural content through youth-oriented social media and offer practical implications for content creators and cultural institutions leveraging digital media for cultural preservation. Full article

Following the significant disruption of traditional teaching by the COVID-19 pandemic, gamified education—an approach integrating technology and cognitive strategies—has gained widespread attention and use among educators and learners. This study explores how game-based learning, supported by situated learning theory and game design elements, can boost learner motivation and knowledge construction. Using 20,293 user comments from the Chinese video platform Bilibili, the study applies sentiment analysis and LDA to uncover users’ sentimental tendencies and cognitive themes. The analysis identifies four core themes: (1) The application of contextual strategies in language learning, (2) Autonomous exploration and active participation in gamified learning, (3) Progressive enhancement of logical thinking in gamified environments, and (4) Teaching innovation in promoting knowledge construction and deepening. Building on these findings, the study further develops a cognitive scaffolding model integrating “contextualization–procedural logic–active construction” to explain the mechanisms of motivation–cognition interaction in gamified learning. Methodologically, this study innovatively combines LDA topic modeling with sentiment analysis, offering a new approach for multidimensional measurement of learner attitudes in gamified education. Theoretically, it extends the application of situated learning theory to digital education, providing systematic support for instructional design and meaning-making. Findings enrich empirical research on gamified learning and offer practical insights for optimizing educational platforms and personalized learning support. Full article