ArticlePloS one2013
Predicting national suicide numbers with social media data.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Emotional and sentiment analyses of MSM geo-social networking app users in Shenzhen: implications for mental health interventions.BMC public health · 2026Article
- Online propagation of emotions: A study of resharing dynamics on social media following celebrity suicides.PloS one · 2025Article
- Predicting state level suicide fatalities in the united states with realtime data and machine learning.Npj mental health research · 2024Article
- Dynamic reciprocal relationships between traditional media reports, social media postings, and youth suicide in Taiwan between 2012 and 2021.SSM - population health · 2023Article
- 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
- Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.Journal of medical Internet research · 2023Article
- Association of 7 million+ tweets featuring suicide-related content with daily calls to the Suicide Prevention Lifeline and with suicides, United States, 2016-2018.The Australian and New Zealand journal of psychiatry · 2023Article
- Development of a Machine Learning Model to Estimate US Firearm Homicides in Near Real Time.JAMA network open · 2023Article
- Hindcasts and forecasts of suicide mortality in US: A modeling study.PLoS computational biology · 2023Observational
- Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach.Journal of medical Internet research · 2022Article
- The Multidimensional Lexicon of Emojis: A New Tool to Assess the Emotional Content of Emojis.Frontiers in psychology · 2022Article
- Re-evaluating standards of human subjects protection for sensitive health data in social media networks.Social networks · 2021Article
- Suicidal Ideation Risk and Socio-Cultural Factors in China: A Longitudinal Study on Social Media from 2010 to 2018.International journal of environmental research and public health · 2021Article
- Insights into elections: An ensemble bot detection coverage framework applied to the 2018 U.S. midterm elections.PloS one · 2021Article
- How Search Engines Handle Suicide Queries.Journal of online trust & safety · 2021Article
- Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.JAMA network open · 2020Article
- A machine learning approach predicts future risk to suicidal ideation from social media data.NPJ digital medicine · 2020Article
- Identifying long-term periodic cycles and memories of collective emotion in online social media.PloS one · 2019Article
- Ethical Issues in Addressing Social Media Posts About Suicidal Intentions During an Online Study Among Youth: Case Study.JMIR mental health · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors - consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.
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