Machine-Learning-Based Suitability Prediction for Mobile Applications for Kids

Xianjun Meng, Shaomei Li, Muhammad Mohsin Malik and Qasim Umer ()
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Xianjun Meng: College of Education, Shaanxi Normal University, Xi’an 710062, China
Shaomei Li: College of Education, Shaanxi Normal University, Xi’an 710062, China
Muhammad Mohsin Malik: Faculty of Multi Disciplinary Studies, National University of Medical Sciences, Rawalpindi 46000, Pakistan
Qasim Umer: Department of Computer Sciences, COMSATS University Islamabad, Vehari 61000, Pakistan

Sustainability, 2022, vol. 14, issue 19, 1-14

Abstract: Digital media has a massive presence in the modern world, and it significantly impacts kids’ intellectual, cognitive, ethical, and social development. It is nearly impossible to isolate kids from digital media. Therefore, adult content on mobile applications should be avoided by children. Although mobile operating systems provide parental controls, handling such rules is impossible for illiterate people. Consequently, kids may download and use adults’ mobile applications. Mobile applications for adults often publish age group information to distinguish user segments that can be used to automate the downloading process. Sustainable Development Goal (SDG) #4 emphasizes inclusivity and equitability in terms of quality of education and the facilitation of conditions for the promotion of lifelong learning for everyone. The current study can be counted as being in line with SDG#4, as it proposes a machine-learning-based approach to the prediction of the suitability of mobile applications for kids. The approach first leverages natural language processing (NLP) techniques to preprocess user reviews of mobile applications. Second, it performs feature engineering based on the given bag of words (BOW), e.g., abusive words, and constructs a feature vector for each mobile app. Finally, it trains and tests a machine learning algorithm on the given feature vectors. To evaluate the proposed approach, we leverage the 10-fold cross-validation technique. The results of the 10-fold cross-validation indicate that the proposed solution is significant. The average results of the exploited metrics (precision, recall, and F1-score) are 92.76%, 99.33%, and 95.93%, respectively.

Keywords: machine learning; classification; reliability; kids learning; mobile applications; sustainable learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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