Social Web Mining for Suicide Prevention (Don't Do It)
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Details and patient eligibility
About
According to a recent and alarming WHO (World Health Organisation) report (September 4, 2014), one person dies of suicide every 40 seconds in the world. Suicide is the third-leading cause of death for 15- to 24-year-olds, according to the Centers for Disease Control and Prevention , after accidents and homicide.
This major public health issue need prevention strategies especially directed to at-risk populations. Since 2013, more than 2 billion users are enrolled in social networks such as Twitter or Facebook. Young adults (ages 18 to 29) are the most likely to use social media - fully 90% do.
Consequently, in this project, we focus on suicide prevention in social media network..
The aim of this project is the validation of the algorithm. This algorithm build a decision support system that monitor young people at-risk based on large volume of heterogeneous data collected through social media to improve suicide prevention.
Full description
This study is composed of two steps :
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9 subjects were recruited. After patients agreement, computer scientists were accessing to patient social network profile. Computer scientists were not able to visualize the content of publications, just run the algorithm that will analyse the content of messages (text, frequency, emoticons...)
The algorithm defines the 3 most at-risk periods of suicide behaviors, on the next month. This result were compared to periods found by psychiatric interview. The psychiatrist then confirmed or not to LIRM whether periods found by the algorithm conrrespond to those defined by the psychitrist. No data of the social network were collected.
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the 2nd step aim to improve the algorithm by collecting sociodemographic and clincal data related to patients included.
Enrollment
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Inclusion criteria
- Major patient
- Minor patient with parents approval
- Patient hospitalized due to suicide attempt or suicide ideations
- Frequent active user of social netword (Facebook, Instagram, Youtube, Twitter)
Exclusion criteria
- Patient's refusal to participate
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Data sourced from clinicaltrials.gov
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