소셜 빅데이터를 이용한 낙태의 경향성과 정책적 예방전략
= Induced Abortion Trends and Prevention Strategy Using Social Big-Data
- 저자[authors] 박명배 ( Myung-bae Park ), 채성현 ( Seong Hyun Chae ), 임진섭 ( Jinseop Lim ), 김춘배 ( Chun-bae Kim )
- 학술지명[periodical name] 보건행정학회지
- 권호사항[Volume/Issue] Vol.27No.3[2017]
- 발행처[publisher] 한국보건행정학회
- 자료유형[Document Type] 학술저널
- 수록면[Pagination] 241-246
- 언어[language] Korean
- 발행년[Publication Year] 2017
- 주제어[descriptor] Big-data, Induced abortion, Contraception, Seasonality, Naver
초록[abstracts]
[Background: The purpose of this study is to investigate the trends on the induced abortion in Korea using social big-data and confirm whether there was time series trends and seasonal characteristics in induced abortion. Methods: From October 1, 2007 to October 24, 2016, we used Naver`s data lab query, and the search word was `induced abortion` in Korean. The average trend of each year was analyzed and the seasonality was analyzed using the cosinor model. Results: There was no significant changes in search volume of abortion during that period. Monthly search volume was the highest in May followed by the order of June and April. On the other hand, the lowest month was December followed by the order of January, and September. The cosinor analysis showed statistically significant seasonal variations (amplitude, 4.46; confidence interval, 1.46-7.47; p<0.0036). The search volume for induced abortion gradually increased to the lowest point at the end of November and was the highest at the end of May and declined again from June. Conclusion: There has been no significant changes in induced abortion for the past nine years, and seasonal changes in induced abortion have been identified. Therefore, considering the seasonality of the intervention program for the prevention of induced abortion, it will be effective to concentrate on the induced abortion from March to May.]