ArticleEpidemiologia (Basel, Switzerland)2021
A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration.
Article in Epidemiologia (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed.
- Large language model-assisted research question development in public health: a case study in the Special Supplemental Nutrition Program for Women, Infants, and Children.Public health nutrition · 2026Article
- Public sentiment and thematic evolution in the metaverse: A large-scale computational analysis of Twitter discourse.PloS one · 2026Article
- Auditing the impact of social media's policy shift on anti-vaccine discourse: A large language model-driven empirical study.PloS one · 2026Article
- From Individual Expression to Group Polarization: A Study on Twitter's Emotional Diffusion Patterns in the German Election.Behavioral sciences (Basel, Switzerland) · 2025Article
- Leveraging Large Language Models for Infectious Disease Surveillance-Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis.Journal of medical Internet research · 2025Article
- Signals of propaganda-Detecting and estimating political influences in information spread in social networks.PloS one · 2025Article
- MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data.Scientific reports · 2024Article
- First public dataset to study 2023 Turkish general election.Scientific reports · 2024Article
- The rise of data repositories in materials chemistry.Communications chemistry · 2024Article
- LEXpander: Applying colexification networks to automated lexicon expansion.Behavior research methods · 2024Article
- Modeling the Impacts of Governmental and Human Responses on COVID-19 Spread Using Statistical Machine Learning.International journal of digital earth · 2024Article
- PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data.Frontiers in public health · 2024Article
- A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets.Healthcare analytics (New York, N.Y.) · 2023Article
- Seeking and Providing Social Support on Twitter for Trauma and Distress During the COVID-19 Pandemic: Content and Sentiment Analysis.Journal of medical Internet research · 2023Article
- A multi-modal panel dataset to understand the psychological impact of the pandemic.Scientific data · 2023Article
- Public Opinions About Palliative and End-of-Life Care During the COVID-19 Pandemic: Twitter-Based Content Analysis.JMIR formative research · 2023Article
- COVID-19 and social media: Beyond polarization.PNAS nexus · 2023Article
- Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets.JAMIA open · 2023Article
- BillionCOV: An enriched billion-scale collection of COVID-19 tweets for efficient hydration.Data in brief · 2023Article
- Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts.Journal of medical Internet research · 2023Article
27 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.
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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.