图 书 馆
基于网络效应的在线评论意见欺诈检测Opinion Fraud Detection in Online Reviews by Network Effects |
|
| 课程网址: | http://videolectures.net/icwsm2013_akoglu_network_effects/ |
| 主讲教师: | Leman Akoglu |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  用户生成的在线评论可以在零售产品，酒店，饭店等的成功中发挥重要作用。但是，评论系统通常是意见垃圾邮件发送者的目标，他们试图通过创建欺诈性评论来扭曲产品的感知质量。我们提出了一个快速有效的框架FRAUDEAGLE，用于在在线评论数据集中发现欺诈者和虚假评论。我们的方法具有几个优点：（1）它利用审阅者和产品之间的网络效应，这与绝大多数现有的专注于审阅文本或行为分析的方法不同；（2）它包括两个互补的步骤；为用户和评论进行评分以进行欺诈检测，并进行分组以进行可视化和感官分析；（3）它以完全无监督的方式运行，不需要标记数据，同时仍然合并了边信息（如果有），并且（4）可扩展至大型数据集运行时间随网络规模线性增长。我们在合成数据集和真实数据集上证明了我们框架的有效性； FRAUDEAGLE在这里成功地在大型在线应用程序审查数据库中揭示了欺诈机器人。 |
| 课程简介: | User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database. |
| 关 键 词: | 数据集; 在线评论 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-04-28：zyk |
| 最后编审: | 2021-04-28：zyk |
| 阅读次数: | 71 |