Predicting Users’ Adoptive Behavior From Review Data

Abstract

Review websites have become an important source of information about different businesses. Social influence on these online platforms can result in individuals adopting or promoting ideas and actions resulting in information cascades. Cascades are dynamic aspects of the social networks. Researches on information cascades have been gaining popularity over the last few years as adoption or promotion of product using cascades can help determine important latent patterns of social influence. A study on information cascade helps the businesses in a city to figure out different consumer trends and hence improve their processes and offerings. While cascade models have been studied in isolation, not much work has been done to correlate cascade with the static network parameters. In this study, an attempt is made to correlate findings from static analysis of the social networks with the findings from a dynamic network phenomenon such as cascade. For this work, a data set containing restaurant reviews of the users in a social platform is used.