Unsupervised Summarization of Privacy Concerns in Mobile Application Reviews

The proliferation of mobile applications (app) over the past decade has imposed unprecedented challenges on end-users privacy. Apps constantly demand access to sensitive user information in exchange for more personalized services. These-mostly unjustifiable-data collection tactics have raised major privacy concerns among mobile app users. Such concerns are commonly expressed in mobile app reviews, however, they are typically overshadowed by more generic categories of user feedback, such as app reliability and usability. This makes extracting user privacy concerns manually, or even using automated tools, a challenging and time-consuming task. To address these challenges, in this paper, we propose an effective unsupervised approach for summarizing user privacy concerns in mobile app reviews. Our analysis is conducted using a dataset of 2.6 million app reviews sampled from three different application domains. The results show that users in different application domains express their privacy concerns using domain-specific vocabulary. This domain knowledge can be leveraged to help unsupervised automated text summarization algorithms to generate concise and comprehensive summaries of privacy concerns in app review collections. Our analysis is intended to help app developers quickly and accurately identify the most critical privacy concerns in their domain of operation, and ultimately, alter their data collection practices to address these concerns.