Скачать или смотреть Graph based Online Store Review Spammer Detection

Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. Previous attempts for spammer detection used reviewers’ behaviors, text similarity, linguistics features and rating patterns. These studies are able to identify certain types of spammers, e.g., those who post many similar reviews about one target entity.
In reality, there are other kinds of spammers who can manipulate their behaviors to act just like genuine reviewers, and thus cannot be detected by the available techniques.
A novel concept of a heterogeneous review graph to capture the relationships among reviewers, reviews and stores that the reviewers have reviewed. They also explored how interactions between nodes in this graph can reveal the cause of spam and propose an iterative model to identify suspicious reviewers. Text information is not used to review. This model is complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after evaluation of the results.