Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data

• 발행기관: 대한신경정신의학회
• 저자명: KyungSang Lee Hyewon Lee Woojae Myung GilYoung Song Kihwang Lee Ho Kim Bernard J. Carroll DohKwan Ki
• 간행물 정보: 『Psychiatry Investigation』제15권 제4호, 344~354쪽, 전체 11쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2018.04.30

영문 초록

Objective-Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. Methods-The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Results-Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. Conclusion-These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

ACKNOWLEDGMENTS

REFERENCES

키워드

SNS Sentiment analysis Social Warning signs of suicide