Detecting Spam albums in online social network
A supervised learning solution to detect Spam albums instead of spammers in Photo Spam was proposed.Specifically,the characteristics of Photo Spam and the differences between Photo Spam and traditional Spam were analyzed.Then 12 features which were extracted easily and calculated efficiently were co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2016-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016180/ |
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| _version_ | 1841539616686997504 |
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| author | Shao-qing LYU Yu-qing ZHANG Dong-hang LIU Guang-hua ZHANG |
| author_facet | Shao-qing LYU Yu-qing ZHANG Dong-hang LIU Guang-hua ZHANG |
| author_sort | Shao-qing LYU |
| collection | DOAJ |
| description | A supervised learning solution to detect Spam albums instead of spammers in Photo Spam was proposed.Specifically,the characteristics of Photo Spam and the differences between Photo Spam and traditional Spam were analyzed.Then 12 features which were extracted easily and calculated efficiently were constructed based on the analysis.Next a classification model was built with a dataset of 2 356 labeled albums to identify Spam albums.The model provided excellent performance with true positive rates of Spam albums and normal albums,reaching 100% and 98.2% respectively.Finally,the detection model were applied to 315 115 unlabeled albums and detected 89 163 spam albums with a true positive rate of 97.2%. |
| format | Article |
| id | doaj-art-db0e4d02f04140d2836b2bc409d32d3c |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2016-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-db0e4d02f04140d2836b2bc409d32d3c2025-01-14T06:55:57ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2016-09-0137759159703377Detecting Spam albums in online social networkShao-qing LYUYu-qing ZHANGDong-hang LIUGuang-hua ZHANGA supervised learning solution to detect Spam albums instead of spammers in Photo Spam was proposed.Specifically,the characteristics of Photo Spam and the differences between Photo Spam and traditional Spam were analyzed.Then 12 features which were extracted easily and calculated efficiently were constructed based on the analysis.Next a classification model was built with a dataset of 2 356 labeled albums to identify Spam albums.The model provided excellent performance with true positive rates of Spam albums and normal albums,reaching 100% and 98.2% respectively.Finally,the detection model were applied to 315 115 unlabeled albums and detected 89 163 spam albums with a true positive rate of 97.2%.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016180/social network securityPhoto SpamSpam detectionRenRen |
| spellingShingle | Shao-qing LYU Yu-qing ZHANG Dong-hang LIU Guang-hua ZHANG Detecting Spam albums in online social network Tongxin xuebao social network security Photo Spam Spam detection RenRen |
| title | Detecting Spam albums in online social network |
| title_full | Detecting Spam albums in online social network |
| title_fullStr | Detecting Spam albums in online social network |
| title_full_unstemmed | Detecting Spam albums in online social network |
| title_short | Detecting Spam albums in online social network |
| title_sort | detecting spam albums in online social network |
| topic | social network security Photo Spam Spam detection RenRen |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016180/ |
| work_keys_str_mv | AT shaoqinglyu detectingspamalbumsinonlinesocialnetwork AT yuqingzhang detectingspamalbumsinonlinesocialnetwork AT donghangliu detectingspamalbumsinonlinesocialnetwork AT guanghuazhang detectingspamalbumsinonlinesocialnetwork |