Identifying Users and Developers of Mobile Apps in Social Network Crowd

Abstract

In the last fifteen years, an immense expansion has been witnessed in mobile app usage and production. The intense competition in the tech sector and also the rapidly and constantly evolving user requirements have led to increased burden on mobile app creators. Nowadays, fulfilling users’ expectations cannot be readily achieved and new and unconventional approaches are needed to permit an interested crowd of users to contribute in the introduction of creative mobile apps. Indeed, users and developers of mobile apps are the most influential candidates to engage in any of the requirements engineering activities. The place where both can best be found is on Twitter, one of the most widely used social media platforms. More interestingly, Twitter is considered as a fertile ground for textual content generated by the crowd that can assist in building robust predictive classification models using machine learning (ML) and natural language processing (NLP) techniques. Therefore, in this study, we have built two classification models that can identify mobile apps users and developers using tweets. A thorough empirical comparison of different feature extraction techniques and machine learning classification algorithms were experimented with to find the best-performing mobile app user and developer classifiers. The results revealed that for mobile app user classification, the highest accuracy achieved was ≈0.86, produced via logistic regression (LR) using Term Frequency Inverse Document Frequency (TF-IDF) with N-gram (unigram, bigram and trigram), and the highest precision was ≈0.86, produced via LR using Bag-of-Words (BOW) with N-gram (unigram and bigram). On the other hand, for mobile app developer classification, the highest accuracy achieved was ≈0.87, produced by random forest (RF) using BOW with N-gram (unigram and bigram), and the highest precision was ≈0.88, produced by multi-layer perception neural network (MLP NN) using BERTweet for feature extraction. According to the results, we believe that the developed classification models are efficient and can assist in identifying mobile app users and developers from tweets. Moreover, we envision that our models can be harnessed as a crowd selection approach for crowdsourcing requirements engineering activities to enhance and design inventive and satisfying mobile apps.