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Recent studies showed that the dialogs between app developers and app userson app stores are important to increase user satisfaction and app's overallratings. However, the large volume of reviews and the limitation of resourcesdiscourage app developers from engaging with customers through this channel.One solution to this problem is to develop an Automated Responding System fordevelopers to respond to app reviews in a manner that is most similar to ahuman response. Toward designing such system, we have conducted an empiricalstudy of the characteristics of mobile apps' reviews and their human-writtenresponses. We found that an app reviews can have multiple fragments at sentencelevel with different topics and intentions. Similarly, a response also can bedivided into multiple fragments with unique intentions to answer certain partsof their review (e.g., complaints, requests, or information seeking). We havealso identified several characteristics of review (rating, topics, intentions,quantitative text feature) that can be used to rank review by their priority ofneed for response. In addition, we identified the degree of re-usability ofpast responses is based on their context (single app, apps of the samecategory, and their common features). Last but not least, a responses can bereused in another review if some parts of it can be replaced by a placeholderthat is either a named-entity or a hyperlink. Based on those findings, wediscuss the implications of developing an Automated Responding System to helpmobile apps' developers write the responses for users reviews more effectively.

On building an automated responding system for app reviews: What are the characteristics of reviews and their responses?