Survey on Astroturfing Detection and Analysis from an Information Technology Perspective

Abstract

With the development of the Internet, user comments produced an unprecedented impact on information acquisition, goods purchase, and other aspects. For example, the user comments can quickly render a topic the focus of discussion in social networks. It can promote the sales of goods in e-commerce, and it influences the ratings of books, movies, or albums. Among these network applications and services, “astroturfing,” a kind of online suspicious behavior, can generate abnormal, damaging, and even illegal behaviors in cyberspace that mislead public perception and bring a bad effect on Internet users and society. Hence, the manner of detecting and combating astroturfing behavior has become highly urgent, attracting interest from researchers both from information technology and sociology. In the current paper, we restudy it mainly from the perspective of information technology, summarize the latest research findings of astroturfing detection, analyze the astroturfing feature, classify the machine learning-based detection methods and evaluation criteria, and introduce the main applications. Different from the previous surveys, we also discuss the new future directions of astroturfing detection, such as cross-domain astroturfing detection and user privacy protection.