Which goods are most likely to be subject to click farming? An evidence from the Taobao platform

Abstract Click farming is common in online shopping. It is thus important to identify click farming and compare its performance across different categories of online goods. To this end, we conduct an empirical analysis of click farming on the Taobao platform in China. First, we extract several new features from three sources, namely main goods, online shop itself, and online reviews, based on the formation mechanism of click farming. Second, we investigate their usefulness in identifying click farming among different online goods, including importance analysis and partial dependence analysis. Third, we further investigate the contribution of constructed features to predicting click farming. Our findings confirm the effectiveness of our created features and the heterogeneity of click farming among different online goods. Specifically, click farming is most likely to happen in clothing-related goods, then followed by electronic goods and service-related goods. Our results are significant for consumers to understand online information and for online business platforms to reduce the occurrence of click farming.