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Published on 17.10.16 in Vol 2, No 2 (2016): Jul-Dec

This paper is in the following e-collection/theme issue:

Works citing "Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity"

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.5869):

(note that this is only a small subset of citations)

  1. Shelton RC, Lee M, Brotzman LE, Crookes DM, Jandorf L, Erwin D, Gage-Bouchard EA. Use of social network analysis in the development, dissemination, implementation, and sustainability of health behavior interventions for adults: A systematic review. Social Science & Medicine 2019;220:81
    CrossRef
  2. Muralidhara S, Paul MJ. #Healthy Selfies: Exploration of Health Topics on Instagram. JMIR Public Health and Surveillance 2018;4(2):e10150
    CrossRef
  3. Pearce JR. Complexity and Uncertainty in Geography of Health Research: Incorporating Life-Course Perspectives. Annals of the American Association of Geographers 2018;108(6):1491
    CrossRef
  4. Timmins KA, Green MA, Radley D, Morris MA, Pearce J. How has big data contributed to obesity research? A review of the literature. International Journal of Obesity 2018;42(12):1951
    CrossRef
  5. Cao X, MacNaughton P, Deng Z, Yin J, Zhang X, Allen J. Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA. International Journal of Environmental Research and Public Health 2018;15(2):250
    CrossRef
  6. Nguyen TT, Meng H, Sandeep S, McCullough M, Yu W, Lau Y, Huang D, Nguyen QC. Twitter-derived measures of sentiment towards minorities (2015–2016) and associations with low birth weight and preterm birth in the United States. Computers in Human Behavior 2018;89:308
    CrossRef
  7. DeGregory KW, Kuiper P, DeSilvio T, Pleuss JD, Miller R, Roginski JW, Fisher CB, Harness D, Viswanath S, Heymsfield SB, Dungan I, Thomas DM. A review of machine learning in obesity. Obesity Reviews 2018;19(5):668
    CrossRef
  8. Velasco K, Paxton P, Ressler RW, Weiss I, Pivnick L. Do National Service Programs Improve Subjective Well-Being in Communities?. The American Review of Public Administration 2018;:027507401881488
    CrossRef
  9. Luhmann M. Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28
    CrossRef
  10. Delir Haghighi P, Kang Y, Buchbinder R, Burstein F, Whittle S. Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter. JMIR Public Health and Surveillance 2017;3(1):e4
    CrossRef
  11. Nguyen QC, Brunisholz KD, Yu W, McCullough M, Hanson HA, Litchman ML, Li F, Wan Y, VanDerslice JA, Wen M, Smith KR. Twitter-derived neighborhood characteristics associated with obesity and diabetes. Scientific Reports 2017;7(1)
    CrossRef
  12. Meng H, Kath S, Li D, Nguyen QC, Giraud-Carrier C. National substance use patterns on Twitter. PLOS ONE 2017;12(11):e0187691
    CrossRef
  13. Roccetti M, Marfia G, Salomoni P, Prandi C, Zagari RM, Gningaye Kengni FL, Bazzoli F, Montagnani M. Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts. JMIR Public Health and Surveillance 2017;3(3):e51
    CrossRef
  14. Nguyen Q, Meng H, Li D, Kath S, McCullough M, Paul D, Kanokvimankul P, Nguyen T, Li F. Social media indicators of the food environment and state health outcomes. Public Health 2017;148:120
    CrossRef
  15. Nguyen QC, McCullough M, Meng H, Paul D, Li D, Kath S, Loomis G, Nsoesie EO, Wen M, Smith KR, Li F. Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015–2016. American Journal of Public Health 2017;107(11):1776
    CrossRef
  16. Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1
    CrossRef