Detecting Spam albums in online social network - Shao-qing LYU; Yu-qing ZHANG; Dong-hang LIU; Guang-hua ZHANG (Tongxin xuebao)
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%.