Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/86562
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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Mitchell, L. | - |
dc.contributor.author | Frank, M. | - |
dc.contributor.author | Harris, K. | - |
dc.contributor.author | Dodds, P. | - |
dc.contributor.author | Danforth, C. | - |
dc.contributor.editor | Sánchez, A. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | PLoS One, 2013; 8(5):e64417-1-e64417-15 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | http://hdl.handle.net/2440/86562 | - |
dc.description.abstract | We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates. | - |
dc.description.statementofresponsibility | Lewis Mitchell, Morgan R. Frank, Kameron Decker Harris, Peter Sheridan Dodds, Christopher M. Danforth | - |
dc.language.iso | en | - |
dc.publisher | Public Library of Science | - |
dc.rights | © 2013 Mitchell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | - |
dc.source.uri | http://dx.doi.org/10.1371/journal.pone.0064417 | - |
dc.subject | Humans | - |
dc.subject | Cluster Analysis | - |
dc.subject | Emotions | - |
dc.subject | Happiness | - |
dc.subject | Health Status | - |
dc.subject | Geography | - |
dc.subject | Algorithms | - |
dc.subject | Socioeconomic Factors | - |
dc.subject | Internet | - |
dc.subject | Urban Population | - |
dc.subject | United States | - |
dc.title | The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1371/journal.pone.0064417 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Mitchell, L. [0000-0001-8191-1997] | - |
Appears in Collections: | Aurora harvest 2 Mathematical Sciences publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_86562.pdf | Published version | 3.41 MB | Adobe PDF | View/Open |
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