Mobile application (app) reviews contain valuable information for app developers. A plethora of supervised and unsupervised techniques have been proposed in the literature to synthesize useful user feedback from app reviews. However, traditional supervised classification algorithms require extensive manual effort to label ground truth data, while unsupervised text mining techniques, such as topic models, often produce suboptimal results due to the sparsity of useful information in the reviews. To overcome these limitations, in this paper, we propose a fully automatic and unsupervised approach for extracting useful information from mobile app reviews. The proposed approach is based on keyATM, a keyword-assisted approach for generating topic models. keyATM overcomes the problem of data sparsity by using seeding keywords extracted directly from the review corpus. These keywords are then used to generate meaningful domain-specific topics. Our approach is evaluated over two datasets of mobile app reviews sampled from the domains of Investing and Food Delivery apps. The results show that our approach produces significantly more coherent topics than traditional topic modeling techniques.

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.

Modern application stores enable developers to classify their apps by choosing from a set of generic categories, or genres, such as health, games, and music. These categories are typically static—new categories do not necessarily emerge over time to reflect innovations in the mobile software landscape. With thousands of apps classified under each category, locating apps that match a specific consumer interest can be a challenging task. To overcome this challenge, in this article, we propose an automated approach for classifying mobile apps into more focused categories of functionally related application domains. Our aim is to enhance apps visibility and discoverability. Specifically, we employ word embeddings to generate numeric semantic representations of app descriptions. These representations are then classified to generate more cohesive categories of apps. Our empirical investigation is conducted using a dataset of 600 apps, sampled from the Education, Health&Fitness, and Medical categories of the Apple App Store. The results show that our classification algorithms achieve their best performance when app descriptions are vectorized using GloVe, a count-based model of word embeddings. Our findings are further validated using a dataset of Sharing Economy apps and the results are evaluated by 12 human subjects. The results show that GloVe combined with Support Vector Machines can produce app classifications that are aligned to a large extent with human-generated classifications.

Applications of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main facilitator of economic growth and shared prosperity in modern-day societies. However, recent research has revealed that the participation of minority groups in DSE activities is often hindered by different forms of bias and discrimination. Evidence of such behavior has been documented across almost all domains of DSE, including ridesharing, lodging, and freelancing. However, little is known about the underlying design decisions of DSE platforms which allow certain demographics of the market to gain unfair advantage over others. To bridge this knowledge gap, in this paper, we systematically synthesize evidence from 58 interdisciplinary studies to identify the pervasive discrimination concerns affecting DSE platforms along with their triggering features and mitigation strategies. Our objective is to consolidate such interdisciplinary evidence from a software design point of view. Our results show that existing evidence is mainly geared towards documenting and mitigating issues of racism and sexism affecting platforms of ridesharing, lodging, and freelancing. Our review further shows that discrimination concerns in the DSE market are commonly enabled by features of user profiles and commonly impact reputation systems.

Recent research has exposed a serious discrimination problem affecting applications of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit. To control for this problem, several DSE apps have crafted a new form of usage policies, known as non-discrimination policies (NDPs). These policies are intended to outline end-users' rights of equal treatment and describe how acts of bias and discrimination over DSE apps are identified and prevented. However, there is still a major knowledge gap in how such non-code artifacts can be formulated, structured, and evolved. To bridge this gap, in this paper, we introduce a first-of-its-kind framework for analyzing and evaluating the content of NDPs in the DSE market. Our analysis is conducted using a dataset of 108 DSE apps, sampled from a broad range of application domains. Our results show that, a) most DSE apps do not provide a separate NDP, b) the majority of existing policies are either extremely brief or combined as sub-statements of other usage policies, and c) most apps do not provide a clear statement of how their NDPs are enforced. Our analysis in this paper is intended to assist DSE app developers with drafting and evolving more comprehensive NDPs as well as help end-users of these apps to make more informed socioeconomic decisions in one of the fastest growing software ecosystems in the world.

Recent evidence has revealed that Sharing Economy platforms such as Uber, Airbnb, and TaskRabbit, have become active hubs for digital discrimination. This new form of discrimination refers to a phenomenon where a business transaction is influenced by race, gender, age, or any other non-business related characteristic of providers or consumers. Existing research often tackles this problem from a socio-economic and regulatory points of view. However, the research on the design aspects of Sharing Economy software, which enable such complex sociotechnical problems to emerge online, is still underdeveloped. To bridge this gap, in this paper, we propose a new perspective on digital discrimination, tackling the problem from a Requirements Engineering point of view. Specifically, we analyze a large dataset of online user feedback as well as synthesize existing literature to identify and classify pervasive discrimination concerns in the Sharing Economy market. Based on this analysis, we devise a crowd-driven domain model to represent these concerns along with their relations to the functional features and user goals of Sharing Economy platforms. This model is intended to provide requirements engineers, working on Sharing Economy software, with systematic insights into the complex types of socio-technical problems that can emerge in the operational environments of their systems.