On the public citation graph

Public Perception of the Use of Digital Contact-Tracing Tools After the COVID-19 Lockdown: Sentiment Analysis and Opinion Mining

https://doi.org/10.2196/33314
0 signed · 34 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/14651858.cd013699openalex-snapshot
Digital contact tracing technologies in epidemics: a rapid review
inferredcites →https://doi.org/10.1007/978-981-15-6202-0_25openalex-snapshot
A Real-Time Sentiments Analysis System Using Twitter Data
inferredcites →https://doi.org/10.1007/s11845-020-02389-yopenalex-snapshot
A national survey of attitudes to COVID-19 digital contact tracing in the Republic of Ireland
inferredcites →https://doi.org/10.1007/s11845-021-02529-yopenalex-snapshot
Sentiment analysis of user feedback on the HSE’s Covid-19 contact tracing app
inferredcites →https://doi.org/10.1016/j.cmi.2021.01.007openalex-snapshot
Awareness, acceptance, and adoption of the national digital contact tracing tool post COVID-19 lockdown among visitors to a public hospital in Singapore
inferredcites →https://doi.org/10.1016/j.jjimei.2021.100021openalex-snapshot
Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore
inferredcites →https://doi.org/10.1016/s2468-2667(20)30157-2openalex-snapshot
Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study
inferredcites →https://doi.org/10.1038/s41467-020-20817-6openalex-snapshot
A population-based controlled experiment assessing the epidemiological impact of digital contact tracing
inferredcites →https://doi.org/10.1080/14494035.2020.1783789openalex-snapshot
Policy capacity and Singapore’s response to the COVID-19 pandemic
inferredcites →https://doi.org/10.1098/rsif.2020.1000openalex-snapshot
Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data
inferredcites →https://doi.org/10.1109/access.2020.3027350openalex-snapshot
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets
inferredcites →https://doi.org/10.1136/bmj.m1859openalex-snapshot
Covid-19 contact tracing: a briefing
inferredcites →https://doi.org/10.1136/medethics-2020-106958openalex-snapshot
Ethics of digital contact tracing wearables
inferredcites →https://doi.org/10.1145/3173574.3173986openalex-snapshot
Addressing Age-Related Bias in Sentiment Analysis
inferredcites →https://doi.org/10.1177/0002716215570866openalex-snapshot
Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites
inferredcites →https://doi.org/10.1177/0894439318788322openalex-snapshot
Potential Biases in Big Data: Omitted Voices on Social Media
inferredcites →https://doi.org/10.1371/journal.pone.0241473openalex-snapshot
Facilitators and barriers to engagement with contact tracing during infectious disease outbreaks: A rapid review of the evidence
inferredcites →https://doi.org/10.1371/journal.pone.0255615openalex-snapshot
COVID-19 sentiment analysis via deep learning during the rise of novel cases
inferredcites →https://doi.org/10.15557/pimr.2020.0005openalex-snapshot
Contact tracing in the context of COVID-19. Interim guidance
inferredcites →https://doi.org/10.2196/19447openalex-snapshot
Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends
inferredcites →https://doi.org/10.2196/23148openalex-snapshot
Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study
inferredcites →https://doi.org/10.2196/25525openalex-snapshot
Early Perceptions of COVID-19 Contact Tracing Apps in German-Speaking Countries: Comparative Mixed Methods Study
inferredcites →https://doi.org/10.2196/jmir.9702openalex-snapshot
Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study
inferredcites →https://doi.org/10.3390/v12040372openalex-snapshot
Virology, Epidemiology, Pathogenesis, and Control of COVID-19

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s12553-024-00857-4openalex-snapshot
Digital contact tracing technology in the COVID-19 pandemic: a systematic review
inferred← citeshttps://doi.org/10.1016/j.techfore.2024.123775openalex-snapshot
Social media data-based spatio-temporal assessment of public attitudes towards digital contact tracing applications: A case of health code application in mainland China
inferred← citeshttps://doi.org/10.1049/ccs2.12076openalex-snapshot
Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic
inferred← citeshttps://doi.org/10.1109/ic3i56241.2022.10073045openalex-snapshot
An Improved Accuracy in Anticipating the User Nature using a novel logistic regression algorithm throughout the pandemic across online social media based on Indian metrics over Support Vector Clustering Algorithm
inferred← citeshttps://doi.org/10.1145/3528575.3551439openalex-snapshot
Understanding Student Perceptions Around Mandatory Use of Smartphone Apps for COVID-19
inferred← citeshttps://doi.org/10.1177/00469580241290095openalex-snapshot
The Growing Impact of Natural Language Processing in Healthcare and Public Health
inferred← citeshttps://doi.org/10.1186/s12889-026-27638-0openalex-snapshot
Trust vs. knowledge during COVID-19: the dominance of trust in promoting preventive behaviours and its role in technology acceptance in Germany and India
inferred← citeshttps://doi.org/10.3390/app15020865openalex-snapshot
Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
inferred← citeshttps://doi.org/10.36948/ijfmr.2023.v05i05.6090openalex-snapshot
Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification
inferred← citeshttps://doi.org/10.5121/ijaia.2023.14203openalex-snapshot
Psychological Emotion Recognition of Students using Machine Learning based Chatbot
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.