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Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews

Authors:

Eman Abdullah AlOmar,

Wajdi Aljedaani,

Murtaza Tamjeed,

Mohamed Wiem Mkaouer,

Yasmine N. El-GlalyAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 387, Pages 1 - 15

https://doi.org/10.1145/3411764.3445281

Published: 07 May 2021 Publication History

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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.

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Cited By

Oliveira ASantos PAljedaani WEler M(2024)Exploring the Influence of Software Evolution on Mobile App Accessibility: Insights from User ReviewsJournal of the Brazilian Computer Society10.5753/jbcs.2024.432130:1(584-607)Online publication date: 2-Nov-2024
https://doi.org/10.5753/jbcs.2024.4321
Prakash Raj Ojha (2024)Democratizing Code: How GPT and Large Language Models Are Reshaping the Landscape of Software CreationInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105103110:5(503-512)Online publication date: 9-Oct-2024
https://doi.org/10.32628/CSEIT241051031
Oliveira AEler M(2024)Exploring Accessibility of Mobile Applications Through User Feedback: Insights from App Reviews in a Systematic Literature ReviewProceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems10.1145/3702038.3702094(1-15)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3702038.3702094
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Index Terms

Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
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Aaron Quigley
University of New South Wales, Australia
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Katherine Isbister
University of California Santa Cruz, USA
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Takeo Igarashi
The University of Tokyo, Japan
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Pernille Bjørn
University of Copenhagen, Denmark
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Steven Drucker
Microsoft Research, USA

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Cited By

Oliveira ASantos PAljedaani WEler M(2024)Exploring the Influence of Software Evolution on Mobile App Accessibility: Insights from User ReviewsJournal of the Brazilian Computer Society10.5753/jbcs.2024.432130:1(584-607)Online publication date: 2-Nov-2024
https://doi.org/10.5753/jbcs.2024.4321
Prakash Raj Ojha (2024)Democratizing Code: How GPT and Large Language Models Are Reshaping the Landscape of Software CreationInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105103110:5(503-512)Online publication date: 9-Oct-2024
https://doi.org/10.32628/CSEIT241051031
Oliveira AEler M(2024)Exploring Accessibility of Mobile Applications Through User Feedback: Insights from App Reviews in a Systematic Literature ReviewProceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems10.1145/3702038.3702094(1-15)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3702038.3702094
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Lee SLaw MHoffman G(2024)When and How to Use AI in the Design Process? Implications for Human-AI Design CollaborationInternational Journal of Human–Computer Interaction10.1080/10447318.2024.235345141:2(1569-1584)Online publication date: 22-May-2024
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Wang XZhang TTan YShang WLi Y(2024)How to effectively mine app reviews concerning software ecosystem? A survey of review characteristicsJournal of Systems and Software10.1016/j.jss.2024.112040213(112040)Online publication date: Jul-2024
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