Research Article
Review Trade: Everything Is Free in Incentivized Review Groups
@INPROCEEDINGS{10.1007/978-3-030-63086-7_19, author={Yubao Zhang and Shuai Hao and Haining Wang}, title={Review Trade: Everything Is Free in Incentivized Review Groups}, proceedings={Security and Privacy in Communication Networks. 16th EAI International Conference, SecureComm 2020, Washington, DC, USA, October 21-23, 2020, Proceedings, Part I}, proceedings_a={SECURECOMM}, year={2020}, month={12}, keywords={Incentivized review groups Co-review graph Community detection}, doi={10.1007/978-3-030-63086-7_19} }- Yubao Zhang
Shuai Hao
Haining Wang
Year: 2020
Review Trade: Everything Is Free in Incentivized Review Groups
SECURECOMM
Springer
DOI: 10.1007/978-3-030-63086-7_19
Abstract
Online reviews play a crucial role in the ecosystem of e-commerce business. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange forincentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who request incentivized reviews and reviewers who seek incentives formsincentivized review groups. In this paper, we focus on the incentivized review groups in e-commerce platforms. We perform data collections from various social networking platforms, including Facebook, WeChat, and Douban. A measurement study of incentivized review groups is conducted with regards to group members, group activities, and products. To identify the incentivized review groups, we propose a new detection approach based on co-review graphs. Specifically, we employ the community detection method to find suspicious communities from co-review graphs. Also, we build a “gold standard” dataset from the data we collected, which contains the information of reviewers who belong to incentivized review groups. We utilize the “gold standard” dataset to evaluate the effectiveness of our detection approach.
