Journal articles on the topic 'Video analysis'
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Luo, Yong, Guochang Zhou, Jianping Li, and Xiao Xiao. "A MOOC Video Viewing Behavior Analysis Algorithm." Mathematical Problems in Engineering 2018 (October 16, 2018): 1–7. http://dx.doi.org/10.1155/2018/7560805.
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MOOCs (massive open online courses) are developing rapidly, but they also face many problems. As the MOOC’s most important resource, the course videos have a very important influence on the learning. This article defines the ratio R (R=Average viewing duration/Video length), which reflects the popularity of the video. By analyzing the relationship between the video length, release time, and R, we found a significant negative linear correlation between video length and R and video release time and R. However, when the number of videos is less than the threshold, the release time has less influence on R. This paper presents a video viewing behavior analysis algorithm based on multiple linear regression. The residual independence test proved that the algorithm has a good approximation to the data. It can predict the popularity of similar course videos to help producers optimize video design.
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Gallis, Michael R. "Artificial Video for Video Analysis." Physics Teacher 48, no. 1 (January 2010): 32–34. http://dx.doi.org/10.1119/1.3274357.
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Kumar, Anil, and Umesh Chandra Jaiswal. "Comparative Analysis of Sentiments in Children with Neurodevelopmental Disorders." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12 (December 29, 2023): e31469. http://dx.doi.org/10.14201/adcaij.31469.
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In-group favoritism is the tendency of people where, individuals tend to punish transgressors with varying intensity based on whether they belong to their own group or not. In this cross-sectional analytical study, we examine matched samples of children with developmental disorders, observing their perspectives on punishment after watching two videos in which rules are broken. Data (video 1) shows a football player from the viewer’s country scoring a handball goal, while in data (video 2), a foreign player replicates the same action against the host nation. Every contestant viewed both videos, and their responses were then compared. Our proposed methods compare and analyze the data to determine player’s opinions using artificial intelligence-based machine learning such as text analysis and opinion, extract on- favorable, unfavorable, neutral feelings, or emotions. In both sets of data, the autism spectrum disorder (ASD) group displayed negative emotions for both video 1 (M = −.1; CI 90% −.41 to .21) and video 2 (t (7) = 1.54, p =.12; M = -.42; CI 90% 76 to -.08). On the contrary, the groups with attention deficit hyperactivity disorder (ADHD), learning disabilities (LD), and intellectual disability (ID) had a favorable reaction to video1 but an unfavorable reaction to video 2. Children diagnosed with ASD typically display a consistent adherence to rules, even when those breaking the rules are not part of their group. This behavior may be linked to lower levels of empathy.
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Tait, D. Margaret. "Video Analysis." Ear and Hearing 14, no. 6 (December 1993): 378–89. http://dx.doi.org/10.1097/00003446-199312000-00002.
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Chang, Yuchou, and Hong Lin. "Irrelevant frame removal for scene analysis using video hyperclique pattern and spectrum analysis." Journal of Advanced Computer Science & Technology 5, no. 1 (February 6, 2016): 1. http://dx.doi.org/10.14419/jacst.v5i1.4035.
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<p>Video often include frames that are irrelevant to the scenes for recording. These are mainly due to imperfect shooting, abrupt movements of camera, or unintended switching of scenes. The irrelevant frames should be removed before the semantic analysis of video scene is performed for video retrieval. An unsupervised approach for automatic removal of irrelevant frames is proposed in this paper. A novel log-spectral representation of color video frames based on Fibonacci lattice-quantization has been developed for better description of the global structures of video contents to measure similarity of video frames. Hyperclique pattern analysis, used to detect redundant data in textual analysis, is extended to extract relevant frame clusters in color videos. A new strategy using the k-nearest neighbor algorithm is developed for generating a video frame support measure and an h-confidence measure on this hyperclique pattern based analysis method. Evaluation of the proposed irrelevant video frame removal algorithm reveals promising results for datasets with irrelevant frames.</p>
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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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Al-Tamimi, Abdel-Karim, Raj Jain, and Chakchai So-In. "High-Definition Video Streams Analysis, Modeling, and Prediction." Advances in Multimedia 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/539396.
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High-definition video streams' unique statistical characteristics and their high bandwidth requirements are considered to be a challenge in both network scheduling and resource allocation fields. In this paper, we introduce an innovative way to model and predict high-definition (HD) video traces encoded with H.264/AVC encoding standard. Our results are based on our compilation of over 50 HD video traces. We show that our model, simplified seasonal ARIMA (SAM), provides an accurate representation for HD videos, and it provides significant improvements in prediction accuracy. Such accuracy is vital to provide better dynamic resource allocation for video traffic. In addition, we provide a statistical analysis of HD videos, including both factor and cluster analysis to support a better understanding of video stream workload characteristics and their impact on network traffic. We discuss our methodology to collect and encode our collection of HD video traces. Our video collection, results, and tools are available for the research community.
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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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Riudin, Hartini, Kasman Arifin, and Murni Sabilu. "Analysis of project-based learning videos on biology subjects." BIO-INOVED : Jurnal Biologi-Inovasi Pendidikan 4, no. 2 (June 26, 2022): 201. http://dx.doi.org/10.20527/bino.v4i2.12753.
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This study aims to analyze project-based learning videos on biology subjects on YouTube. This type of research is descriptive research with qualitative approach. The object of the research was a real teaching video about project-based learning in biology subjects on YouTube. The instrument in this research is an observation sheet that contains aspects of the activities carried out in project-based learning which have been validated by three learning experts. The data analysis technique used is descriptive analysis, by observing learning videos and evaluating through instrument, then the data is processed and classified into categories. The results of observations of four video real teaching project-based learning in biology subjects on YouTube are in the good and sufficient categories. Each video category is video 1 received a score of 85 which was classified as good category, the videos 2 and 3 scored 71 in the good category, and the video 4 scored 61 in the sufficient category.Abstrak Penelitian ini bertujuan untuk menganalisis video pembelajaran berbasis proyek pada mata pelajaran biologi di YouTube. Jenis penelitian ini adalah penelitian deskriptif dengan pendekatan kualitatif. Objek penelitian berupa video real teaching mengenai pembelajaran berbasis proyek pada mata pelajaran Biologi di YouTube. Instrumen dalam penelitian ini berupa lembar pengamatan yang berisi aspek-aspek kegiatan yang dilakukan dalam pembelajaran berbasis proyek yang telah divalidasi oleh tiga pakar pembelajaran. Teknik analisis data yang digunakan adalah analisis deskriptif, dengan mengamati video pembelajaran dan dilakukan penilaian melalui instrumen, kemudian data diolah dan diklasifikasikan dalam kategori. Hasil pengamatan terhadap empat video real teaching pembelajaran berbasis proyek pada mata pelajaran biologi di YouTube berada pada kategori baik dan cukup. Masing-masing kategori video yaitu video 1 memperoleh nilai sebesar 85 tergolong kategori baik, video 2 dan 3 memperoleh nilai sebesar 71 tergolong kategori baik, dan video 4 memperoleh nilai sebesar 61 tergolong kategori cukup.
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Kamble, Shailesh D., Dilip Kumar Jang Bahadur Saini, Sachin Jain, Kapil Kumar, Sunil Kumar, and Dharmesh Dhabliya. "A novel approach of surveillance video indexing and retrieval using object detection and tracking." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 341–50. http://dx.doi.org/10.47974/jim-1665.
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The problem of searching videos in large databases i.e. multimedia applications is a major challenge. Therefore, video indexing is used to search the location of the particular video in a large database quickly. Quickly locating the video in the large database is the good quality of indexing. Still, there is a scope of improvement in quickly searching a video in a large database in terms of assigning labels to video. In computer vision, real-time object detection and tracking is a gigantic, vibrant yet indecisive and intricate area. You only look once (YOLO) algorithm is used to detect the object and background subtraction is used to track the object. In this paper, video indexing using object detection / tracking can be performed on single object in a video. In future, video indexing can be performed on multiple objects in a video.
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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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He, Tianyao, Huabin Liu, Yuxi Li, Xiao Ma, Cheng Zhong, Yang Zhang, and Weiyao Lin. "Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (March 24, 2024): 2112–20. http://dx.doi.org/10.1609/aaai.v38i3.27983.
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Video Correlation Learning (VCL), which aims to analyze the relationships between videos, has been widely studied and applied in various general video tasks. However, applying VCL to instructional videos is still quite challenging due to their intrinsic procedural temporal structure. Specifically, procedural knowledge is critical for accurate correlation analyses on instructional videos. Nevertheless, current procedure-learning methods heavily rely on step-level annotations, which are costly and not scalable. To address this problem, we introduce a weakly supervised framework called Collaborative Procedure Alignment (CPA) for procedure-aware correlation learning on instructional videos. Our framework comprises two core modules: collaborative step mining and frame-to-step alignment. The collaborative step mining module enables simultaneous and consistent step segmentation for paired videos, leveraging the semantic and temporal similarity between frames. Based on the identified steps, the frame-to-step alignment module performs alignment between the frames and steps across videos. The alignment result serves as a measurement of the correlation distance between two videos. We instantiate our framework in two distinct instructional video tasks: sequence verification and action quality assessment. Extensive experiments validate the effectiveness of our approach in providing accurate and interpretable correlation analyses for instructional videos.
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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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Liu, Yuyang, Ce Zhu, Min Mao, Fangliang Song, Frederic Dufaux, and Xiang Zhang. "Video analytical coding: When video coding meets video analysis." Signal Processing: Image Communication 67 (September 2018): 48–57. http://dx.doi.org/10.1016/j.image.2018.05.012.
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Garn, H., G. Kloesch, D. Wong, G. Mcallister, A. Barbosa, E. Vatikiotis-Bateson, S. Stockler, and O. Ipsiroglu. "Expert video analysis (EVA)-video-viewer-prototype for annotating sleep/wake behaviour videos." Sleep Medicine 16 (December 2015): S30. http://dx.doi.org/10.1016/j.sleep.2015.02.074.
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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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Cambron, Julia C., Kirk D. Wyatt, Christine M. Lohse, Page Y. Underwood, and Thomas R. Hellmich. "Medical Videography Using a Mobile App: Retrospective Analysis." JMIR mHealth and uHealth 7, no. 12 (December 3, 2019): e14919. http://dx.doi.org/10.2196/14919.
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Background As mobile devices and apps grow in popularity, they are increasingly being used by health care providers to aid clinical care. At our institution, we developed and implemented a point-of-care clinical photography app that also permitted the capture of video recordings; however, the clinical findings it was used to capture and the outcomes that resulted following video recording were unclear. Objective The study aimed to assess the use of a mobile clinical video recording app at our institution and its impact on clinical care. Methods A single reviewer retrospectively reviewed video recordings captured between April 2016 and July 2017, associated metadata, and patient records. Results We identified 362 video recordings that were eligible for inclusion. Most video recordings (54.1%; 190/351) were captured by attending physicians. Specialties recording a high number of video recordings included orthopedic surgery (33.7%; 122/362), neurology (21.3%; 77/362), and ophthalmology (15.2%; 55/362). Consent was clearly documented in the medical record in less than one-third (31.8%; 115/362) of the records. People other than the patient were incidentally captured in 29.6% (107/362) of video recordings. Although video recordings were infrequently referenced in notes corresponding to the clinical encounter (12.2%; 44/362), 7.7% (22/286) of patients were video recorded in subsequent clinical encounters, with 82% (18/22) of these corresponding to the same finding seen in the index video. Store-and-forward telemedicine was documented in clinical notes in only 2 cases (0.5%; 2/362). Videos appeared to be of acceptable quality for clinical purposes. Conclusions Video recordings were captured in a variety of clinical settings. Documentation of consent was inconsistent, and other individuals were incidentally included in videos. Although clinical impact was not always clearly evident through retrospective review because of limited documentation, potential uses include documentation for future reference and store-and-forward telemedicine. Repeat video recordings of the same finding provide evidence of use to track the findings over time. Clinical video recordings have the potential to support clinical care; however, documentation of consent requires standardization.
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Abbott, David. "The LivePhoto Physics videos and video analysis site." Physics Teacher 47, no. 6 (September 2009): 396. http://dx.doi.org/10.1119/1.3204133.
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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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Shi, Ao, Joe El Haddad, Peng Cai, Shijie Song, Yuan Jia Wang, Qi Liu, and Pengyang Li. "Mpox (monkeypox) information on TikTok: analysis of quality and audience engagement." BMJ Global Health 8, no. 3 (March 2023): e011138. http://dx.doi.org/10.1136/bmjgh-2022-011138.
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BackgroundOnline information on mpox (monkeypox) is not well studied. We have analysed the video content, information quality, and audience engagement of mpox-related videos on TikTok.MethodsUsing a hashtag-based searching strategy, we identified 2462 mpox-related videos on TikTok from 1 January to 11 August 2022; 85 were included after exclusion criteria screening. Videos were evaluated for content on features and treatment of mpox. Video and information quality was assessed using the DISCERN instrument and theJournal of the American Medical Association(JAMA) criteria. We recorded video source, evaluation scores, and viewer engagement metrics. The Kruskal-Wallis test was used for statistical analysis and multiple linear regression for factor-association studies.ResultsOf the 85 videos, two assessed all content topics and highlighted 33% of all content items in clinical guidelines. The overall average score for the videos was 39.56 of 80 on the DISCERN instrument and 1.93 of 4 on the JAMA criteria. No video met all JAMA criteria. Subgroup analysis based on author identity suggested the variance in video scores by source (p<0.05 for all). Overall scores were higher for videos produced by doctors and science communicators than for those made by institutional users, nurses, and the general public. Multiple linear regression analysis showed that having people in the video (69.20, p=0.0001) and including information on treatment choices (1.15, p=0.045) were significant, independent determinants of audience engagement.ConclusionPublic-directed TikTok videos on mpox frequently provide incomplete, inaccurate information, highlighting the potential risks of using TikTok as a health information source.
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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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Thakur, Nirmalya, Shuqi Cui, Victoria Knieling, Karam Khanna, and Mingchen Shao. "Investigation of the Misinformation about COVID-19 on YouTube Using Topic Modeling, Sentiment Analysis, and Language Analysis." Computation 12, no. 2 (February 6, 2024): 28. http://dx.doi.org/10.3390/computation12020028.
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The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas—Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories Regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative. For the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative. For the videos belonging to the theme of Conspiracy Theories Regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative. For the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were neutral. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions for each topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels, such as Facebook and Twitter, were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of tweets and the correlation between the length of the video title and the number of Facebook posts were statistically significant.
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Yan, Bingbing, Chixiang Ma, Mingfei Wang, and Ana Isabel Molina. "Application of Short Video Semantic Understanding Technology Based on Big Data Analysis in Education Management." International Journal of Web-Based Learning and Teaching Technologies 19, no. 1 (December 11, 2023): 1–20. http://dx.doi.org/10.4018/ijwltt.334708.
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With the emergence of short video and the development of mobile internet, short video software, such as TikTok and Kwai, has emerged. Based on the semantic understanding technology of teaching short videos, a teaching management platform was built to push healthy and positive short video for students' content in a targeted way. Taking the 21st grade students majoring in Chinese in Guizhou Normal University as an example, the authors discusses the effect of teaching management platform on college students. In this process, the following conclusions are drawn: (1) Among college students, the viewing rate of short videos has exceeded 95%, and short videos have become an indispensable entertainment for most college students. (2) Through short video semantic understanding technology and short video screening program, excellent short video can be effectively pushed to students. (3) The actual effect shows that the short video teaching management platform can effectively improve the values of the cultural level of students.
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Li, Jiangfeng, Ziyu Li, Xiaofeng Ma, Qinpei Zhao, Chenxi Zhang, and Gang Yu. "Sentiment Analysis on Online Videos by Time-Sync Comments." Entropy 25, no. 7 (July 2, 2023): 1016. http://dx.doi.org/10.3390/e25071016.
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Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.
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Gadhia, Bijal U., and Shahid S. Modasiya. "An Evaluation-based Analysis of Video Summarising Methods for Diverse Domains." Journal of Innovative Image Processing 5, no. 2 (June 2023): 127–39. http://dx.doi.org/10.36548/jiip.2023.2.005.
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As technology progresses, a gigantic amount of video data is generated day-by-day. Processing of such a huge video requires time, increased storage, and a computational power. Sometimes it is convenient for the user to watch a summary or highlight rather than watching a complete video, which is a time-consuming task. So, a fully automated solution is required to extract important segments from video. Researchers have proposed multiple approaches / techniques for summarizing the videos which resolve the problem of long videos and summarize them according to the video type. This survey and comparative evaluation of video summarizing techniques based on several domains are presented in this study. Primarily, these methods are classified into different categories based on their methods or techniques used. Furthermore, an overview of some of the latest literature is presented with the dataset and the evaluation approaches used. The review is also made related to the domain direction, and is concluded by presenting the benefits and difficulties associated with the current video summarization techniques.
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Essel, Daniel Danso, Ben-Bright Benuwa, and Benjamin Ghansah. "Video Semantic Analysis." International Journal of Computer Vision and Image Processing 11, no. 2 (April 2021): 1–21. http://dx.doi.org/10.4018/ijcvip.2021040101.
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Sparse Representation (SR) and Dictionary Learning (DL) based Classifier have shown promising results in classification tasks, with impressive recognition rate on image data. In Video Semantic Analysis (VSA) however, the local structure of video data contains significant discriminative information required for classification. To the best of our knowledge, this has not been fully explored by recent DL-based approaches. Further, similar coding findings are not being realized from video features with the same video category. Based on the foregoing, a novel learning algorithm, Sparsity based Locality-Sensitive Discriminative Dictionary Learning (SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experimental results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with state-of-the-art approaches. The proposed approach also shows robustness to diverse video environments, proving the universality of the novel approach.
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Zhang, Songhai, Yifei Zhang, Tao Chen, Peter M. Hall, and Ralph Martin. "Video structure analysis." Tsinghua Science and Technology 12, no. 6 (December 2007): 714–18. http://dx.doi.org/10.1016/s1007-0214(07)70180-3.
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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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Pal, Ratnabali, Arif Ahmed Sekh, Debi Prosad Dogra, Samarjit Kar, Partha Pratim Roy, and Dilip K. Prasad. "Topic-based Video Analysis." ACM Computing Surveys 54, no. 6 (July 2021): 1–34. http://dx.doi.org/10.1145/3459089.
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Manual processing of a large volume of video data captured through closed-circuit television is challenging due to various reasons. First, manual analysis is highly time-consuming. Moreover, as surveillance videos are recorded in dynamic conditions such as in the presence of camera motion, varying illumination, or occlusion, conventional supervised learning may not work always. Thus, computer vision-based automatic surveillance scene analysis is carried out in unsupervised ways. Topic modelling is one of the emerging fields used in unsupervised information processing. Topic modelling is used in text analysis, computer vision applications, and other areas involving spatio-temporal data. In this article, we discuss the scope, variations, and applications of topic modelling, particularly focusing on surveillance video analysis. We have provided a methodological survey on existing topic models, their features, underlying representations, characterization, and applications in visual surveillance’s perspective. Important research papers related to topic modelling in visual surveillance have been summarized and critically analyzed in this article.
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Smith, Peter E., James McGuire, Michael Falci, Dilli Ram Poudel, Richard Kaufman, Mary Ann Patterson, Benjamin Pelleschi, and Esther Shin. "Analysis of YouTube as a Source of Information for Diabetic Foot Care." Journal of the American Podiatric Medical Association 109, no. 2 (March 1, 2019): 122–26. http://dx.doi.org/10.7547/16-149.
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Background: Increasing amounts of diabetes-focused content is being posted to YouTube with little regulation as to the quality of the content. Diabetic education has been shown to reduce the risk of ulceration and amputation. YouTube is a frequently visited site for instructional and demonstrational videos posted by individuals, advertisers, companies, and health-care organizations. We sought to evaluate the usefulness of diabetic foot care video information on YouTube. Methods: YouTube was queried using the keyword phrase diabetic foot care. Original videos in English, with audio, less than 10 min long within the first 100 video results were evaluated. Two reviewers classified each video as useful or nonuseful/misleading. A 14-point usefulness criteria checklist was used to further categorize videos as most useful, somewhat useful, or nonuseful/misleading. Video sources were categorized by user type, and additional video metrics were collected. Results: Of 87 included videos, 56 (64.4%), were classified as useful and 31 (35.6%) as nonuseful/misleading. A significant difference in the mean length of useful videos vs nonuseful/misleading videos was observed (3.33 versus 1.73 min; P < .0001). There was no significant difference in terms of popularity metrics (likes, views, subscriptions, etc) between useful and nonuseful/misleading videos. Conclusions: This study demonstrates that although most diabetic foot care videos on YouTube are useful, many are still nonuseful/misleading. More concerning is the lack of difference in popularity between useful and nonuseful videos. Podiatric physicians should alert patients to possibly misleading information and offer a curated list of videos.
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Zekovic, Amela, and Irini Reljin. "Multifractal analysis of multiview 3D video with different quantization parameters applying histogram method." Serbian Journal of Electrical Engineering 11, no. 1 (2014): 25–34. http://dx.doi.org/10.2298/sjee131130003z.
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In this paper, multifractal properties of multiview 3D video are determined. Multifractal spectra are determined by using the histogram method. For the analysis of multiview video, long video traces are used, for multiview video with two views. Differences between multifractal properties of different views of multiview video and different types of frames are highlighted. Additional analysis was performed for the left view of multiview 3D videos for different quantization parameters of the frames.
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Peng, Pengfei, Guoqing Liang, and Tao Luan. "Multi-View Inconsistency Analysis for Video Object-Level Splicing Localization." International Journal of Emerging Technologies and Advanced Applications 1, no. 3 (April 24, 2024): 1–5. http://dx.doi.org/10.62677/ijetaa.2403111.
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In the digital era, the widespread use of video content has led to the rapid development of video editing technologies. However, it has also raised concerns about the authenticity and integrity of multimedia content. Video splicing forgery has emerged as a challenging and deceptive technique used to create fake video objects, potentially for malicious purposes such as deception, defamation, and fraud. Therefore, the detection of video splicing forgery has become critically important. Nevertheless, due to the complexity of video data and a lack of relevant datasets, research on video splicing forgery detection remains relatively limited. This paper introduces a novel method for detecting video object splicing forgery, which enhances detection performance by deeply exploring inconsistent features between different source videos. We incorporate various feature types, including edge luminance, texture, and video quality information, and utilize a joint learning approach with Convolutional Neural Network (CNN) and Vision Transformer (ViT) models. Experimental results demonstrate that our method excels in detecting video object splicing forgery, offering promising prospects for further advancements in this field.
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Liu, Jie, and Haiping Lv. "Recommendation of Micro Teaching Video Resources Based on Topic Mining and Sentiment Analysis." International Journal of Emerging Technologies in Learning (iJET) 17, no. 06 (March 29, 2022): 243–56. http://dx.doi.org/10.3991/ijet.v17i06.30011.
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Video learning resources are preferred by many students, owing to their intuitiveness and attractiveness. It is of practical significance to study the recommendation methods of video learning resources. Most of the existing research methods treat the scoring matrix as the main element, failing to consider video contents and learner interests. As a result, few of them can realize precise recommendation of videos. To solve the problem, this paper explores the recommendation of micro teaching video resources based on topic mining and sentiment analysis. Firstly, the dialog text features of English dialog videos and learner interest features were mined based on the deep word vector, and a topic mining model was established to achieve similarity-based resource recommendation. Next, the micro teaching videos with text information were subjected to sentiment analysis, improving the pushing accuracy of micro teaching videos. Finally, the scientific nature of our algorithm was demonstrated through experiments.
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Jiang, Shuhan. "Analysis of the Rise of Douyin." Lecture Notes in Education Psychology and Public Media 4, no. 1 (May 17, 2023): 230–36. http://dx.doi.org/10.54254/2753-7048/4/20220264.
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Douyin is a short video-sharing mobile app that is similar to famous social media platforms like Instagram and Tiktok. The paper highlights the key features of Douyin, and its effect on short-video marketing in the field of literature and research. The paper will introduce the development of short video marketing, discuss the impact of Douyin on short video market, and provide suggestions for companies to better utilize Douyin. Moreover, the paper will highlight the development trends of Douyin and its impact on short video marketing in the future. The research method applied in the paper was focused on the four channels of sales promotion, individual behavior, content creation and influencer endorsement. The survey included companies such as Instagram, Twitter, and Snapchat. Document analysis included the specific promotion channels, the promotion time frame and promotion percentage. The results of this research showed that almost three-quarters of the companies (72.2%) employed social media marketing strategies. Douyin has played a significant role in the rise of short video marketing. Douyin has opened a new channel for product promotion and brand-building activities. Douyin's rapid development has brought about a change in distribution strategies of online content. Short videos are more attractive than traditional videos because of their shortness and popularity among people. In addition, it can make product promotion more effective and convenient because companies do not have to pay for ads on TV advertisements anymore due to the high cost. Douyin can also be used as a channel for recruitment and influencer endorsements. The rapid development of Douyin has brought about changes in how people consume media.
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Koçyiğit, Burhan Fatih, Ahmet Akyol, and Ahmet Riza Şahin. "ANALYSIS OF YOUTUBE VIDEOS ON PULMONARY REHABILITATION IN COVID-19." Central Asian Journal of Medical Hypotheses and Ethics 2, no. 1 (April 2, 2021): 36–42. http://dx.doi.org/10.47316/cajmhe.2021.2.1.06.
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Introduction: YouTube is a popular social media platform frequently searched by online users for retrieving health-related information. Pulmonary rehabilitation programs have an important place in the COVID-19 treatment protocols. The aim of this study was to evaluate COVID-19 pulmonary rehabilitation videos on YouTube.
Methods: A total of 180 videos tagged with the search terms “COVID-19 pulmonary rehabilitation”, “COVID-19 pulmonary exercise” and “COVID-19 pulmonary physiotherapy” were retrieved. Of these, 63 videos met the study inclusion criteria. The Global Quality Scale (GQS) and the modified DISCERN tool were performed for quality and reliability assessments. Duration of video, upload date, number of views, likes, dislikes, and comments were recorded. Video sources were determined.
Results: Of the total 63 videos, 22 (34.9%) were classified in the high-quality group, 19 (30.2%) intermediate quality group, and 22 (34.9%) low quality group. The main sources of the high-quality videos were universities and physicians. Others, patients, independent users and health related websites produced high rates of low-quality videos. No significant difference was detected in views, likes, dislikes, and comments per day between the quality groups (p > 0.05).
Conclusion: Numbers of high, intermediate and low-quality videos were very close to each other. It is necessary to consider the video sources in order to find videos that contains accurate information. Video parameters other than sources should not be considered as quality indicators.
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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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Exarchakos, George, Luca Druda, Vlado Menkovski, and Antonio Liotta. "Network analysis on Skype end-to-end video quality." International Journal of Pervasive Computing and Communications 11, no. 1 (April 7, 2015): 17–42. http://dx.doi.org/10.1108/ijpcc-08-2014-0044.
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Purpose – This paper aims to argue on the efficiency of Quality of Service (QoS)-based adaptive streaming with regards to perceived quality Quality of Experience (QoE). Although QoS parameters are extensively used even by high-end adaptive streaming algorithms, achieved QoE fails to justify their use in real-time streaming videos with high motion. While subjective measurements of video quality are difficult to be applied at runtime, objective QoE assessment can be easier to automate. For end-to-end QoS optimization of live streaming of high-motion video, objective QoE is a more applicable approach. This paper contributes to the understanding of how specific QoS parameters affect objective QoE measurements on real-time high-motion video streaming. Design/methodology/approach – The paper approached the question through real-life and extensive experimentation using the Skype adaptive mechanisms. Two Skype terminals were connected through a QoS impairment box. A reference video was used as input to one Skype terminal and streamed on one direction. The impairment box was stressing the stream with different conditions. Received video was stored and compared against the reference video. Findings – After the experimental analysis, the paper concludes that adaptive mechanisms based on QoS-related heuristics fail to follow unexpected changes to stream requirements. High-motion videos are an example of this variability, which makes the perceived quality sensitive to jitter more than to packet loss. More specifically, Skype seems to use if-else heuristics to decide its behavior to QoS changes. The weaknesses to high-motion videos seem to lie on this rigidity. Research limitations/implications – Due to the testbed developed, the results may be different if experiments are run over networks with simultaneous streams and a variety of other traffic patterns. Finally, other streaming clients and algorithms would contribute to a more reliable generalization. Practical implications – The paper motivates video streaming engineers to emphasize their efforts toward QoE and end-to-end optimization. Originality/value – The paper identifies the need of a generic adaptive streaming algorithm able to accommodate a big range of video characteristics. The effect of QoS variability to high-motion video streaming helps in modeling and design.
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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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Chen, Yirui. "Analysis of Bilibilis Profit Model." Advances in Economics, Management and Political Sciences 74, no. 1 (April 17, 2024): 75–79. http://dx.doi.org/10.54254/2754-1169/74/20241500.
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The main popular videos are short videos these years, Bilibili is still popular among young people and earns a lot of revenue even though they are focused on long videos of their unique products. In this paper, an introduction about the general background of video websites and the background of Bilibili is given, and others progress in this topic is also presented. Moreover, the situations of other video websites and Bilibili are analyzed in the second part. This part talks about the situations and reasons why consumers choose them in this era. Then the analysis of sources of profits online and offline is conducted. This paper found that in both online and offline sections, mobile games occupy the largest amount of revenue but the proportion of mobile games occupied in the general revenue is declining.
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Wang, Han, Shangyu Xie, and Yuan Hong. "VideoDP: A Flexible Platform for Video Analytics with Differential Privacy." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (October 1, 2020): 277–96. http://dx.doi.org/10.2478/popets-2020-0073.
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AbstractMassive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.
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Nita Sukma Fitria. "A Semiotics Analysis Found on Music Video of You Belong with Me by Taylor Swift." PRAGMATICA : Journal of Linguistics and Literature 1, no. 1 (July 17, 2023): 39–46. http://dx.doi.org/10.60153/pragmatica.v1i1.17.
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This research entitles “A Semiotics Analysis Found on Music Vidio of You Belong with Me”.The aim of this research was to investigate and analyze the verbal and visual signs and the meaning itself in the music video of “You Belong with Me” by Taylor Swift. The type of this research was qualitative research. In collecting data, the writer used the method of observation and documentation by classifying videos into pictures in the form of sequences.The results of this study indicate that the semiotic signs contained in this music video are in the form of visual displays contained in body language in the music video which tells about a male friend that Swift likes who actually has a lover, and verbal signs contained in the music video is a paper that contains writing that is used to communicate. Based on the result of the analysis,it can be concluded as there are two classifications,namely: verbal sign and visual sign. In verbal sign, it was found eight data. In visual sign, it was found seven data. The concept of music video of You Belong With Me describe someone who is in love with someone where that person has been with a lover who doesn't appreciate it at all. In the data found, verbal and visual sign explained about caring, disappointment, jealousy, and express feelings.
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Kristiana, Uning, Alfirna Rizqi Lahitani, Chanief Budi Setiawan, and Nafisa Alfi Sa'diya. "Analisis Forensik Digital Pada Komentar Youtube Live Menggunakan Sentiment Analysis." Teknomatika: Jurnal Informatika dan Komputer 15, no. 1 (May 27, 2023): 23–27. http://dx.doi.org/10.30989/teknomatika.v15i1.1115.
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The development of increasingly sophisticated technology can have a positive influence on various aspects of our daily lives. From the survey results of the Indonesian Internet Services Association (APJII) in the second quarter of 2019-2020, it shows that Internet users of the operator spend more time watching online videos. Youtube video content watching is open to the public and all ages can freely watch it. However, the content and comments are not necessarily suitable for audiences of all ages to read. Of course, Youtube video content can also affect behavior, especially minors.The purpose of this research is to conduct digital forensic analysis on Youtube Live Comments using sentiment analysis.The research method used applies the NIST SP 800-86 method, namely Collection, Examination, Analysis, and Reporting. Sentiment analysis resulted in 0.01 in the comments on the two videos tested, namely the PUBG and Free Fire video games. Sentiment analysis resulted in 0.01 in the comments on the two videos tested, namely the PUBG and Free Fire video games.
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Adenowo, A. A., and L. F. Oderinu. "PERFORMANCE ANALYSIS OF ENHANCED VIDEO ENCRYPTION ALGORITHM." Engineering and Technology Research Journal 5, no. 2 (September 20, 2020): 67–75. http://dx.doi.org/10.47545/etrj.2020.5.2.066.
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The prevalence of internet, as well as low-cost mobile computing devices, makes video the preferred option for information archival and transmission. Also, the geometric growth in the generation and use of digital videos is massive and challenging to determine. Meanwhile, this growth has come with security and privacy issues such as unauthorized access, piracy, hacking and other digital attack every year. Restricting unauthorized access measure can be adopted to protect multimedia information, but does not guarantee the physical security of information. Cryptography which is a better and more secure approach is therefore required. Thus, this paper presents performance evaluation of Enhanced Novel Selective Video Encryption Algorithm, an AES-based video encryption algorithm. Properties of I frame are used randomly to generate the encryption key and a jump factor is used to determine which of the remaining P and B frame is selected for encryption. The results show that the more frames selected for encryption/decryption, the higher the encryption/decryption time and the higher the encrypted video size. Also, the PSNR value of this algorithm is around 40dB which indicates that the quality of the decrypted video is as high as that of the original video. The performance of this algorithm does not diminish with increase in size of video and number of frames. Hence, this algorithm is scalable, fast and highly secure for video encryption.
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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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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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Elshenawy, Ahmad, Steele Carter, and Daniela Braga. "It’s Not Just What You Say, But How You Say It: Muiltimodal Sentiment Analysis Via Crowdsourcing." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (March 28, 2016): 9–15. http://dx.doi.org/10.1609/hcomp.v3i1.13264.
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This paper examines the effect of various modalities of expression on the reliability of crowdsourced sentiment polarity judgments. A novel corpus of YouTube video reviews was created, and sentiment judgments were obtained via Amazon Mechanical Turk. We created a system for isolating text, video, and audio modalities from YouTube videos to ensure that annotators could only see the particular modality or modalities being evaluated. Reliability of judgments was assessed using Fleiss Kappa inter-annotator agreement values. We found that the audio only modality produced the most reliable judgments for video fragments and that across modalities video fragments are less ambiguous than full videos.
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Cha, Sang Yoon, and Hyunho Kim. "Policy Suggestions through Social Perception Analysis of Defense YouTube Channels." Crisis and Emergency Management: Theory and Praxis 13, no. 7 (July 31, 2023): 19–36. http://dx.doi.org/10.14251/jscm.2023.7.19.
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YouTube is a platform that delivers video content on diverse topics and enables communication, making it the most utilized service for conveying knowledge and exchanging information. The Korean Army is also proactively operating official Defense YouTube channels related to the military and producing videos to promote a positive perception and instill faith in the military among the general public and soldiers. However, there is a lack of research on the current status of the content and societal perceptions of defense-related channels. To understand societal awareness, we qualitatively analyzed video content and comments. In addition, we used big data analysis methods such as keyword derivation, term frequency analysis, word cloud visualization, word association analysis, and sentiment analysis of comments based on a Korean sentiment dictionary. These analyses identify current defense channel strategies and society's awareness of these strategies and provide a foundation for improving and perfecting future video creation and operation processes.
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Duan, Feifei, and Xiawei Lu. "Analysis of College Art Teaching System under the Background of Video Big Data Technology." Mathematical Problems in Engineering 2022 (June 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/2720959.
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Art teaching needs not only learning art knowledge but also a lot of practice and aesthetic appreciation. However, traditional teaching methods cannot provide students with a large number of relevant learning materials, which is not conducive to improving students’ classroom enthusiasm. This paper presents the design and implementation of college art teaching system based on video big data technology and combines video recommendation algorithm with the Django teaching video website. The system analyzes the preference needs according to the behavior data of students watching videos and recommends videos for students. At the same time, the system can also evaluate the quality of the video content according to the behavior of students watching videos, reverse classify the video, and then optimize the recommendation results. The video recommendation algorithm model based on user behavior is better than the traditional collaborative filtering recommendation algorithm and fully connected neural network collaborative filtering algorithm. It can reduce the range of users who need similarity calculation and improve the accuracy of recommendation algorithm. The experimental results show that the fully connected neural network collaborative filtering algorithm has good recommendation performance and stability, can reduce the computational complexity, and can improve the recommendation accuracy. The teaching technology integrated through the Internet can greatly improve students’ enthusiasm for art teaching.
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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.