Video Quality Research Articles

Videos in Short-Video Sharing Platforms as Sources of Information on Colorectal Polyps: Cross-Sectional Content Analysis Study.

Short videos have demonstrated huge potential in disseminating health information in recent years. However, to our knowledge, no study has examined information about colorectal polyps on short-video sharing platforms. This study aimed to analyze the content and quality of colorectal polyps-related videos on short-video sharing platforms. The terms "" (intestinal polyps) or "" (colonic polyps) or "" (rectal polyps) or "" (colorectal polyps) or "" (polyps of large intestine) were used to search in TikTok (ByteDance), WeChat (Tencent Holdings Limited), and Xiaohongshu (Xingyin Information Technology Limited) between May 26 and June 8, 2024, and then the top 100 videos for each search term on different platforms were included and recorded. The Journal of American Medical Association (JAMA) score, the Global Quality Scale (GQS), the modified DISCERN, and the Patient Education Materials Assessment Tool (PEMAT) were used to evaluate the content and quality of selected videos by 2 independent researchers. SPSS (version 22.0; IBM Corp) and GraphPad Prism (version 9.0; Dotmatics) were used for analyzing the data. Descriptive statistics were generated, and the differences between groups were compared. Spearman correlation analysis was used to evaluate the relationship between quantitative variables. A total of 816 eligible videos were included for further analysis, which mainly conveyed disease-related knowledge (n=635, 77.8%). Most videos were uploaded by physicians (n=709, 86.9%). These videos had an average JAMA score of 2.0 (SD 0.6), GQS score of 2.5 (SD 0.8), modified DISCERN score of 2.5 (SD 0.8), understandability of 80.4% (SD 15.6%), and actionability of 42.2% (SD 36.1%). Videos uploaded by news agencies were of higher quality and received more likes and comments (all P<.05). The number of collections and shares of videos about posttreatment caveats were more than those for other content (P=.03 and P=.006). There was a positive correlation between the number of likes, comments, collections, and shares (all P<.001). The duration and the number of fans were positively correlated with the quality of videos (all P<.05). There are numerous videos about colorectal polyps on short-video sharing platforms, but the reliability and quality of these videos are not good enough and need to be improved.

The Reliability of YouTube as an Information Resource for Parents About Retinopathy of Prematurity.

To investigate the quality and reliability of YouTube videos about retinopathy of prematurity (ROP) to direct parents of infants with the disease to access more accurate content. The term "retinopathy of prematurity" has been searched on YouTube containing all of the videos between January 2 and February 2, 2024. The first 200 videos were evaluated by two ophthalmologists. Duplicated-split videos, videos shorter than 60 seconds, videos presented in languages other than English or with an incomprehensible accent, and videos unrelated to ROP were excluded. Video uploaders, types, continental origins, durations, and viewer interactions were noted. DISCERN, The Journal of the American Medical Association (JAMA), and the Global Quality Score (GQS) scoring systems were used to evaluate the quality of the videos. The mean quality of all videos was poor in all scoring systems. Academic societies and medical institutes scored highest in video uploaders, followed by physicians; patient experience videos had the lowest quality. Of the video types, the medical education seminars were of the highest quality. Although a strong positive correlation was detected between video duration and video quality, this same strong correlation was not observed between viewer interactions and video quality. There was no significant difference between video origins in terms of video quality. It would be wiser to direct the parents of patients with ROP to watch longer videos uploaded by the academic community, medical institute, or physicians, and to watch the medical training seminars. Also, it might be important to warn them not to take user interactions too seriously. [J Pediatr Ophthalmol Strabismus. 20XX;XX(X):XXX-XXX.].

Artificial intelligence-guided screening of under-recognized cardiomyopathies adapted for point-of-care echocardiography

Abstract Background Point-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols. Purpose To develop and validate deep learning models for detecting underdiagnosed cardiomyopathies on cardiac POCUS, leveraging a novel acquisition quality-adapted modeling strategy. Methods To develop the models, we identified transthoracic echocardiograms (TTEs) of patients across five hospitals in a large U.S. health system with transthyretin amyloid cardiomyopathy (ATTR-CM, confirmed by Tc99m-pyrophosphate imaging), hypertrophic cardiomyopathy (HCM, confirmed by cardiac magnetic resonance), and random controls from the same period enriched for the presence of severe AS. In a sample of 290,245 TTE videos, we used novel augmentation approaches and a customized loss function to weigh image and view quality to train a multi-label, view agnostic video-based convolutional neural network (CNN) to discriminate the presence of ATTR-CM, HCM, and/or AS+ and AS- controls. Models were tested across 3,758 real-world POCUS videos from 1,879 studies in 1,330 independent emergency department (ED) patients from 2011 through 2023 (A). Results Our multi-label, view-agnostic classifier demonstrated state-of-the-art performance in discriminating ATTR-CM (AUROC 0.98 [95%CI: 0.96-0.99]) and HCM (AUROC 0.95 [95% CI: 0.94-0.96]) on standard TTE studies. Automated metrics of anatomical view correctness confirmed significantly lower quality in POCUS vs TTE videos (median view classifier confidence of 0.63 [IQR: 0.44-0.88] vs 0.93 [IQR: 0.69-1.00], p&amp;lt;0.001). When deployed to POCUS videos, our algorithm effectively discriminated ATTR-CM and HCM with AUROC of up to 0.94 (parasternal long-axis (PLAX)), and 0.85 (apical 4 chamber (A4C)) (B), corresponding to positive diagnostic odds ratios of 46.7, and 25.5, respectively. In total, 18/35 (51.4%) of ATTR-CM and 32/57 (41.1%) of HCM patients in the POCUS cohort had a positive AI screen in the year before their eventual confirmatory imaging (C). Conclusions We define and validate an AI framework that enables scalable, opportunistic screening of under-diagnosed cardiomyopathies using POCUS.

Evaluation of the Educational Quality of Endonasal Endoscopic Dacryocystorhinostomy Videos on YouTube with IVORY and LAP-VEGaS Guidelines.

Objective: The aim of this study was to evaluate the educational quality of endonasal endoscopic dacryocystorhinostomy (EE-DCR) videos on YouTube with Instructional Videos in Otorhinolaryngology by YO-IFOS (IVORY) and LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) guidelines and to evaluate the correlation of the 2 guidelines. Methods: EE-DCR videos were queried using search terms on YouTube. Views, likes, likes/dislikes ratio, age, and length of videos were noted. Videos were evaluated using the IVORY and LAP-VEGaS guidelines. Two IVORY scores were created: total (IVORY-1) and organ-specific (IVORY-2). The correlation analysis between video features and guideline scores was performed. Results: A total of 61 EE-DCR videos were evaluated. The mean score of LAP-VEGaS was 10.3 (±SD 2.7), the mean IVORY-1 score was 22.5 (±SD 5.5), and the mean IVORY-2 score was 10.6 (±SD 1.94). Correlation analysis revealed a statistically significant correlation between the IVORY-1 total score and the number of likes, the duration of the video, the age of the video, and the LAP-VEGaS score. Linear regression analysis showed that higher IVORY-1 scores predicted longer video duration, newer video age, and higher LAP-VEGaS scores. There was a significant association between LAP-VEGaS categories and the IVORY-1 total score (P < .001). Conclusion: The quality of EE-DCR videos is generally low to moderate. The IVORY and LAP-VEGaS guidelines were found to be correlated with each other. Both guidelines can be used to evaluate EE-DCR videos and otolaryngology surgical education videos in general. We believe that scales such as IVORY and LAP-VEGaS may be improved according to specific surgical procedures.

Enhancing Multimodal Emotional Information Extraction in Film and Television through Adaptive Feature Fusion with DenseNe, Transformer, and 3D CNN Models

ABSTRACT The extraction of multimodal emotional information enables a more nuanced representation of the emotional subtleties embedded in film and television works. However, conventional approaches that independently extract features from images and text fail to capture the rich semantic interplay between these modalities, impeding the feature learning process within each modality. To address this, this paper presents an innovative model for extracting emotional information from multimodal film and television content. The model utilizes DenseNe for image feature extraction, enhancing network depth via the MSC module. Text feature extraction is achieved through a Transformer encoder, while video feature extraction employs a 3D CNN model. Refinements are made to the number and placement of convolutional layers, planar convolution size, and 3D convolution depth. Moreover, a multi-head scaled dot-product attention mechanism is incorporated into the interaction module to compute the similarity between each image block within the sequence and every word in the text sequence. Experimental evaluations on the CMU-MOSEI and CMU-MOSI datasets showcase superior performance compared to the baseline model, achieving accuracy and F1 scores of 0.708 and 0.698, respectively. Noteworthy is the proposed adaptive feature fusion module, which enriches the expressiveness of pivotal emotional features while eliminating redundant data.

The impact of multimodal information features of short sales videos on consumer engagement behavior: A multi-method approach

In the emerging business model of short video commerce, consumers can engage in short sales videos by liking, commenting, and sharing. Compared with traditional shopping contexts, consumer engagement behavior in short video commerce settings is affected quickly and simultaneously by text, audio, and visual information, but the extant literature lacks sufficient exploration of the effects of such multimodal information. Based on the multimodal theory and signaling theory, this study aims to investigate the role of multimodal information including textual sentiment score, audio spectrum and visual effects of short sales videos on consumer engagement behavior. We collect 4292 veridical short sales videos from short video platform Douyin, while based on a multi-method approach of multiple regression analysis (MRA) and fuzzy set qualitative comparative analysis (fsQCA), it is found that the multimodal information features of short sales videos contribute to the promotion of consumer engagement behavior, and the cross-modal configurational solutions to enhance engagement is not unique. This study bridges the gap in the existing literature by capturing the impact of multimodal information features on consumer engagement behaviors and their asymmetric effects, which also provides practical implications for sellers and marketers on attracting consumers to engage in short video commerce.

Accuracy, Quality, and Misinformation of YouTube Abortion Procedural Videos: Cross-Sectional Study.

The internet is often the first source patients turn to for medical information. YouTube is a commonly used internet-based resource for patients seeking to learn about medical procedures, including their risks, benefits, and safety profile. Abortion is a common yet polarizing medical procedure. People interested in obtaining an abortion are likely to use the internet to learn more about abortion procedures and may encounter misinformed and biased information. This is troubling as information found on the internet can significantly alter perceptions and understanding of these procedures. There is no current research that evaluates the accuracy, quality, and misinformation of instructional abortion videos available to patients. The purpose of this study was to assess if any given video can deliver accurate and quality information about this topic in an unbiased manner and to assess the level of factually incorrect, distorted, or medically irrelevant information in any given video. Procedural methods of abortion were queried on YouTube on August 22, 2022. The videos were screened with strict exclusion criteria. Videos were categorized into "video slants" based on the language and attitudes expressed in each video. Video accuracy was calculated using the Surgical Curriculum in Obstetrics and Gynecology (SCOG) checklist for each corresponding procedure. Video quality was calculated using the Laparoscopic Surgery Video Educational Guidelines (LAP-VEGaS) criteria. The level of misinformation was assessed with the evidence-based Anti-Choice Rubric, which scores the amount of factually incorrect, distorted, or medically irrelevant information in each video. A total of 32 videos were analyzed and categorized into 3 "video slant" groups: neutral (n=23, 72%), antichoice (n=4, 12%), and prochoice (n=5, 16%). Using the SCOG checklist, neutral videos had the highest median accuracy (45.9%), followed by antichoice videos (24.6%) and prochoice videos (18.5%). None of the videos met the LAP-VEGaS quality control criteria, (score>11, indicating adequate quality). Neutral videos had a median score of 8.8 out of 18, with antichoice videos scoring 10.75 and prochoice videos scoring 6.2. Using the Anti-Choice Rubric, neutral videos mentioned only 1 factually incorrect piece of information. Antichoice videos mentioned 12 factually incorrect pieces of information, 8 distortions, and 3 medically irrelevant pieces of information. Prochoice videos did not mention any of the 3 themes. Using the SCOG checklist, the accuracy of instructional videos were inconsistent across the 3 identified "video slants." Using LAP-VEGaS criteria, the quality of educational videos were also inconsistent across the 3 "video slants." Prochoice videos had the lowest level of misinformation, with no mentions of any of the 3 themes. Antichoice videos had the highest levels of misinformation, with mentions in all 3 themes. Health care professionals should consider this when counseling patients who may watch YouTube videos for information regarding abortion procedures.

Assessment of the Quality of Stapedotomy Videos on YouTube Using the IVORY Grading System.

Assessment of the Quality of Stapedotomy Videos on YouTube Using the IVORY Grading System.

YOUTUBE VIDEOS AS A SOURCE OF INFORMATION ON INTRAMUSCULAR INJECTION APPLICATION WITH THE Z TECHNIQUE: CONTENT, QUALITY AND RELIABILITY ANALYSIS

YouTube videos about intramuscular injection with the Z technique were evaluated for content, quality and reliability. A search of YouTube was made on 13 November 2023 using the keywords of “Z technique” and “intramuscular injection”. 40 videos that fulfilled the inclusion criteria were assessed for usefulness using the Z Technique Intramuscular Injection Administration Checklist (ZIAC), reliability using the Quality Criteria for Consumer Health (DISCERN), and quality using the Global Quality Scale (GQS) scores. Most of the videos used in the study belonged to individual content producers. The videos were determined to be of medium quality, and reliability, but their usefulness was not at the desired level based on their ZIAC, DISCERN, and GQS scores (respectively; 16.92±2.72; 2.92±0.763; 3.03±0.77). Furthermore, the videos' usefulness (ZIAC), reliability (DISCERN), and quality (GQS) scores all showed a strong positive correlation. It was determined that the dorsogluteal region was preferred in 48.1% of the videos (n=13), and the region was determined correctly in 67.5% of the videos (n=27). To provide the most reliable information to healthcare professionals, particularly nurses, it is recommended that expert nurses and educators create qualified videos about the use of intramuscular injection with the Z technique and share them on YouTube.

Evaluation of the information quality related to osteoporosis on TikTok

BackgroundOsteoporosis is currently considered the most common bone disease in the world and is characterized by low bone mass, deterioration of the bone tissue microstructure, and decreased bone strength. With the increasing popularity of smartphones and short videos, many patients search for various types of health information through social media, such as short videos. As one of China’s short video giants, TikTok has played a significant role in spreading health information. We found that there are many videos about osteoporosis on TikTok; however, the quality of these short videos has not yet been evaluated.ObjectiveThe purpose of this study was to evaluate the information quality of osteoporosis videos on the domestic TikTok platform.MethodsWe retrieved and screened 100 videos about osteoporosis from TikTok, extracted the basic information, encoded the video content, and recorded the source of each video. Two independent raters evaluated the information quality of each video via the DISCERN rating scale.ResultThe videos were divided into three groups according to their source: medical personnel, science communicators, and news media, with medical personnel posting the most videos. The content of the video is divided into 7 groups, namely, disease prevention, disease diagnosis, disease symptoms, disease overview, life-style, drug knowledge, and drug treatment, with the most videos related to disease overview. The average DISCERN score of the videos is 37.69 (SD = 6.78), mainly within the ‘poor’ (54/100, 54%) and ‘appropriate’ (43/100, 43%) rating ranges, with overall quality being low. Further analysis revealed a positive correlation between the number of shares, comments, likes, and favorites, and a positive correlation between the DISCERN score and the number of shares and favorites.ConclusionThe overall quality of videos concerning osteoporosis on TikTok is lower, but the quality of videos varies significantly across different sources. We should be selective and cautious when watching videos about osteoporosis on TikTok.

Quantifying marine growth on pipelines with archival remotely operated vehicle footage – a case study

Abstract. Marine growth can substantially affect the hydrodynamic properties of subsea infrastructure. The ability to accurately quantify this growth is essential for designing and maintaining subsea structures. Traditionally, quantifying marine growth has been done by applying simple ratio equations putting in relationship the known distance of an object and its length observed in the image to the length of an unknown object only observed in an image. This method is very sensitive if measurements are not taken in the same plane. Hence, this study aims to establish a method for creating a 3D model of a subsea pipeline segment based on Structure from Motion (SfM). The proof of concept is done using a case study. For this study, Remotely Operated Vehicle (ROV) footage from a 2019 inspection survey is utilised. The scenario in which the procedure is tested represents a difficult, but realistic test case. Despite the lack of metadata, video quality and obstructions (e.g. watermarks), meaningful results were produced. The SfM workflow included masking obstructions (e.g. watermarks), and an iterative camera alignment procedure to generate 3D models, enabling the measurement of marine growth using either the models or ortho images created. While the resulting models were sufficiently accurate to provide meaningful measurements for harder growth types, the method faced challenges in accurately modelling softer growth types, suggesting the need for further refinement. Nevertheless, SfM was proven to be a more reliable method of measuring biofouling than the ratio approach that been traditionally used.

Analyzing dissemination, quality, and reliability of Chinese brain tumor-related short videos on TikTok and Bilibili: a cross-sectional study

Analyzing dissemination, quality, and reliability of Chinese brain tumor-related short videos on TikTok and Bilibili: a cross-sectional study

Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers

The growth of the influencer marketing industry warrants an empirical examination of the effect of posting sponsored videos on influencers’ reputations. We collected a novel data set of user-generated YouTube videos created by prominent English-speaking influencers in the beauty and style category. We extracted a rich set of theory-driven video features and used DiNardo-Fortin-Lemieux reweighting to construct comparable treatment and control groups matched at the influencer-video level. The empirical analysis of the matched sample revealed a reputation-burning effect; that is, posting a sponsored video, compared with posting an equivalent organic video, cost the influencers 0.19% of their reputation (operationalized as the number of subscribers). The reputation-burning effect was stronger among the influencers with larger audiences. An analysis of likes, comments, and comment texts revealed a larger gap in audience response between sponsored and organic videos among the influencers with larger (versus smaller) audiences. The reputation-burning effect was mitigated when a high fit existed between the sponsored content and the influencer’s “usual” content and when the promoted brand was less well known. Our study empirically tested the assumption of several theoretical works. Moreover, it contributes to the literature on influencer marketing and celebrity endorsements and provides managerial implications for influencers, brands, and social media platforms. This paper was accepted by D. J. Wu, information systems. Funding: This research was supported by funding provided by Harvard Business School. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00193 .

Extract And Reconstruct: Research On the Content Of “Second Creation” Short Videos for Film Promotion

Short videos have surged in popularity, particularly on platforms like Douyin, Kuaishou, WeChat Channel, and Little Red Book in China. These brief yet engaging videos are extensively produced and consumed, making them a potent tool for promoting theatrical films. Among the various types of short videos, second-creation short videos—those that creatively reinterpret or remix original content—are particularly effective in driving platform users to theaters. This research focuses on these second-creation short videos used for film promotion. Through extensive viewing and creating these videos during an internship, the author observed notable deviations in meaning between the original films and their second-creation counterparts. To delve deeper, this study analyzes short videos related to “The Boy and the Heron”. Starting from the unique characteristics of second-creation short videos, the research identifies issues such as the narrowing of meaning that can occur in the adaptation process. By highlighting these problems, the study aims to offer insights into how audiences should interpret short videos in the context of film promotion. Finally, the research proposes recommendations for better alignment between original films and their promotional short videos, ensuring a more accurate representation of the cinematic works.

Content-Adaptive Bitrate Ladder Estimation in High-Efficiency Video Coding Utilizing Spatiotemporal Resolutions

The constant increase in multimedia Internet traffic in the form of video streaming requires new solutions for efficient video coding to save bandwidth and network resources. HTTP adaptive streaming (HAS), the most widely used solution for video streaming, allows the client to adaptively select the bitrate according to the transmission conditions. For this purpose, multiple presentations of the same video content are generated on the video server, which contains video sequences encoded at different bitrates with resolution adjustment to achieve the best Quality of Experience (QoE). This set of bitrate–resolution pairs is called a bitrate ladder. In addition to the traditional one-size-fits-all scheme for the bitrate ladder, context-aware solutions have recently been proposed that enable optimum bitrate–resolution pairs for video sequences of different complexity. However, these solutions use only spatial resolution for optimization, while the selection of the optimal combination of spatial and temporal resolution for a given bitrate has not been sufficiently investigated. This paper proposes bit-ladder optimization considering spatiotemporal features of video sequences and usage of optimal spatial and temporal resolution related to video content complexity. Optimization along two dimensions of resolution significantly increases the complexity of the problem and the approach of intensive encoding for all spatial and temporal resolutions in a wide range of bitrates, for each video sequence, is not feasible in real time. In order to reduce the level of complexity, we propose a data augmentation using a neural network (NN)-based model. To train the NN model, we used seven video sequences of different content complexity, encoded with the HEVC encoder at five different spatial resolutions (SR) up to 4K. Also, all video sequences were encoded using four frame rates up to 120 fps, presenting different temporal resolutions (TR). The Structural Similarity Index Measure (SSIM) is used as an objective video quality metric. After data augmentation, we propose NN models that estimate optimal TR and bitrate values as switching points to a higher SR. These results can be further used as input parameters for the bitrate ladder construction for video sequences of a certain complexity.

A mixed-method exploration of #vapingcessation videos on TikTok.

E-cigarette use has risen rapidly, especially among young people. TikTok is one of the most commonly used social media platforms among young people. The representation of vaping cessation messages in TikTok content remains unexplored. We aimed to address this gap by analyzing the vaping cessation content prevalent on TikTok. This cross-sectional mixed methods study analyzed the top 100 TikTok videos-determined based on view-counts-related to vaping cessation. Our quantitative analysis examined video characteristics, while our qualitative analysis identified key thematic representations. The final sample included 87 videos, with a combined total of over 100 billion views. Most videos were created by regular users (general TikTok content creators) rather than those identifying as health experts or professionals such as doctors, acupuncturists or dental hygienists. Very few posts mentioned evidence-based cessation aids or support. Videos scored high on understandability but low on actionability based on standardized criteria. Qualitative analysis identified nine key themes: motivations for quitting, barriers to quitting, use of aids and strategies, relapse experiences, identity and self-image, celebrating cessation success, emotional journey of quitting vaping, seeking social support, and limited mentions of healthcare providers. TikTok appears to be a useful influential forum for studying quitting behavior through the sharing of personal experiences and perspectives on vaping cessation. Opportunities exist to integrate evidence-based messaging and support into this platform. Findings can inform social media interventions designed to counter pro-vaping content and promote quitting e-cigarette use. Partnerships among health professionals, public health experts, and social media influencers may be leveraged to disseminate proven vaping cessation techniques on leading social media platforms such as TikTok.

Enhanced Infant Movement Analysis Using Transformer-Based Fusion of Diverse Video Features for Neurodevelopmental Monitoring

Neurodevelopment is a highly intricate process, and early detection of abnormalities is critical for optimizing outcomes through timely intervention. Accurate and cost-effective diagnostic methods for neurological disorders, particularly in infants, remain a significant challenge due to the heterogeneity of data and the variability in neurodevelopmental conditions. This study recruited twelve parent–infant pairs, with infants aged 3 to 12 months. Approximately 25 min of 2D video footage was captured, documenting natural play interactions between the infants and toys. We developed a novel, open-source method to classify and analyse infant movement patterns using deep learning techniques, specifically employing a transformer-based fusion model that integrates multiple video features within a unified deep neural network. This approach significantly outperforms traditional methods reliant on individual video features, achieving an accuracy of over 90%. Furthermore, a sensitivity analysis revealed that the pose estimation contributed far less to the model’s output than the pre-trained transformer and convolutional neural network (CNN) components, providing key insights into the relative importance of different feature sets. By providing a more robust, accurate and low-cost analysis of movement patterns, our work aims to enhance the early detection and potential prediction of neurodevelopmental delays, whilst providing insight into the functioning of the transformer-based fusion models of diverse video features.

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.

Query-aware multi-scale proposal network for weakly supervised temporal sentence grounding in videos

Recently, weakly supervised temporal sentence grounding in videos (TSGV) has attracted extensive attention because it does not require precise start-end time annotations during training, and it can quickly retrieve interesting segments according to user needs. In weakly supervised TSGV, query reconstruction (QR)-based methods are the current mainstream, and the quality of proposals determines their performance. QR-based methods have two problems in proposal quality. First, a multi-modal global token is usually mapped to proposals with limited duration diversity, making it difficult to capture relevant segments at varying durations in real scenarios. Additionally, Gaussian functions are typically used to generate relatively fixed weights for frames within proposals, which weigh the original video features to generate proposal-specific features. This results in query-irrelevant frames affecting the discrimination of the proposal features. In this study, we propose a query-aware multi-scale proposal network (QMN). Initially, pre-trained encoders are used to extract video and query features. Subsequently, a multi-scale proposal generation module is designed to refine video features guided by queries and diversify the duration of the proposal. This module performs multi-modal interaction and multi-scale modeling to obtain proposals of different durations. Furthermore, to extract discriminative proposal features and enhance the modeling of proposal frame correlation, a query-aware weight generator is constructed to learn frame weights to suppress query-irrelevant frame representations through contrastive learning. Finally, the masked query is reconstructed using the proposal features to select the best proposal. The effectiveness of the proposed QMN is verified through experiments on the Charades-STA and ActivityNet-Captions datasets.

Medical misinformation in social media: Representations of gastrointestinal disorders on a short video platform

Background and Objectives: Short video platforms have become one of the most common methods for disseminating medical information on social media. We analysed gastrointestinal (GI)-related content on TikTok, focusing on the creators’ background, patterns of content utilisation and overall content quality and understandability, using validated metrics to quantify the degree of misinformation. Methods: Twelve hashtags related to common GI conditions on TikTok and the 20 most engaging posts associated with each hashtag were selected for review. The ‘most engaging’ status was determined by the application’s internal algorithm. Six GI board-certified physicians evaluated the videos using two validated instruments: DISCERN quality criteria and Patient Education Materials Assessment Tool (PEMAT-A/V) survey. Results: In total, 253 videos were reviewed with 18.6 billion likes, 191,000 comments, 935,000 saves and 927,000 shares. IBS hashtag had the greatest number of views (2.2 billion), while videos on haemorrhoids had the most likes (4.3 million), comments (59,000) and shares (288,000). Most videos 177/253 (70%) were created by non-healthcare workers. The median video quality was 2/5 on DISCERN Likert-type scale with 23/75 DISCERN score. Of 186 videos, 114 (61.3%) were rated ‘very poor’; 58/186 (31.2%) ‘poor’, 13/186 (7%) ‘fair’ and only one video (0.5%) was rated ‘excellent’ on DISCERN grading system. The median PEMAT-A/V scores were 7/12 (73.9%) for understandability and 1/3 (33.3%) for actionability. Conclusion: Our study highlights significant public interest in common GI disorders. However, most posts lacked substantive information and did not have verifiable sources.