Protecting Your Children from Inappropriate Content in Mobile Apps: An Automatic Maturity Rating Framework.

Mobile applications (Apps) could expose children or adolescents to mature themes such as sexual content, violence and drug use, which results in an inappropriate security and privacy risk for them. Therefore, mobile platforms provide rating policies to label the maturity levels of Apps and the reasons why an App has a given maturity level, which enables parents to select maturity-appropriate Apps for their children. However, existing approaches to implement these maturity rating policies are either costly (because of expensive manually labeling) or inaccurate (because of no centralized controls). In this work, we aim to design and build a machine learning framework to automatically predict maturity levels for mobile Apps and the associated reasons with a high accuracy and a low cost. To this end, we take a multi-label classification approach to predict the mature contents in a given App and then label the maturity level according to a rating policy. Specifically, we extract novel features from App descriptions by leveraging deep learning technique to automatically capture the semantic similarity of pairwise words and adapt Support Vector Machine to capture label correlations with pearson correlation in a multi-label classification setting. Moreover, we evaluate our approach and various baseline methods using datasets that we collected from both App Store and Google Play. We demonstrate that, with only App descriptions, our approach already achieves 85% Precision for predicting mature contents and 79% Precision for predicting maturity levels, which substantially outperforms baseline methods.