Journal article

Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods

Kokil Jaidka, Salvatore Giorgi, H Andrew Schwartz, Margaret L Kern, Lyle H Ungar, Johannes C Eichstaedt

Proceedings of the National Academy of Sciences of USA | NATL ACAD SCIENCES | Published : 2020

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..

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Grants

Awarded by Templeton Religion Trust


Funding Acknowledgements

We thank T.J.V., the PNAS editorial staff, the anonymous reviewers, and JamesW. Pennebaker for their generous and insightful suggestions. Support for this research was provided by a Nanyang Presidential Postdoctoral Award, an Adobe Research Award, a RobertWood Johnson Foundation Pioneer Award, and Templeton Religion Trust Grant TRT0048.