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Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics
- Source :
- Health informatics journal. 26(2)
- Publication Year :
- 2019
-
Abstract
- A valid mechanism for suicide detection and intervention to a wider population online has not yet been fully established. With the increasing suicide rate, we proposed an approach that aims to examine temporal patterns of potential suicidal ideations and behaviors on Twitter to better understand their risk factors and time-varying features. It identifies latent suicide topics and then models the suicidal topic–related score time series to quantitatively represent behavior patterns on Twitter. After evaluated on a collection of suicide-related tweets in 2016, 13 key risk factors were discovered and the temporal patterns of suicide behavior on different days during 1 week were identified to highlight the distinct time-varying features related to different risk factors. This study is practical to help public health services and others to develop refined prevention strategies, to monitor and support a population of high-risk at right moments.
- Subjects :
- medicine.medical_specialty
Applied psychology
Population
Health Informatics
Suicidal Ideation
03 medical and health sciences
0302 clinical medicine
Risk Factors
Intervention (counseling)
medicine
Humans
Social media
030212 general & internal medicine
education
education.field_of_study
Mechanism (biology)
business.industry
Public health
Suicide
Suicide behavior
Suicidal behavior
Analytics
Psychology
business
Social Media
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 17412811
- Volume :
- 26
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Health informatics journal
- Accession number :
- edsair.doi.dedup.....2948a3e563facf30c2986779e261d8f6