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Journal articles on the topic 'Video content analysis'
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Liang, Chao, Changsheng Xu, and Hanqing Lu. "Personalized Sports Video Customization Using Content and Context Analysis." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/836357.
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We present an integrated framework on personalized sports video customization, which addresses three research issues: semantic video annotation, personalized video retrieval and summarization, and system adaptation. Sports video annotation serves as the foundation of the video customization system. To acquire detailed description of video content, external web text is adopted to align with the related sports video according to their semantic correspondence. Based on the derived semantic annotation, a user-participant multiconstraint 0/1 Knapsack model is designed to model the personalized video customization, which can unify both video retrieval and summarization with different fusion parameters. As a measure to make the system adaptive to the particular user, a social network based system adaptation algorithm is proposed to learn latent user preference implicitly. Both quantitative and qualitative experiments conducted on twelve broadcast basketball and football videos validate the effectiveness of the proposed method.
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Gad, Gad, Eyad Gad, Korhan Cengiz, Zubair Fadlullah, and Bassem Mokhtar. "Deep Learning-Based Context-Aware Video Content Analysis on IoT Devices." Electronics 11, no. 11 (June 4, 2022): 1785. http://dx.doi.org/10.3390/electronics11111785.
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Integrating machine learning with the Internet of Things (IoT) enables many useful applications. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, limited energy and computation resources present a major challenge. Video captioning is one type of VCA that describes a video with a sentence or a set of sentences. This work proposes an IoT-based deep learning-based framework for video captioning that can (1) Mine large open-domain video-to-text datasets to extract video-caption pairs that belong to a particular domain. (2) Preprocess the selected video-caption pairs including reducing the complexity of the captions’ language model to improve performance. (3) Propose two deep learning models: A transformer-based model and an LSTM-based model. Hyperparameter tuning is performed to select the best hyperparameters. Models are evaluated in terms of accuracy and inference time on different platforms. The presented framework generates captions in standard sentence templates to facilitate extracting information in later stages of the analysis. The two developed deep learning models offer a trade-off between accuracy and speed. While the transformer-based model yields a high accuracy of 97%, the LSTM-based model achieves near real-time inference.
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Cui, Limeng, and Lijuan Chu. "YouTube Videos Related to the Fukushima Nuclear Disaster: Content Analysis." JMIR Public Health and Surveillance 7, no. 6 (June 7, 2021): e26481. http://dx.doi.org/10.2196/26481.
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Background YouTube (Alphabet Incorporated) has become the most popular video-sharing platform in the world. The Fukushima Daiichi Nuclear Power Plant (FDNPP) disaster resulted in public anxiety toward nuclear power and radiation worldwide. YouTube is an important source of information about the FDNPP disaster for the world. Objective This study's objectives were to examine the characteristics of YouTube videos related to the FDNPP disaster, analyze the content and comments of videos with a quantitative method, and determine which features contribute to making a video popular with audiences. This study is the first to examine FDNPP disaster–related videos on YouTube. Methods We searched for the term “Fukushima nuclear disaster” on YouTube on November 2, 2019. The first 60 eligible videos in the relevance, upload date, view count, and rating categories were recorded. Videos that were irrelevant, were non-English, had inappropriate words, were machine synthesized, and were <3 minutes long were excluded. In total, 111 videos met the inclusion criteria. Parameters of the videos, including the number of subscribers, length, the number of days since the video was uploaded, region, video popularity (views, views/day, likes, likes/day, dislikes, dislikes/day, comments, comments/day), the tone of the videos, the top ten comments, affiliation, whether Japanese people participated in the video, whether the video recorder visited Fukushima, whether the video contained theoretical knowledge, and whether the video contained information about the recent situation in Fukushima, were recorded. By using criteria for content and technical design, two evaluators scored videos and grouped them into the useful (score: 11-14), slightly useful (score: 6-10), and useless (score: 0-5) video categories. Results Of the 111 videos, 43 (38.7%) videos were useful, 43 (38.7%) were slightly useful, and 25 (22.5%) were useless. Useful videos had good visual and aural effects, provided vivid information on the Fukushima disaster, and had a mean score of 12 (SD 0.9). Useful videos had more views per day (P<.001), likes per day (P<.001), and comments per day (P=.02) than useless and slightly useful videos. The popularity of videos had a significant correlation with clear sounds (likes/day: P=.001; comments/day: P=.02), vivid information (likes/day: P<.001; comments/day: P=.007), understanding content (likes/day: P=.001; comments/day: P=.04). There was no significant difference in likes per day (P=.72) and comments per day (P=.11) between negative and neutral- and mixed-tone videos. Videos about the recent situation in Fukushima had more likes and comments per day. Video recorders who personally visited Fukushima Prefecture had more subscribers and received more views and likes. Conclusions The possible features that made videos popular to the public included video quality, videos made in Fukushima, and information on the recent situation in Fukushima. During risk communication on new forms of media, health institutes should increase publicity and be more approachable to resonate with international audiences.
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Thinh, Bui Van, Tran Anh Tuan, Ngo Quoc Viet, and Pham The Bao. "Content based video retrieval system using principal object analysis." Tạp chí Khoa học 14, no. 9 (September 20, 2019): 24. http://dx.doi.org/10.54607/hcmue.js.14.9.291(2017).
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Video retrieval is a searching problem on videos or clips based on the content of video clips which relates to the input image or video. Some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. We propose a content based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is evaluated on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show.
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Faeruz, Ratna, Maila D. H. Rahiem, Nur Surayyah Madhubala Abdullah, Dzikri Rahmat Romadhon, Ratna Sari Dewi, Rahmatullah Rahmatullah, and Dede Rosyada. "Child Educational Content on Digital Folklore "Pak Lebai Malang": A Qualitative Content Analysis." Al-Athfal: Jurnal Pendidikan Anak 7, no. 2 (December 22, 2021): 111–22. http://dx.doi.org/10.14421/al-athfal.2021.72-02.
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Purpose – The purpose of this study was to explore child educational content digital folklore on YouTube, and It is used to teach young children about science, language, and values. The unit analysis on this research was the video of Pak Lebai Malang from West Sumatera, Indonesia.Design/methods/approach – The qualitative content analysis method was used in this study. The content analyzed was digital folklore based on the Minangkabau story Pak Lebai Malang. The process began with downloading the video, creating a transcript, taking notes on the text, language, and context, re-watching the video, comparing and contrasting it to the memo, and eliciting evidence from the video.Findings – The data revealed the following ways in which digital folklore on YouTube teaches science, language, and values: 1) digital technology illustrates science concepts with simple-to-understand videos; 2) by repeating the words and visualizing each spoken word, YouTube videos teach children new vocabulary. 3) the characters’ expressions and intonation in the video teach children about social values.Research implications/limitations – This research could serve as a springboard for future research on the use of digital folklores in early childhood classrooms. It is advised that additional research be conducted to improve the interest, effectiveness, and applicability of digital folklore in the early childhood learning process and design more effective programs for teaching science, language, and value to young people children. The study’s drawback is that it analyzes only one video. If it is compared to other videos, it may provide a complete view. Practical implications – This study informs educators on the potential for using digital folklore to teach science, language, and values. It entails the implementation of more creative strategies in early childhood education. Additionally, the study inspires innovative content creators on YouTube to make their videos more relevant to young children’s learning. Additionally, parents may discover that something as simple as a YouTube video could be an incredible resource for their child’s development.Originality/value – The study explains child educational content based on local wisdom. The digital form of Pak Lebai Malang folklore can facilitate accessibility and acceptability.
Paper type Research paper
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Jacob, Jaimon, M. Sudheep Elayidom, and V. P. Devassia. "Video content analysis and retrieval system using video storytelling and indexing techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6019. http://dx.doi.org/10.11591/ijece.v10i6.pp6019-6025.
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Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed.
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Duan, Yamin. "Analysis Of Competitive Strategy Of Bilibili Content Ecology." BCP Business & Management 34 (December 14, 2022): 865–72. http://dx.doi.org/10.54691/bcpbm.v34i.3106.
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With the development of The Times, the Internet industry has gradually penetrated all aspects of our daily life, especially in the field of pan-entertainment, among which the video industry is the gathering place of the Internet industry. Long video websites include iQiyi, Youku, and Tencent, while short video websites include Douyin and Kuaishou. Bilibili, an especially popular video website in recent years, has attracted many young people as its users. Bilibili, as a video website with both long and short videos, mainly focuses on PUGC content but also has rich self-made content. It has strong competition from many video websites, but it also successfully occupies a considerable part of the market with its unique content ecology and bullet screen culture. This paper will analyze the external and internal competition pattern of Bilibili, then analyze its differentiated competition strategy and competitive advantages, and finally put forward suggestions and prospects for its future development direction.
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Eide, Viktor S. Wold, Ole-Christoffer Granmo, Frank Eliassen, and Jørgen Andreas Michaelsen. "Real-time video content analysis." ACM Transactions on Multimedia Computing, Communications, and Applications 2, no. 2 (May 2006): 149–72. http://dx.doi.org/10.1145/1142020.1142024.
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Overmeire, Luk, Lode Nachtergaele, Fabio Verdicchio, Joeri Barbarien, and Peter Schelkens. "Constant quality video coding using video content analysis." Signal Processing: Image Communication 20, no. 4 (April 2005): 343–69. http://dx.doi.org/10.1016/j.image.2005.01.001.
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Pan, Peng, Changhua Yu, Tao Li, Xilei Zhou, Tingting Dai, Hanhan Tian, and Yaozu Xiong. "Xigua Video as a Source of Information on Breast Cancer: Content Analysis." Journal of Medical Internet Research 22, no. 9 (September 29, 2020): e19668. http://dx.doi.org/10.2196/19668.
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Background Seeking health information on the internet is a popular trend. Xigua Video, a short video platform in China, ranks among the most accessed websites in the country and hosts an increasing number of videos with medical information. However, the nature of these videos is frequently unscientific, misleading, or even harmful. Objective Little is known about Xigua Video as a source of information on breast cancer. Thus, the study aimed to investigate the contents, quality, and reliability of breast cancer–related content on Xigua Video. Methods On February 4, 2020, a Xigua Video search was performed using the keyword “breast cancer.” Videos were categorized by 2 doctors based on whether the video content provided useful or misleading information. Furthermore, the reliability and quality of the videos were assessed using the 5-point DISCERN tool and 5-point global quality score criteria. Results Out of the 170 videos selected for the study, 64 (37.6%) were classified as useful, whereas 106 (62.4%) provided misleading information. A total of 41.8% videos (71/170) were generated by individuals compared to 19.4% videos (33/170) contributed by health care professionals. The topics mainly covered etiology, anatomy, symptoms, preventions, treatments, and prognosis. The top topic was “treatments” (119/170, 70%). The reliability scores and global quality scores of the videos in the useful information group were high (P<.001). No differences were observed between the 2 groups in terms of video length, duration in months, and comments. The number of total views was higher for the misleading information group (819,478.5 vs 647,940) but did not reach a level of statistical significance (P=.112). The uploading sources of the videos were mainly health care professionals, health information websites, medical advertisements, and individuals. Statistical differences were found between the uploading source groups in terms of reliability scores and global quality scores (P<.001). In terms of total views, video length, duration, and comments, no statistical differences were indicated among the said groups. However, a statistical difference was noted between the useful and misleading information video groups with respect to the uploading sources (P<.001). Conclusions A large number of Xigua videos pertaining to breast cancer contain misleading information. There is a need for accurate health information to be provided on Xigua Video and other social media; health care professionals should address this challenge.
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Pipek, P., J. Jeleníková, and L. Sarnovský. "The use of video image analysis for fat content estimation." Czech Journal of Animal Science 49, No. 3 (December 12, 2011): 115–20. http://dx.doi.org/10.17221/4288-cjas.
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The composition of selected cuts of cattle carcasses was determined in connection with the search for new methods of carcass classification. The content of adipose tissue and intramuscular fat in the cross-section of beef loin was estimated. A total of 79 samples was taken for investigations in a broad range of cattle category. The classical extraction method in Soxhlet extractor was compared with video image analysis, which measured the ratio of muscle to fat areas. The size and the shape of the musculus longissimus lumborum et thoracis (MLLT) and its ratio in the loin cross-section was also estimated. A good correlation (r = 0.99, P < 0.05) between both methods was found for the estimation of intramuscular fat in MLLT. The correlation in the case of the whole cross-section was influenced by the connective tissue that gives also white areas similarly like the adipose tissue, but the fat content is different.
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Abu Daabes, Ajayeb S., and Faten F. Kharbat. "A content analysis of Arabic YouTube videos for cancer treatment." International Journal of Health Governance 24, no. 4 (November 21, 2019): 267–73. http://dx.doi.org/10.1108/ijhg-05-2019-0035.
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Purpose The purpose of this paper is to describe and assess Arabic videos related to cancer treatment to gain insights about the nature of health information as it is shared on YouTube. Accordingly, future strategies for different bodies are suggested to promote effective communication. Design/methodology/approach The approach is to select a representative sample of YouTube videos for certain search terms related to cancer treatment in the Arabic language. In order to identify the search terms, Google Trends is utilized. To retrieve the most relevant videos, a simple python tool is developed using YouTube API V3. For this study, the first 150 relevant videos are quantitatively and qualitatively analyzed. Objective data and subjective data are collected for each video and analyzed. Objective data include video title, URL, length, view count, like count, dislike count, comment count and the associated tags. For content analysis, coding themes are defined for the subjective data as follows: video format, video authorship and video content. Video content includes three categories: types of treatments, targeted part and evidence-based indicators. Findings The study included 150 videos, from which 30 videos were not content related; therefore, 120 videos remain in the analysis. Using rounding values, it can be observed that the average video lasted 10 min, had 184,966 views, was commented on 263 times, was liked by 2,295 users and disliked by 148 users. Non-professional individuals (46 percent) posted less than half of the videos, whereas public institutions posted only 18 percent of videos. More than half of videos (56 percent) promoted using herbal, botanical, and other natural products for cancer treatment. The majority of YouTube video formats were videos (52 percent), followed by audio with captions (30 percent). News and stories were the dominant videos, with (16 percent), and other types of videos were mostly testimonials and private centers promotions. Only 6 and 9 percent of videos targeted the genetic and immune systems, respectively. Out of the 120 analyzed videos, 86 percent did not mention any risk factor for the recommended treatment, and 73 percent did not offer the details of their usage direction. Research limitations/implications Researchers need to understand the information that is currently available on social media platforms related to the high-risk diseases in order to design initiatives, tools, and actions to allow an easy effective transfer of knowledge. Practical implications Recounting in-depth knowledge of YouTube cancer treatment contents will allow policy makers, YouTube management, medical organizations, and government agencies to understand the viewers’ behavior of YouTube and their needs to provide accurate and trustworthy information to adopt evidence-based resources. Social implications Creating the suitable content, in terms of health promotion strategies, associated with the appropriate format and understandable language that people need will be one of the major responsibilities of YouTube management, government and professional bodies. The well-designed health messages will enhance users’ engagement and attention to health issues from trusted sources. Originality/value There is very less information about Arabic messages in social media, YouTube in particular, specifically regarding cancer treatment. Thus, this study is one of the first studies to explore how Arabic messages are presented on YouTube. The aim of the assessment is to extract the current status and suggest future strategies for different bodies to have effective communication toward the Arabic communities.
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Jeong, Tae-ho. "The Content Analysis of Video Contents in Multicultural Education." Korean Association for the Social Studies 27, no. 2 (May 30, 2020): 27–47. http://dx.doi.org/10.37409/rsse.27.2.27.
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Lena, Yağmur, and Furkan Dindaroğlu. "Lingual orthodontic treatment: A YouTube™ video analysis." Angle Orthodontist 88, no. 2 (December 21, 2017): 208–14. http://dx.doi.org/10.2319/090717-602.1.
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ABSTRACT Objectives: The aim of this study was to examine the quality of information offered for patients seeking information on lingual orthodontic treatment. Materials and Methods: YouTube™ was searched for videos about lingual orthodontic treatment using the key word “lingual braces” from the Google Trends application. One hundred and four videos were selected to be analyzed from the first 120 results. The video content was evaluated using a 10-point score, which was used for classifying low- and high-content video groups. The video information and quality index (VIQI) was applied to determine the quality of the videos. The Mann-Whitney U-test, Chi-square test, and logistic regression analysis were used, and the Pearson correlation coefficient was calculated for statistical evaluations. Results: We classified 32 videos as high-content and 72 as low-content. Most videos were uploaded by laypeople (58.7%, n = 61). Definition of lingual braces and psychological impact were the most commonly discussed topics (51.0%), followed by speech performance (47.1%), pain (44.2%), tongue soreness (37.5%), and biomechanics (14.4%). Compared to the low-content group, the high-content video group had a significantly higher mean number of views (19,867.41 vs 6720.08, P = .002) and more “likes” (135.88 vs 13.01, P < .001), “dislikes” (4.34 vs 0.81, P < .001), and “comments” (26.28 vs 5.31, P = .002). There was no difference in the total VIQI score between the groups (P = .009). Conclusions: The content of YouTube™ videos for lingual orthodontics was generally incomplete. Most videos mentioned psychological effects, but few videos discussed the biomechanics or procedure. Orthodontists should be aware of the information available on YouTube™.
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Grillon, Marlene, and Andy Wai Kan Yeung. "Content Analysis of YouTube Videos That Demonstrate Panoramic Radiography." Healthcare 10, no. 6 (June 13, 2022): 1093. http://dx.doi.org/10.3390/healthcare10061093.
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In this digital era, dental students often search for online resources for self-directed learning. YouTube is one of the most commonly sought online platforms for educational or instructional videos. No prior study has examined the validity of panoramic radiography videos available on YouTube. This study provides a content analysis of these YouTube videos. A search for relevant YouTube videos was conducted in April 2022. The search string was: (panoramic OR pan OR OPG) AND (dental OR dentistry OR X-ray). The first 100 videos that resulted from the search and their related videos were screened. Exclusion criteria included irrelevance (e.g., no demonstration of panoramic radiography procedures) and non-English videos. For each included video, the following parameters were recorded: image receptor type, patient age, patient type (real patient, animation, or phantom head), patient preparation procedures, machine preparation, patient positioning, and operator safety. The number of views, comments, likes, and channel subscribers were recorded, as well as the video duration and the age of the video. Forty videos were included and analyzed. Most of the videos demonstrated digital panoramic radiography with an adult patient. Procedures on the patient and machine preparations as well as patient positioning were generally explained well. However, most videos did not well-demonstrate operator safety details concerning the use of adequate personal protective equipment. View count, comment count, and channel subscriber count positively correlated with the like count. Clinicians and students should carefully critique the content of such instructional videos and refer to the contents from other sources such as user manuals and latest recommendations from local authorities.
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Sigonney, François, Camille Steltzlen, Pierre Alban Bouché, and Nicolas Pujol. "A quality analysis of the YouTube video content on meniscus repair surgery." Orthopaedic Journal of Sports Medicine 9, no. 2_suppl (February 1, 2021): 2325967121S0001. http://dx.doi.org/10.1177/2325967121s00013.
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Objectives: The Internet, especially YouTube, is an important and growing source of medical information. The content of this information is poorly evaluated. The objective of this study was to analyze the quality of YouTube video content on meniscus repair. The hypothesis was that this source of information is not relevant for patients. Methods: A YouTube search was carried out using the keywords "meniscus repair". Videos had to have had more than 10,000 views to be included. The videos were analyzed by two evaluators. Various features of the videos were recorded (number of views, date of publication, "likes", "don’t likes", number of comments, source, type of content and the origin of the video). The quality of the video content was analyzed by two validated information system scores: the JAMA benchmark score (0 to 4) and the Modified DISCERN score (0 to 5). A specific meniscus repair score (MRSS scored out of 22) was developed for this study, in the same way that a specific score has been developed for other similar studies (anterior cruciate ligament, spine, etc.). Results: Forty-four (44) videos were included in the study. The average number of views per video was 180,100 (± 222,000) for a total number of views of 7,924,095. The majority of the videos were from North America (90.9%). In most cases, the source (uploader) that published the video was a doctor (59.1%). A manufacturer, an institution and a non-medical source were the other sources. The content actually contained information on meniscus repair in only 50% of the cases. The mean scores for the JAMA benchmark, MD score and MRSS were 1.6/4± 0.75, 1.2/5 ± 1.02 and 4.5/22 (± 4.01) respectively. No correlation was found between the number of views and the quality of the videos. The quality of videos from medical sources was not superior to those from other sources. Conclusion: The content of YouTube videos on meniscus repair is of very low quality. Physicians should inform patients and, more importantly, contribute to the improvement of these contents.
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Dağdelen, Kenan. "Quality Assessment and Content Analysis of Youtube Videos on Glaucoma Surgery." British Journal of Multidisciplinary and Advanced Studies 3, no. 1 (October 12, 2022): 20–28. http://dx.doi.org/10.37745/bjmas.2022.0010.
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The aim of this study is to evaluate the accuracy, quality and reliability of the videos on glaucoma surgery on YouTube which is an online video-sharing platform.The material has been obtained by a video search carried out on the Youtube -online video platform- with the keyword “Trabeculectomy” is kind of glaucoma surgery. A total of 44 videos for the keyword “trabeculectomy” were analyzed. When evaluated according to the exclusion criteria, a total of 10 videos were included in the analysis, and the 34 videos were excluded. Thus, after carving out the secondary data, a number of statistical analyses were performed namely Shapiro-Wilks, Kruskall-Wallis, Mann-Whitney U and Backward Linear Regression. In this framework, statistical analyzes were made via using the Stata software. Statistical significance value (threshold) was accepted as %10 (p<0.05). When the videos were evaluated according to the information content they provided, it was found that 5 videos had low quality information content, 4 videos had medium quality information content, 1 video had good quality information content. Moreover, it was statistically determined that the parameter affecting the number of views was the number of likes (p<0.05). This is the first study in ophthalmology to analyze publicly available online data evaluating glaucoma patients.YouTube videos are essentially insufficient as an educational material and an English source of information for the glaucoma surgery. Health professionals need to pay more attention to online platforms so that patients can access accurate information.
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Sadry, Sanaz, and Ece Buyukbasaran. "Content analysis and reliability of YouTube™ videos on clear aligner." APOS Trends in Orthodontics 11 (October 5, 2021): 209–17. http://dx.doi.org/10.25259/apos_46_2021.
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Objectives: The aim of the present study is to evaluate the accuracy and reliability of the information provided in the videos about the clear aligner on the Youtube video platform, an internet information source. Materials and Methods: A video search was performed on the Youtube video platform using three keywords determined with the Google Trends application. After the videos were ranked based on their view counts, a total of 75 videos were analyzed, 47 being the first for each term. After applying the exclusion criteria (repetitive content, no audio content or title information, irrelevant to the topic, only promotional content, duration over 15 min, and addressing other orthodontic treatments), the remaining 47 videos were evaluated. Regarding the videos, the following parameters were recorded: View count, number of likes, number of dislikes, number of comments, video duration, and days since the uploaded date. A scoring system consisting of seven parameters was used to classify the video content quality as low and high. The video information and quality index were utilized to evaluate the quality of the videos universally. Statistical analyses were performed with Stata/IC computer program using Shapiro–Wilks, Kruskall–Wallis, Mann–Whitney U, and linear regression (Backward method) tests. Statistical significance value was set at P < 0.05. Results: Evaluation of the videos based on their upload source revealed that 27 videos were downloaded from individuals, ten videos from doctors and ten videos from commercial sources, and the number of subscribers to these sources varied significantly (P < 0.05). Evaluation of the videos based on the information provided, revealed that 32 videos had poor quality, ten videos had moderate quality and five videos had good quality information content, and the video durations varied significantly (P < 0.05). It was seen that of the 75 videos viewed, only 10 (13.3%) provided information of moderate-good quality. The number of likes was found to be the parameter that significantly affects the view count (P < 0.05). Conclusion: YouTube videos prove insufficient as a source of information on orthodontic treatment with clear aligner. Health-care professionals should be aware of online platforms to ensure that patients can access accurate information.
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Sadry, Sanaz, and Ece Buyukbasaran. "Content analysis and reliability of YouTube™ videos on clear aligner." APOS Trends in Orthodontics 11 (October 5, 2021): 209–17. http://dx.doi.org/10.25259/apos_46_2021.
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Objectives: The aim of the present study is to evaluate the accuracy and reliability of the information provided in the videos about the clear aligner on the Youtube video platform, an internet information source. Materials and Methods: A video search was performed on the Youtube video platform using three keywords determined with the Google Trends application. After the videos were ranked based on their view counts, a total of 75 videos were analyzed, 47 being the first for each term. After applying the exclusion criteria (repetitive content, no audio content or title information, irrelevant to the topic, only promotional content, duration over 15 min, and addressing other orthodontic treatments), the remaining 47 videos were evaluated. Regarding the videos, the following parameters were recorded: View count, number of likes, number of dislikes, number of comments, video duration, and days since the uploaded date. A scoring system consisting of seven parameters was used to classify the video content quality as low and high. The video information and quality index were utilized to evaluate the quality of the videos universally. Statistical analyses were performed with Stata/IC computer program using Shapiro–Wilks, Kruskall–Wallis, Mann–Whitney U, and linear regression (Backward method) tests. Statistical significance value was set at P < 0.05. Results: Evaluation of the videos based on their upload source revealed that 27 videos were downloaded from individuals, ten videos from doctors and ten videos from commercial sources, and the number of subscribers to these sources varied significantly (P < 0.05). Evaluation of the videos based on the information provided, revealed that 32 videos had poor quality, ten videos had moderate quality and five videos had good quality information content, and the video durations varied significantly (P < 0.05). It was seen that of the 75 videos viewed, only 10 (13.3%) provided information of moderate-good quality. The number of likes was found to be the parameter that significantly affects the view count (P < 0.05). Conclusion: YouTube videos prove insufficient as a source of information on orthodontic treatment with clear aligner. Health-care professionals should be aware of online platforms to ensure that patients can access accurate information.
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Hakami, Zaki, Prabhadevi C. Maganur, Sanjeev B. Khanagar, Sachin Naik, Khalid Alhakami, Omar A. Bawazeer, Ahmed Mohammed Alassiry, and Satish Vishwanathaiah. "Thumb-Sucking Habits and Oral Health: An Analysis of YouTube Content." Children 9, no. 2 (February 8, 2022): 225. http://dx.doi.org/10.3390/children9020225.
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This study aims to investigate the content and usefulness of YouTube videos on** thumb-sucking habits. Methods: YouTube was systematically searched for all relevant videos on thumb sucking using primary keywords, such as thumb, finger, and digit sucking. Video information was assessed, such as the type of video, number of likes or dislikes, number of views, and duration of upload. The usefulness of videos was analyzed, and information about treatment modalities was evaluated. Results: A total of 331 YouTube videos (314 educational offerings and 17 testimonials) were included in the analysis. Individual users uploaded (36.6%), followed by healthcare professionals (30.5%). Only 4.83% of the videos were classified as having “very useful” general information content, whereas 51.1% were rated as “slightly useful.” There was no significant correlation between the usefulness score and the interaction rate, video length, or viewing rate. The videos advised a psychosocial approach and mechanical or reminder therapy in 32.33% and 25.07% of videos, respectively. Preventive methods accounted for 7.26%, and chemical treatments were discussed in 5.44% of the videos. Conclusion: Information on YouTube about thumb-sucking habits was unsatisfactory and should be improved by oral healthcare professionals and organizations.
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Gupta, Yamini. "A Survey on Video Content Analysis." INTERNATIONAL JOURNAL OF RESEARCH IN ADVANCE ENGINEERING 1, no. 2 (February 28, 2015): 16. http://dx.doi.org/10.26472/ijrae.v1i2.23.
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Chen, Wei, and Yu-Jin Zhang. "Parametric model for video content analysis." Pattern Recognition Letters 29, no. 3 (February 2008): 181–91. http://dx.doi.org/10.1016/j.patrec.2007.09.020.
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Loukas, Constantinos. "Video content analysis of surgical procedures." Surgical Endoscopy 32, no. 2 (October 26, 2017): 553–68. http://dx.doi.org/10.1007/s00464-017-5878-1.
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Grillon, Marlene, and Andy Wai Kan Yeung. "Content Analysis of YouTube Videos That Demonstrate Periapical Radiography." Applied Sciences 12, no. 19 (September 24, 2022): 9602. http://dx.doi.org/10.3390/app12199602.
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No prior study has evaluated the content of YouTube videos that demonstrated how to take periapical radiographs, one of the commonest radiographic examinations in dentistry. This study provided a content analysis on these YouTube videos. Three separate searches were performed with the following search strings: (1) posterior periapical, (2) molar periapical, and (3) anterior periapical. The videos resulting from the searches were sorted by view count, and their related videos (as listed by YouTube on the right of the screen) were also screened. Exclusion criteria included irrelevance to periapical radiography, no real patient involved, no demonstration of periapical radiography procedures), non-English video, and duplicate videos. Finally, 21 videos were included and analyzed. For each included video, the following aspects were evaluated: general settings (types of image receptor, patient age, and radiographic technique), patient preparation, machine preparation, receptor placement, and operator safety. All but one were demonstrated with an adult patient. The videos were largely based on digital imaging with the use of either intraoral sensors or phosphor plates, and mostly used paralleling technique. Several common issues were identified, such as not removing the metallic objects from the patient’s head and neck region, not showing the selection of radiation dose, not placing the orientation dot on the film or phosphor plate on the occlusal side, and not wearing proper personal protective equipment. More instructional videos are definitely required. Future videos should eliminate these issues, and also demonstrate with pediatric patients. Perhaps the dental radiology department of a well-established university should be enticed to produce an instructional video that presents the technique in a flawless presentation, to facilitate the learning of dental students, dental hygienists and dental assistants who have yet to master their skills.
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NGO, CHONG-WAH, TING-CHUEN PONG, and HONG-JIANG ZHANG. "RECENT ADVANCES IN CONTENT-BASED VIDEO ANALYSIS." International Journal of Image and Graphics 01, no. 03 (July 2001): 445–68. http://dx.doi.org/10.1142/s0219467801000268.
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In this paper, we present major issues in video parsing, abstraction, retrieval and semantic analysis. We discuss the success, the difficulties and the expectations in these areas. In addition, we identify important opened problems that can lead to more sophisticated ways of video content analysis. For video parsing, we discuss topics in video partitioning, motion characterization and object segmentation. The success in video parsing, in general, will have a great impact on video representation and retrieval. We present three levels of abstracting video content by scene, keyframe and key object representations. These representation schemes in overall serve as a good start for video retrieval. We then describe visual features, in particular motion, and similarity measures adopted for retrieval. Next, we discuss the recent computational approaches in bridging the semantic gap for video content understanding.
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Yu, Shiqi. "Analysis on the Trend of Content Production of "Fans Create Videos" Website." BCP Social Sciences & Humanities 20 (October 18, 2022): 593–97. http://dx.doi.org/10.54691/bcpssh.v20i.2420.
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"Fan-created video" is one of the text forms reproduced by fans. It is the second creation of the original video text by fans according to their own narrative logic and value trend, and it is the positive practice of subculture groups in the new cultural pattern. The development of Internet media technology has further promoted the enthusiasm of fans to participate and the diversity of expression. When fans create videos, they use audio, video, pictures, words and other audio-visual materials to interpret their favorite idols. In this culture, fans turn media consumption into the production of new texts and construct new meanings. In the Internet era, barrage has developed into a social activity, and barrage users show the characteristics of socialization in the process of interaction, and the individual viewing activities of viewers gradually turn into group ritual activities. Taking bilibili barrage video network as an example, this paper analyzes the production mechanism and communication characteristics of "Fan-created videos" in China, and explores the trend of content production of "Fan-created videos" websites.
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Li, Jiahui. "Research on Personal Brand Established on the Short Video Platform From the Perspective of Dramaturgy Theory." Asian Journal of Social Science Studies 7, no. 7 (August 1, 2022): 29. http://dx.doi.org/10.20849/ajsss.v7i7.1240.
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At present, short videos are still the trend of the industry. The research of this paper will be based on the theoretical perspective of dramaturgy theory and use the typical research methods named case analysis and content analysis to analyze the popular video blogger ‘Zhihao will’ on a specific short video platform Douyin, which is an another official version of Tiktok used in the mainland of China, to conduct analysis and research, and to find out how to establish personal brands basically in the short videos era. After selecting and doing a research on the video contents released by the research subjects from 2018 to 2022, it is found that in the process of using short video as a medium to transmit content, a certain label for characters in short videos are necessarily needed for spreading its desired aims and contents to leave the first impression on audiences.
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Elgamml, Mohamed M., Fazly S. Abas, and H. Ann Goh. "Semantic Analysis in Soccer Videos Using Support Vector Machine." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (December 20, 2019): 2055018. http://dx.doi.org/10.1142/s0218001420550186.
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A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotating the highlights from a soccer video. The proposed framework identifies the interesting clips containing possible scenes of interest, such as goals, penalty kicks, etc. by parsing and processing the audio/video components. The framework analyzes, separates and annotates the individual scenes inside the video clips and saves using kernel support vector machine. The results show that semantic analysis of videos using kernel support vector machines is a reliable method to separate and annotate events of interest in a soccer game.
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Lin, Yucheng. "Analysis on the Creation of Popular Videos in The New Era." International Journal of Education and Humanities 5, no. 3 (November 11, 2022): 68–70. http://dx.doi.org/10.54097/ijeh.v5i3.2451.
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The content of videos in the current new media environment varies greatly. Analyzing the creation concept of the well-known blogger of Bilibili ‘Hi teacher, my name is Student He’ is beneficial to the creation and research of popular video content in the new era. This article takes the popular video as the research object, and analyzes the 5G video of "Hello teacher, my name is Student He" as a case study, and analyzes the reasons for Student He's success and the creative ideas of his video. In today’s media environment, how can video producers meet the needs of the audience for high-quality content and create qualified hit works is an topic that worth discussing.
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Zhao, Nan, Löic Baud, and Patrick Bellot. "Exploring Video Sharing Websites Content with Machine Learning." International Journal of Distributed Systems and Technologies 5, no. 4 (October 2014): 31–50. http://dx.doi.org/10.4018/ijdst.2014100103.
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This article studies the characteristics of content on video sharing websites. A better understanding on online video content can help to analyse Internet users' behaviour and improve the video-sharing service. We improved an existing graph-sampling algorithm so that it could be more adapted to sample over the video sharing websites. A newly category system is defined in this paper, which can be applied on many different video sharing websites for content analysis. We also implement machine learning to realize the content re-classification with the newly defined category system. The efficiency reaches at 90%. From the classified content analysis, we find the content category distribution is not constant, and nowadays, cultural goods content take about 70% over all the sampled videos.
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Oh, Juyeon, SeolHwa Moon, and Kyongok Park. "Amyotrophic lateral sclerosis in social media: Content analysis of YouTube videos." Health Informatics Journal 28, no. 2 (January 2022): 146045822211059. http://dx.doi.org/10.1177/14604582221105999.
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The purpose of this study is to analyze the characteristics, content, and quality of information available on YouTube regarding amyotrophic lateral sclerosis (ALS), and explore the search results and number of views and quality of the videos. A search was performed on YouTube on 10 June 2020, using the search term ‘‘Lou Gehrig’s disease” in the Korean language, and the first 100 videos were targeted for analysis. General characteristics (e.g., number of views, video length, etc.) were collected, and upload source, target audience, subject of the video, and quality of the videos using a global quality score (GQS) were assessed. About two-thirds of the videos were for the general public (62.0%), while the other third were made for patients and their families with ALS (38.0%). The mean GQS was 3.70 ± 1.42, and 60 videos were classified as high-quality videos. ALS-related videos on YouTube play a role in raising awareness among the general public of this devastating disease, however, practical information regarding disease management for patients and family is relatively insufficient. The quality and characteristics of the videos varied greatly and sometimes the lowest quality videos were shown in the top pages of search results.
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Syamsi, Nur. "Identifikasi Nilai Karakter Religius dalam Video Karya Youtuber Millenial." Tarbiyah Wa Ta'lim: Jurnal Penelitian Pendidikan dan Pembelajaran 7, no. 1 (March 1, 2020): 37–48. http://dx.doi.org/10.21093/twt.v7i1.2073.
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The YouTube page is the most favorite page for millennials. Although the main purpose of the content in the videos is to entertain, but unconsciously it is also able to influence the audience both cognitive, affective, and behavioral. Therefore, it is necessary to identify whether in the video content there are religious characters that can have a positive impact on the audience. This research aims to identify the character of religious values in videos by millennial YouTuber. Data collection method used is the documentation method, data were analyzed using descriptive qualitative content analysis techniques. The results showed that from the entire video uploaded on Ria Ricis Official, Atta Halilintar, and Rans Entertainment YouTube, there were 131 video contents those were used as analysis material. Based on the video analyzed, religious values are obtained which include the values of faith, Islam, ihsan, science, and charity.
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Tang, Sheng, Ke Gao, Xiaoguang Gu, Chenggang Yan, and Yongdong Zhang. "High-Throughput Content-Based Video Analysis Technologies." Journal of Engineering Studies 06, no. 03 (September 1, 2014): 294–306. http://dx.doi.org/10.3724/sp.j.1224.2014.00294.
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Aita, Mari, Shoko Hirakawa, Nobuko Ihori, and Akira Sakamoto. "Content Analysis of Educational Video Game Software." Journal of Digital Games Research 3, no. 1 (2009): 13–25. http://dx.doi.org/10.9762/digraj.3.1_13.
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Kasim, Hendy, Asnan Furinto, and Ronnie Resdianto Masman. "Analysis on Students’ Online Video Content Preference." Advanced Science Letters 24, no. 1 (January 1, 2018): 524–26. http://dx.doi.org/10.1166/asl.2018.12058.
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Baveye, Yoann, Christel Chamaret, Emmanuel Dellandrea, and Liming Chen. "Affective Video Content Analysis: A Multidisciplinary Insight." IEEE Transactions on Affective Computing 9, no. 4 (October 1, 2018): 396–409. http://dx.doi.org/10.1109/taffc.2017.2661284.
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Ewerth, Ralph, Markus Mühling, and Bernd Freisleben. "Robust Video Content Analysis via Transductive Learning." ACM Transactions on Intelligent Systems and Technology 3, no. 3 (May 2012): 1–26. http://dx.doi.org/10.1145/2168752.2168755.
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Dimitrova, N., Hong-Jiang Zhang, B. Shahraray, I. Sezan, T. Huang, and A. Zakhor. "Applications of video-content analysis and retrieval." IEEE Multimedia 9, no. 3 (July 2002): 42–55. http://dx.doi.org/10.1109/mmul.2002.1022858.
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Li, Y., and C. Dorai. "Instructional Video Content Analysis Using Audio Information." IEEE Transactions on Audio, Speech and Language Processing 14, no. 6 (November 2006): 2264–74. http://dx.doi.org/10.1109/tasl.2006.872602.
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Sahouria, E., and A. Zakhor. "Content analysis of video using principal components." IEEE Transactions on Circuits and Systems for Video Technology 9, no. 8 (1999): 1290–98. http://dx.doi.org/10.1109/76.809163.
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Liu, Jingfang, Caiying Lu, and Shuangjinhua Lu. "Increase the Number of Views and Shares of COVID-19 Videos: Content Relevance and Emotional Consistency with Virus Variant Topics." Healthcare 11, no. 1 (December 30, 2022): 119. http://dx.doi.org/10.3390/healthcare11010119.
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(1) Background: The coronavirus variants have posed serious challenges for the prevention and control of the COVID-19 pandemic. Individuals selectively watch and forward videos that help them reduce the damage caused by the virus. Therefore, the factors influencing video viewing and sharing in the context of the COVID-19 pandemic caused by virus variation must be explored. (2) Method: Based on a combination of uncertainty reduction theory and functional emotion theory, this paper designed hypotheses regarding how content relevance and emotional consistency affect video views and shares. We used the support vector machine (SVM) classification algorithm to measure the content relevance between videos and virus variant topics. We performed sentiment analysis of video text to evaluate the emotional consistency between videos and virus variant topics. Then, we used empirical analysis to build the model. (3) Results: The trained SVM classifier was effective in judging whether the video text was related to virus variant topics (F = 88.95%). The content relevance between COVID-19 videos and virus variant topics was generally low. The results showed that the higher the content relevance, the more views (IRR = 1.005, p = 0.017) and shares (IRR = 1.008, p = 0.009) the video received. Individuals were more willing to view (IRR = 1.625, p < 0.001) and share (IRR = 1.761, p < 0.001) COVID-19 videos with high emotional consistency with virus variant topics. (4) Conclusions: The results of empirical analysis showed that content relevance and emotional consistency between videos and virus variant topics significantly positively impacted video views and shares. The trained SVM classifier can support public health departments in monitoring and assessing the COVID-19 pandemic. Our study provides management advice while helping individuals reduce harm and inform next-step decisions.
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Manchaiah, Vinaya, Monica L. Bellon-Harn, Itzel Mercado Godina, Eldré W. Beukes, and Vinay. "Portrayal of Hearing Loss in YouTube Videos: An Exploratory Cross-Sectional Analysis." American Journal of Audiology 29, no. 3 (September 3, 2020): 450–59. http://dx.doi.org/10.1044/2020_aja-19-00121.
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Objective The objective of the current study was to examine the source, content, understandability, and actionability of hearing loss information on YouTube videos. Method The study used a cross-sectional design. One hundred of the most frequently viewed YouTube videos were identified, and various data were manually coded (i.e., video source, video content, popularity measures such as number of views, likes, and dislikes). In addition, the understandability and actionability of each video were evaluated using the Patient Education Materials Assessment Tool for Audiovisual Materials rating scale. Results Of the 100 most viewed videos, 16 were created by consumers, 62 were professional created, and 22 were media based. Symptoms, causes, and treatment or management of hearing loss were the most frequently discussed content categories, with over 60% of all videos commenting on these areas. The overall understandability and actionability scores for the 100 videos included were 77% and 31%, respectively, indicating adequate understandability and poor actionability. Conclusions The YouTube videos on hearing loss focus on a range of issues. The poor actionability of these videos was a concern, as these videos may not lead to appropriate consumer actions in addressing their hearing loss. Efforts are needed to improve the quality and content of these videos to promote appropriate behavior change.
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Bellon-Harn, Monica L., Vinaya Manchaiah, and Lekeitha R. Morris. "A cross-sectional descriptive analysis of portrayal of autism spectrum disorders in YouTube videos: A short report." Autism 24, no. 1 (July 13, 2019): 263–68. http://dx.doi.org/10.1177/1362361319864222.
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Professionals have expressed concerns about the quality of autism-related information available from Internet-based sources. The purpose of this study was to examine the source, content, usability, and actionability of autism spectrum disorder–related information contained in 100 different videos directed to families of children with autism spectrum disorder uploaded to YouTube. Upload sources were identified, and video content was coded. Understandability and actionability of the videos were examined using Patient Education Materials Assessment Tool for Audiovisual Materials. The collective number of views of the videos was almost 100 million. The length of videos was 691.17 min (i.e. 11.5 h) with the shortest video being 30 s and the longest video being 37.36 min. The YouTube videos related to autism spectrum disorder covered a range of issues, although much of the content was focused on signs and symptoms. No difference in content reporting was noted based on sources for most categories, although differences were noted in some categories (e.g. professionals mentioned diagnosis and resources more frequently). Poor understandability and actionability scores (i.e. below 70%) were reported for all videos regardless of video source. However, the videos generated by the professionals were superior in terms of understandability. Study implications and recommendations for further research are discussed.
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Cui, Yansong, Weizheng Ren, and Zhongliang Deng. "Fast Mode Decision for HD Scalable Video Coding via Statistical Content Analysis." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 08 (April 8, 2018): 1855011. http://dx.doi.org/10.1142/s021800141855011x.
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Scalable video coding (SVC) can provide different bitrate, resolution, or quality formats of the same video through the one-time encoding process. However, SVC also increases encoding complexity significantly, making real-time implementations difficult especially when applied to high definition videos. In this work, we proposed to utilize a fast mode decision algorithm to speed up the SVC encoder. A simple model of the relationship of the video complexity is proposed, the rate-distortion (RD) cost and the statistical mode distribution of the enhancement layer (EL). The proposed mode decision algorithm evaluates the complexity of the video content, then sets the rate distortion RD cost thresholds for inter coding modes. The method also optimizes the process INTRA_BL for I slices. We demonstrate the method using four different resolutions (1080p, 720p, VGA and QVGA), four temporal layers, and two quality layers. We find that the proposed algorithm can reduce the computational complexity significantly while maintaining or even improving encoder efficiency.
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Ma, Zirui, and Bin Gu. "The influence of firm-Generated video on user-Generated video: Evidence from China." International Journal of Engineering Business Management 14 (January 2022): 184797902211186. http://dx.doi.org/10.1177/18479790221118628.
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We examined the impact of firm-generated content on firm-related user-generated content. Researches have proven that firm-related user-generated content impacts firm revenue. Therefore, it is necessary to determine what factors stimulate the creation of firm-related user-generated content. Creating user-generated videos related to companies (e.g. E-sports) on Chinese online video platforms often includes the use of firm-generated videos as material. It may suggest that firm-generated content may play an essential role in influencing Internet users to create firm-related user-generated content. We collected a unique dataset using a Python web crawler and used the LSDV model for empirical analysis, including 2977 firm-generated videos and 49860 user-generated videos, to explore the impact of firm-generated video attributes on user-generated videos. The results show that some attributes of firm-generated video have a significant impact on user-generated video. The number of comments and coins on firm-generated videos positively affects user-generated videos, while the number of favorite on firm-generated videos negatively affects user-generated videos. We also found that the difference in how users feel about firm-generated videos affects the likelihood of users creating their original videos. User engagement and user brand identity in FGV positively impact stimulating user-generated videos. The authors suggest that companies can maximize the impact on user-generated content by targeting the creation of firm-generated content based on these video attributes. The authors also suggest that firms further investigate theories related to motivational factors, such as the impact of consumer engagement, brand identity, and perceived usefulness on user-generated content.
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Salsabila, Iskandarsyah Siregar, and Somadi Sosrohadi. "Analysis of Code Mixing in Jerome Polin Youtube Content “Nihongo Mantappu”." International Journal of Linguistics, Literature and Translation 4, no. 12 (December 7, 2021): 01–08. http://dx.doi.org/10.32996/ijllt.2021.4.12.1.
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Jerome Polin Sijabat is an Indonesian YouTuber. Jerome Polin is known after starting a YouTube channel called Nihongo Mantappu, which shares his personal life in Japan. Apart from speaking Indonesian, Jerome Polin also uses other languages, such as English and Japanese. Jerome Polin's mastery of the language causes code-mixing in the video. This study describes the forms of code-mixing and the factors that cause code-mixing in videos on Jerome Polin's YouTube channel. This study uses a qualitative descriptive method with a sample of conversational quotations. The data collection technique used is the listening method using note-taking techniques and free-involved-talk listening techniques. The results showed that the forms of code-mixing insertion in Jerome Polin's YouTube video include elements of words, phrases, and clauses. The types of code-mixing in Jerome Polin's YouTube videos are outer code-mixing.
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Guangyu, Han. "Analysis of Sports Video Intelligent Classification Technology Based on Neural Network Algorithm and Transfer Learning." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/7474581.
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With the rapid development of information technology, digital content shows an explosive growth trend. Sports video classification is of great significance for digital content archiving in the server. Therefore, the accurate classification of sports video categories is realized by using deep neural network algorithm (DNN), convolutional neural network (CNN), and transfer learning. Block brightness comparison coding (BICC) and block color histogram are proposed, which reflect the brightness relationship between different regions in video and the color information in the region. The maximum mean difference (MMD) algorithm is adopted to achieve the purpose of transfer learning. On the basis of obtaining the features of sports video images, the sports video image classification method based on deep learning coding model is adopted to realize sports video classification. The results show that, for different types of sports videos, the overall classification effect of this method is obviously better than other current sports video classification methods, which greatly improves the classification effect of sports videos.
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Choi, Bogeum, Heejun Kim, and Jina Huh-Yoo. "Seeking Mental Health Support Among College Students in Video-Based Social Media: Content and Statistical Analysis of YouTube Videos." JMIR Formative Research 5, no. 11 (November 11, 2021): e31944. http://dx.doi.org/10.2196/31944.
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Background Mental health is a highly stigmatized disease, especially for young people. Due to its free, accessible format, college students increasingly use video-based social media for many aspects of information needs, including how-to tips, career, or health-related needs. The accessibility of video-based social media brings potential in supporting stigmatized contexts, such as college students’ mental health. Understanding which kinds of videos about college students’ mental health have increased viewer engagement will help build a foundation for exploring this potential. Little research has been done to identify video types systematically, how they have changed over time, and their associations on viewer engagement both short term and long term. Objective This study aims to identify strategies for using video-based social media to combat stigmatized diseases, such as mental health, among college students. We identify who, with what perspective, purpose, and content, makes up the videos available on social media (ie, YouTube) about college students’ mental health and how these factors associate with viewer engagement. We then identify effective strategies for designing video-based social media content for supporting college students’ mental health. Methods We performed inductive content analysis to identify different types of YouTube videos concerning college students’ mental health (N=452) according to video attributes, including poster, perspective, and purpose. Time analysis showed how video types have changed over time. Fisher’s exact test was used to examine the relationships between video attributes. The Mann-Whitney U test was used to test the association between video types and viewer engagement. Lastly, we investigated the difference in viewer engagement across time between two major types of videos (ie, individuals’ storytelling and organization’s informational videos). Results Time trend analysis showed a notable increase in the number of (1) videos by individuals, (2) videos that represent students’ perspectives, and (3) videos that share stories and experiential knowledge over the recent years. Fisher’s exact test found all video attributes (ie, poster, perspective, and purpose) are significantly correlated with each other. In addition, the Mann-Whitney U test found that poster (individual vs organization) and purpose (storytelling vs sharing information) type has a significant association with viewer engagement (P<.001). Lastly, individuals’ storytelling videos had a greater engagement in the short term and the long term. Conclusions The study shows that YouTube videos on college students’ mental health can be well differentiated by the types of posters and the purpose of the videos. Taken together, the videos where individuals share their personal stories, as well as experiential knowledge (ie, tips and advice), engaged more viewers in both the short term and long term. Individuals’ videos on YouTube showed the potential to support college students' mental health in unique ways, such as providing social support, validating experience, and sharing the positive experience of help-seeking.
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Liu, Jingfang, Caiying Lu, and Shuangjinhua Lu. "Research on the Influencing Factors of Audience Popularity Level of COVID-19 Videos during the COVID-19 Pandemic." Healthcare 9, no. 9 (September 4, 2021): 1159. http://dx.doi.org/10.3390/healthcare9091159.
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(1) Background: During the COVID-19 pandemic, users share and obtain COVID-19 information through video platforms, but only a few COVID-19 videos become popular among most audiences. Therefore, it is a very interesting and important research question to explore the influencing factors of the popularity of COVID-19 videos during the COVID-19 pandemic; (2) Our research collects video data related to the keyword “COVID-19” on video platform, the data are analyzed by content analysis and empirical analysis. We then constructed a theoretical model based on the information adoption model; (3) A total of 251 videos were divided into three categories. The least common category was the data and analysis category (11.2%), followed by the prevention and control status category (13.5%); the knowledge and general science category was the most common (75.3%). From the perspective of video quality, the information sources of most videos are relatively reliable, and the content of medical information is low. The research results showed that short video lengths, longer descriptions, more reliable video sources and lower medical information content were more popular with audiences. Audiences are more likely to be attracted to videos in the prevention and control status category and knowledge and general science category. Videos uploaded by uploaders who have a higher influence are more popular with audiences; (4) Conclusions: During the COVID-19 pandemic, information quality (video length, description length, video content type, and medical information and content index) and source credibility (information source reliability, influence and certification type) all significantly influence the popularity level of COVID-19 videos. Our research conclusions can provide management suggestions for the platform, make videos released by uploaders more popular with audiences, and help audiences better understand COVID-19 information and make prevention and control efforts.
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O’Leary, Emmett James. "The ukulele and YouTube: A content analysis of seven prominent YouTube ukulele channels." Journal of Popular Music Education 4, no. 2 (July 1, 2020): 175–91. http://dx.doi.org/10.1386/jpme_00024_1.
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This study is a content analysis of seven prominent ukulele instruction YouTube channels examining the following elements: (1) types of videos on channels, (2) musical content included, (3) teaching strategies used in videos and (4) audience engagement. Tutorial videos were the most common type of video produced and viewed across the channels. Musical content was diverse, including 988 unique songs by 545 unique artists, with 79 per cent of songs and 74 per cent of artists appearing on only one channel. The most viewed tutorials often included songs with connections to the ukulele in popular culture. Video characteristics and teaching methods were similar across all channels, and each taught using a direct instruction method. Channels had engaged audiences who actively commented and voted on videos through the YouTube platform. Findings are discussed in connection to prior research on the ukulele, learning on and through YouTube, and media studies.