Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics

作者：Jianhong Luo, Jingcheng Du, Cui Tao, Hua Xu, Yaoyun Zhang · 发表于：Health Informatics Journal · 年份：2019 · DOI：10.1177/1460458219832043 · 被引用次数：40 · 研究领域：Mental Health via Writing、Suicide and Self-Harm Studies、Digital Mental Health Interventions

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.