Psychiatry Investig.  2018 Apr;15(4):344-354. 10.30773/pi.2017.10.15.

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

• Lee KS
• Lee H
• Myung W
• Song GY
• Lee K
• Kim H
• Carroll BJ
• Kim DK

• KMID: 2411385
• DOI: http://doi.org/10.30773/pi.2017.10.15

Abstract

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.

Keyword

SNS; Sentiment analysis; Social; Warning signs of suicide

MeSH Terms

Forecasting
Public Health
Social Media*
Suicide*
Sunlight