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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. 

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.