Estimating county health statistics with twitter

Conference on Human Factors in Computing Systems - Proceedings

Volumn , Issue , 2014, Pages 1335-1344

  Culotta, Aron ^a  

^a Illinois Institute of Technology   (United States)

Author keywords

Natural language processing; Public health; Social media

Indexed keywords

HEALTH INSURANCE; HUMAN ENGINEERING; NATURAL LANGUAGE PROCESSING SYSTEMS; NUTRITION; POPULATION STATISTICS; SOCIAL NETWORKING (ONLINE);

DEMOGRAPHIC VARIABLES; INSURANCE COVERAGES; LARGE-SCALE STUDIES; LINGUISTIC ANALYSIS; NATURAL LANGUAGE PROCESSING; PREDICTIVE ACCURACY; SOCIAL MEDIA; TRADITIONAL MODELS;

PUBLIC HEALTH;

EID: 84900461706     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2556288.2557139     Document Type: Conference Paper

References (32)

1. Anselin, L. Spatial econometrics: Methods and models. Kluwer Academic Publishers, Dordrecht; Boston, 1988.
2. Chen, M. K. The effect of language on economic behavior: Evidence from savings rates, health behaviors and retirement assets. American Economic Review 103, 2 (Apr. 2013), 690-731.
3. Chiswick, B. R., and Miller, P. W. The economics of language international analyses. Routledge, London; New York, 2007.
4. Clifford, P., Richardson, S., and Hmon, D. Assessing the significance of the correlation between two spatial processes. Biometrics 45, 1 (Mar. 1989), 123-134. PMID: 2720048.
5. Culotta, A. Towards detecting influenza epidemics by analyzing twitter messages. In Proceedings of the First Workshop on Social Media Analytics, ACM (New York, NY, USA, 2010), 115-122.
6. Culotta, A. Lightweight methods to estimate influenza rates and alcohol sales volume from twitter messages. Lang. Resour. Eval. 47, 1 (Mar. 2013), 217238.
7. Danner, D. D., Snowdon, D. A., and Friesen, W. V. Positive emotions in early life and longevity: Findings from the nun study. Journal of personality and social psychology 80, 5 (May 2001), 804-813. PMID: 11374751.
8. De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. Predicting depression via social media. In ICWSM (2013).
9. Dredze, M. How social media will change public health. IEEE Intelligent Systems 27, 4 (2012), 81-84.
10. Duggan, M., and Brenner, J. The demographics of social media users-2012. Pew Internet &American Life Project, Feb 2013.
11. Flores, B. E. A pragmatic view of accuracy measurement in forecasting. Omega 14, 2 (1986), 93-98.
12. Ghosh, D. D., and 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.
13. Gottschalk, L. A., and Gleser, G. C. The Measurement of Psychological States Through the Content Analysis of Verbal Behavior. University of California Press, Jan. 1979.
14. Graham, L E, n., Scherwitz, L., and Brand, R. Self-reference and coronary heart disease incidence in the western collaborative group study. Psychosomatic medicine 51, 2 (Apr. 1989), 137-144. PMID: 2710908.
15. Hanson, C. L., Burton, S. H., Giraud-Carrier, C., West, J. H., Barnes, M. D., and Hansen, B. Tweaking and tweeting: Exploring twitter for nonmedical use of a psychostimulant drug (adderall) among college students. Journal of Medical Internet Research 15, 4 (Apr. 2013), e62.
16. Hecht, B., Hong, L., Suh, B., and Chi, E. H. Tweets from Justin Bieber's heart: The dynamics of the location field in user profiles. In CHI (New York, NY, USA, 2011), 237-246.
17. Howell, R. T., Kern, M. L., and Lyubomirsky, S. Health benefits: Meta-analytically determining the impact of well-being on objective health outcomes. Health Psychology Review 1, 1 (2007), 83-136.
18. James W Pennebaker, M. R. M. Psychological aspects of natural language. use: Our words, our selves. Annual review of psychology 54 (2003), 547-77.
19. Jamison-Powell, S., Linehan, C., Daley, L., Garbett, A., and Lawson, S. "I can't get no sleep": Discussing #insomnia on Twitter. In CHI, ACM (New York, NY, USA, 2012), 1501-1510.
20. Lampos, V., De Bie, T., and Cristianini, N. Flu detector: Tracking epidemics on twitter. In ECML/PKDD (2010), 599-602.
21. Messer, L. C. Neighborhood-level characteristics as predictors of preterm birth: Examples from wake county, north carolina. Tech. rep., North Carolina Department of Health and Human Services, 2005.
22. Paul, M. J., and Dredze, M. You are what you tweet: Analyzing Twitter for public health. In ICWSM (2011).
23. Pedregosa, F., et al. Scikit-learn: Machine learning in python. Machine Learning Research 12 (2011), 28252830.
24. Pennebaker, J., Francis, J., and Booth, R. Linguistic inquiry and word count: LIWC 2001. World Journal of the International Linguistic Association (2001).
25. Qiu, L., Lin, H., Ramsay, J., and Yang, F. You are what you tweet: Personality expression and perception on twitter. Journal of Research in Personality 46, 6 (Dec. 2012), 710-718.
26. Rabi, D. M., et al. Association of socio-economic status with diabetes prevalence and utilization of diabetes care services. BMC Health Services Research 6, 1 (Oct. 2006), 124. PMID: 17018153.
27. Sadilek, A., Kautz, H., and Silenzio, V. Predicting disease transmission from geo-tagged micro-blog data. In AAAI (Dec. 2012).
28. Schwartz, H. A., et al. Characterizing geographic variation in well-being using tweets. In Seventh International AAAI Conference on Weblogs and Social Media (ICWSM) (2013).
29. Seligman, M. E. P. Flourish: A visionary new understanding of happiness and well-being. Free Press, New York, 2011.
30. Signorini, A., Segre, A. M., and Polgreen, P. M. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza a H1N1 pandemic. PLoS ONE 6, 5 (May 2011), e19467.
31. Sobal, J., and Stunkard, A. J. Socioeconomic status and obesity: A review of the literature. Psychological Bulletin 105, 2 (1989), 260-275.
32. Stewart, A., and Diaz, E. Epidemic intelligence: For the crowd, by the crowd. In Web Engineering, M. Brambilla, T. Tokuda, and R. Tolksdorf, Eds., no. 7387 in Lecture Notes in Computer Science. Springer Berlin Heidelberg, Jan. 2012, 504-505.