Small Number Of Videos Research Articles

Investigating Turf Burn-Related Videos on TikTok: Cross-sectional Study.

Due to the increased use of artificial turf, turf burn has become a common sports injury. Turf burn is caused by exposed skin sliding on artificial turf. Health complications, such as methicillin-resistant Staphylococcus aureus outbreaks, sepsis, and pneumonia, have been linked to untreated turf burns, and many athletes have been turning to social media for advice and companionship regarding their sports injuries. The goal of this study is to categorize and quantitatively assess the percentage of turf burn-related posts on TikTok based on creator type, content, athletes' experiences, and treatment and prevention methods. With these data, we not only investigate if there is room for health care professionals to assist in the distribution of evidence-based health education to athletes to counteract misinformation but also investigate if there is a potential audience of athletes on TikTok who have the potential to develop problematic responses to injuries. By using the Discover page on TikTok, we searched for the term turf burn on October 17, 2021. In total, 100 videos were analyzed. Videos were categorized and analyzed based on creator type, content, experiences of the athletes, and treatment and prevention methods. The number of likes and comments was recorded. Most videos (98/100, 98%) were created by athletes. A small number of videos (2/100, 2%) were created by health care professionals. In terms of content, most videos (67/100, 67%) displayed turf burns. A small amount of videos (15/100, 15%) showed the incidents when turf burns were acquired, while around one-quarter of the videos (23/100, 23%) demonstrated the treatment and prevention of turf burns. Of the 23 treatment and prevention videos, a minority (4/23, 17%) showed the preferred treatment of turf burns, while most videos (19/23, 83%) showed nonpreferred treatments. The smallest amount of videos (2/100, 2%) were about turf burn education. Most of the videos created by athletes (56/98, 57%) depicted the negative experiences that patients had with turf burns. Some videos (37/98, 38%) depicted neutral experiences, while the smallest amount of videos (5/98, 5%) depicted positive experiences. Our study suggests that there is a potential audience of athletes on TikTok who could develop problematic responses to sports injuries, such as turf burns, as most of the people who post videos are athletes, and many of the posts demonstrate negative experiences associated with turf burns. TikTok is a growing social media platform that should be studied to determine if it can be used to create a social support group for injured athletes to prevent the progression of negative emotional responses into problematic responses. Physicians should also have a role in establishing their social media presence on TikTok and offering evidence-based advice to athletes while disproving misinformation on TikTok.

Face Recognition Based on Videos by Using Convex Hulls

A wide range of face appearance variations can be modeled by using set-based recognition approaches effectively, but computational complexity of current methods is highly dependent on the set and class sizes. This paper introduces new video-based classification methods designed for reducing the required disk space of data samples and speed up the testing process in large-scale face recognition systems. In the proposed method, image sets collected from videos are approximated with kernelized convex hulls and it was shown that it is sufficient to use only the samples that participate in shaping the image set boundaries in this setting. The kernelized support vector data description (SVDD) is used to extract those important samples that form the image set boundaries. Moreover, we show that these kernelized hypersphere models can also be used to approximate image sets for classification purposes. Then, we propose a binary hierarchical decision tree approach to improve the speed of the classification system even more. At last, we introduce a new video database that includes 285 people with 8 videos of each person, since the most popular video data sets used for set-based recognition methods include either a few people, or small number of videos per person. The experimental results on varying sized databases show that the proposed methods greatly improve the testing times of the classification system (we obtained speed-ups to a factor of 20) without a significant drop in accuracies.

A critical review of obstetric and gynecological physical examination videos available on YouTube

Background:Video-sharing website “YouTube” is a growing source of healthcare information. But, the videos uploaded on this open platform are not peer reviewed, therefore, the information available needs to be sufficiently evaluated. No studies have been conducted to evaluate the authenticity and utility of obstetrics and gynecology (Obs/Gyne) physical examination YouTube videos. This study was performed to analyze the sources, contents, and quality of videos about the Obs/Gyne clinical examination available on YouTube.Methods:A systematic search was performed on YouTube website using the following key words: “OBSTETRIC,” “GYNECOLOGICAL,” “SPECULUM OBSTETRIC,” “OBSTETRIC CLINICAL,” “BIMANUAL PELVIC,” and “EXAMINATION” to analyze the sources, contents, and the quality of YouTube videos about the Obs/Gyne clinical examination during the period between November 2015 and March 2017. The videos were classified into educationally useful and useless based on the content, accuracy of the knowledge, and the demonstration.Results:Out of total 457 screened videos, 176 (38.51%) videos met the pre-set inclusion criteria. After review, out of 176 pertinent videos, 84 (47.7%) videos were found educationally useful, and out of these 84 useful videos, only 29 (34.5%) were highly educational in nature.Conclusion:YouTube videos showed variable educational value. Only, a small number of videos were identified as useful and can be used by the medical students for self-directed learning and by the clinical teachers for educational purposes or other academic activities.

Age is in the Eye of the Beholder: Examining the Cues Employed to Construct the Illusion of Youth in Teen Pornography

Past research has identified a subgenre of mainstream pornography that attempts to create the illusion for consumers that sex is occurring between an adult and a minor (i.e., a child or young adolescent under the age of 18). This illusion is established through various textual, verbal, visual, and behavioural cues. Although the construction of adult–minor relationships in pornography has received some scholarly attention, there has been no attempt to investigate this phenomenon within pornographic videos available via the Internet. The current study addressed this omission by analyzing for content 150 of the most popular “teen” pornography videos available on three pornography websites. We coded for textual, visual, verbal, and behavioural content that connoted sexual activity between an adult and a minor. Results indicated that a small number of videos (28, 18.7 % of the sample) contained a disproportionate percentage of cues (54.2 %), with the remaining videos containing little or no youth sexualized content. We conclude that only a subsample of videos clearly attempted to portray adult–minor relationships. The prevalence of various cues within the sample was quantified and discussed, as were limitations associated with this study and directions for future research.

Video popularity characterization centered on news-on-demand

Video popularity characterization, in a News-on-demand service, is the core issue of this paper. It will concentrate on the digital edition of six Spanish regional newspapers, where the different news articles are classified into a wide variety of issues. Request video data has been analyzed, including how accesses were distributed along the different topics, during a period of nine months, where the number of requests fluctuated between 85,758 and 586,398 over a minimum of 519 videos and a maximum of 4,221. On the one hand, the statistical function from which video popularity would come from is likely to be unknown. The problem has been tackled with some statistical functions, such as Zipf-like, Mandelbrot and Stretched, which are well-known in the literature. Another method called Box-Cox transformation has been looked into for this specific field. On the other hand, a brief overview has been carried out about the popularity growth tendency, that is how many days a video takes to reach the totality of its requests, and if requests are focused on a small number of videos or they are spread, known as temporal locality. In the end, after an inference and validation process, Box-Cox transformation turned out to be the best choice for the majority of the on-line newspapers. Moreover, the topic related to regional news captured the highest number of requests, so highlighting their regional nature. Finally, new videos achieved 80% of their requests in their first day of life in most of the newspapers, and in four out of six on-line newspapers a high percentage of requests were concentrated on a low percentage of videos (Principle of Pareto).