检测和分析YouTube上的合谋实体
Detecting and analyzing collusive entities on YouTube
摘要：
在这项工作中，我们对YouTube上由各种黑市服务培育的串通实体进行了深入分析。在此之后，我们提出了检测三种类型的串通YouTube实体的模型-寻求串通喜欢的视频，寻求串通订阅的频道和寻求串通评论的视频。第三种类型的实体与时间信息相关联。为了分别检测共谋喜欢和订阅的视频和频道，我们利用在我们策划的共谋实体和一组新特征上训练的一类分类器。基于支持向量机的模型显示出显着的性能与真阳性率分别为0.911和0.910，用于检测串通视频和串通渠道。为了检测寻求共谋评论的视频，我们提出了CollATe，这是一种新型的端到端神经架构，它利用了发布评论的时间序列信息以及视频的静态元数据。CollATe由三个组件组成-元数据特征提取器（从视频中提取基于元数据的特征），异常特征提取器（利用评论时间序列数据来检测评论活动中的突然变化）和评论特征提取器（利用共谋期间发布的评论的文本并计算评论之间的相似性得分）。大量的实验表明，CollATe的有效性（真阳性率为0.905）超过基线。
英文摘要：
In this work, we provide an in-depth analysis of collusive entities on YouTube fostered by various blackmarket services. Following this, we propose models to detect three types of collusive YouTube entities - videos seeking collusive likes, channels seeking collusive subscriptions, and videos seeking collusive comments. The third type of entity is associated with temporal information. To detect videos and channels for collusive likes and subscriptions respectively, we utilize one-class classifiers trained on our curated collusive entities and a set of novel features. The SVM-based model shows significant performance with a true positive rate of 0.911 and 0.910 for detecting collusive videos and collusive channels respectively. To detect videos seeking collusive comments, we propose CollATe, a novel end-to-end neural architecture that leverages time-series information of posted comments along with static metadata of videos. CollATe is composed of three components - metadata feature extractor (which derives metadata-based features from videos), anomaly feature extractor (which utilizes the comment time-series data to detect sudden changes in the commenting activity), and comment feature extractor (which utilizes the text of the comments posted during collusion and computes a similarity score between the comments). Extensive experiments show the effectiveness of CollATe (with a true positive rate of 0.905) over the baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
