A graph neural network approach to detect original review spammers of astroturfing campaigns

Abstract

Online reviews affect consumers’ buying decisions. When astroturfing happens, the posted fake reviews not only cause confusion to the consumers, but in extreme cases, harm the reputation of others. Identifying the original posters of fake review in a discussion forum from the interactions with the peers in a collusive effort is essential to an overall detection strategy. However, existing detection approaches that focus on the mechanic characteristics of the posted reviews may fall short, since fake reviews cause more harm when astroturfing campaign happens involving multiple accomplices. We propose a detection framework with graph neural network, which incorporates the original perpetrator’s stylometric patterns and relationships with other accomplices. The framework was tested against the data collected from real incidents. Multiple deep learning models with fusion techniques were tested. Managerial and theoretical implications are provided.