ArticleNature human behaviour2022
Global evidence of expressed sentiment alterations during the COVID-19 pandemic.
Article in Nature human behaviour, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013-2023.Scientific data · 2026Article
- Reducing social biases in text-based emotion prediction using semantic blinding and semantic propagation graph neural networks.Scientific reports · 2026Article
- Understanding crisis dynamics during public health emergencies: an integrated framework of social consequences, trust, and behavioral responses.Frontiers in public health · 2026Review
- Declining subjective well-being disparities concurrent with urbanization in China.National science review · 2025Article
- Influence of climate change and accidents on perception differs among energy technologies.PNAS nexus · 2025Article
- High risk of political bias in black box emotion inference models.Scientific reports · 2025Article
- Understanding Citizens' Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI.Journal of medical Internet research · 2025Article
- Pathogen stress heightens sensorimotor dimensions in the human collective semantic space.Communications psychology · 2025Article
- Optimizing the detection of emerging infections using mobility-based spatial sampling.International journal of applied earth observation and geoinformation : ITC journal · 2024Article
- SIR+ models: accounting for interaction-dependent disease susceptibility in the planning of public health interventions.Scientific reports · 2024Article
- Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy.Royal Society open science · 2024Article
- Effects of extreme temperatures on public sentiment in 49 Chinese cities.Scientific reports · 2024Article
- Tracking electricity losses and their perceived causes using nighttime light and social media.iScience · 2023Article
- Analysis of the evolving factors of social media users' emotions and behaviors: a longitudinal study from China's COVID-19 opening policy period.BMC public health · 2023Article
- Twitter Sentiment Geographical Index Dataset.Scientific data · 2023Article
- Unveiling the dynamics of emotions in society through an analysis of online social network conversations.Scientific reports · 2023Article
- Effects of public-health measures for zeroing out different SARS-CoV-2 variants.Nature communications · 2023Article
- Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders.Journal of medical Internet research · 2023Article
- Young people's mental and social distress in times of international crisis: evidence from helpline calls, 2019-2022.Scientific reports · 2023Article
- Emotional Expression on Social Media Support Forums for Substance Cessation: Observational Study of Text-Based Reddit Posts.Journal of medical Internet research · 2023Observational
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has created unprecedented burdens on people's physical health and subjective well-being. While countries worldwide have developed platforms to track the evolution of COVID-19 infections and deaths, frequent global measurements of affective states to gauge the emotional impacts of pandemic and related policy interventions remain scarce. Using 654 million geotagged social media posts in over 100 countries, covering 74% of world population, coupled with state-of-the-art natural language processing techniques, we develop a global dataset of expressed sentiment indices to track national- and subnational-level affective states on a daily basis. We present two motivating applications using data from the first wave of COVID-19 (from 1 January to 31 May 2020). First, using regression discontinuity design, we provide consistent evidence that COVID-19 outbreaks caused steep declines in expressed sentiment globally, followed by asymmetric, slower recoveries. Second, applying synthetic control methods, we find moderate to no effects of lockdown policies on expressed sentiment, with large heterogeneity across countries. This study shows how social media data, when coupled with machine learning techniques, can provide real-time measurements of affective states.
Indexed as
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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.