Search Results (79)

The present study explores the use of Instagram as an innovative strategy in the teaching–learning process in the context of animal reproduction topics. In the current era, with digital technology and social media transforming how information is accessed and consumed, it is essential for teachers to adapt and harness the potential of these tools for educational purposes. This article delves into the need for teachers to stay updated with current trends and the importance of promoting digital competences among teachers. This research aims to provide insights into the benefits of integrating social media into the educational landscape. Students of Veterinary Science degrees, Master’s degrees in Equine Sport Medicine as well as vocational education and training (VET) were involved in this study. An Instagram account named “UCOREPRO” was created for educational use, and it was openly available to all users. Instagram usage metrics were consistently tracked. A voluntary survey comprising 35 questions was conducted to collect feedback regarding the educational use of smartphone technology, social media habits and the UCOREPRO Instagram account. The integration of Instagram as an educational tool was positively received by veterinary students. Survey data revealed that 92.3% of respondents found the content engaging, with 79.5% reporting improved understanding of the subject and 71.8% acquiring new knowledge. Students suggested improvements such as more frequent posting and inclusion of academic incentives. Concerns about privacy and digital distraction were present but did not outweigh the perceived benefits. The use of short videos and microlearning strategies proved particularly effective in capturing students’ attention. Overall, Instagram was found to be a promising platform to enhance motivation, engagement, and informal learning in veterinary education, provided that thoughtful integration and clear educational objectives are maintained. In general, students expressed positive opinions about the initiative, and suggested some ways in which it could be improved as an educational tool. Full article

Objective: This cross-sectional content analysis aimed to investigate how oil pulling is portrayed on YouTube Shorts, focusing on the types of speakers, claims made, and alignment with scientific evidence. The study further explored how the content may influence viewer perception, health behaviors, and the potential spread of misinformation. Methods: On 28 January 2025, a systematic search of YouTube Shorts was performed using the term “oil pulling” in incognito mode to reduce algorithmic bias. English language videos with at least 1000 views were included through purposive sampling. A total of 47 Shorts met the inclusion criteria. Data were extracted using a structured coding framework that recorded speaker type (e.g., dentist, hygienist, influencer), engagement metrics, stated benefits, oil type and regimen, the use of disclaimers or citations, and stance toward oil pulling rated on a 5-point Likert scale. Speaker background and nationality were determined through publicly available channel descriptions or linked websites, with user identities anonymized and ethical approval deemed unnecessary due to the use of publicly available content. In total, 47 videos met the inclusion criteria. Results: Of the 47 YouTube Shorts that met the inclusion criteria, most were posted by influencers rather than dental professionals. These videos predominantly encouraged oil pulling, often recommending coconut oil for 10–15 min daily and citing benefits such as reduced halitosis and improved gum health. However, a smaller subset advanced more extreme claims, including reversing cavities and remineralizing enamel. Notably, US-licensed dentists and dental hygienists tended to discourage or express skepticism toward oil pulling, assigning lower Likert scores (1 or 2) to influencers and alternative health practitioners (often 4 or 5). Conclusions: YouTube Shorts largely promote oil pulling through anecdotal and testimonial-driven content, often diverging from evidence-based dental recommendations. The findings reveal a disconnect between professional dental guidance and popular social media narratives. While some benefits like halitosis reduction may have limited support, exaggerated or misleading claims may result in improper oral hygiene practices. Greater engagement from dental professionals and improved health communication strategies are needed to counteract misinformation and reinforce oil pulling’s role, if any, as an adjunct—not a replacement—for standard oral care. Future studies should explore viewer interpretation, behavioral influence, and cross-platform content patterns to better understand the impact of short-form health videos. Full article

This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. Full article

Police officers work in high-stress environments that demand emotional resilience, interpersonal skills, and effective communication. Occupational stress can negatively impact their motivation, conflict resolution abilities, and professional effectiveness. Loving-Kindness Meditation (LKM), a mindfulness-based intervention focused on cultivating compassion and empathy, has shown promise in enhancing prosocial attitudes and emotional regulation. With the rise of short video platforms, digital interventions like video-guided LKM may offer accessible mental health support for law enforcement. This study examines the effects of short video app-guided LKM on police officers’ mindfulness, public service motivation (PSM), conflict resolution skills (CRSs), and communication skills (CSSs). It aims to determine whether LKM can enhance these psychological and professional competencies. A randomized controlled trial (RCT) was conducted with 110 active-duty police officers from a metropolitan police department in China, with 92 completing the study. Participants were randomly assigned to either the LKM group (n = 46) or the waitlist control group (n = 46). The intervention consisted of a 6-week short video app-guided LKM program with daily 10 min meditation sessions. Pre- and post-intervention assessments were conducted using several validated scales: the Mindfulness Attention Awareness Scale (MAAS), the Public Service Motivation Scale (PSM), the Conflict Resolution Styles Inventory (CRSI), and the Communication Competence Scale (CCS). A 2 (Group: LKM vs. Control) × 2 (Time: Pre vs. Post) mixed-design MANOVA was conducted to analyze the effects. Statistical analyses revealed significant group-by-time interaction effects for PSM (F(4,177) = 21.793, p < 0.001, η² = 0.108), CRS (F(4,177) = 20.920, p < 0.001, η² = 0.104), and CSS (F(4,177) = 49.095, p < 0.001, η² = 0.214), indicating improvements in these areas for LKM participants. However, no significant improvement was observed for mindfulness (F(4,177) = 2.850, p = 0.930, η² = 0.016). Short video app-guided LKM improves public service motivation, conflict resolution skills, and communication skills among police officers but does not significantly enhance mindfulness. These findings suggest that brief, digitally delivered compassion-focused programs can be seamlessly incorporated into routine in-service training to strengthen officers’ prosocial motivation, de-escalation competence, and public-facing communication, thereby fostering more constructive police–community interactions. Full article

Every minute, vast amounts of video and image data are uploaded worldwide to the internet and social media platforms, creating a rich visual archive of human experiences—from weddings and family gatherings to significant historical events such as war crimes and humanitarian crises. When properly analyzed, this multimodal data holds immense potential for reconstructing important events and verifying information. However, challenges arise when images and videos lack complete annotations, making manual examination inefficient and time-consuming. To address this, we propose a novel event-based focal visual content text attention (EFVCTA) framework for automated past event retrieval using visual question answering (VQA) techniques. Our approach integrates a Long Short-Term Memory (LSTM) model with convolutional non-linearity and an adaptive attention mechanism to efficiently identify and retrieve relevant visual evidence alongside precise answers. The model is designed with robust weight initialization, regularization, and optimization strategies and is evaluated on the Common Objects in Context (COCO) dataset. The results demonstrate that EFVCTA achieves the highest performance across all metrics (88.7% accuracy, 86.5% F1-score, 84.9% mAP), outperforming state-of-the-art baselines. The EFVCTA framework demonstrates promising results for retrieving information about past events captured in images and videos and can be effectively applied to scenarios such as documenting training programs, workshops, conferences, and social gatherings in academic institutions Full article

Background: Colonoscopy is an important and essential diagnostic and screening tool for colorectal cancer and other pathologies in the colon. High-quality bowel preparation (BP) is a key quality measure of colonoscopy and is critical for maximizing its effectiveness, including enhancing adenoma detection rates. However, inadequate bowel preparation (IBP) remains a frequent challenge and is influenced by multiple factors. This review aims to summarize and evaluate educational and technological interventions implemented before colonoscopy to improve BP quality. Methods: The methodology comprised a structured narrative review of studies published in English, including randomized controlled trials, prospective studies, observational cohorts, and meta-analyses. Interventions were categorized by their delivery mode and impact on BP adequacy. Interventions included written materials, internet-based education modules, short message service (SMS) reminders, visual aids, instructional videos, verbal communication, telephone support, smartphone applications, and virtual reality (VR) platforms. Results: Most studies reported significant improvements in BP quality with enhanced patient education, particularly with the use of instructional videos and smartphone applications. Verbal communication and telephone support also demonstrated positive outcomes but were limited by resource availability. VR represents a promising emerging technology, though its implementation remains costly and complex. Conclusions: Enhanced educational interventions are proven methods to optimize BP quality. The selection of an appropriate modality should consider patient characteristics, technological accessibility, and institutional resources. Personalized strategies targeting high-risk populations can further reduce IBP rates and improve overall colonoscopy outcomes. Full article

In modern communication technology, short videos are increasingly used on social media platforms. The advancement of video codecs is pivotal in communication. In this study, we developed a new scheme to encode the residue of intraframes. For the H.264 baseline profile, we used context-based arithmetic variable-length coding (CAVLC) to encode the residue of integer transforms in a block-wise manner. In the developed method, the DC and AC coefficients are separated. In addition, context assignment, adaptive scanning, range increment, and mutual learning are adopted in a mixture of fixed-length and variable-length schemes, and block-wise compressions of the frequency table are applied to obtain improved compression rates. Compressing the frequency prevents CAVLC from being hindered by horizontally/vertically dominated blocks. The developed method outperforms CAVLC, with average reductions of 7.81, 8.58, and 7.88% in quarter common intermediate format (QCIF), common intermediate format (CIF), and full high-definition (FHD) inputs. Full article

Short video applications have gained increasing prominence as pivotal channels for information acquisition and knowledge dissemination, capturing the attention of scholars. Despite the increasing interest in this area, a general investigation of the factors that facilitate users’ information adoption on TikTok remains insufficient. We combine the flow experience and information adoption theory to construct a novel theoretical model for investigating the antecedents of information adoption intentions within the realm of short video applications. A total of 386 data were collected from TikTok’s users and analyzed using the partial least squares (PLS) approach. The analysis of the data revealed that technology affordance (i.e., accuracy, serendipity, and perceived ease of use) and information quality influence users’ information adoption intentions via flow experience. Users’ interest-type epistemic curiosity can moderate the effect of serendipity on information adoption intentions. This study investigates the interactive effects of individuals, technology, and information on flow and information adoption intentions, offering implications for platform administrators, content creators, and government agencies to understand users’ information adoption. Full article

The present study explores the emerging role of “public affairs live streamers” on the short video platform Kuaishou, examining how these individuals have evolved into new forms of rural elites, expanding opportunities for civic participation and rural governance in China. Through content analysis and case studies, the research identifies six key themes central to the streamers’ discourse: land and property issues, financial aid and subsidies, policy and governance, advocacy for the public, educational outreach, and customs. These themes underscore their role as rural knowledge elites who bridge the gap between the government and rural residents through digital platforms. The study further examines the mechanisms behind the emergence of “public affairs live streamers”. Empowered by the platform, these streamers effectively convert their cultural capital into online social capital, which may then be partially transformed into offline social influence and economic returns. However, their empowerment is constrained by the platform’s algorithm distribution mechanisms and opaque content regulation, making the conversion of capital unstable, traffic-dependent, and challenging to sustain. Linking to the broader socio-political landscape, the study delves into the empowering role of “public affairs live streamers” in society. These online rural elites blend knowledge with digital skills to facilitate public engagement and inspire civic awareness in rural areas through their activities. Nonetheless, their contribution to rural governance and civilization is often limited to informal mediation and emotional expression, with limited capacity to foster rational discourse or deeper improvements in rural governance. Overall, this study contextualizes the rise of rural elites within China’s rapid technological and rural development, offering a fresh perspective on how digital media can supplement governance strategies and enhance grassroots’ civic participation. Based on this analysis, the study proposes recommendations for improving platform governance and integrating public affairs streamers into broader participatory frameworks, thereby stabilizing their empowering effects and promoting sustainable rural governance. Full article

This systematic review analyzed the role of microlearning in basic education, identifying the most widely used Digital Information and Communication Technologies, relevant learning theories, and the role of social technologies from a Science, Technology, Society, and Environment (STSE) perspective. Following PRISMA 2020, searches were conducted in Web of Science, Scopus, ERIC, and IEEE Xplore databases. Studies on microlearning were selected based on previously defined eligibility criteria. The review process in Rayyan involved deduplication, screening, and full-text analysis. Data were qualitatively analyzed using content analysis, and methodological quality was assessed with CASP and the Downs and Black. The findings highlight that microlearning, integrated with digital tools such as online platforms, mobile apps, and short videos, significantly enhances student motivation, performance, and interaction; content in short modules facilitates knowledge retention and connects concepts to real-life situations. Promising trends include mobile technologies and gamification, which foster active, meaningful learning. Grounded in theories like Self-Determination, Constructionism, and Constructivism, microlearning personalizes teaching and promotes engagement, critical thinking, and accessibility, contributing to inclusive and sustainable education. From a STSE perspective, social technologies enhance autonomy, social interaction, and ethical–environmental awareness. In Brazil, further research on digital platforms and gamified strategies is needed to drive innovative educational practices. Full article

Background and Purpose: The proliferation of short-form video content on social media platforms has led to increased user engagement but also raised concerns about potential addictive behaviors and cognitive consequences, particularly among youth. This study explored the prevalence of short-form video addiction (SVA) among Jordanian youth, its correlates, and its impact on attention and memory function, with an emphasis on understanding the mediating and moderating role of attention in the relationship between SVA and memory. Methods: Utilizing a cross-sectional survey design, data were collected from 1029 university students across 25 higher-education institutions in Jordan. Results: Half of the participants exhibited moderate to high levels of SVA. The findings indicated a significant increase in SVA scores among female students (p = 0.003), those of a younger age (p = 0.045), those with lower GPAs (p = 0.013), and those who dedicated fewer hours to study (p = 0.006). Notably, there was a significant and large correlation between SVA scores and students’ perceptions of user-generated content (p < 0.001). Attention partially mediated the relationship between SVA and memory function with excellent model fit indices (χ²(12) = 14.11, p = 0.05, RMSEA = 0.03, GFI = 0.99, IFI = 0.99, TLI = 0.98, CFI = 0.99). However, attention did not moderate this relationship, suggesting that the impact of SVA on memory is consistent across varying levels of attention. Discussion: The findings underscore the significant engagement of Jordanian youth with short-form video content and the potential cognitive risks associated with SVA. Interventions to manage attention could mitigate the adverse effects of SVA on cognitive functions. This study calls for a comprehensive approach to address SVA among youth, including the development of digital literacy programs, mental health support services, and policy interventions that promote a balanced digital ecosystem and responsible media consumption. Full article

To investigate public perceptions regarding tunnel fire disasters and optimize the tunnel fire disaster prevention framework, this study takes the emerging social media platform Douyin as a case study, conducting an in-depth analysis of 2133 short videos related to tunnel fires on the platform. A computational communication method was used for analysis, Latent Dirichlet Allocation was used to cluster the discussion topics of these tunnel fire short videos, and a spatiotemporal evolution analysis of the number of videos posted, user comments, and emotional inclinations across different topics was performed. The findings reveal that there is a noticeable divergence in public opinion regarding emergency decision making in tunnel fires, related to the complexity of tunnel fire incidents, ethical dilemmas in tunnel fire escape scenarios, and insufficient knowledge popularization of fire safety practices. The study elucidates the public’s actual needs during tunnel fire incidents, and a dynamic disaster prevention framework for tunnel fires based on social media and artificial intelligence is proposed on this basis to enhance emergency response capabilities. Utilizing short videos on social media, the study constructs a critical target dataset under real tunnel fire scenarios. It proposes a computer vision-based model for identifying critical targets in tunnel fires. This model can accurately and in real-time identify key targets such as fires, smoke, vehicles, emergency exits, and people in real tunnel fire environments, achieving an average detection precision of 77.3%. This research bridges the cognitive differences between the general public and professionally knowledgeable tunnel engineers regarding tunnel fire evacuation, guiding tunnel fire emergency responses and personnel evacuation. Full article

Critical thinking is essential when navigating, evaluating, and interacting with media; therefore, it is important to investigate if adults’ critical thinking skills can be trained. This paper describes an experiment investigating the impact of video lectures about enthymemes and critical thinking skills on participants’ (N = 176) critical thinking skills, measured by the Watson–Glaser Critical Thinking Appraisal (WGCTA) and on their ability to identify clickbait headlines. Participants were adults recruited through the Prolific Platform, and they were randomly assigned to one of three conditions: an enthymeme lecture, a general critical thinking lecture, or a control condition. The results indicated no significant improvement in critical thinking scores across the conditions, as measured by the WGCTA. Similarly, no significant differences were found in the participants’ ability to identify clickbait headlines. However, a significant positive correlation was observed between higher critical thinking scores and better clickbait recognition. These results suggest that a short lecture-based intervention may not be sufficient to significantly improve adult learners’ critical thinking. Perhaps this study indicates the need for more in-depth or interactive interventions to effectively support media literacy. The material presented here is a kind of counterexample of what should be done. For this reason, it may prove useful in future research to avoid certain experimental dead-ends. Full article

On short video platforms, individuals can not only passively browse videos but also actively engage in behaviors such as liking and commenting on videos. However, the mechanisms by how active and passive usage of short videos affect subjective well-being remains unclear. Thus, this study aims to explore these mechanisms through path analysis and network analysis. We employed the Short Video Usage Questionnaire, Subjective Well-Being Scale, and Interaction Anxiety Scale to survey 1086 participants. The results indicated (1) the active usage positively predicted life satisfaction and positive affect, while passive usage failed to predict any of them. (2) Social anxiety mediated the relationship between short video usage and subjective well-being. Active usage enhanced life satisfaction and positive affect by alleviating social anxiety, while reducing negative affect by decreasing social anxiety. At the same time, passive usage reduced life satisfaction and positive affect by exacerbating social anxiety while increasing negative affect by intensifying social anxiety. (3) Network analysis revealed that “live interaction” behavior was a core activity for alleviating social anxiety and enhancing subjective well-being. The findings highlight the significant role of short video usage in subjective well-being and provide empirical evidence to guide users in the rational and positive usage of short video platforms. Full article

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis. Full article