The lexicocalorimeter: Gauging public health through caloric input and output on social media

PLoS ONE

Volumn 12, Issue 2, 2017, Pages

  Alajajian, Sharon E ^a,b   Williams, Jake Ryland ^a,b,c   Reagan, Andrew J ^a,b   Alajajian, Stephen C ^d   Frank, Morgan R ^e   Mitchell, Lewis ^f   Lahne, Jacob ^g   Danforth, Christopher M ^a,b   Dodds, Peter Sheridan ^a,b  

^a University of Vermont ^*  (United States)

^b UNIVERSITY OF VERMONT   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d Infants and Children   (United States)

^e MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^f UNIVERSITY OF ADELAIDE   (Australia)

^g DREXEL UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CALORIC INTAKE; CALORIMETER; DIABETES MELLITUS; EMOTIONAL STABILITY; HEALTH; HUMAN; LEXICOCALORIMETER; MEDICAL INFORMATION; ONLINE SYSTEM; PHYSICAL ACTIVITY; PHYSICAL WELL-BEING; PSYCHOLOGICAL WELL-BEING; PUBLIC HEALTH; SOCIAL MEDIA; SOCIAL WELLBEING; UNITED STATES; WELLBEING; WORK ENVIRONMENT; EPIDEMIOLOGICAL MONITORING; FOOD; PROCEDURES; STATISTICS AND NUMERICAL DATA;

ENERGY INTAKE; EPIDEMIOLOGICAL MONITORING; FOOD; HUMANS; PUBLIC HEALTH; SOCIAL MEDIA; UNITED STATES;

EID: 85012289107     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0168893     Document Type: Article

References (41)

1. Health-related quality of life: Well-being concepts; 2013. Health-related quality of life: Well-being concepts. http://www.cdc.gov/hrqol/wellbeing.htm; Accessed March 29, 2014.
2. Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM. Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS ONE. 2011; 6:e26752. Draft version available at http://arxiv.org/abs/1101.5120v4. Accessed November 15, 2014. doi: 10.1371/journal.pone.0026752 PMID: 22163266
3. Diener E, Diener M, Diener C. Factors predicting the subjective well-being of nations. Journal of Personality and Social Psychology. 1995; 69:851-864. PMID: 7473035
4. State of the States. http://www.gallup.com/poll/125066/State-States.aspx; Accessed March 29, 2014.
5. Stimmungsgasometer. http://xn-fhlometer-q9a.de/; Accessed March 29, 2014.
6. Siebens J. Extended measures of well-being: Living conditions in the United States: 2011; 2013. Accessed on March 15, 2014. Available from: http://www.census.gov/prod/2013pubs/p70-136.pdf.
7. Duggan M, Brenner J. The demographics of social media users:2012; 2013. Accessed on March 15,2014. Available from: http://www.pewinternet.org/files/old-media//Files/Reports/2013/PIP-SocialMediaUsers.pdf.
8. Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PLoS ONE. 2011; 6:e19467. doi: 10.1371/journal.pone.0019467 PMID: 21573238
9. Prieto VM, Matos S, Alvarez M, Cacheda F, Oliveira JL. Twitter: A good place to detect health conditions. PLoS ONE. 2014; 9:e86191. doi: 10.1371/journal.pone.0086191 PMID: 24489699
10. Chew C, Eysenbach G. Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE. 2010; 5:e14118. doi: 10.1371/journal.pone.0014118 PMID: 21124761
11. Paul MJ, Dredze M. You are what you tweet: Analyzing Twitter for public health. ICWSM. 2011;20:265-272.
12. Watts DJ, Muhamad R, Medina D, Dodds PS. Multiscale, resurgent epidemics in a hierarchcial metapopulation model. Proc Natl Acad Sci. 2005; 102(32):11157-11162. doi: 10.1073/pnas.0501226102PMID: 16055564
13. Google Flu Trends, https://www.google.org/flutrends/; accessed March 1, 2015.
14. Lazer D, Kennedy R, King G, Vespignani A. The parable of Google Flu: Traps in Big Data analysis. Science Magazine. 2014; 343:1203-1205.
15. Mitchell L, Frank MR, Dodds PS, Danforth CM. The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE. 2013; 8: e64417. doi: 10.1371/journal.pone.0064417 PMID: 23734200
16. Dodds PS, Clark EM, Desu S, Frank MR, Reagan AJ, Williams JR, et al. Human language reveals a universal positivity bias. Proc Natl Acad Sci. 2015; 112(8):2389-2394. doi: 10.1073/pnas.1411678112PMID: 25675475
17. Chunara R, Bouton L, Ayers JW, Brownstein JS. Assessing the online social environment for surveillance of obesity prevalence. PLoS ONE. 1995; 8:e61373. doi: 10.1371/journal.pone.0061373
18. West R, White RW, Horvitz E. From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. In: Proceedings of the 22nd international conference on World Wide Web. ACM; 2013. p. 1399-1410.
19. Eichstaedt JC, Schwartz HA, Kern ML, Park G, Labarthe DR, Merchant RM, et al. Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science. 2015;.
20. Culotta A. Estimating County Health Statistics with Twitter. In: Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systemes. CHI'14. New York, NY, USA: ACM; 2014. p. 1335-1344. Available from: http://doi.acm.org/10.1145/2556288.2557139.
21. Abbar S, Mejova Y, Weber I. You tweet what you eat: Studying food consumption through Twitter. New York, NY, USA: ACM; 2015. Available from: http://doi.acm.org/10.1145/2702123.2702153.
22. Walpole SC, Prieto-Merino D, Edwards P, Cleland J, Stevens G, Roberts I. The weight of nations: an estimation of adult human biomass. BMC Public Health. 2012; 12:439. doi: 10.1186/1471-2458-12-439PMID: 22709383
23. Hedonometer 2.0: Measuring happiness and using word shifts; Computational Story Lab blog; October 6, 2014; http://compstorylab.org/2014/10/06/hedonometer-2-0-measuring-happiness-and-using-wordshifts/;Accessed on March 1, 2015.
24. Americas Health Rankings report:State Health Statistics; http://AmericasHealthRankings.org,Accessed March 15, 2014.
25. Centers for Disease Control and Prevention; http://www.cdc.gov, Accessed March 15, 2014.
26. CNBC overall rankings 2012; http://www.cnbc.com/id/100016697, Accessed March 15, 2014.
27. State Health Facts:The Henry J. Kaiser Family Foundation; http://kff.org/statedata, Accessed March15, 2014.
28. State indicator report on fruits and vegetables. National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition, Physical Activity, and Obesity. Centers for Disease Control and Prevention, US Department of Health and Human Services, 2013; http://www.cdc.gov/nutrition/downloads/State-Indicator-Report-Fruits-Vegetables-2013.pdf, Accessed March 15, 2014.
29. America's Brain Health Index; http://www.beautiful-minds.com/AmericasBrainHealthIndex, Accessed March 15, 2014.
30. US Census American FactFinder; http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml, Accessed March 15, 2014.
31. Rentfrow PJ, Gosling SD, Jokela M, Stillwell DJ, Kosinski M, Potter J. Divided we stand: Three psychological regions of the United States and their political, economic, social, and health correlates. Journal of Personality and Social Psychology. 2013; 105(6):996-1012. doi: 10.1037/a0034434 PMID: 24128185
32. Strolling of the Heiders Locavore Index; http://www.strollingoftheheifers.com/locavoreindex/, Accessed March 15, 2014.
33. Freedom in the 50 states, Mercatus Center, George Mason University; http://freedominthe50states.org/, Accessed March 15, 2014.
34. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological). 1995; 57:289-300.
35. French MT, Popovici I, Maclean JC. Do alcohol consumers exercise more? Findings from a national survey. Am J Health Promot. 2009; 24:2-10. doi: 10.4278/ajhp.0801104 PMID: 19750956
36. Pesta BJ, Bertsch S, McDaniel MA, Mahoney CB, Poznanski PJ. Differential epidemiology: IQ, neuroticism, and chronic disease by the 50 U.S. states. Intelligence. 2012; 40:107-114. doi: 10.1016/j.intell.2012.01.011
37. Williams JR, Lessard PR, Desu S, Clark EM, Bagrow JP, Danforth CM, et al. Zipf's law holds for phrases, not words. Nature Scientific Reports. 2015; 5:12209. Available online at http://arxiv.org/abs/1406.5181. doi: 10.1038/srep12209
38. U.S. Department of Agriculture, Agricultural Research Service, USDA National Nutrient Database for Standard Reference, release 25; 2013; http://www.ars.usda.gov/ba/bhnrc/ndl; Accessed March 15, 2014.
39. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C, et al. The Compendium of Physical Activities Tracking Guide. Healthy Lifestyles Research Center, College of Nursing & Health Innovation, Arizona State University; 2013.
40. USDA MyPlate food groups; http://www.choosemyplate.gov/food-groups/; Accessed May 15, 2015.
41. Williams JR, Clark EM, Bagrow JP, Danforth CM, Dodds PS. Identifying missing dictionary entries with frequency-conserving context models. Physical Review E. 2015; 92:042808. doi: 10.1103/PhysRevE.92.042808