Video Quality Research Articles

A compressed video quality enhancement algorithm based on CNN and transformer hybrid network

A compressed video quality enhancement algorithm based on CNN and transformer hybrid network

#TraumaTok—TikTok Videos Relating to Trauma: Content Analysis

Background Experiencing a traumatic event can significantly impact mental and emotional well-being. Social media platforms offer spaces for sharing stories, seeking support, and accessing psychoeducation. TikTok (ByteDance), a rapidly growing social media platform, is increasingly used for advice, validation, and information, although the content of this requires further study. Research is particularly needed to better understand TikTok content relating to trauma and the potential implications for young viewers, considering the distressing nature of the subject and the possibility of users experiencing vicarious trauma through exposure to these videos. Objective This study aims to explore the content of trauma-related videos on TikTok, focusing on hashtags related to trauma. Specifically, this study analyzes how TikTok videos present information, advice, stories, and support relating to trauma. Methods A quantitative cross-sectional descriptive content analysis was performed on TikTok in December 2022. A total of 5 hashtags related to trauma were selected: #trauma, #traumatized, #traumatok, #traumatic, and #traumabond, with the top 50 videos from each hashtag analyzed (total N=250 videos). A standardized codebook was developed inductively to analyze the content of the videos, while an existing generic codebook was used to collect the video features (eg, age of people in the video) and metadata (likes, comments, and shares) for each video. Results A total of 2 major content themes were identified, which were instructional videos (54/250, 21.6%) and videos disclosing personal stories (168/250, 67.3%). The videos garnered significant engagement, with a total of 296.6 million likes, 2.3 million comments, and 4.6 million shares, indicating that users find this content engaging and useful. Alarmingly, only 3.7% (9/250) of videos included a trigger warning, despite many featuring highly distressing stories that young people and those with trauma may be exposed to. Conclusions The study highlights the potential risks of vicarious trauma due to trauma dumping without trigger warnings on TikTok, and the need for further research to assess the accuracy of advice and information in these videos. However, it also underscores the platform’s potential to foster social connections, provide validation, and reduce stigma around mental health issues. Public health professionals should leverage social media to disseminate accurate mental health information, while promoting user education and content moderation to mitigate potential harms. People often use social media, such as TikTok to share advice, stories, and support around mental health, including their experiences with trauma. Out of 250 videos, most were either giving advice (54/250, 21.6%) or sharing personal experiences (168/250, 67.3%). The study found many videos lacked warnings about upsetting content, which could potentially harm young viewers or people suffering from trauma. While TikTok can help people feel connected and reduce the stigma around mental health, it is important to seek support from professionals when needed.

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.

Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models.

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models.

Weakly supervised video anomaly detection based on hyperbolic space

In recent years, there has been a proliferation of weakly supervised methods in the field of video anomaly detection. Despite significant progress in existing research, these efforts have primarily focused on addressing this issue within Euclidean space. Conducting weakly supervised video anomaly detection in Euclidean space imposes a fundamental limitation by constraining the ability to model complex patterns due to the dimensionality constraints of the embedding space and lacking the capacity to model long-term contextual information. This inadequacy can lead to misjudgments of anomalous events due to insufficient video representation. However, hyperbolic space has shown significant potential for modeling complex data, offering new insights. In this paper, we rethink weakly supervised video anomaly detection with a novel perspective: transforming video features from Euclidean space into hyperbolic space may enable the network to learn implicit relationships in normal and anomalous videos, thereby enhancing its ability to effectively distinguish between them. Finally, to validate our approach, we conducted extensive experiments on the UCF-Crime and XD-Violence datasets. Experimental results show that our method not only has the lowest number of parameters but also achieves state-of-the-art performance on the XD-Violence dataset using only RGB information.

Trust and confidence in telehealth-delivered services: a nation-wide cross-sectional study

Sustaining telehealth uptake hinges on people’s desire and ability to effectively engage with it. We explored trust and confidence in telehealth delivered by medical and allied health using cross-sectional survey of 1,116 Australians. Descriptive analysis presented factors that would improve trust and confidence in telehealth cross-tabulated with video consultation experience. Inferential statistics compared levels of trust in medical and allied health telehealth with user-related variables. Trust in medical telehealth was higher than in allied health, but practice with video calls, experience with high-quality telehealth, and good internet were associated with greater levels of trust in both groups. Telehealth with a known health professional and no additional costs were top-ranked factors to improve trust and confidence. Participants confident in troubleshooting trusted telehealth more. This first cross-sectional study on trust and confidence in telehealth suggests that digital upskilling and promoting quality video consultations can potentially enhance telehealth adoption.

The Influence of Social Presence on Purchase Intention in Live Video Commerce (Empirical Study in Bandung City)

The rapid development of internet technology has changed the way we live and interact. The increasing number of internet users in Indonesia has encouraged business actors to be more innovative in marketing their products digitally. The emergence of direct shopping features on various social media platforms has revolutionized the way consumers shop, allowing transactions to occur instantly and interactively. There is competition between e-commerce that uses live video features such as Shopee, TikTok, Tokopedia, and Lazada. This study aims to determine the role of social presence on purchase intention mediated by immersive experience and trust in live video commerce features in Indonesia. The research methodology employed is a descriptive and quantitative approach, which utilizes data acquisition techniques through online questionnaires to obtain 385 respondents. Purposive sampling is implemented in conjunction with nonprobability sampling. The data collected will be analyzed using SmartPLS version 3.9. The results of this study found that social presence has no effect on purchase intention. Keywords: Social Presence, Live Video Commerce, Purchase Intention.

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 .