Mining user reviews of COVID contact-tracing apps: An exploratory analysis of nine European apps

Ahmed, 2020, A survey of covid-19 contact tracing apps, IEEE Access, 8, 134577, 10.1109/ACCESS.2020.3010226 Altmann, 2020, Acceptability of app-based contact tracing for COVID-19: Cross-country survey study, JMIR MHealth UHealth, 8, e19857, 10.2196/19857 Anon, 2020 Aslam, 2015, Research ideas: Correlation does not imply causation, Br. Dent. J., 219, 49, 10.1038/sj.bdj.2015.585 Baltes, 2020 Basili, 1992 Blom, 2021 Braithwaite, 2020 Browne, 2015, Modeling contact tracing in outbreaks with application to Ebola, J. Theoret. Biol., 384, 33, 10.1016/j.jtbi.2015.08.004 Budd, 2020, Digital technologies in the public-health response to COVID-19, Nature Med., 1 Cho, 2020 Danquah, 2019, Use of a mobile application for ebola contact tracing and monitoring in Northern Sierra leone: a proof-of-concept study, BMC Infect. Dis., 19, 810, 10.1186/s12879-019-4354-z Davidson, 1996, ’searching for Mary, Glasgow’: Contact tracing for sexually transmitted diseases in twentieth-century Scotland, Soc. Hist. Med., 9, 195, 10.1093/shm/9.2.195 Farronato, 2020 Fenn, 2008 Fernández, 2019 Garousi, V., 2009. Evidence-based Insights about Issue Management Processes: An Exploratory Study. In: Proceedings of the International Conference on Software Process. ICSP, pp. 112–123. Genc-Nayebi, 2017, A systematic literature review: Opinion mining studies from mobile app store user reviews, J. Syst. Softw., 125, 207, 10.1016/j.jss.2016.11.027 Gomez, 2012, Overview and evaluation of bluetooth low energy: An emerging low-power wireless technology, Sensors, 12, 11734, 10.3390/s120911734 Groen, 2015, Towards crowd-based requirements engineering a research preview, 247 Guttal, 2020 Guzman, 2017, A little bird told me: Mining tweets for requirements and software evolution, 11 Guzman, E., Maalej, W., 2014. How do users like this feature? a fine grained sentiment analysis of app reviews. In: IEEE international requirements engineering conference. pp. 153–162. Hayden, 2006 Henry, 1990, Practical sampling, Sage Hoepman, 2020 Horvath, 2020, Citizens’ attitudes to contact tracing apps, J. Exp. Political Sci., 1 Hu, 2019, Studying the consistency of star ratings and reviews of popular free hybrid android and iOS apps, Empir. Softw. Eng., 24, 7, 10.1007/s10664-018-9617-6 Husmann, M., Spiegel, M., Murolo, A., Norrie, M.C., 2016. UI testing cross-device applications. In: Proceedings of the 2016 ACM International Conference on Interactive Surfaces and Spaces. pp. 179–188. Iacob, C., Harrison, R., 2013. Retrieving and analyzing mobile apps feature requests from online reviews, In: Working conference on Mining Software Repositories. pp. 41–44. Jha, 2017, Mining user requirements from application store reviews using frame semantics, 273 Jha, 2019, Mining non-functional requirements from app store reviews, Empir. Softw. Eng., 24, 3659, 10.1007/s10664-019-09716-7 Joorabchi, M.E., Mesbah, A., Kruchten, P., 2013. Real challenges in mobile app development, In: ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. pp. 15–24. Kazman, 2019, Software engineering in society, IEEE Softw., 37, 7, 10.1109/MS.2019.2949322 Khalid, 2014, What do mobile app users complain about?, IEEE Softw., 32, 70, 10.1109/MS.2014.50 Ksir, 2016, Correlation still does not imply causation, Lancet Psychiatry, 3, 401, 10.1016/S2215-0366(16)30005-0 Kukuk, 2020 Leith, 2020 Li, 2020 Lim, 2014, Investigating country differences in mobile app user behavior and challenges for software engineering, IEEE Trans. Softw. Eng., 41, 40, 10.1109/TSE.2014.2360674 Liu, 2012, Sentiment analysis and opinion mining, Synth. Lect. Hum. Lang. Technol., 5, 1, 10.1007/978-3-031-02145-9 Lu, M., Liang, P., 2017. Automatic classification of non-functional requirements from augmented app user reviews. In: Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering. pp. 344–353. Maalej, 2015, Bug report, feature request, or simply praise? on automatically classifying app reviews, 116 Maalej, 2019, Data-driven requirements engineering-an update, 289 Martens, D., Johann, T., 2017. On the emotion of users in app reviews. In: IEEE/ACM International Workshop on Emotion Awareness in Software Engineering. SEmotion, pp. 8–14. Martin, 2020, Demystifying COVID-19 digital contact tracing: A survey on frameworks and mobile apps, Wirel. Commun. Mob. Comput., 2020, 10.1155/2020/8851429 Martuscelli, 2020 Morales-Ramirez, 2015, An ontology of online user feedback in software engineering, Appl. Ontog., 10, 297, 10.3233/AO-150150 Nayebi, 2017, Crowdsourced exploration of mobile app features: A case study of the fort McMurray wildfire, 57 Nebeling, M., Husmann, M., Zimmerli, C., Valente, G., Norrie, M.C., 2015. XDSession: integrated development and testing of cross-device applications, In: Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems. pp. 22–27. Nicholas, 2020 Pagano, 2013, User feedback in the appstore: An empirical study, 125 Potter, 2006, Methods for presenting statistical information: The box plot, 97 Redmiles, 2020 Rekanar, 2020 Rizzo, 2020 Runeson, 2009, Guidelines for conducting and reporting case study research in software engineering, Empir. Softw. Eng., 14, 131, 10.1007/s10664-008-9102-8 Sacks, 2015, Introduction of mobile health tools to support Ebola surveillance and contact tracing in guinea, Glob. Health Sci. Prac., 3, 646, 10.9745/GHSP-D-15-00207 Scherr, 2019, Listen to your users–quality improvement of mobile apps through lightweight feedback analyses, 45 Scudellari, 2020, COVID-19 digital contact tracing: Apple and google work together as MIT tests validity, IEEE Spectr., 13 Stoyanov, 2015, Mobile app rating scale: a new tool for assessing the quality of health mobile apps, JMIR MHealth UHealth, 3, e27, 10.2196/mhealth.3422 Sun, 2020 Trang, 2020, One app to trace them all? Examining app specifications for mass acceptance of contact-tracing apps, Eur. J. Inf. Syst., 1 Wallach, H.M., 2006. Topic modeling: beyond bag-of-words. In: Proceedings of the international conference on Machine learning. pp. 977–984. Walrave, 2020, Adoption of a contact tracing app for containing COVID-19: A health belief model approach, JMIR Public Health Surv., 6, e20572, 10.2196/20572 Wang, 2020, A new system for surveillance and digital contact tracing for COVID-19: spatiotemporal reporting over network and GPS, JMIR MHealth UHealth, 8, e19457, 10.2196/19457 Webber, 2015 Wen, H., Zhao, Q., Lin, Z., Xuan, D., Shroff, N., 2020. A study of the privacy of covid-19 contact tracing apps. In: International Conference on Security and Privacy in Communication Networks. Williams, 2017, Mining twitter feeds for software user requirements, 1 Wohlin, 2000 Zarandy, 2020 Zhao, Q., Wen, H., Lin, Z., Xuan, D., Shroff, N., 2020. On the accuracy of measured proximity of bluetooth-based contact tracing apps In: International Conference on Security and Privacy in Communication Networks.