Search Results (119)

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

Face-to-face interaction is one of the most natural forms of human communication. Unsurprisingly, Video Conferencing (VC) Applications have experienced a significant rise in demand over the past decade. With the widespread availability of cellular devices equipped with high-resolution cameras, Instant Messaging Video Call Applications (IMVCAs) now constitute a substantial portion of VC communications. Given the multitude of IMVCA options, maintaining a high Quality of Experience (QoE) is critical. While content providers can measure QoE directly through end-to-end connections, Internet Service Providers (ISPs) must infer QoE indirectly from network traffic—a non-trivial task, especially when most traffic is encrypted. In this paper, we analyze a large dataset collected from WhatsApp IMVCA, comprising over 25,000 s of VC sessions. We apply four Machine Learning (ML) algorithms and a Large Multimodal Model (LMM)-based agent, achieving mean errors of 4.61%, 5.36%, and 13.24% for three popular QoE metrics: BRISQUE, PIQE, and FPS, respectively. Full article

The authenticity of digital video content has become a critical issue in multimedia security due to the significant rise in video editing and manipulation in recent years. The detection of interframe forgeries is essential for identifying manipulations, including frame duplication, deletion, and insertion. These are popular techniques for altering video footage without leaving visible visual evidence. This study provides a detailed review of various methods for detecting video forgery, with a primary focus on interframe forgery techniques. The article evaluates approaches by assessing key performance measures. According to a statistical overview, machine learning has traditionally been used more frequently, but deep learning techniques are gaining popularity due to their outstanding performance in handling complex tasks and robust post-processing capabilities. The study highlights the significance of interframe forgery detection for forensic analysis, surveillance, and content moderation, as demonstrated through both evaluation and case studies. It aims to summarize existing studies and identify limitations to guide future research towards more robust, scalable, and generalizable methods, such as the development of benchmark datasets that reflect real-world video manipulation diversity. This emphasizes the necessity of creating large public datasets of manipulated high-resolution videos to support reliable integrity evaluations in dealing with widespread media manipulation. Full article

Background/objectives: Social media has significantly enhanced access to medical knowledge by enabling rapid information sharing. With YouTube being the second-most popular website, we intended to evaluate the quality of its content as a source of information for patients and relatives for information about cerebral palsy. Methods: The first 30 videos for search terms “Cerebral palsy”, “Spastic cerebral palsy”, “Dyskinetic cerebral palsy”, “Worster-Drought syndrome”, and “Ataxic cerebral palsy” were selected for inquiry. Out of 150 films, a total of 83 were assessed with a mixed method approach by two independent raters utilizing evidence-based quality scales such as Quality Criteria for Consumer Health Information (DISCERN), the Journal of the American Medical Association instrument (JAMA), and the Global Quality Score (GQS). Furthermore, audience engagement was analyzed, and the Video Power Index (VPI) was calculated for each video. Results: The mean total DISCERN score excluding the final question (subjective assessment of the video) was 30.5 ± 8.7 (out of 75 points), implying that the quality of the videos was poor. The global JAMA score was 2.36 ± 0.57 between the raters. The mean GQS score reached 2.57 ± 0.78. The videos had statistically higher DISCERN scores when they included treatment options, risk factors, anatomy, definition, information for doctors, epidemiology, doctor as a speaker, and patient experience. Conclusions: YouTube seems to be a poor source of information for patients and relatives on cerebral palsy. The analysis can contribute to creating more engaging, holistic, and informative videos regarding this topic. Full article

Immersive content streaming services are becoming increasingly popular on video on demand (VoD) platforms due to the growing interest in extended reality (XR) and spatial experiences. Unlike traditional VoD, immersive VoD (IVoD) offers more engaging and interactive content beyond conventional 2D video. IVoD requires substantial bandwidth and minimal latency to deliver its interactive XR experiences. This research examines intelligent resource allocation for IVoD services across NG-EPON and B5G X-haul converged networks. A proposed software-defined networking (SDN) framework employs artificial neural networks (ANN) with a backpropagation technique to predict bandwidth control based on traffic patterns and network conditions. The new immersive video storage, field-programmable gate array (FPGA), Queue Manager, and logical layer components are added to the existing OLT and ONU hardware architecture to implement the SDN framework. The SDN framework manages the entire network, predicts bandwidth requirements, and operates the immersive media dynamic bandwidth allocation (IMS-DBA) algorithm to efficiently allocate bandwidth to IVoD network traffic, ensuring that QoS metrics are met for IM services. Simulation results demonstrate that the proposed framework significantly enhances mean packet delay by up to 3% and improves packet drop probability by up to 4% as the traffic load varies from light to high across different scenarios, leading to enhanced overall QoS performance. Full article

TikTok is a popular social media platform offering educational opportunities for health issues such as rheumatic fever, which primarily affects 4–19-year-olds globally. This content analysis aimed to explore the type of rheumatic fever content available and popular on TikTok and the role that rheumatic fever representation may play in shaping public understanding and attitudes. The top 100 TikTok video posts under the hashtag #rheumaticfever were examined. Descriptive statistics were used to summarize video metrics and deductive thematic analysis enabled the coding of video content. The majority of TikTok users creating rheumatic fever content were patients or family members of people suffering from rheumatic fever (42%), followed by health professionals (30%). Forty-three percent of videos had negative connotations and personal stories were the most commonly coded type of video (42%). In terms of rheumatic fever content, symptoms (n = 59), medications/treatment (n = 37) and disease pathogenesis (n = 36) were the most common themes. Misinformation was identified in 3% of videos. This study provides a unique insight into who is making rheumatic fever-related content on TikTok and the primarily negative framing of narratives people are exposed to. There are opportunities for future health promotion strategies to focus on the gaps identified in this study, including information on where to seek health services, primordial prevention and stories of recovery. Full article

With the growing interest in health and fitness, whey protein supplements are becoming increasingly popular among fitness enthusiasts and athletes. The surge in demand for whey protein supplements highlights the need for cost-effective methods to characterise product quality throughout the food supply chain. This study presents a rapid and low-cost method for authenticating sports whey protein supplements using smartphone video imaging (SVI) combined with machine learning. A gradient of colours ranging from purple to red is displayed on the front screen of a smartphone to illuminate the sample. The colour change on the sample surface is captured in a short video by the front-facing camera. Then, the video is split into frames, decomposed into RGB colour channels, and converted into spectral data. The relationship between video data and sample labels is established using machine learning models. The proposed method is tested on five tasks, including identifying 15 brands of whey protein concentrate (WPC), quantifying fat content and energy levels, detecting three types of adulterants, and quantifying adulterant levels. Moreover, the performance of SVI was compared to that of hyperspectral imaging (HSI), which has an equipment cost of around 80 times that of SVI. The proposed method achieves accuracies of 0.933 and 0.96 in WPC brand identification and adulterant detection, respectively, which are only around 0.05 lower than those of HSI. It obtains coefficients of determination of 0.897, 0.906 and 0.963 for the quantification of fat content, energy levels and milk powder adulteration, respectively. Such results demonstrate that the combination of smartphones and machine learning offers a low-cost and viable preliminary screening tool for verifying the authenticity of whey protein supplements. Full article

The Korean popular music (K-pop) industry, with its global popularity and increasing multilingual orientation, serves as a suitable context for exploring language perceptions. This research examines the metalinguistic commentary on K-pop idols’ English accents on YouTube. Specifically, we investigate how online users evaluate the idols’ English accents and how their metalinguistic comments communicate linguistic ideologies that favor a “native” way of speaking. Our dataset consists of 602 metalinguistic comments drawn from four popular YouTube videos featuring the evaluation of K-pop idols’ accents. We employ content analysis to first categorize comments into positive, negative, and neutral evaluations, then focus on aspects being evaluated in the users’ comments (e.g., social attractiveness and (non-)nativeness). The results indicate that a vast majority of comments (88.1%) convey positive evaluations, largely associating the idols’ accents with social appeal and native-like accents. Although a few neutral and negative evaluations exist, our result shows a dominant preference for inner circle accents and complex attitudes toward accented speech in digital spaces. We conclude by highlighting the influence of digital platforms in shaping language perceptions and the implications for linguistic stereotyping in the context of K-pop culture. Full article

Massive Online Open Courses (MOOCs) have been growing rapidly in the past few years. Video content is an important carrier for cultural exchange and education popularization, and needs to be translated into multiple language versions to meet the needs of learners from different countries and regions. However, current MOOC video processing solutions rely excessively on manual operations, resulting in low efficiency and difficulty in meeting the urgent requirement for large-scale content translation. Key technical challenges include the accurate localization of embedded text in complex video frames, maintaining style consistency across languages, and preserving text readability and visual quality during translation. Existing methods often struggle with handling diverse text styles, background interference, and language-specific typographic variations. In view of this, this paper proposes an innovative cross-language style transfer algorithm that integrates advanced techniques such as attention mechanisms, latent space mapping, and adaptive instance normalization. Specifically, the algorithm first utilizes attention mechanisms to accurately locate the position of each text in the image, ensuring that subsequent processing can be targeted at specific text areas. Subsequently, by extracting features corresponding to this location information, the algorithm can ensure accurate matching of styles and text features, achieving an effective style transfer. Additionally, this paper introduces a new color loss function aimed at ensuring the consistency of text colors before and after style transfer, further enhancing the visual quality of edited images. Through extensive experimental verification, the algorithm proposed in this paper demonstrated excellent performance on both synthetic and real-world datasets. Compared with existing methods, the algorithm exhibited significant advantages in multiple image evaluation metrics, and the proposed method achieved a 2% improvement in the FID metric and a 20% improvement in the IS metric on relevant datasets compared to SOTA methods. Additionally, both the proposed method and the introduced dataset, PTTEXT, will be made publicly available upon the acceptance of the paper. For additional details, please refer to the project URL, which will be made public after the paper has been accepted. Full article

Background and Objectives: Schizophrenia is a significant public health issue, and YouTube has become an increasingly popular source of health information. This study aims to assess the quality and validity of YouTube videos about schizophrenia, focusing on the presence of scientific evidence and the role of healthcare professionals in content quality. Methods: A retrospective, cross-sectional observational study was conducted. One hundred videos in Spanish were selected using NodeXL Pro software, based on specific keywords and hashtags. The videos were categorized by content type and assessed using the DISCERN and Global Quality Scale [GQS] tools to evaluate quality and reliability. Results: Only 39% of the videos referenced scientific articles or technical documents. The videos created by healthcare professionals exhibited a higher quality and reliability. Significant differences were found in the DISCERN and GQS scores between the videos presenting personal opinions and those providing scientific information, favoring the latter. Conclusion: There is a prevalence of misinformation about schizophrenia on YouTube. To enhance the educational value of the platform and reduce misinformation risks, involving healthcare professionals in content creation and implementing control mechanisms is essential. Full article

Background: Physical activities in childhood are important. However, a lack of exercise among children and adolescents is becoming a global reality. Moreover, following the coronavirus disease 2019 pandemic, the increase in time spent at home has led to qualitative changes, such as at-home exercises and the use of YouTube content. This study aimed to conduct qualitative assessments of YouTube-based exercise education programs, such as video content and exercise education programs. Methods: A Python-based (version 3.11.6) video data crawl of YouTube using the keywords “children + exercise”, “kid + exercise”, “child + physical activity”, and “kid + physical activity” was conducted on 27 November 2023. Duplicate, non-English, outdated (over 5 years old), short (<60 s) or long (>30 min) videos, and irrelevant content were excluded. Basic video characteristics, video popularity metrics, and qualitative analyses (m-DISCERN, GQS, i-CONTENT, CONTENT, CERT) were collected and assessed. Results: Of the 2936 retrieved videos, 126 were selected. Approximately 10% of the videos were uploaded by health professionals, and most videos covered aerobic and muscle-strengthening exercises. A qualitative analysis of the video content showed moderate to high quality, while only a few videos satisfied the criteria of an effective exercise program, especially in terms of “Type and timing of outcome assessment”, “Qualified supervisor”, “Patient eligibility”, “Adherence to the exercise program”, and “Dosage parameters (frequency, intensity, time)”. In the correlation analysis of video content and exercise program quality, only a few items showed a statistically significant correlation. Conclusions: YouTube exercise-related educational content targeting children may be inadequate and is not correlated with video popularity. Although an overall weak to moderate correlation was observed between the quality evaluation of exercise education and video content, the use of video quality assessment tools to evaluate exercise program quality was insufficient. Full article

Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such as the balance between style and content, the model generalization ability, and diversity. This article first introduces the origin and development process of style transfer and provides a brief overview of existing methods. Next, this article explores research work related to style transfer, introduces some metrics used to evaluate the effect of style transfer, and summarizes datasets. Subsequently, this article focuses on the application of the currently popular deep learning technology for style transfer and also mentions the application of style transfer in video. Finally, the article discusses possible future directions for this field. Full article

Livestock behavior serves as a crucial indicator of physiological health. Leveraging deep learning techniques to automatically recognize dairy goat behaviors, particularly abnormal ones, enables early detection of potential health and environmental issues. To address the challenges of recognizing small-target behaviors in complex environments, a multi-scale and lightweight behavior recognition model for dairy goats called GSCW-YOLO was proposed. The model integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features (CARAFE) upsampling operator, enhancing the YOLOv8n framework’s attention to behavioral features, reducing interferences from complex backgrounds, and improving the ability to distinguish subtle behavior differences. Additionally, GSCW-YOLO incorporates a small-target detection layer and optimizes the Wise-IoU loss function, increasing its effectiveness in detecting distant small-target behaviors and transient abnormal behaviors in surveillance videos. Data for this study were collected via video surveillance under varying lighting conditions and evaluated on a self-constructed dataset comprising 9213 images. Experimental results demonstrated that the GSCW-YOLO model achieved a precision of 93.5%, a recall of 94.1%, and a mean Average Precision (mAP) of 97.5%, representing improvements of 3, 3.1, and 2 percentage points, respectively, compared to the YOLOv8n model. Furthermore, GSCW-YOLO is highly efficient, with a model size of just 5.9 MB and a frame per second (FPS) of 175. It outperforms popular models such as CenterNet, EfficientDet, and other YOLO-series networks, providing significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thus advancing the modernization of the dairy goat industry. Full article

With the increasing integration of multimedia elements into webpages, videos have emerged as a popular medium for enhancing user engagement and knowledge retention. However, irrelevant or poorly placed videos can hinder readability and distract users from the core content of a webpage. This paper proposes a novel approach leveraging natural language processing (NLP) techniques to assess the relevance of video content on educational websites, thereby enhancing readability and user engagement. By using a cosine similarity-based relevance scoring method, we measured the alignment between video transcripts and webpage text, aiming to improve the user’s comprehension of complex topics presented on educational platforms. Our results demonstrated a strong correlation between automated relevance scores and user ratings, with an improvement of over 35% in relevance alignment. The methodology was evaluated across 50 educational websites representing diverse subjects, including science, mathematics, and language learning. We conducted a two-phase evaluation process: an automated scoring phase using cosine similarity, followed by a user study with 100 participants who rated the relevance of videos to webpage content. The findings support the significance of integrating NLP-driven video relevance assessments for enhanced readability on educational websites, highlighting the potential for broader applications in e-learning. Full article

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement. Full article