ArticlePloS one2021
How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance.Science advances · 2026Article
- What We Ask about When We Ask about Quarantine? Content and Sentiment Analysis on Online Help-Seeking Posts during COVID-19 on a Q&A Platform in China.International journal of environmental research and public health · 2022Article
- Can people hear others' crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China.Information processing & management · 2022Article
- Facilitators and Barriers of COVID-19 Vaccine Promotion on Social Media in the United States: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Article
- Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter.Science advances · 2021Article
- A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets.Online social networks and media · 2021Article
- Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning.International journal of environmental research and public health · 2021Article
- Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic.EPJ data science · 2021Article
- Hurricanes and hashtags: Characterizing online collective attention for natural disasters.PloS one · 2021Article
- Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning.Frontiers in artificial intelligence · 2021Article
- Political polarization drives online conversations about COVID-19 in the United States.Human behavior and emerging technologies · 2020Article
- Online mental health services in Indonesia during the COVID-19 outbreak.Asian journal of psychiatry · 2020Article
- "Show this thread": policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic.Crime science · 2020Article
- Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada.Frontiers in public healthArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 2000 day-scale time series of 1- and 2-grams across 24 languages on Twitter that are most 'important' for April 2020 with respect to April 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for 'virus' in January 2020 followed by a decline through February and then a surge through March and April. The world's collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations.
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