The structure and evolution of online rating biases in the sharing economy

A wave of sharing economy companies are profoundly changing the market landscape, disrupting traditional businesses alongside the social fabrics of exchange. A critical challenge to their growth, however, is that how to generate trust from online to offline transactions. Users in many online platforms rely on reputational systems such as ratings to infer quality and make decisions. However, ratings are biased by behavioral tendencies, such as homophily and power dependence. Our project examines the structure and evolution of rating biases by analyzing massive amount of platform data. Using big data techniques on leading sharing economy platforms, we identify the structure and evolution of biases, attempting to correct the tendencies in system design. We examine rating biases and their relationships to social distance among heterogeneous user populations. The coevolution of reputational systems and trust further implies long-term behavioral trends, which are critical to investigate for business growth.