Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods
Proceedings of the National Academy of Sciences of the United States of America, ISSN: 1091-6490, Vol: 117, Issue: 19, Page: 10165-10171
2020
- 154Citations
- 254Captures
- 6Mentions
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Metrics Details
- Citations154
- Citation Indexes144
- 144
- CrossRef42
- Policy Citations10
- Policy Citation10
- Captures254
- Readers254
- 254
- Mentions6
- News Mentions4
- News4
- Blog Mentions1
- Blog1
- References1
- Wikipedia1
Most Recent News
Twitter can reveal the well-being of a whole community
Social media can reveal the psychological states of an entire population, according to new research. The results show that through machine-learning—teaching a computer to identify and analyze patterns in large datasets—researchers can see, in principle, how a society is doing in real-time. “These methods really show how to do psychological measurement in the 21st century in our digital world,” say
Article Description
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 r = 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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85084502628&origin=inward; http://dx.doi.org/10.1073/pnas.1906364117; http://www.ncbi.nlm.nih.gov/pubmed/32341156; https://pnas.org/doi/full/10.1073/pnas.1906364117; https://dx.doi.org/10.1073/pnas.1906364117; https://www.pnas.org/content/117/19/10165
Proceedings of the National Academy of Sciences
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