{"title":"使用数据挖掘技术检测阿拉伯语YouTube垃圾邮件","authors":"Yahya M. Tashtoush, Areen Magableh, Omar Darwish, Lujain Smadi, Omar Alomari, Anood ALghazoo","doi":"10.1109/ISDFS55398.2022.9800840","DOIUrl":null,"url":null,"abstract":"Since YouTube became one of the sources of income, the number of users has increased significantly and the number of spammers who aim to spread viruses or to promote their videos and channels. These behaviors have led many YouTubers to close their channels or to disable the comments because YouTube does not have enough tools to prevent it. Filtering Arabic spam comments is a big challenge at all according to various dialects which hold a huge number of synonyms. In this work, we have classified these comments using different algorithms such as Decision Tree(DT), Support Vector Machine (SVM), Naive Bayes(NB), Random Forest, and k-Nearest Neighbor (k-NN).","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Detecting Arabic YouTube Spam Using Data Mining Techniques\",\"authors\":\"Yahya M. Tashtoush, Areen Magableh, Omar Darwish, Lujain Smadi, Omar Alomari, Anood ALghazoo\",\"doi\":\"10.1109/ISDFS55398.2022.9800840\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since YouTube became one of the sources of income, the number of users has increased significantly and the number of spammers who aim to spread viruses or to promote their videos and channels. These behaviors have led many YouTubers to close their channels or to disable the comments because YouTube does not have enough tools to prevent it. Filtering Arabic spam comments is a big challenge at all according to various dialects which hold a huge number of synonyms. In this work, we have classified these comments using different algorithms such as Decision Tree(DT), Support Vector Machine (SVM), Naive Bayes(NB), Random Forest, and k-Nearest Neighbor (k-NN).\",\"PeriodicalId\":114335,\"journal\":{\"name\":\"2022 10th International Symposium on Digital Forensics and Security (ISDFS)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 10th International Symposium on Digital Forensics and Security (ISDFS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDFS55398.2022.9800840\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDFS55398.2022.9800840","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting Arabic YouTube Spam Using Data Mining Techniques
Since YouTube became one of the sources of income, the number of users has increased significantly and the number of spammers who aim to spread viruses or to promote their videos and channels. These behaviors have led many YouTubers to close their channels or to disable the comments because YouTube does not have enough tools to prevent it. Filtering Arabic spam comments is a big challenge at all according to various dialects which hold a huge number of synonyms. In this work, we have classified these comments using different algorithms such as Decision Tree(DT), Support Vector Machine (SVM), Naive Bayes(NB), Random Forest, and k-Nearest Neighbor (k-NN).