No abstract

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5,326 mobile app reviews. The findings show that (1) our model can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves an accuracy of 85% with relatively small training dataset; however, the accuracy improves as we increase the size of the training dataset. CCS CONCEPTS• Human-centered computing → Empirical studies in accessibility; Ubiquitous and mobile devices.

Despite increasing work investigating the accessibility of research tools, most accessibility research has traditionally focused on popular, mainstream, or web technologies. We investigated barriers and workarounds blind and low vision doctoral students in computing-intensive disciplines experienced and engaged, respectively, when using advanced technical tools for research tasks. We conducted an observation and interview study with eight current and former Ph.D. students, closely analyzing the accessibility of specific tasks. Our findings contextualize how inaccessible tools complicate research tasks, adding time and effort, and exacerbating social entanglements in collaborative relationships. This work contributes empirical data that extricates how in/accessibility of advanced technical tools used in research influences productivity and collegial efforts.