Affiliations: [a] Department of Information Sciences, Kanagawa University, Kanagawa 259-1293, Japan | [b] Department of Integrated Information Technology, Aoyama Gakuin University, Kanagawa 252-5258, Japan | [c] Department of Electronics and Informatics, Ryukoku University, Shiga 520-2194, Japan | [d] Institute of Scientific and Industrial Research, Osaka University, Osaka 567-0047, Japan | [e] School of Computing and Information Systems, University of Tasmania, Hobart TAS 7001, Australia
Abstract: At its heart the act of reviewing is very subjective, but in reality many factors would influence user’s decision. This can be called social influence bias. We pick two factors, “Who” and “When” and discuss which factor is more influential when a user posts his/her own rate in an online review system. We consider two kinds of users: real and virtual. In the former each user has its own metric, but in the latter the metric is assigned to the order of review posting actions (rating). We propose a weighted multinomial generative model that can learn the factor metric quite efficiently from a vast amount of data already available in many online review systems. If the model can explain the data well enough, this implies that such a social bias does exist. We evaluate the proposed method and confirm its effectiveness by five review datasets, and empirically clarify that there is no universal solution, but the social bias does exist. In reality the influential factor depends on each dataset, the majority of users is normal (average), and there are two small groups of users, each with high metric value and low metric value.
Keywords: Social media, influence, review score, machine learning
