Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing
<h3>Background</h3><p dir="ltr">Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. To this aim, several mobile apps have been developed. However, there are ever-growing concerns over the working mechanism and performance of t...
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2022
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| _version_ | 1864513519183986688 |
|---|---|
| author | Kashif Ahmad (12592762) |
| author2 | Firoj Alam (14158866) Junaid Qadir (16494902) Basheer Qolomany (16855527) Imran Khan (109715) Talhat Khan (18387126) Muhammad Suleman (3829027) Naina Said (16869939) Syed Zohaib Hassan (18387129) Asma Gul (5980553) Mowafa Househ (9154124) Ala Al-Fuqaha (4434340) |
| author2_role | author author author author author author author author author author author |
| author_facet | Kashif Ahmad (12592762) Firoj Alam (14158866) Junaid Qadir (16494902) Basheer Qolomany (16855527) Imran Khan (109715) Talhat Khan (18387126) Muhammad Suleman (3829027) Naina Said (16869939) Syed Zohaib Hassan (18387129) Asma Gul (5980553) Mowafa Househ (9154124) Ala Al-Fuqaha (4434340) |
| author_role | author |
| dc.creator.none.fl_str_mv | Kashif Ahmad (12592762) Firoj Alam (14158866) Junaid Qadir (16494902) Basheer Qolomany (16855527) Imran Khan (109715) Talhat Khan (18387126) Muhammad Suleman (3829027) Naina Said (16869939) Syed Zohaib Hassan (18387129) Asma Gul (5980553) Mowafa Househ (9154124) Ala Al-Fuqaha (4434340) |
| dc.date.none.fl_str_mv | 2022-05-11T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.2196/36238 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Global_User-Level_Perception_of_COVID-19_Contact_Tracing_Applications_Data-Driven_Approach_Using_Natural_Language_Processing/25611627 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems COVID-19 sentiment analysis contact tracing applications NLP text classification BERT fastText transformers RoBerta |
| dc.title.none.fl_str_mv | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. To this aim, several mobile apps have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community’s response to the applications by analyzing information from different sources, such as news and users’ reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users’ reviews and extracts the evoked sentiments. We believe such solutions combined with a user-friendly interface can be used as a rapid surveillance tool to monitor how effective an application is and to make immediate changes without going through an intense participatory design method.</p><h3>Objective</h3><p dir="ltr">In this paper, we aim to analyze the efficacy of AI and NLP techniques for automatically extracting and classifying the polarity of users’ sentiments by proposing a sentiment analysis framework to automatically analyze users’ reviews on COVID-19 contact tracing mobile apps. We also aim to provide a large-scale annotated benchmark data set to facilitate future research in the domain. As a proof of concept, we also developed a web application based on the proposed solutions, which is expected to help the community quickly analyze the potential of an application in the domain.</p><h3>Methods</h3><p dir="ltr">We propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding with the development and training of artificial intelligence (AI) models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large-scale data set of user reviews on COVID-19 contact tracing applications. We used both classical and deep learning methods for classification experiments.</p><h3>Results</h3><p dir="ltr">We used 8 different methods on 3 different tasks, achieving up to an average F1 score of 94.8%, indicating the feasibility of the proposed solution. The crowd-sourcing activity resulted in a large-scale benchmark data set composed of 34,534 manually annotated reviews.</p><h3>Conclusions</h3><p dir="ltr">The existing literature mostly relies on the manual or exploratory analysis of users’ reviews on applications, which is tedious and time-consuming. In existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and natural language processing techniques provide good results for analyzing and classifying users’ sentiments’ polarity and that automatic sentiment analysis can help to analyze users’ responses more accurately and quickly. We also provided a large-scale benchmark data set. We believe the presented analysis, data set, and proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile apps deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Formative Research<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/36238" target="_blank">https://dx.doi.org/10.2196/36238</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_04a486724fed811cf8ea3a1abc93e940 |
| identifier_str_mv | 10.2196/36238 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25611627 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language ProcessingKashif Ahmad (12592762)Firoj Alam (14158866)Junaid Qadir (16494902)Basheer Qolomany (16855527)Imran Khan (109715)Talhat Khan (18387126)Muhammad Suleman (3829027)Naina Said (16869939)Syed Zohaib Hassan (18387129)Asma Gul (5980553)Mowafa Househ (9154124)Ala Al-Fuqaha (4434340)Health sciencesHealth services and systemsCOVID-19sentiment analysiscontact tracing applicationsNLPtext classificationBERTfastTexttransformersRoBerta<h3>Background</h3><p dir="ltr">Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. To this aim, several mobile apps have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community’s response to the applications by analyzing information from different sources, such as news and users’ reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users’ reviews and extracts the evoked sentiments. We believe such solutions combined with a user-friendly interface can be used as a rapid surveillance tool to monitor how effective an application is and to make immediate changes without going through an intense participatory design method.</p><h3>Objective</h3><p dir="ltr">In this paper, we aim to analyze the efficacy of AI and NLP techniques for automatically extracting and classifying the polarity of users’ sentiments by proposing a sentiment analysis framework to automatically analyze users’ reviews on COVID-19 contact tracing mobile apps. We also aim to provide a large-scale annotated benchmark data set to facilitate future research in the domain. As a proof of concept, we also developed a web application based on the proposed solutions, which is expected to help the community quickly analyze the potential of an application in the domain.</p><h3>Methods</h3><p dir="ltr">We propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding with the development and training of artificial intelligence (AI) models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large-scale data set of user reviews on COVID-19 contact tracing applications. We used both classical and deep learning methods for classification experiments.</p><h3>Results</h3><p dir="ltr">We used 8 different methods on 3 different tasks, achieving up to an average F1 score of 94.8%, indicating the feasibility of the proposed solution. The crowd-sourcing activity resulted in a large-scale benchmark data set composed of 34,534 manually annotated reviews.</p><h3>Conclusions</h3><p dir="ltr">The existing literature mostly relies on the manual or exploratory analysis of users’ reviews on applications, which is tedious and time-consuming. In existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and natural language processing techniques provide good results for analyzing and classifying users’ sentiments’ polarity and that automatic sentiment analysis can help to analyze users’ responses more accurately and quickly. We also provided a large-scale benchmark data set. We believe the presented analysis, data set, and proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile apps deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Formative Research<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/36238" target="_blank">https://dx.doi.org/10.2196/36238</a></p>2022-05-11T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.2196/36238https://figshare.com/articles/journal_contribution/Global_User-Level_Perception_of_COVID-19_Contact_Tracing_Applications_Data-Driven_Approach_Using_Natural_Language_Processing/25611627CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256116272022-05-11T03:00:00Z |
| spellingShingle | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing Kashif Ahmad (12592762) Health sciences Health services and systems COVID-19 sentiment analysis contact tracing applications NLP text classification BERT fastText transformers RoBerta |
| status_str | publishedVersion |
| title | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing |
| title_full | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing |
| title_fullStr | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing |
| title_full_unstemmed | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing |
| title_short | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing |
| title_sort | Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing |
| topic | Health sciences Health services and systems COVID-19 sentiment analysis contact tracing applications NLP text classification BERT fastText transformers RoBerta |