Detecting fake-review buyers using network structure: Direct evidence from Amazon

Abstract

Online reviews significantly impact consumers’ decision-making process and firms’ economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve for over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews for thousands of Amazon products and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using laboratory-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We show that products that buy fake reviews are highly clustered in the product reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews. We show that our network-based approach is also successful at detecting fake review buyers even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation.

A study on the propagation of online public opinion by internet water army

“…(Chen et al (2013); Zeng et al (2014); Wang et al (2014)). At present, many foreign scholars study the identification of Internet water armies for false comments (Jabeur et al (2023); Bathla et al (2022); Vidanagama et al (2022); He et al (2022); Lee et al (2022)); domestic scholars mainly study the identification of Internet water armies and the governance of rumors (He et al (2023); Li et al (2014); Peng et al (2023); Zhang et al (2019); Yan et al (2023); Chen and Du (2023); Zhang et al (2023)). However, most of these researches use different algorithms to accurately identify Internet water armies, and there is a lack of research on how Internet water armies promote the development of Internet public opinion communication.…”