ReviewSocial network analysis and mining2021
An augmented multilingual Twitter dataset for studying the COVID-19 infodemic.
Review in Social network analysis and mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Dynamic Analysis of a Cross-Lingual Coupled Rumor Propagation Model with Response Delay in Online Social Networks.Entropy (Basel, Switzerland) · 2026Article
- Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.BMC medical informatics and decision making · 2025Article
- Multilingual SEIR public opinion propagation model with social enhancement mechanism and cross transmission mechanisms.Scientific reports · 2024Article
- Article
- Streamlining social media information retrieval for public health research with deep learning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.Journal of medical Internet research · 2024Article
- Enabling Semantic Topic Modeling on Twitter Using MetaMap.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study.Journal of medical Internet research · 2023Article
- Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Social media mining under the COVID-19 context: Progress, challenges, and opportunities.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Applications of machine learning for COVID-19 misinformation: a systematic review.Social network analysis and mining · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work presents an openly available dataset to facilitate researchers' exploration and hypothesis testing about the social discourse of the COVID-19 pandemic. The dataset currently consists of over 2.2 billions tweets (count as of September, 2021), from all over the world, in multiple languages. Tweets start from January 22, 2020, when the total cases of reported COVID-19 were below 600 worldwide. The dataset was collected using the Twitter API and by rehydrating tweets from other available datasets, data collection is ongoing as of the time of writing. To facilitate hypothesis testing and exploration of social discourse, the English and Spanish tweets have been augmented with state-of-the-art Twitter Sentiment and Named Entity Recognition algorithms. The dataset and the summary files provided allow researchers to avoid some computationally intensive analyses, facilitating more widespread use of social media data to gain insights on issues such as (mis)information diffusion, semantic networks, sentiments, and the evolution of COVID-19 discussions. In addition, the dataset provides an archive for researchers in the social sciences wishing to have access to a dataset covering the entire duration of the pandemic.
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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.