Fig 2 - uploaded by Jane C. Walsh
Content may be subject to copyright.
Source publication
Background
Digital Contact Tracing is seen as a key tool in reducing the propagation of Covid-19. But it requires high uptake and continued participation across the population to be effective. To achieve sufficient uptake/participation, health authorities should address, and thus be aware of, user concerns.
Aim
This work manually analyzes user rev...
Context in source publication
Citations
... 2 Since the outbreak of COVID-19, various digital contact tracing technologies have been developed to prevent and control the spread of existing infectious diseases. [3][4][5][6][7][8][9][10] This technology verifies information regarding an infectious disease risk area and an infected individual, records the contaminated area data, tracks the location, and compares this location with the data on the infectious disease-contaminated area. When the location corresponds to an infectious diseasecontaminated area, it is designed to receive the log record from a mobile terminal that sends the visitation log of the infectious disease-contaminated area. ...
Objectives: Rapid epidemiological investigations are fundamental to prevent the spread of infectious diseases such as coronavirus disease 2019. An epidemiological investigation presents significant challenges for both epidemiologists and infected individuals. It requires creating an environment that enables people to independently manage infectious diseases and voluntarily participate in epidemiological investigations. Methods: We developed the KODARI application, an epidemiological investigation support system that users can voluntarily use. We developed the questionnaires based on literature reviews. We evaluated the application through an online survey from December 2 to 14, 2022. Results: The application automatically or manually collect epidemiological investigation information. The application improved data accuracy through accurate information collection. It voluntarily can transmit self-management information to epidemiologist terminals or users in real time. We collected 248 users from an online survey. Most users had high ratings and willingness to use. They have willingness to manage infectious patients was substantial. The application was evaluated as helpful for epidemiological investigations and could shorten the time required for epidemiological investigations by more than 30 min. Conclusion: The application proposes a model based on people’s voluntary participation. We demonstrated that the application could enhance epidemiological investigations and diminish the duration of existing epidemiological investigation processes.
... Kaavya et al. [43] examine user feedback on the Irish Health Service Executive's (HSE) Contact Tracker app manually in order to determine user issues and create the framework for later, extensive automated reviews analysis. This research determines the features of the app that users concentrated on and the level of positive and negative emotion conveyed, a manual study of 1287 user reviews from the Google and Apple playstores was conducted. ...
We live in an age where the use of smart devices and Internet are redefining our community standards. Additionally, the pandemic Covid-19 enforced the community to use applications on smart devices for various activities. Currently, many organizations are developing their applications that are accessible through various platforms, including Windows Phone Store, Apple App Store, and Google Play. To facilitate the customer the banking sector is also providing their mobile applications for various online services. Mobile banking applications (mbanking apps) have considerably upgraded the efficiency of the banks and living standards of the people. The people can easily download applications from app stores and are permitted to leave reviews or comments on the mobile application. The sentiment analysis is an area that allows us to examine the user opinion to improve the online services. Therefore, for any organization it is of prime importance to explore and evaluate the weaknesses affecting the delivery of their online services. In this work, sentiment analysis is performed to evaluate ten (10) mbanking apps of Pakistan using valence aware dictionary for sentiment reasoning and machine learning (ML) based approaches. Performance of three classifiers through supervised ML techniques multinomial Naïve Bayes, logistic regression, support vector machine, and ensemble model is compared and employed. Moreover, the thematic analysis of reviews is also performed to discover various factors as themes that affect the effectiveness of the mbanking apps by using Top2Vec Model. The results indicate that the ensemble model is best performing model with f1-score of 90%. The thematical analysis uncovers 346 positive themes like ease of use, helpful, reliable, user friendly, good aesthetics, convenience, secured and many more, whereas 441 negative themes comprise performance issue, poor updates/new version in apps, account registration issue, app crash problem, etc.
... While previous studies [4][5][6][7][8][9][10] have examined the privacy and security aspects of contact tracing apps during the COVID-19 pandemic, our research aims to go beyond these investigations. In addition to evaluating the existing concerns, we place special emphasis on the impact of educating the public regarding these issues. ...
... Most of the conducted studies [4][5][6][7][8]18] show that privacy concerns and lack of knowledge about contact tracing apps were the primary reasons that discouraged individuals from adopting CTAs. ...
COVID-19 was an unprecedented pandemic that changed the lives of everyone. To handle the virus’s rapid spread, governments and big tech companies, such as Google and Apple, implemented Contact Tracing Applications (CTAs). However, the response by the public was different in each country. While some countries mandated downloading the application for their citizens, others made it optional, revealing contrasting patterns to the spread of COVID-19. In this study, in addition to investigating the privacy and security of the Canadian CTA, COVID Alert, we aim to disclose the public’s perception of these varying patterns. Additionally, if known of the results of other nations, would Canadians sacrifice their freedoms to prevent the spread of a future pandemic? Hence, a survey was conducted, gathering responses from 154 participants across Canada. Next, we questioned the participants regarding the COVID-19 pandemic and their knowledge and opinion of CTAs before presenting our findings regarding other countries. After showing our results, we then asked the participants their views of CTAs again. The arrangement of the preceding questions, the findings, and succeeding questions to identify whether Canadians’ opinions on CTAs would change, after presenting the proper evidence, were performed. Among all of our findings, there is a clear difference between before and after the findings regarding whether CTAs should be mandatory, with 34% of participants agreeing before and 56% agreeing afterward. This hints that all the public needed was information to decide whether or not to participate. In addition, this exposes the value of transparency and communication when persuading the public to collaborate. Finally, we offer three recommendations on how governments and health authorities can respond effectively in a future pandemic and increase the adoption rate for CTAs to save more lives.
... In Ref. [11], the authors analysed user reviews collected from the Irish Health Service Executive's (HSE) Contact Tracker app. The app was developed with the aim of identifying large-scale and automated analysis of reviews. ...
If understanding sentiments is already a difficult task in human‐human communication, this becomes extremely challenging when a human‐computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network‐based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID‐19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID‐19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian‐language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER.
... Experiments to show the effectiveness of vector-based features for sentiment analysis show that an 85.77% F1 score is obtained using Naive Bayes (NB). The study (Rekanar et al., 2022) performs sentiment analysis on an Irish health service executive's COVID-19 contact tracing app. Manual sentiment analysis on 1287 reviews extracted from Google and Apple play stores is performed. ...
Online meeting applications (apps) have emerged as a potential solution for conferencing, education and meetings, etc. during the COVID-19 outbreak and are used by private companies and governments alike. A large number of such apps compete with each other by providing a different set of functions towards users’ satisfaction. These apps take users’ feedback in the form of opinions and reviews which are later used to improve the quality of services. Sentiment analysis serves as the key function to obtain and analyze users’ sentiments from the posted feedback indicating the importance of efficient and accurate sentiment analysis. This study proposes the novel idea of self voting classification (SVC) where multiple variants of the same model are trained using different feature extraction approaches and the final prediction is based on the ensemble of these variants. For experiments, the data collected from the Google Play store for online meeting apps were used. Primarily, the focus of this study is to use a support vector machine (SVM) with the proposed SVC approach using both soft voting (SV) and hard voting (HV) criteria, however, decision tree, logistic regression, and k nearest neighbor have also been investigated for performance appraisal. Three variants of models are trained on a bag of words, term frequency-inverse document frequency, and hashing features to make the ensemble. Experimental results indicate that the proposed SVC approach can elevate the performance of traditional machine learning models substantially. The SVM obtains 1.00 and 0.98 accuracy scores, using HV and SV criteria, respectively when used with the proposed SVC approach. Topic-wise sentiment analysis using the latent Dirichlet allocation technique is performed as well for topic modeling.
... An analysis of COVID-19 app user reviews has been conducted in some studies. In [36], nearly one thousand reviews have been manually analysed to identify users' sentiments and general satisfaction levels with using the Irish COVID-19 app. In [19], nearly two million user reviews were analysed to identify the differences between users' privacy concerns in social media apps versus COVID-19 apps. ...
... A limited number of studies have also been conducted to understand citizens' opinion on contact tracing in different countries through the analysis of user-generated content (UGC) on social media such as Twitter. While in Ireland, citizens exhibited mostly positive opinion (Rekanar et al., 2021), in some other countries such as India and Brazil, citizens exhibited more negative opinions (Praveen et al., 2020a;Praveen et al., 2021). Privacy concerns have been identified as one of the major reasons behind negative feelings (Praveen et al., 2020a;Praveen et al., 2021;Crable & Sena, 2020). ...
The extant contact tracing privacy literature is yet to explore the significance of user emotions in privacy-related decision-making such as whether to use such potentially privacy-invasive apps. Using social media analytics, the present study examines users’ privacy-related emotions stimulated by privacy-related aspects of contact tracing apps. A text-Convolutional Neural Network (Text-CNN)-based emotion analysis of tweets on the Indian contact tracing app Aarogya Setu and its Singaporean counterpart TraceTogether conducted in the paper reveals that users expressed negative privacy-related emotions towards these apps indicating high levels of perceived privacy risks and the perceived lack of privacy protection. For TraceTogether, users have also exhibited positive emotions to appreciate the steps taken by the government to protect their privacy. Based on these findings, the government/data controllers can devise strategies to assuage users’ negative emotions and promote positive emotions to encourage the adoption of contact tracing apps. This work incorporates privacy related emotions as key informants about user privacy concerns within the Privacy Calculus Theory. By relying on candid user opinions available through rich but inexpensive user-generated content, the research provides a quick, reliable, and cost-effective approach to study potential app users’ emotions to gain insights into privacy concerns related to any e-governance platform.
... 17,18 Although this problem was quickly contained, preliminary analyses suggest this resulted in a significant number of de-installations. 19 However, the COVID Tracker App, built by the Irish company NearForm, has generally been considered a success. It is open-source software and since July 2020 has thus far has been used in nine jurisdictions across North America and Europe, providing digital contact tracing for 55 million people. ...
Objective
This study aims to gather public opinion on the Irish “COVID Tracker” digital contact tracing (DCT) App, with particular focus on App usage, usability, usefulness, technological issues encountered, and potential changes to the App.
Methods
A 35-item online questionnaire was deployed for 10 days in October 2020, 3 months after the launch of the Irish DCT App.
Results
A total of 2889 completed responses were recorded, with 2553 (88%) respondents currently using the App. Although four in five users felt the App is easy to download, is easy to use and looks professional, 615 users (22%) felt it had slowed down their phone, and 757 (28%) felt it had a negative effect on battery life. Seventy-nine percent of respondents reported the App's main function is to aid contact tracing. Inclusion of national COVID-19 trends is a useful ancillary function according to 87% of respondents, and there was an appetite for more granular local data. Overall, 1265 (44%) respondents believed the App is helping the national effort, while 1089 (38%) were unsure.
Conclusions
DCT Apps may potentially augment traditional contact tracing methods. Despite some reports of negative effects on phone performance, just 7% of users who have tried the App have deleted it. Ancillary functionality, such as up-to-date regional COVID-19, may encourage DCT App use. This study describes general positivity toward the Irish COVID Tracker App among users but also highlights the need for transparency on effectiveness of App-enabled contact tracing and for study of non-users to better establish barriers to use.
... Such studies adopted both qualitative (e.g. focus groups and "think aloud" interviews) and quantitative methods (sentiment analysis of user reviews from the Google/Apple playstores) to investigate the usability of the COVID tracker app [18] and explore unmet user needs and concerns [52]. ...
The COVID-19 pandemic has led governments worldwide to introduce various measures restricting human activity and mobility. Along with the administration of COVID-19 vaccinations and rapid testing, socio-technological solutions such as digital COVID-19 certificates have been considered as a strategy to lessen these restrictions and allow the resumption of routine activities. Using a mixed-methods approach-a survey (n=1008) and 27 semi-structured interviews-this study explores the attitudes of residents in the Republic of Ireland towards the idea of introducing digital COVID-19 certificates. We examine the topics of acceptability, fairness, security and privacy of COVID-related personal data, and practical considerations for implementation. Our study reveals the conditional and contextual nature of the acceptability of digital certificates, identifying specific factors that affect it, associated data practices, and related public concerns and expectations of such technologies.
... Manual contact tracing works only when the infected person knows who has been in physical contact with him or her, which reduces the effectiveness of the method. Moreover, manual contact tracing is a very time-and resource-consuming process [2,3]. ...
... We believe an analysis of users' reviews on these applications will lead to a better understanding of the concerns over these applications. There are already some efforts in this regard [3,9,10]. However, the majority of the methods rely on exploratory and manual analysis of the users' reviews, which is a resource-and time-consuming process. ...
... There are already some efforts in this direction. For instance, Rekanar et al [3] provided a detailed analysis of users' feedback on HSE3 in terms of usability, functional effectiveness, and performance. However, the authors relied on manual analysis only, which is a time-consuming process. ...
Background: Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several mobile applications 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 which, although in normal circumstances is optimal, but not optimal in emergency situations where a mobile device needs to be deployed immediately with little to no user input from the beginning for the greater public good.
Objective: In this paper, we aim to analyze the efficacy of AI models and Natural Language Processing (NLP) techniques in 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 applications. We also aim to provide a large-scale annotated benchmark dataset to facilitate future research in the domain. As a proof of concepts, we also develop a potential web application, based on the proposed solutions, with a user-friendly interface to automatically analyze and classify users’ reviews on the COVID-19 contact tracing applications. The proposed framework combined with the interface which is expected to help the community in quickly analyzing users’ perception about such mobile applications and can be used as a rapid surveillance tool to monitor effectiveness of mobile applications and to make immediate changes without going through an intense participatory design method in emergency situations.
Methods: We propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large- scale dataset of Android and iOS mobile applications users’ reviews for COVID-19 contact tracing. After manually analyzing and annotating users’ reviews, we employed both classical (i.e., Naïve Bayes, SVM, Random Forest) and deep learning (i.e., fastText, and different transformers) methods for classification experiments. This resulted in eight different classification models.
Results: We employed eight different methods on three different tasks achieving up to an average F1-Scores 94.8% indicating the feasibility and applicability of automatic sentiment analysis of users’ reviews on the COVID-19 contact tracing applications. Moreover, the crowd-sourcing activity resulted in a large-scale benchmark dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The resulted dataset is also made publicly available for research usage.
Conclusions: The existing literature mostly relies on the manual/exploratory analysis of users’ reviews on the application, which is a tedious and time-consuming process. Moreover, in the existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and NLP techniques provide good results in analyzing and classifying users’ sentiments’ polarity, and that the automatic sentiment analysis can help in analyzing users’ responses to the application more quickly with a significant accuracy. Moreover, we also provided a large-scale benchmark dataset composed of 34,534 reviews from 47 different applications. We believe the presented analysis, dataset, and the proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile applications deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.
