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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Infodemiology and Infoveillance (1349) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (111) Happiness (10)

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

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

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

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

Authors of this article:

Quynh C Nguyen1 Author Orcid Image ;   Dapeng Li2 Author Orcid Image ;   Hsien-Wen Meng1 Author Orcid Image ;   Suraj Kath3 Author Orcid Image ;   Elaine Nsoesie4 Author Orcid Image ;   Feifei Li3 Author Orcid Image ;   Ming Wen5 Author Orcid Image
Article Authors Cited by (67) Tweetations (104) Metrics

Journals

  1. Muralidhara S, Paul M. #Healthy Selfies: Exploration of Health Topics on Instagram. JMIR Public Health and Surveillance 2018;4(2):e10150 View
  2. Nguyen T, Criss S, Allen A, Glymour M, Phan L, Trevino R, Dasari S, Nguyen Q. Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US. International Journal of Environmental Research and Public Health 2019;16(10):1766 View
  3. Zhu B, Zheng X, Liu H, Li J, Wang P. Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons & Fractals 2020;140:110123 View
  4. Luhmann M. Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28 View
  5. 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 View
  6. Huang Y, Huang D, Nguyen Q. Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015–2018. International Journal of Environmental Research and Public Health 2019;16(6):975 View
  7. Leatherdale S, Lee J. Artificial intelligence (AI) and cancer prevention: the potential application of AI in cancer control programming needs to be explored in population laboratories such as COMPASS. Cancer Causes & Control 2019;30(7):671 View
  8. Nguyen T, Adams N, Huang D, Glymour M, Allen A, Nguyen Q. The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study. JMIR Public Health and Surveillance 2020;6(3):e17103 View
  9. Timmins K, Green M, Radley D, Morris M, Pearce J. How has big data contributed to obesity research? A review of the literature. International Journal of Obesity 2018;42(12):1951 View
  10. Shelton R, Lee M, Brotzman L, Crookes D, Jandorf L, Erwin D, Gage-Bouchard E. 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 View
  11. 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 View
  12. Cesare N, Dwivedi P, Nguyen Q, Nsoesie E. Use of social media, search queries, and demographic data to assess obesity prevalence in the United States. Palgrave Communications 2019;5(1) View
  13. Hobbiss M, Fairnie J, Jafari K, Lavie N. Attention, mindwandering, and mood. Consciousness and Cognition 2019;72:1 View
  14. Gibbons J, Malouf R, Spitzberg B, Martinez L, Appleyard B, Thompson C, Nara A, Tsou M, Danforth C. Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE 2019;14(7):e0219550 View
  15. Roccetti M, Marfia G, Salomoni P, Prandi C, Zagari R, Gningaye Kengni F, 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 View
  16. Huang D, Huang Y, Khanna S, Dwivedi P, Slopen N, Green K, He X, Puett R, Nguyen Q. Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample. JMIR Public Health and Surveillance 2020;6(3):e17969 View
  17. DePriest K, Shields T, Curriero F. Returning to our roots: The use of geospatial data for nurse‐led community research. Research in Nursing & Health 2019;42(6):467 View
  18. Pearce J. Complexity and Uncertainty in Geography of Health Research: Incorporating Life-Course Perspectives. Annals of the American Association of Geographers 2018;108(6):1491 View
  19. Felmlee D, Blanford J, Matthews S, MacEachren A, Tang W. The geography of sentiment towards the Women’s March of 2017. PLOS ONE 2020;15(6):e0233994 View
  20. Liu S, Chen B, Kuo A. Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study. Journal of Medical Internet Research 2019;21(6):e12394 View
  21. Cesare N, Oladeji O, Ferryman K, Wijaya D, Hendricks‐Muñoz K, Ward A, Nsoesie E. Discussions of miscarriage and preterm births on Twitter. Paediatric and Perinatal Epidemiology 2020;34(5):544 View
  22. Nguyen Q, Brunisholz K, Yu W, McCullough M, Hanson H, Litchman M, Li F, Wan Y, VanDerslice J, Wen M, Smith K. Twitter-derived neighborhood characteristics associated with obesity and diabetes. Scientific Reports 2017;7(1) View
  23. Wilkins E, Aravani A, Downing A, Drewnowski A, Griffiths C, Zwolinsky S, Birkin M, Alvanides S, Morris M. Evidence from big data in obesity research: international case studies. International Journal of Obesity 2020;44(5):1028 View
  24. Meng H, Kath S, Li D, Nguyen Q, Giraud-Carrier C. National substance use patterns on Twitter. PLOS ONE 2017;12(11):e0187691 View
  25. Nguyen T, Meng H, Sandeep S, McCullough M, Yu W, Lau Y, Huang D, Nguyen Q. 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 View
  26. DeGregory K, Kuiper P, DeSilvio T, Pleuss J, Miller R, Roginski J, Fisher C, Harness D, Viswanath S, Heymsfield S, Dungan I, Thomas D. A review of machine learning in obesity. Obesity Reviews 2018;19(5):668 View
  27. Velasco K, Paxton P, Ressler R, Weiss I, Pivnick L. Do National Service Programs Improve Subjective Well-Being in Communities?. The American Review of Public Administration 2019;49(3):275 View
  28. 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 View
  29. Nguyen Q, McCullough M, Meng H, Paul D, Li D, Kath S, Loomis G, Nsoesie E, Wen M, Smith K, Li F. Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015–2016. American Journal of Public Health 2017;107(11):1776 View
  30. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  31. Stirling E, Willcox J, Ong K, Forsyth A. Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours. Public Health Nutrition 2021;24(6):1193 View
  32. Alshamsi A, Bayari R, Salloum S. Sentiment Analysis in English Texts. Advances in Science, Technology and Engineering Systems Journal 2020;5(6):1683 View
  33. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660 View
  34. Park Y, Kim M, Seong K. Happy neighborhoods: Investigating neighborhood conditions and sentiments of a shrinking city with Twitter data. Growth and Change 2021;52(1):539 View
  35. Elnagar A, Yagi S, Nassif A, Shahin I, Salloum S. Systematic Literature Review of Dialectal Arabic: Identification and Detection. IEEE Access 2021;9:31010 View
  36. Morgenstern J, Rosella L, Costa A, de Souza R, Anderson L. Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology. Advances in Nutrition 2021;12(3):621 View
  37. Bour C, Ahne A, Schmitz S, Perchoux C, Dessenne C, Fagherazzi G. The Use of Social Media for Health Research Purposes: Scoping Review. Journal of Medical Internet Research 2021;23(5):e25736 View
  38. Kino S, Hsu Y, Shiba K, Chien Y, Mita C, Kawachi I, Daoud A. A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects. SSM - Population Health 2021;15:100836 View
  39. Karami A, Kadari R, Panati L, Nooli S, Bheemreddy H, Bozorgi P. Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?. ISPRS International Journal of Geo-Information 2021;10(6):373 View
  40. Lupton‐Smith C, Badillo‐Goicochea E, Chang T, Maniates H, Riehm K, Schmid I, Stuart E. Factors associated with county‐level mental health during the COVID‐19 pandemic. Journal of Community Psychology 2022;50(5):2431 View
  41. Abdullah A, Law J, Perlman C, Butt Z. Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study. JMIR Public Health and Surveillance 2022;8(7):e34782 View
  42. Karami A, Clark S, Mackenzie A, Lee D, Zhu M, Boyajieff H, Goldschmidt B. 2020 U.S. presidential election in swing states: Gender differences in Twitter conversations. International Journal of Information Management Data Insights 2022;2(2):100097 View
  43. Côté M, Lamarche B. Artificial intelligence in nutrition research: perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism 2022;47(1):1 View
  44. Sigalo N, St Jean B, Frias-Martinez V. Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets. JMIR Public Health and Surveillance 2022;8(7):e34285 View
  45. Golder S, Stevens R, O'Connor K, James R, Gonzalez-Hernandez G. Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review. Journal of Medical Internet Research 2022;24(4):e35788 View
  46. Siriaraya P, Zhang Y, Kawai Y, Jeszenszky P, Jatowt A, Zhao J. A city-wide examination of fine-grained human emotions through social media analysis. PLOS ONE 2023;18(2):e0279749 View
  47. Hernandez M, Modi S, Mittal K, Dwivedi P, Nguyen Q, Cesare N, Nsoesie E. Diet during the COVID-19 pandemic: An analysis of Twitter data. Patterns 2022;3(8):100547 View
  48. Mane H, Yue X, Yu W, Doig A, Wei H, Delcid N, Harris A, Nguyen T, Nguyen Q. Examination of the Public’s Reaction on Twitter to the Over-Turning of Roe v Wade and Abortion Bans. Healthcare 2022;10(12):2390 View
  49. Granheim S, Løvhaug A, Terragni L, Torheim L, Thurston M. Mapping the digital food environment: A systematic scoping review. Obesity Reviews 2022;23(1) View
  50. Rostami M, Oussalah M, Berahmand K, Farrahi V. Community Detection Algorithms in Healthcare Applications: A Systematic Review. IEEE Access 2023;11:30247 View
  51. Nguyen T, Merchant J, Criss S, Makres K, Gowda K, Mane H, Yue X, Hswen Y, Glymour M, Nguyen Q, Allen A. Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021. Journal of Medical Internet Research 2023;25:e44990 View
  52. Yong K, Liew J. The more "similar" the happier: Augmenting text using similarity scoring with neural embeddings for happiness classification. Journal of Intelligent Information Systems 2023;60(3):631 View
  53. Khudri M, Rhee K, Hasan M, Ahsan K, Ahmad T. Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach. PLOS ONE 2023;18(5):e0277738 View
  54. Luca M, Campedelli G, Centellegher S, Tizzoni M, Lepri B. Crime, inequality and public health: a survey of emerging trends in urban data science. Frontiers in Big Data 2023;6 View
  55. Sigalo N, Frias-Martinez V. Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study. JMIR Infodemiology 2023;3:e43700 View
  56. Sun P, Zhao H, Lu W. How urban environments affect public sentiment and physical activity using a cognitive computing framework. Frontiers of Architectural Research 2024;13(5):946 View
  57. Lee S, Cho M, Peng T. Understanding sentiment toward racial unrest through temporal and geographic lenses: a multilevel-analysis across metropolitan areas in the United States. Frontiers in Communication 2024;9 View
  58. Cesare N, Nguyen Q, Grant C, Nsoesie E. Social media captures demographic and regional physical activity. BMJ Open Sport & Exercise Medicine 2019;5(1):e000567 View

Books/Policy Documents

  1. Sandhu M, Giabbanelli P, Mago V. Social Computing and Social Media. Design, Human Behavior and Analytics. View
  2. Spitzberg B, Tsou M, Jung C. The Handbook of Applied Communication Research. View
  3. Lim K, Lee K, Kendal D, Rashidi L, Naghizade E, Feng Y, Wang J. Smart Cities: Issues and Challenges. View
  4. Batan H, Radpour D, Kehlbacher A, Klein-Seetharaman J, Paul M. Explainable AI in Healthcare and Medicine. View
  5. Zhang X, Athanasiadou R, Razavian N. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. View