ArticleProceedings of the National Academy of Sciences of the United States of America2020
Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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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
68 citing papers in PubMed.
- Estimating Longitudinal Changes in Workday Happiness From Daily Report Texts Among Employees: Retrospective Feasibility Study.JMIR formative research · 2026Article
- Decriminalizing same-sex relationships and its impact on social media discussions in Asia.Nature human behaviour · 2026Article
- Trust dynamics in social media crises: a topic-aspect-sentiment analysis of public evaluations during a school food safety incident.Frontiers in psychology · 2026Article
- Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis.Journal of medical Internet research · 2025Article
- Influence of climate change and accidents on perception differs among energy technologies.PNAS nexus · 2025Article
- Prediction of subjective well-being level in residents of Dali City: Where modern tourism meets traditional ethnic culture.PloS one · 2025Article
- Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study.Journal of medical Internet research · 2024Article
- Article
- Applying deep learning on social media to investigate cultural ecosystem services in protected areas worldwide.Scientific reports · 2024Article
- Robust language-based mental health assessments in time and space through social media.NPJ digital medicine · 2024Article
- A systematic evaluation of text mining methods for short texts: Mapping individuals' internal states from online posts.Behavior research methods · 2024Article
- The social anatomy of climate change denial in the United States.Scientific reports · 2024Article
- Twitter (X) use predicts substantial changes in well-being, polarization, sense of belonging, and outrage.Communications psychology · 2024Article
- LEXpander: Applying colexification networks to automated lexicon expansion.Behavior research methods · 2024Article
- Article
- A longitudinal analysis of usage patterns, topics, and information dissemination related to five names for cultured meat on social media.Current research in food science · 2024Article
- Editorial: Digital mental health and wellbeing under crisis.Frontiers in digital health · 2024Article
- When I am sixty-four… evaluating language markers of well-being in healthy aging narratives.PloS one · 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
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to
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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.