A cross-modal CCA-based astroturfing detection approach

Abstract

In recent years, astroturfing can generate abnormal, damaging even illegal behaviors in cyberspace which may mislead the public perception and bring a bad effect on both Internet users and society. This paper aims to design a algorithm to detect astroturfing in online shopping effectively and help users to identify potential online astroturfers quickly. The previous work used single method text-text or image-image to detect astroturfing, while in this paper we first propose a cross-modal canonical correlation analysis model (CCCA) which combines text and images. First, we identify several features of astroturfing and analysis these features. Then, we use feature extraction algorithm, image similarity algorithm and CCA algorithm, and propose a cross-modal method to detect astroturfing which release comments with pictures. We also conduct an experiment on a Taobao dataset to verify our method. The experimental results show that the supervised method proposed is effective.