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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\n            <jats:italic>r<\/jats:italic>\n            = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used.\n          <\/jats:p>","DOI":"10.1073\/pnas.1906364117","type":"journal-article","created":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T00:25:14Z","timestamp":1588033514000},"page":"10165-10171","update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":172,"title":["Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods"],"prefix":"10.1073","volume":"117","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8127-1157","authenticated-orcid":false,"given":"Kokil","family":"Jaidka","sequence":"first","affiliation":[{"name":"Department of Communications and New Media, National University of Singapore, Singapore 117416;"},{"name":"Centre for Trusted Internet and Community, National University of Singapore, Singapore 117416;"}]},{"given":"Salvatore","family":"Giorgi","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104;"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6383-3339","authenticated-orcid":false,"given":"H. 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