User generated micro-reviews: characterization and popularity prediction

Abstract

Since the popularization of the Web 2.0, people are becoming increasingly engaged expressing their opinions with reviews about products and services. As any other type of user-generated content, online reviews come in various forms, sizes and qualities. Such quality variability is particularly prominent in textual reviews produced on mobile apps, often called micro-reviews or tips, due to their inherent conciseness. In such content abundant environment, being able to estimate the helpfulness of an online (micro-)review, and ultimately predict its future popularity among users as accurately and early as possible, can greatly benefit content filtering and recommendationmethods, helping users find valuable reviews and providing quick feedback to business owners and future customers. In this context, we investigate how users exploit micro-reviews, focusing particularlyon Foursquare tips, an increasingly popular type of review whose high degree of informality and briefness offers extra difficulties to the design of effective prediction methods. Using collected data from Foursquare, we also investigate how tip popularity, given by the number of times the tip received a like from a user, evolves over time and which factors impact this popularity evolution. Then, we explore how these factors can be combined to develop models to predict tip popularity at a given point in time in the future. We develop solutions to two different prediction tasks: predicting the popularity ranking of a set of tips and predicting the popularity level a particular tip will achieve. Our experimental results show that a multidimensional set of predictor variables, which considers features of both the user who posted the tip and the venue where it was posted, leads to more accurate results than using each set of features inisolation. Our models, when applied to Foursquare tips, are also more robust than state-of-the-art popularity prediction methods, as they can be applied to any tip, at or after posting time.