Video Sources Research Articles

Positive Attributable Visual Sources Attenuate the Impact of Trigger Sounds in Misophonia

Abstract Misophonia is characterized by strong negative reactions to everyday sounds, such as chewing, slurping or breathing, that can have negative consequences for daily life. Here, we investigated the role of visual stimuli in modulating misophonic reactions. We recruited 26 misophonics and 31 healthy controls and presented them with 26 sound-swapped videos: 13 trigger sounds paired with the 13 Original Video Sources (OVS) and with 13 Positive Attributable Visual Sources (PAVS). Our results show that PAVS stimuli significantly increase the pleasantness and reduce the intensity of bodily sensations associated with trigger sounds in both the misophonia and control groups. Importantly, people with misophonia experienced a larger reduction of bodily sensations compared to the control participants. An analysis of self-reported bodily sensation descriptions revealed that PAVS-paired sounds led participants to use significantly fewer words pertaining to body parts compared to the OVS-paired sounds. We also found that participants who scored higher on the Duke Misophonia Questionnaire (DMQ) symptom severity scale had higher auditory imagery scores, yet visual imagery was not associated with the DMQ. Overall, our results show that the negative impact of misophonic trigger sounds can be attenuated by presenting them alongside PAVSs.

Evaluation of the content and quality of schizophrenia on TikTok: a cross-sectional study

Schizophrenia is a common and serious heterogeneous mental disorder that has a significant health and economic impact on families and society. As the prevalence of schizophrenia increases each year, public awareness of the disorder is growing. However, it remains unclear whether the quality of information presented in these videos is satisfactory. Therefore, a comprehensive assessment of the quality and content of schizophrenia-related videos on video-sharing platforms is necessary. We screened 123 schizophrenia-related videos from the video-sharing platform TikTok that met the criteria, and evaluated and analyzed them. First, the basic information provided in the videos was recorded and analyzed. The source and content type of each video was then determined. The educational content and quality of all videos were then evaluated using three rating instruments, Modified DISCERN, Global Quality Scale, and Journal of the American Medical Association (JAMA). Videos from different sources were also compared to see if they were statistically different in terms of educational content and quality. We identified 4 categories of content for the videos. The science introduction category had the largest number of 50, and the least number of videos was etiology and causation at 15. Six categories of sources of videos were also identified: the for-profit organizations category has the lowest number of 10, the health professionals category has the highest number of 45. There was a significant correlation between video source and duration (P = 0.014). The JAMA score was significantly positively correlated with the number of video likes (r = 0.721, P < 0.001). This study evaluated the content and information quality of 123 videos related to schizophrenia on the video-sharing platform TikTok. The findings indicate that while these videos offer some valuable insights into schizophrenia, the overall quality falls short of satisfactory levels, with inadequate reliability and accuracy.

LAMB: An open-source software framework to create artificial intelligence assistants deployed and integrated into learning management systems

This paper presents LAMB (Learning Assistant Manager and Builder), an innovative open-source software framework designed to create AI-powered Learning Assistants tailored for integration into learning management systems. LAMB addresses critical gaps in existing educational AI solutions by providing a framework specifically designed for the unique requirements of the education sector. It introduces novel features, including a modular architecture for seamless integration of AI assistants into existing LMS platforms and an intuitive interface for educators to create custom AI assistants without coding skills. Unlike existing AI tools in education, LAMB provides a comprehensive framework that addresses privacy concerns, ensures alignment with institutional policies, and promotes using authoritative sources. LAMB leverages the capabilities of large language models and associated generative artificial intelligence technologies to create generative intelligent learning assistants that enhance educational experiences by providing personalized learning support based on clear directions and authoritative fonts of information. Key features of LAMB include its modular architecture, which supports prompt engineering, retrieval-augmented generation, and the creation of extensive knowledge bases from diverse educational content, including video sources. The development and deployment of LAMB were iteratively refined using a minimum viable product approach, exemplified by the learning assistant: “Macroeconomics Study Coach,” which effectively integrated lecture transcriptions and other course materials to support student inquiries. Initial validations in various educational settings demonstrate the potential that learning assistants created with LAMB have to enhance teaching methodologies, increase student engagement, and provide personalized learning experiences. The system's usability, scalability, security, and interoperability with existing LMS platforms make it a robust solution for integrating artificial intelligence into educational environments. LAMB's open-source nature encourages collaboration and innovation among educators, researchers, and developers, fostering a community dedicated to advancing the role of artificial intelligence in education. This paper outlines the system architecture, implementation details, use cases, and the significant benefits and challenges encountered, offering valuable insights for future developments in artificial intelligence assistants for any sector.

Deep‐Learning‐Based Facial Retargeting Using Local Patches

AbstractIn the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch‐based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re‐enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.

Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function.

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain-based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort.

Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos

Heart rate (HR) is an important indicator reflecting the overall physical and mental health of the human body, playing a crucial role in diagnosing cardiovascular and neurological diseases. Recent research has revealed that variations in the light absorption of human skin captured through facial video over the cardiac cycle, due to changes in blood volume, can be utilized for non-contact HR estimation. However, most existing methods rely on single-modal video sources (such as RGB or NIR), which often yield suboptimal results due to noise and the limitations of a single information source. To overcome these challenges, this paper proposes a multimodal information fusion architecture named the spatiotemporal sensitive network (SS-Net) for non-contact heart rate estimation. Firstly, spatiotemporal feature maps are utilized to extract physiological signals from RGB and NIR videos effectively. Next, a spatiotemporal sensitive (SS) module is introduced to extract useful physiological signal information from both RGB and NIR spatiotemporal maps. Finally, a multi-level spatiotemporal context fusion (MLSC) module is designed to fuse and complement information between the visible light and infrared modalities. Then, different levels of fused features are refined in task-specific branches to predict both remote photoplethysmography (rPPG) signals and heart rate (HR) signals. Experiments conducted on three datasets demonstrate that the proposed SS-Net achieves superior performance compared to existing methods.

Accessing the Impact of TikTok’s Algorithm on Regional Inequality in Health Information

ABSTRACT This study aims to audit the potential algorithmic bias in TikTok’s health-related video recommendation toward geographically diverse groups in China. We employed 120 cloud phones and conducted two agent-based testing experiments simulating users’ geographical locations and online behaviors. The results indicated significant regional inequality in video sources recommended by the TikTok algorithm, t(118) = 3.02, p = .003, with users from developed cities encountering a higher proportion of professional videos than those from underdeveloped cities. However, when users from both regions expressed a similar preference for the same type of information, an equal proportion of professional videos was recommended. Our findings suggest that widely used algorithms may covertly perpetuate social inequities and reinforce preexisting class-based inequalities, particularly affecting vulnerable population from low-income regions. This study also highlights the importance of enhancing eHealth literacy among disadvantaged users to mitigate problematic outcomes in the AI-based communication landscape.

Evaluating the quality and reliability of oncologic breast reconstruction videos on youtube

Oncologic breast reconstruction (OBR) is a complex process that requires consideration of multiple factors, including chemoradiation, extent of cancer treatment, and surgical approach. Patients often feel uncertain about the numerous surgical options and may turn to popular social media platforms like YouTube for information. Thus, this study aims to assess the quality and reliability of YouTube videos related to OBR.We conducted a retrospective, cross-sectional analysis of YouTube videos related to OBR. Search terms were obtained from plasticsurgery.org and Google Trends. The first ten videos for each search term were analyzed. Videos were categorized by source and subject matter and independently reviewed by three evaluators using the DISCERN scale.This study examined 172 YouTube videos. Five video source categories were identified: Health Care Administrations, Physicians, Non-physician Providers, News Organizations, and Patients. Health Care Administration accounts received the highest overall DISCERN score of 3.6 ± 0.51, followed by Physicians at 2.98 ± 0.93, with News Organization accounts scoring the lowest at 2.22 ± 0.60 (p < 0.001). Videos from academic sources (Physicians and Health Care Administrators) had higher DISCERN scores compared to non-academic sources (News Organizations and Patients), 2.98 ± 0.95 versus 2.28 ± 0.77, respectively (p < 0.001).Our findings indicate that videos from academic sources generally exhibit higher DISCERN scores, pointing to a higher content quality and reliability standard. Given the increasing reliance on YouTube for healthcare information, our study underscores the need for healthcare professionals to engage more actively in content creation and dissemination.

Video source identification using machine learning: A case study of 16 instant messaging applications

In recent years, there has been a notable increase in the prevalence of cybercrimes related to video content, including the distribution of illegal videos and the sharing of copyrighted material. This has led to the growing importance of identifying the source of video files to trace the owner of the files involved in the incident or identify the distributor. Previous research has concentrated on revealing the device (brand and/or model) that “originally” created a video file. This has been achieved by analysing the pattern noise generated by the image sensor in the camera, the storage structural features of the file, and the metadata patterns. However, due to the widespread use of mobile environments, instant messaging applications (IMAs) such as Telegram and Wire have been utilized to share illegal videos, which can result in the loss of information from the original file due to re-encoding at the application level, depending on the transmission settings. Consequently, it is necessary to extend the scope of existing research to identify the various applications that are capable of re-encoding video files in transit. Furthermore, it is essential to determine whether there are features that can be leveraged to distinguish them from the source identification perspective. In this paper, we propose a machine learning-based methodology for classifying the source application by extracting various features stored in the storage format and internal metadata of video files. To conduct this study, we analyzed 16 IMAs that are widely used in mobile environments and generated a total of 1974 sample videos, taking into account both the transmission options and encoding settings offered by each IMA. The training and testing results on this dataset indicate that the ExtraTrees model achieved an identification accuracy of approximately 99.96 %. Furthermore, we developed a proof-of-concept tool based on the proposed method, which extracts the suggested features from videos and queries a pre-trained model. This tool is released as open-source software for the community.

Continuous Space-Time Video Super-Resolution with Multi-Stage Motion Information Reorganization

Space-time video super-resolution (ST-VSR) aims to simultaneously expand a given source video to a higher frame rate and resolution. However, most existing schemes either consider fixed intermediate time and scale or fail to exploit long-range temporal information due to model design or inefficient motion estimation and compensation. To address these problems, we propose a continuous ST-VSR method to convert the given video to any frame rate and spatial resolution with Multi- s tage M otion information r eorganization (MsMr). To achieve time-arbitrary interpolation, we propose a forward warping guided frame synthesis module and an optical flow-guided context consistency loss to better approximate extreme motion and preserve similar structures among input and prediction frames. To realize continuous spatial upsampling, we design a memory-friendly cascading depth-to-space module. Meanwhile, with the sophisticated reorganization of optical flow, MsMr realizes more efficient motion estimation and motion compensation, making it possible to propagate information from long-range neighboring frames and achieve better reconstruction quality. Extensive experiments show that the proposed algorithm is flexible and performs better on various datasets than the state-of-the-art methods. The code will be available at https://github.com/hahazh/LD-STVSR .

YouTube Videos Provide Low-Quality Educational Content About the Anterolateral Ligament of the Knee

PurposeThe purpose of the current study is to evaluate the reliability and quality of the educational content of YouTube videos about anterolateral ligament (ALL). MethodsA standard search was performed on the YouTube database. All English-language videos were included for analysis. Video reliability was assessed with the use of the DISCERN tool, the Journal of American Medical Association (JAMA) benchmark criteria, and the Global Quality Score (GQS). Data regarding the duration of the videos, the date of publication, and the number of likes and views were collected. Furthermore, videos were categorized based on video source (health professionals, company, private user), the type of information (surgical technique, overview, anatomy, radiology and patient experience) and video content (education, patient experience/testimony). ResultsA total of 94 videos were included in the analysis. Eighty-eight (93.6%) videos were published by health professionals, while 4 (4.3%) were published by companies and 2 (2.1%) were published by private users. Most of the videos were about surgical technique (57.4%), and almost all the videos (98.9%) had an educational aim except one that reported a patient experience (1.1%). The mean length of the videos was 648.4±973.5 seconds, and the mean online period was 34.5±27.0 months. The mean DISCERN score, JAMA score and GQS were 32.9±15.9, 1.5±0.9 and 2.3±1.0, respectively. Videos that provided an overview of the ALL were the best in terms of all quality scores and were significantly higher quality than videos about surgical technique and anatomy, radiology and patient experience for all the scores (p<0.001). No difference was found between surgical technique and anatomy, radiology and patient experience (DISCERN score p>0.99; JAMA score: p=0.839; GQS: p>0.99). ConclusionsThe educational content of YouTube videos about the anterolateral ligament of the knee showed low quality and validity based on the DISCERN, JAMA and GQS scores.

I Know It When I Hear It

In the following article I consider a catalog of audio description porn videos available on the online adult entertainment platform Pornhub. Attending to the visual and aural elements of the original source video and their simultaneous narration in the audio descriptive tracks, I argue that this transcription offers us, the viewer/auditor, an ekphrastic encounter with the pornographic moving image mediated by the acousmatic gendered voice of the audio describer. While academic scholarship on pornography informs my discussion of the source videos, theories of ekphrasis ranging from its usage in Ancient Greek rhetoric, to its formulation in twentieth-century anglophone literary theory, and finally to its application in digital visual culture, will underpin my analysis of the audio description. Through ekphrasis I ultimately distinguish the audio description of pornography as a creative genre in its own right, as well as offer an extensive and interdisciplinary approach that extends beyond the recursive or self-reflexive lens of porn studies scholarship, and newly positions hardcore film pornography within a larger body of academic inquiry in conversation with epic poetry and art history.

Temporally Coherent Video Cartoonization for Animation Scenery Generation

The automatic transformation of short background videos from real scenarios into other forms with a visually pleasing style, like those used in cartoons, holds application in various domains. These include animated films, video games, advertisements, and many other areas that involve visual content creation. A method or tool that can perform this task would inspire, facilitate, and streamline the work of artists and people who produce this type of content. This work proposes a method that integrates multiple components to translate short background videos into other forms that contain a particular style. We apply a fine-tuned latent diffusion model with an image-to-image setting, conditioned with the image edges (computed with holistically nested edge detection) and CLIP-generated prompts to translate the keyframes from a source video, ensuring content preservation. To maintain temporal coherence, the keyframes are translated into grids and the style is interpolated with an example-based style propagation algorithm. We quantitatively assess the content preservation and temporal coherence using CLIP-based metrics over a new dataset of 20 videos translated into three distinct styles.

Video quality of nonalcoholic fatty liver disease on TikTok: A cross-sectional study.

The short-video application TikTok shows great potential for disseminating health information. We assessed the content, sources, and quality of information in videos related to nonalcoholic fatty liver disease (NAFLD) on TikTok. Our study aims to identify upload sources, content, and characteristic information for NAFLD videos on TikTok and further evaluate factors related to video quality. We investigated the top 100 videos related to NAFLD on TikTok and analyzed the upload sources, content, and characteristics of these videos. Evaluate video quality using the DISCERN tool and Global Quality Score (GQS). In addition, the correlation between video quality and video characteristics is further studied. In terms of video sources, the majority of NAFLD videos on TikTok (85/100, 85%) were posted by doctors, ensuring the professionalism of the content, and among the video content, disease knowledge was the most dominant video content, accounting for 57% (57/100) of all videos, and the average DISCERN and GQS scores of all 100 videos were 39.59 (SD 3.31) and 2.99 (SD 0.95), respectively. DISCERN and GQS data show that videos related to NAFLD do not have high-quality scores on TikTok, mainly fair (68/100, 68%) and moderate (49/100, 49%). In general, the quality of NAFLD video information from professional content and professional sources was higher than that of nonprofessional sources and nonprofessional content, the video quality of general surgeons was better than that of other department physicians, and the video quality of junior physicians was better than that of senior physicians. In terms of video correlation, durations, the number of fans, and the total number of works were negatively correlated with DISCERN scores (R < 0, P < .05), while likes, comments, collections, shares, and days since upload were not significantly correlated with DISCERN and GQS scores (P > .05). The medical information on TikTok is not rigorous enough to guide patients to make accurate judgments, platforms should monitor and guide publishers to help promote and disseminate quality content.

Recognition of domestic &lt;em&gt;Felis Catus&lt;/em&gt; vocalisations using a computational associative model

The cat (Felis catus) lives around or with humans and is considered one of the most popular pets in the world. Domestic cats have an extensive and complex vocalisation repertoire, and little is known about the meaning of their vocalisations; these animal sounds have been studied to determine their behavioural characteristics. This work implemented a computational model for cat vocalisation as a signal process to characterise it. Additionally, a recognition model is proposed to identify and associate domestic cat vocalisations with cat intentions. Domestic cat vocalisations were studied by obtaining sounds from audio and video sources stored in the web cloud and processing signals (distress, hiss, howl, meow, fright, purr, sexual call, and trill vocalisations) that belong to some cat situations (soliciting attention, complaining, surprise, threatening, welcoming, fighting, feeling hungry, mating, etc.). In measured signals, environmental noise was removed using a computational algorithm to increase the effectiveness of the recognition process. As a first approach, cat vocalisation patterns were extracted using Welch's method for spectral density estimation, appreciating vocalisation frequency characteristics and statistics. Additionally, a similarity function to compare cat vocalisation signals was developed, excluding useless patterns that could introduce computational problems in the learning phase of the recognition model. Then, an associative memory, the Lernmatrix Thresholded model, was used to recognise the domestic cat vocalisation needed, and pair associations between sounds and moods related to the recognition process result were established, i.e., identifying cat intention through vocalisation by analysing the signals emitted. The experimental results show the performance of the thresholded Lernmatrix among different pattern recognition models, where accurate vocalisation recognition rates are obtained. This computational model emerges as a suitable tool that could be helpful in animal care and ethology science to characterise the behaviour of animals through their vocalisation.

Landscape in the Twentieth Century Composing in the Aspect of Performing Interpretation

The aim of the study is to cover the topic of landscape in the composer’s practice of the 20th century from the perspective of performing interpretation, as a complex artistic phenomenon consisting of a number of elements. The following research methods were chosen: monitoring the processes of performance of works; systematization of video sources; audio analysis; comparison and synthesis. The conclusions of the study were theses that emphasize the importance of: a deep sense of style, which includes works dedicated to the landscape; perfect performance technique; a creative approach to the pace and nature of the interpretation; the ability of musicians to create a special atmosphere of natural pictures in the sound fabric. The academic novelty of the article is researching the landscape in academic music of the 20th century from the perspective of performance interpretation as a single complex multi-component system. Its practical significance is emphasized by the understanding of the key components of the process of performing such works. Keywords: miniature, components of interpretation, timbre, choral style, dynamic shades

Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey

Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction of large-scale datasets and deep learning-based representations. However, video models trained on existing datasets suffer from significant performance degradation when deployed directly to real-world applications due to domain shifts between the training public video datasets (source video domains) and real-world videos (target video domains). Further, with the high cost of video annotation, it is more practical to use unlabeled videos for training. To tackle performance degradation and address concerns in high video annotation cost uniformly, the video unsupervised domain adaptation (VUDA) is introduced to adapt video models from the labeled source domain to the unlabeled target domain by alleviating video domain shift, improving the generalizability and portability of video models. This paper surveys recent progress in VUDA with deep learning. We begin with the motivation of VUDA, followed by its definition, and recent progress of methods for both closed-set VUDA and VUDA under different scenarios, and current benchmark datasets for VUDA research. Eventually, future directions are provided to promote further VUDA research. The repository of this survey is provided at https://github.com/xuyu0010/awesome-video-domain-adaptation.

Evaluating the quality and reliability of YouTube videos on myopia: a video content analysis.

To evaluate the quality and reliability of YouTube videos as an educational resource about myopia. The videos were identified by searching YouTube with the keywords 'myopia' and 'nearsightedness', using the website's default search settings. The number of views, likes, dislikes, view ratio, source of the upload, country of origin, video type, and described treatment techniques were assessed. Each video was evaluated using the DISCERN, Journal of the American Medical Association (JAMA), Ensuring Quality Information for Patients (EQIP), Health On the Net Code of Conduct Certification (HONcode), and the Global Quality Score (GQS) scales. A total of 112 videos were included. The classification of videos by source indicated that the top three contributors were health channels (30 videos [26.8%]), physicians (24 videos [21.4%]), and academic centers (19 videos [16.9%]). Most of these videos originated from the United States (74 videos [66.1%]) and focused on the pathophysiology (n = 89, 79.4%) and the treatment (n = 77, 68.7%) of myopia. Statistical comparisons among the groups of video sources showed no significant difference in the mean DISCERN score (p = 0.102). However, significant differences were noted in the JAMA (p = 0.011), GQS (p = 0.009), HONcode (p = 0.011), and EQIP (p = 0.002) scores. This study underscored the variability in the quality and reliability of YouTube videos related to myopia, with most content ranging from 'weak to moderate' quality based on the DISCERN and GQS scales, yet appearing to be 'excellent' according to the HONcode and EQIP scales. Videos uploaded by physicians generally exhibited higher standards, highlighting the importance of expert involvement in online health information dissemination. Given the potential risks of accessing incorrect medical data that can affect the decision-making processes of patients, caution should be exercised when using online content as a source of information.

A Social Media Content Analysis of Dental Health Information Involving the Use of Miswak (Salvadora persica) Chewing Stick on YouTube™.

Background The widespread availability of Internet access and the rising popularity of social media platforms have facilitated the dissemination of health-related information, including dental health practices. However, assessing the quality and effectiveness of such information remains a challenge, particularly concerning traditional practices such as Miswak (Salvadora persica) usage. This study aims to assess the description, use, and effectiveness of the Miswak (Salvadora persica) chewing stick posted as video clips on YouTube™ and provide considerations for future interventions. Methodology YouTube videos were searched using the terms "Miswak," "Siwak," "Salvadora persica," and "Chewing stick." Each video's descriptive features, i.e., title, links, country of origin, upload date, running time, views, comments, likes, and dislikes, were recorded. Content quality was assessed using the DISCERN tool, which rates the reliability, dependability, and trustworthiness of online sources across 16 items. Scores were aggregated for analysis. The statistical analysis examined video features and associations between the speaker, video type, source, and quality, with significance set at a p-value <0.05 using SPSS Statistics Version 20 (IBM Corp., Armonk, NY, USA). Results A total of 45 videos were included in the study, with the majority (62%) created by the "other professionals" category. Almost three-quarters (73.3%) of the videos were educational. The quality of the video clips was correlated with the speaker source and category of "other," revealing that high-quality information was considered such when the source was other than a dentist. Further, we found that a video's source did not elicit differences in the opinion of the video's quality. Conclusions This social media analysis provides considerations and implications for future research on the potential use of YouTube as a platform for Miswak educational interventions.

Quality and Reliability Analysis of Videos About Thulium Laser Enucleation of the Prostate on YouTube

ObjectivesTo assess the reliability and quality analyses of ThuLEP videos on YouTube, as a source of public information. Materials and methodsIn this study, a YouTube search with the keyword “ThuLEP” was performed on November 15, 2022 and 142 videos were listed according to relevance. Video features and source of upload were recorded. The quality of videos was evaluated by using both the Journal of American Medical Association (JAMA) score and the Global Quality Score(GQS); the reliability of videos was evaluated by using 5-point modified DISCERN tool,respectively. The correlation analysis was performed by using Spearman test between video features and these three scores. Results77 videos were analysed after exclusion and the most common source of upload were urologists (54.5%) and the videos containing only ThuLEP surgery (74%) were the majority of the videos. The median JAMA score, 5-point modified DISCERN score, and GQS were 2, 1, 1, respectively. There were no statistical differences in these three scores according to the source of the upload. All three scores were analysed separately by language and no significant statistical difference was found.There was a positive correlation between the video power index and as well as JAMA, GQS and m. DISCERN scores. ConclusionsDespite abundant videos on ThuLEP on YouTube, most of these videos are not targeted to public and information provided may not be as useful for patients. Information presented in these videos may be inaccurate and not reliable.