ArticleDigital health
A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada.Frontiers in digital health · 2023Article
- Article
- Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases.Annual review of biomedical data science · 2022Review
- Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.Frontiers in public health · 2022Review
- Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics.Journal of personalized medicine · 2021Review
- Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.NPJ digital medicine · 2021Article
- Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation.JMIR medical informatics · 2021Article
- Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.Journal of medical Internet research · 2021Article
- Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.JMIR medical informatics · 2021Review
- Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis.Journal of medical Internet research · 2020Article
- Analysis and Evaluation of COVID-19 Web Applications for Health Professionals: Challenges and Opportunities.Healthcare (Basel, Switzerland) · 2020Article
- Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK.medRxiv : the preprint server for health sciences · 2020Article
- Ideas for how informaticians can get involved with COVID-19 research.BioData mining · 2020Article
- A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK.Digital healthArticle
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Authors and funding
7 authors.
Funding
Abstract
Objective: Given the uncertainty about the trends and extent of the rapidly evolving COVID-19 outbreak, and the lack of extensive testing in the United Kingdom, our understanding of COVID-19 transmission is limited. We proposed to use Twitter to identify personal reports of COVID-19 to assess whether this data can help inform as a source of data to help us understand and model the transmission and trajectory of COVID-19. Methods: We used natural language processing and machine learning framework. We collected tweets (excluding retweets) from the Twitter Streaming API that indicate that the user or a member of the user's household had been exposed to COVID-19. The tweets were required to be geo-tagged or have profile location metadata in the UK. Results: We identified a high level of agreement between personal reports from Twitter and lab-confirmed cases by geographical region in the UK. Temporal analysis indicated that personal reports from Twitter appear up to 2 weeks before UK government lab-confirmed cases are recorded. Conclusions: Analysis of tweets may indicate trends in COVID-19 in the UK and provide signals of geographical locations where resources may need to be targeted or where regional policies may need to be put in place to further limit the spread of COVID-19. It may also help inform policy makers of the restrictions in lockdown that are most effective or ineffective.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.