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Citing this Article

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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"

Quynh C Nguyen, Dapeng Li, Hsien-Wen Meng, Suraj Kath, Elaine Nsoesie, Feifei Li, Ming Wen

JMIR Public Health Surveill 2016 (Oct 17); 2(2):e158 

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. Muralidhara S, Paul MJ. #Healthy Selfies: Exploration of Health Topics on Instagram. JMIR Public Health and Surveillance 2018;4(2):e10150
    CrossRef
  2. Nguyen TT, Criss S, Allen AM, Glymour MM, Phan L, Trevino R, Dasari S, Nguyen QC. 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
    CrossRef
  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
    CrossRef
  4. . Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28
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  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
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  6. Huang Y, Huang D, Nguyen QC. 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
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  7. Leatherdale ST, 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
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  8. Nguyen TT, Adams N, Huang D, Glymour MM, Allen AM, Nguyen QC. 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
    CrossRef
  9. 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
  10. 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
  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
    CrossRef
  12. Cesare N, Dwivedi P, Nguyen QC, Nsoesie EO. Use of social media, search queries, and demographic data to assess obesity prevalence in the United States. Palgrave Communications 2019;5(1)
    CrossRef
  13. Hobbiss MH, Fairnie J, Jafari K, Lavie N. Attention, mindwandering, and mood. Consciousness and Cognition 2019;72:1
    CrossRef
  14. Gibbons J, Malouf R, Spitzberg B, Martinez L, Appleyard B, Thompson C, Nara A, Tsou M, Danforth CM. Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE 2019;14(7):e0219550
    CrossRef
  15. 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
  16. Huang D, Huang Y, Khanna S, Dwivedi P, Slopen N, Green KM, 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
    CrossRef
  17. DePriest KN, Shields TM, Curriero FC. Returning to our roots: The use of geospatial data for nurse‐led community research. Research in Nursing & Health 2019;42(6):467
    CrossRef
  18. . Complexity and Uncertainty in Geography of Health Research: Incorporating Life-Course Perspectives. Annals of the American Association of Geographers 2018;108(6):1491
    CrossRef
  19. Felmlee DH, Blanford JI, Matthews SA, MacEachren AM, Tang W. The geography of sentiment towards the Women’s March of 2017. PLOS ONE 2020;15(6):e0233994
    CrossRef
  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
    CrossRef
  21. Cesare N, Oladeji O, Ferryman K, Wijaya D, Hendricks‐Muñoz KD, Ward A, Nsoesie EO. Discussions of miscarriage and preterm births on Twitter. Paediatric and Perinatal Epidemiology 2020;34(5):544
    CrossRef
  22. 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
  23. Wilkins E, Aravani A, Downing A, Drewnowski A, Griffiths C, Zwolinsky S, Birkin M, Alvanides S, Morris MA. Evidence from big data in obesity research: international case studies. International Journal of Obesity 2020;44(5):1028
    CrossRef
  24. Meng H, Kath S, Li D, Nguyen QC, Giraud-Carrier C. National substance use patterns on Twitter. PLOS ONE 2017;12(11):e0187691
    CrossRef
  25. 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
  26. 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
  27. 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 2019;49(3):275
    CrossRef
  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
    CrossRef
  29. 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
  30. Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1
    CrossRef
  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
    CrossRef
  32. Alshamsi A, Bayari R, Salloum S. Sentiment Analysis in English Texts. Advances in Science, Technology and Engineering Systems Journal 2020;5(6):1683
    CrossRef
  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
    CrossRef
  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
    CrossRef
  35. Elnagar A, Yagi SM, Nassif AB, Shahin I, Salloum SA. Systematic Literature Review of Dialectal Arabic: Identification and Detection. IEEE Access 2021;9:31010
    CrossRef
  36. Morgenstern JD, Rosella LC, Costa AP, de Souza RJ, Anderson LN. Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology. Advances in Nutrition 2021;12(3):621
    CrossRef
  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
    CrossRef
  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
    CrossRef
  39. Karami A, Kadari RR, Panati L, Nooli SP, 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
    CrossRef
  40. Lupton‐Smith C, Badillo‐Goicochea E, Chang T, Maniates H, Riehm KE, Schmid I, Stuart EA. Factors associated with county‐level mental health during the COVID‐19 pandemic. Journal of Community Psychology 2022;50(5):2431
    CrossRef
  41. Abdullah AYM, Law J, Perlman CM, Butt ZA. Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study. JMIR Public Health and Surveillance 2022;8(7):e34782
    CrossRef
  42. Karami A, Clark SB, Mackenzie A, Lee D, Zhu M, Boyajieff HR, 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  47. Hernandez MA, Modi S, Mittal K, Dwivedi P, Nguyen QC, Cesare NL, Nsoesie EO. Diet during the COVID-19 pandemic: An analysis of Twitter data. Patterns 2022;3(8):100547
    CrossRef
  48. Mane H, Yue X, Yu W, Doig AC, Wei H, Delcid N, Harris A, Nguyen TT, Nguyen QC. Examination of the Public’s Reaction on Twitter to the Over-Turning of Roe v Wade and Abortion Bans. Healthcare 2022;10(12):2390
    CrossRef
  49. Granheim SI, Løvhaug AL, Terragni L, Torheim LE, Thurston M. Mapping the digital food environment: A systematic scoping review. Obesity Reviews 2022;23(1)
    CrossRef
  50. Rostami M, Oussalah M, Berahmand K, Farrahi V. Community Detection Algorithms in Healthcare Applications: A Systematic Review. IEEE Access 2023;11:30247
    CrossRef
  51. Nguyen TT, Merchant JS, Criss S, Makres K, Gowda KN, Mane H, Yue X, Hswen Y, Glymour MM, Nguyen QC, Allen AM. 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
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  52. Yong KS, Liew JSY. 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
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  53. Khudri MM, Rhee KK, Hasan MS, Ahsan KZ, 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
    CrossRef
  54. Luca M, Campedelli GM, 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
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  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
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  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;
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  57. Lee S, Cho MS, 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
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According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.5869):

  1. Sandhu M, Giabbanelli PJ, Mago VK. Social Computing and Social Media. Design, Human Behavior and Analytics. 2019. Chapter 31:434
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  2. Spitzberg BH, Tsou M, Jung C. The Handbook of Applied Communication Research. 2020. :163
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  3. Lim KH, Lee KE, Kendal D, Rashidi L, Naghizade E, Feng Y, Wang J. Smart Cities: Issues and Challenges. 2019. :77
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  4. Batan H, Radpour D, Kehlbacher A, Klein-Seetharaman J, Paul MJ. Explainable AI in Healthcare and Medicine. 2021. Chapter 16:179
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  5. Zhang X, Athanasiadou R, Razavian N. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. 2021. Chapter 12:141
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