Characterizing diabetes, diet, exercise, and obesity comments on Twitter

International Journal of Information Management

Volumn 38, Issue 1, 2018, Pages 1-6

  Karami, Amir ^a   Dahl, Alicia A ^a   Turner McGrievy, Gabrielle ^a   Kharrazi, Hadi ^b   Shaw, George ^a  

^a UNIVERSITY OF SOUTH CAROLINA   (United States)

^b JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Diabetes; Diet; Exercise; Health; Obesity; Text mining; Topic model; Twitter

Indexed keywords

BLOOD PRESSURE; COMPUTER GAMES; HEALTH; MEDICAL PROBLEMS; PUBLIC HEALTH; SEMANTICS; SOCIAL NETWORKING (ONLINE);

EXERCISE; OBESITY; TEXT MINING; TOPIC MODELING; TWITTER;

NUTRITION;

EID: 85029596678     PISSN: 02684012     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijinfomgt.2017.08.002     Document Type: Article

References (74)

1. Arnold, C., Speier, W., A topic model of clinical reports. Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, 2012, ACM, 1031–1032.
2. Arnold, C.W., El-Saden, S.M., Bui, A.A., Taira, R., Clinical case-based retrieval using latent topic analysis. AMIA annual symposium proceedings, Vol. 2010, 2010, American Medical Informatics Association, 26.
3. Asou, T., Eguchi, K., Predicting protein–protein relationships from literature using collapsed variational latent Dirichlet allocation. Proceedings of the 2nd international workshop on data and text mining in bioinformatics, 2008, ACM, 77–80.
4. Association, A.D., et al. Physical activity/exercise and diabetes. Diabetes Care 27:suppl 1 (2004), s58–s62.
5. Barnard, N.D., Cohen, J., Jenkins, D.J., Turner-McGrievy, G., Gloede, L., Green, A., et al. A low-fat vegan diet and a conventional diabetes diet in the treatment of type 2 diabetes: A randomized, controlled, 74-wk clinical trial. The American Journal of Clinical Nutrition, 2009, 26736H.
6. Blei, D.M., Ng, A.Y., Jordan, M.I., Latent Dirichlet allocation. Journal of Machine Learning Research 3 (2003), 993–1022.
7. Bollen, J., Mao, H., Zeng, X., Twitter mood predicts the stock market. Journal of Computational Science 2:1 (2011), 1–8.
8. Boyington, J.E., Carter-Edwards, L., Piehl, M., Hutson, J., Langdon, D., McManus, S., Cultural attitudes toward weight, diet, and physical activity among overweight African American girls. Preventing Chronic Disease, 5(2), 2008.
9. Boyle, J.P., Honeycutt, A.A., Narayan, K.V., Hoerger, T.J., Geiss, L.S., Chen, H., et al. Projection of diabetes burden through 2050. Diabetes Care 24:11 (2001), 1936–1940.
10. Buntain, C., Golbeck, J., This is your Twitter on drugs: Any questions?. Proceedings of the 24th international conference on World Wide Web, 2015, ACM, 777–782.
11. Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K., From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. NAACL HLT 2015, 2015, 1.
12. Cotman, C.W., Berchtold, N.C., Exercise: A behavioral intervention to enhance brain health and plasticity. Trends in Neurosciences 25:6 (2002), 295–301.
13. Culotta, A., Towards detecting influenza epidemics by analyzing Twitter messages. Proceedings of the first workshop on social media analytics, 2010, ACM, 115–122.
14. Dahl, A.A., Hales, S.B., Turner-McGrievy, G.M., Integrating social media into weight loss interventions. Current Opinion in Psychology 9 (2016), 11–15.
15. Eichstaedt, J.C., Schwartz, H.A., Kern, M.L., Park, G., Labarthe, D.R., Merchant, R.M., et al. Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science 26:2 (2015), 159–169.
16. Flegal, K.M., Carroll, M.D., Kit, B.K., Ogden, C.L., Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. The Journal of the American Medical Association 307:5 (2012), 491–497.
17. Fu, K.-W., Liang, H., Saroha, N., Tse, Z.T.H., Ip, P., Fung, I.C.-H., How people react to Zika virus outbreaks on Twitter? A computational content analysis. American Journal of Infection Control 44:12 (2016), 1700–1702.
18. Ghosh, D., Guha, R., What are we ‘tweeting’ about obesity? Mapping tweets with topic modeling and geographic information system. Cartography and Geographic Information Science 40:2 (2013), 90–102.
19. Golder, S.A., Macy, M.W., Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333:6051 (2011), 1878–1881.
20. Hanson, C.L., Cannon, B., Burton, S., Giraud-Carrier, C., An exploration of social circles and prescription drug abuse through Twitter. Journal of Medical Internet Research, 15(9), 2013, e189.
21. Harris, J.K., Moreland-Russell, S., Tabak, R.G., Ruhr, L.R., Maier, R.C., Communication about childhood obesity on Twitter. American Journal of Public Health 104:7 (2014), e62–e69.
22. Harris, J.K., Mueller, N.L., Snider, D., Haire-Joshu, D., Peer reviewed: Local health department use of Twitter to disseminate diabetes information, United States. Preventing Chronic Disease, 10, 2013.
23. Hartz, A.J., Rupley, D.C., Kalkhoff, R.D., Rimm, A.A., Relationship of obesity to diabetes: Influence of obesity level and body fat distribution. Preventive Medicine 12:2 (1983), 351–357.
24. Harvard HSPH, Obesity trends. 2017 https://www.hsph.harvard.edu/obesity-prevention-source/obesity-trends/.
25. Hill, J.O., Wyatt, H.R., Peters, J.C., Energy balance and obesity. Circulation 126:1 (2012), 126–132.
26. Hong, L., Davison, B.D., Empirical study of topic modeling in Twitter. Proceedings of the first workshop on social media analytics, 2010, ACM, 80–88.
27. Karami, A., Fuzzy topic modeling for medical corpora (Ph.D. thesis). 2015, University of Maryland, Baltimore County.
28. Karami, A., Gangopadhyay, A., FFTM: A fuzzy feature transformation method for medical documents. Proceedings of the conference of the Association for Computational Linguistics (ACL), Vol. 128, 2014.
29. Karami, A., Gangopadhyay, A., Zhou, B., Kharrazi, H., FLATM: A fuzzy logic approach topic model for medical documents. Proceedings of the annual meeting of the North American Fuzzy Information Processing Society (NAFIPS), 2015, IEEE.
30. Karami, A., Gangopadhyay, A., Zhou, B., Kharrazi, H., A fuzzy approach model for uncovering hidden latent semantic structure in medical text collections. Proceedings of the iConference, 2015.
31. Karami, A., Gangopadhyay, A., Zhou, B., Kharrazi, H., Fuzzy approach topic discovery in health and medical corpora. International Journal of Fuzzy Systems, 2017, 1–12.
32. Karami, A., Zhou, B., Online review spam detection by new linguistic features. iConference 2015 proceedings, 2015.
33. Karami, A., Zhou, L., Exploiting latent content based features for the detection of static SMS spams. The 77th annual meeting of the Association for Information Science and Technology (ASIST), 2014.
34. Karami, A., Zhou, L., Improving static SMS spam detection by using new content-based features. The 20th Americas Conference on Information Systems (AMCIS), 2014.
35. Kivipelto, M., Ngandu, T., Fratiglioni, L., Viitanen, M., Kåreholt, I., Winblad, B., et al. Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Archives of Neurology 62:10 (2005), 1556–1560.
36. Kopelman, P.G., Obesity as a medical problem. Nature 404:6778 (2000), 635–643.
37. Laaksonen, D.E., Lindström, J., Lakka, T.A., Eriksson, J.G., Niskanen, L., Wikström, K., et al. Physical activity in the prevention of type 2 diabetes. Diabetes 54:1 (2005), 158–165.
38. Lampos, V., Cristianini, N., Tracking the flu pandemic by monitoring the social web. 2010 2nd international workshop on cognitive information processing, 2010, IEEE, 411–416.
39. Lampos, V., Cristianini, N., Nowcasting events from the social web with statistical learning. ACM Transactions on Intelligent Systems and Technology, 3(4), 2012, 72.
40. Lampos, V., De Bie, T., Cristianini, N., Flu detector-tracking epidemics on Twitter. Joint European conference on machine learning and knowledge discovery in databases, 2010, Springer, 599–602.
41. Lazard, A.J., Scheinfeld, E., Bernhardt, J.M., Wilcox, G.B., Suran, M., Detecting themes of public concern: A text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat. American Journal of Infection Control 43:10 (2015), 1109–1111.
42. Lu, Y., Mei, Q., Zhai, C., Investigating task performance of probabilistic topic models: An empirical study of PLSA and LDA. Information Retrieval 14:2 (2011), 178–203.
43. Luchsinger, J.A., Cheng, D., Tang, M.X., Schupf, N., Mayeux, R., Central obesity in the elderly is related to late onset Alzheimer's disease. Alzheimer Disease and Associated Disorders, 26(2), 2012, 101.
44. Mcauliffe, J.D., Blei, D.M., Supervised topic models. Proceedings of the advances in neural information processing systems, 2008, 121–128.
45. McCallum, A.K., Mallet: A machine learning for language toolkit. 2002.
46. Mejova, Y., Weber, I., Macy, M.W., Twitter: A digital socioscope. 2015, Cambridge University Press.
47. Nasukawa, T., Yi, J., Sentiment analysis: Capturing favorability using natural language processing. Proceedings of the 2nd international conference on knowledge capture, 2003, ACM, 70–77.
48. Odlum, M., Yoon, S., What can we learn about the Ebola outbreak from tweets?. American Journal of Infection Control 43:6 (2015), 563–571.
49. Olanoff, D., Twitter Monthly Active Users Crawl To 316M, Dorsey: We are not satisfied. 2015 http://apps.who.int/iris/bitstream/10665/204871/1/9789241565257_eng.pdf.
50. Paul, M.J., Dredze, M., You are what you tweet: Analyzing Twitter for public health. ICWSM, Vol. 20, 2011, 265–272.
51. Paul, M.J., Dredze, M., A model for mining public health topics from Twitter. Health, 11, 2012 16-6.
52. PLAY 60 Challenge, NFL. 2017 http://www.nflrush.com/play60/.
53. Pokeman-Go Game, Niantic, Inc. 2017 http://www.pokemongo.com/.
54. Prier, K.W., Smith, M.S., Giraud-Carrier, C., Hanson, C.L., Identifying health-related topics on Twitter. International conference on social computing, behavioral-cultural modeling, and prediction, 2011, Springer, 18–25.
55. Ritterman, J., Osborne, M., Klein, E., Using prediction markets and Twitter to predict a swine flu pandemic. 1st international workshop on mining social media, Vol. 9, 2009, 9–17.
56. Ross, A., Thomas, S., The health benefits of yoga and exercise: A review of comparison studies. The Journal of Alternative and Complementary Medicine 16:1 (2010), 3–12.
57. Ross, R., Dagnone, D., Jones, P.J., Smith, H., Paddags, A., Hudson, R., et al. Reduction in obesity and related comorbid conditions after diet-induced weight loss or exercise-induced weight loss in men. A randomized, controlled trial. Annals of Internal Medicine 133:2 (2000), 92–103.
58. Scanfeld, D., Scanfeld, V., Larson, E.L., Dissemination of health information through social networks: Twitter and antibiotics. American Journal of Infection Control 38:3 (2010), 182–188.
59. Schwarzer, R., Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology 57:1 (2008), 1–29.
60. Statista, Number of social media users worldwide from 2010 to 2020. 2017 https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/.
61. Szomszor, M., Kostkova, P., De Quincey, E., # swineflu: Twitter predicts swine flu outbreak in 2009. International conference on electronic healthcare, 2010, Springer, 18–26.
62. Tompson, T., Benz, J., Agiesta, J., Brewer, K., Bye, L., Reimer, R., et al. Obesity in the United States: Public perceptions. The Food Industry, 53(26), 2012, 21.
63. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M., Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM 10, 2010.
64. Turner-McGrievy, G.M., Beets, M.W., Tweet for health: Using an online social network to examine temporal trends in weight loss-related posts. Translational Behavioral Medicine 5:2 (2015), 160–166.
65. Twitter, Twitter developer documentation. 2017 https://dev.twitter.com/docs.
66. Verdile, G., Keane, K.N., Cruzat, V.F., Medic, S., Sabale, M., Rowles, J., et al. Inflammation and oxidative stress: The molecular connectivity between insulin resistance, obesity, and Alzheimer's disease. Mediators of Inflammation, 2015, 2015.
67. Vickey, T.A., Ginis, K.M., Dabrowski, M., Twitter classification model: The ABC of two million fitness tweets. Translational Behavioral Medicine 3:3 (2013), 304–311.
68. Wallach, H.M., Murray, I., Salakhutdinov, R., Mimno, D., Evaluation methods for topic models. Proceedings of the 26th annual international conference on machine learning, 2009, ACM, 1105–1112.
69. Wartell, D., The geography of obesity: Predicting obesity rates in California based on access to health care. 2015, 10.7910/DVN/LQ4DY6.
70. Wiebe, J., Breck, E., Buckley, C., Cardie, C., Davis, P., Fraser, B., et al. Recognizing and organizing opinions expressed in the world press. New directions in question answering, 2003, 12–19.
71. Wing, R.R., Goldstein, M.G., Acton, K.J., Birch, L.L., Jakicic, J.M., Sallis, J.F., et al. Behavioral science research in diabetes lifestyle changes related to obesity, eating behavior, and physical activity. Diabetes Care 24:1 (2001), 117–123.
72. World Health Organization Fact Sheet, Obesity and overweight. 2016 http://www.who.int/mediacentre/factsheets/fs311/en/.
73. Zabin, J., Jefferies, A., Social media monitoring and analysis: Generating consumer insights from online conversation. Aberdeen Group Benchmark Report, 37(9), 2008.
74. Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., et al. Comparing Twitter and traditional media using topic models. European conference on information retrieval, 2011, Springer, 338–349.