Predicting national suicide numbers with social media data.
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3632511?pdf=render |
| _version_ | 1818692387013132288 |
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| author | Hong-Hee Won Woojae Myung Gil-Young Song Won-Hee Lee Jong-Won Kim Bernard J Carroll Doh Kwan Kim |
| author_facet | Hong-Hee Won Woojae Myung Gil-Young Song Won-Hee Lee Jong-Won Kim Bernard J Carroll Doh Kwan Kim |
| author_sort | Hong-Hee Won |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T12:56:58Z |
| format | Article |
| id | doaj.art-a06323e703824999ad1cf73906e0ec21 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T12:56:58Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a06323e703824999ad1cf73906e0ec212022-12-21T21:47:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0184e6180910.1371/journal.pone.0061809Predicting national suicide numbers with social media data.Hong-Hee WonWoojae MyungGil-Young SongWon-Hee LeeJong-Won KimBernard J CarrollDoh Kwan KimSuicide 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.http://europepmc.org/articles/PMC3632511?pdf=render |
| spellingShingle | Hong-Hee Won Woojae Myung Gil-Young Song Won-Hee Lee Jong-Won Kim Bernard J Carroll Doh Kwan Kim Predicting national suicide numbers with social media data. PLoS ONE |
| title | Predicting national suicide numbers with social media data. |
| title_full | Predicting national suicide numbers with social media data. |
| title_fullStr | Predicting national suicide numbers with social media data. |
| title_full_unstemmed | Predicting national suicide numbers with social media data. |
| title_short | Predicting national suicide numbers with social media data. |
| title_sort | predicting national suicide numbers with social media data |
| url | http://europepmc.org/articles/PMC3632511?pdf=render |
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