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Journal Article

Citation

Luo J, Du J, Tao C, Xu H, Zhang Y. Health Informatics J. 2019; ePub(ePub): ePub.

Affiliation

The University of Texas School of Biomedical Informatics, USA.

Copyright

(Copyright © 2019, SAGE Publishing)

DOI

10.1177/1460458219832043

PMID

30866708

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.


Language: en

Keywords

Twitter; behavior; social media; suicide; temporal patterns; time series

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