This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (434) JMIR Theme Issue: COVID-19 Special Issue (2391) Outbreak and Pandemic Preparedness and Management (1675) Digital Contact Tracing, Digital Proximity Tracing, Precision Public Health (138) Theme Issue: Apps for COVID-19 (#Apps4Covid) (133) Natural Language Processing (644)

Published on in Vol 6, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36238, first published .
Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing

Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing

Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing

Authors of this article:

Kashif Ahmad1 Author Orcid Image ;   Firoj Alam2 Author Orcid Image ;   Junaid Qadir3 Author Orcid Image ;   Basheer Qolomany4 Author Orcid Image ;   Imran Khan5 Author Orcid Image ;   Talhat Khan5 Author Orcid Image ;   Muhammad Suleman5 Author Orcid Image ;   Naina Said5 Author Orcid Image ;   Syed Zohaib Hassan6 Author Orcid Image ;   Asma Gul7 Author Orcid Image ;   Mowafa Househ1 Author Orcid Image ;   Ala Al-Fuqaha1 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

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  2. Hang C, Tsai Y, Yu P, Chen J, Tan C. Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review. Big Data and Cognitive Computing 2023;7(2):108 View
  3. Zheng C, Li W, Wang S, Ye H, Xu K, Fang W, Dong Y, Wang Z, Qiao T. Automated detection of steps in videos of strabismus surgery using deep learning. BMC Ophthalmology 2024;24(1) View