DOI QR코드

DOI QR Code

Predicting tobacco risk factors by using social big data

소셜 빅데이터를 활용한 담배 위험 예측

  • Song, Tae Min (Information and Statistics Department, Korea Institute for Health and Social Affairs) ;
  • Song, Juyoung (Department of Criminal Justice, Pennsylvania State University) ;
  • Cheon, Mi Kyung (Information and Statistics Department, Korea Institute for Health and Social Affairs)
  • 송태민 (한국보건사회연구원 정보통계연구실) ;
  • 송주영 (펜실베니아주립대학 범죄학과) ;
  • 천미경 (한국보건사회연구원 정보통계연구실)
  • Received : 2015.08.05
  • Accepted : 2015.09.24
  • Published : 2015.09.30

Abstract

This study will predict risk factors associated with cigarettes in Korea by analyzing the social big data collected from the internet such as blogs, cafes, and SNSes in Korea, using data mining techniques. The key analysis results are as follows. First, when "raising cigarette price"is mentioned online, the negative group (i.e., the proportion of people holding negative views about smoking) increased from 58.6% to 74.8%, and when "lung cancer" is mentioned, it increased to 73.1%. Second, with regard to cigarettes in general, the positive group (i.e., the proportion of people holding positive views about smoking) decreased by 5.6% after the raising of cigarette prices, while the negative group increased by 6.1%. Third, when policies related to "FCTC, raising cigarette price, non-smoking laws, smoking regulations, non-smoking ads, and nonsmoking business" are more frequently mentioned online, the positive group tended to decrease. Finally, when "non-smoking drugs, non-smoking patches, and non-smoking gums" are more frequently mentioned online, the positive group tended to decrease. However, when "electronic cigarettes and supplements" are more frequently mentioned online, the positive group increased.

본 연구는 국내의 블로그, 카페, SNS 등 인터넷을 통해 수집된 소셜 빅데이터를 데이터마이닝 분석 기법을 적용하여 우리나라 국민의 담배에 대한 위험요인을 예측하고자 하였다. 주요분석 결과는 다음과 같다. 첫째, 온라인상에 '담뱃값인상'이 언급될 경우 담배에 대한 일반군 (negative)이 58.6%에서 74.8%로 증가하며, '폐암'이 언급될 경우 73.1%로 증가하는 것으로 나타났다. 둘째, 담뱃값인상 이후 담배에 대한 위험군 (positive)은 5.6% 감소하고, 일반군은 6.1% 증가한 것으로 나타났다. 셋째, 'FCTC, 담뱃값인상, 금연관련법, 흡연규제, 금연광고, 금연사업'과 관련된 정책이 온라인상에 많이 언급될수록 담배에 대한 위험군이 감소하는 것으로 나타났다. 마지막으로 '금연약, 금연패치, 금연껌'이 온라인 상에 언급될수록 담배에 대한 위험군이 감소하나, '전자담배와 보조제'가 온라인상에 언급될수록 담배에 대한 위험군을 증가시키는 것으로 나타났다.

Keywords

References

  1. Campaign for Tobacco-Free Kids. (2013). Increasing the federal tobacco tax reduces tobacco use, Washington DC.
  2. Carter, B. D., Abnet, C. C., Feskanich, D., Freedman, N. D., Hartge, P., Lewis, C. E., Ockene, J. K., Prentice, R. L., Speizer, F. E., Thun, M. J. and Jacobs, E. J. (2015). Smoking and mortality : Beyond established causes. New England Journal of Medicine, 372, 631-640. https://doi.org/10.1056/NEJMsa1407211
  3. Center for Disease Control and Prevention. (2010). How tobacco smoke cause disease : The biology and behavioral basis for smoking attributable disease: A report of the surgeon genera, US Department of Health and Human Services, Atlanta, GA.
  4. Centers for Disease Control and Prevention. (2014). Cigarette prices and smoking prevalence after a tobacco tax increase-Turkey, 2008 and 2012. MMWR Morbidity and Mortality Weekly Report, 63, 457-461.
  5. Chun, H. (2015). The comparison of coauthor networks of two statistical Journals of the Korean Statistical Society using social network analysis. Journal of the Korean Data & Information Science Society, 26, 335-346. https://doi.org/10.7465/jkdi.2015.26.2.335
  6. Chun, H. and Leem. B. (2014). Face/non-face channel fit comparison of life insurance company and non-life insurance company using social network analysis. Journal of the Korean Data & Information Science Society, 25, 1207-1219. https://doi.org/10.7465/jkdi.2014.25.6.1207
  7. Hammond, D., Fong, G. T., McDonald, P. W., Brown, K. S. and Cameron, R. (2004). Graphic Canadian cigarette warning labels and adverse outcomes. American Journal of Public Health, 94, 1442-1445. https://doi.org/10.2105/AJPH.94.8.1442
  8. Hong, Y. (2014). A study on the invigorating strategies for open government data. Journal of the Korean Data & Information Science Society, 25, 769-777. https://doi.org/10.7465/jkdi.2014.25.4.769
  9. Ji, S., Jung, K., Jeon, C., Kim, H., Yun, Y. and Kim, I. (2014). Smoking attributable risk and medical care cost in 2012 in Korea. Journal of Health Informatics and Statistics, 39, 25-41.
  10. Jung, K., Yun, Y., Baek, S., Jee, S. and Kim, I. (2013). Smoking-attributable mortality among Korean adults, 2012. Journal of Health Informatics and Statistics, 38, 36-48.
  11. Kang, E. and Lee, J. (2011) Factor related to willingness-to-quit smoking cigarette price among Korean adults. Korean Journal of Health Education and Promotion, 28, 125-137.
  12. Ministry of Health and Welfare. (2014). Korea health statistics 2013: Korea national health and nutrition examination survey, Ministry of Health and Welfare, Korea.
  13. Ministry of Health and Welfare. (2014) press release. Government-wide, No smoking comprehensive plan retrieved September 11, 2014.
  14. Organization for Economic Cooperation and Development. (2014). Health data 2014, Paris, OECD.
  15. Park, H. C. (2013). Proposition of causal association rule thresholds. Journal of the Korean Data & Information Science Society, 24, 1189-1197. https://doi.org/10.7465/jkdi.2013.24.6.1189
  16. Song, T. M. (2015). Predicting tobacco risk factors by using social big data, Health and Social Welfare Issue & Focus, Korea Institute for Health and Social affairs, Korea.
  17. Song, T. M., Song, J., An, J. Y. and Jin, D. (2013). Multivariate analysis of factors for search on suicide using social big data. Korean Journal of Health Education and Promotion, 30, 59-73. https://doi.org/10.14367/kjhep.2013.30.3.059
  18. Thun, M. J., Carter, B. D., Feskanich, D., Freedman, N. D., Prentice, R., Lopez, A. D., Hartge, P. and Gapstur, S. M. (2013). 50-year trends in smoking-related mortality in the United States. New England Journal of Medicine, 368, 351-364. https://doi.org/10.1056/NEJMsa1211127
  19. World Health Organization. (2008). Report on the global tobacco epidemic - The MPOWER package, World Health Organization, Geneva.
  20. Zheng, W., McLerran, D. F., Rolland, B. A., Fu, Z., Boffetta, P., He, J., Gupta, P. C., Ramadas, K., Tsugane, S., Irie, F., Tamakoshi, A., Gao, Y. T., Koh, W. P., Shu, X. O., Ozasa, K., Nishino, Y., Tsuji, I., Tanaka, H., Chen, C. J., Yuan, J. M., Ahn, Y. O., Yoo, K. Y., Ahsan, H., Pan, W. H., Qiao, Y. L., Gu, D., Pednekar, M. S., Sauvaget, C., Sawada, N., Sairenchi, T., Yang, G., Wang, R., Xiang, Y. B., Ohishi, W., Kakizaki, M., Watanabe, T., Oze, I., You, S. L., Sugawara, Y., Butler, L. M., Kim, D. H., Park, S. K., Parvez, F., Chuang, S. Y., Fan, J. H., Shen, C. Y., Chen, Y., Grant, E. J., Lee, J. E., Sinha, R., Matsuo, K., Thornquist, M., Inoue, M., Feng, Z., Kang, D. and Potter, J. D. (2014). Burden of total and cause-specific mortality related to tobacco smoking among adults aged 45 years in Asia: A pooled analysis of 21 cohorts. Public Library of Science Medicine, 11, e1001631.

Cited by

  1. Effect on ambulatory dental visitation frequency according to pack-years of smoking vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.419
  2. Study on Recognitions of Luxury Brands by Using Social Big Data vol.18, pp.1, 2016, https://doi.org/10.5805/SFTI.2016.18.1.1
  3. Dynamic ontology construction algorithm from Wikipedia and its application toward real-time nation image analysis vol.27, pp.4, 2016, https://doi.org/10.7465/jkdi.2016.27.4.979
  4. 소셜 빅데이터를 이용한 낙태의 경향성과 정책적 예방전략 vol.27, pp.3, 2015, https://doi.org/10.4332/kjhpa.2017.27.3.241