ArticleJournal of computational social science2022
A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic.
Article in Journal of computational social science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Mapping Five Years of #FOAMed: Trends, Engagement, and Shifting Topics on Twitter/X.The western journal of emergency medicine · 2025Article
- Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response.Open forum infectious diseases · 2024Article
- Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.Behavioral sciences (Basel, Switzerland) · 2024Article
- Enhanced sentiment analysis regarding COVID-19 news from global channels.Journal of computational social science · 2023Article
- Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources.Frontiers in sociology · 2022Article
- Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has deeply impacted all aspects of social, professional, and financial life, with concerns and responses being readily published in online social media worldwide. This study employs probabilistic text mining techniques for a large-scale, high-resolution, temporal, and geospatial content analysis of Twitter related discussions. Analysis considered 20,230,833 English language original COVID-19-related tweets with global origin retrieved between January 25, 2020 and April 30, 2020. Fine grain topic analysis identified 91 meaningful topics. Most of the topics showed a temporal evolution with local maxima, underlining the short-lived character of discussions in Twitter. When compared to real-world events, temporal popularity curves showed a good correlation with and quick response to real-world triggers. Geospatial analysis of topics showed that approximately 30% of original English language tweets were contributed by USA-based users, while overall more than 60% of the English language tweets were contributed by users from countries with an official language other than English. High-resolution temporal and geospatial analysis of Twitter content shows potential for political, economic, and social monitoring on a global and national level.
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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.