ArticlePsychiatry investigation2018
Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data.
Article in Psychiatry investigation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 44 citations in OpenAlex.
- Temporal analysis of posts on a Japanese online message board for suicide risk monitoring.BMC psychiatry · 2025Article
- National Trends in Suicides and Male Twin Live Births in the US, 2003 to 2019: An Updated Test of Collective Optimism and Selection in Utero.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2023Article
- Hindcasts and forecasts of suicide mortality in US: A modeling study.PLoS computational biology · 2023Observational
- The mental health and well-being profile of young adults using social media.Npj mental health research · 2022Article
- Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.JAMA network open · 2020Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSuicide 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.
methodsThe 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.
resultsOur 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.
conclusionThese results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.
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