{"title":"Age-Based Ranking of YouTube Videos for Improved Parental Controls in Smart TV Environment","authors":"I. Alam, Azhar R Uddin, Shah Khusro","doi":"10.1109/ICETECC56662.2022.10069109","DOIUrl":null,"url":null,"abstract":"YouTube is a popular social media networking site that contains billions of videos. Many YouTube videos target children of different ages with offensive, inappropriate, violent, etc. Numerous one-size-fits-all countermeasures and research work have been deployed and suggested. However, these solutions are ineffective in accurately detecting inappropriate content for a diverse audience of different needs and requirements. In this research work, instead of one-size-fits-all, we consider a mutable age-based context, where different Age-Groups (AG) have different choices and needs. A novel and real-time approach have been proposed to prevent and allow the audience of varying AG towards the diverse content of YouTube, specifically in a smart TV-watching scenario. The proposed system analyses the running video through its metadata for teenagers, children, and adults. In parallel with data, the proposed model captures the viewers in real-time, detects their age, and checks the displayed video against the detected AG for appropriateness. The same system responds to parental consent and overrides its stop/play policies according to the direct input provided by parents/guardians with a diverse psychological setup, developed by their beliefs, religion, and cultural sensitivities. The proposed solution leverages Random Forest Classifier–a supervised text classification approach with 80% accuracy and Convolutional Neural Network for age determination using the Caffe model.","PeriodicalId":364463,"journal":{"name":"2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETECC56662.2022.10069109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
YouTube is a popular social media networking site that contains billions of videos. Many YouTube videos target children of different ages with offensive, inappropriate, violent, etc. Numerous one-size-fits-all countermeasures and research work have been deployed and suggested. However, these solutions are ineffective in accurately detecting inappropriate content for a diverse audience of different needs and requirements. In this research work, instead of one-size-fits-all, we consider a mutable age-based context, where different Age-Groups (AG) have different choices and needs. A novel and real-time approach have been proposed to prevent and allow the audience of varying AG towards the diverse content of YouTube, specifically in a smart TV-watching scenario. The proposed system analyses the running video through its metadata for teenagers, children, and adults. In parallel with data, the proposed model captures the viewers in real-time, detects their age, and checks the displayed video against the detected AG for appropriateness. The same system responds to parental consent and overrides its stop/play policies according to the direct input provided by parents/guardians with a diverse psychological setup, developed by their beliefs, religion, and cultural sensitivities. The proposed solution leverages Random Forest Classifier–a supervised text classification approach with 80% accuracy and Convolutional Neural Network for age determination using the Caffe model.