Abstract—Identifying review manipulation has become one of
hot research issues in e-commerce because more and more
customers make their purchase decisions based on some personal
comments from virtual communities and e-business websites.
Customers consider these personal reviews are more reliable
than the existing internet advertisements. Consequently, some
enterprises attempt to create fake personal comments to affect
customer behaviors and increase their sales. But, how to identify
those manipulated reviews is a difficult task for customers.
Therefore, this study employs Decision Tree (DT) to improve the
classification performance of review manipulation by introducing
eight potential review manipulation attributes. In addition, we
attempted to discover the important factors of identifying
manipulated reviews using correlation analysis and extracted
knowledge rules. Finally, a real case of online users’ comments
regarding smart phones has been employed to testify the
effectiveness of the proposed method.