How features in iOS App Store Reviews can Predict Developer Responses.

Until recently, communications regarding apps on the iOS App Store have been one-way from users to developers, with developers unable to respond to reviews directly. While studies have shown that responding to reviews improves an app's overall rating and user satisfaction, resource limitations make it so developers can usually only respond to some of the reviews. Although developers' response behavior has been studied, little is known about which features (aspects) of user reviews spur their responses. Motivated by these observations, we investigate a wide range of features that can be extracted from a user review and apply a random forest algorithm and the features it extracts to predict whether developers will respond to that review. We then determine the importance of these features in distinguishing reviews that receive a developer response from those that do not. Through a case study of three popular free-to-download iOS apps, we find that although features such as rating and review length are among the most important features for all apps, each app has its own individual feature importance ranking, indicating that developers assign different feature weights when prioritizing reviews. Our results may help guide research or the development of tools that are more in line with developers' actual response behavior.