Paper Info
Title
Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics.
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.
Year
DOI
Venue
2020
10.1177/1460458219832043
HEALTH INFORMATICS JOURNAL
Keywords
DocType
Volume
behavior,social media,suicide,temporal patterns,time series,Twitter
Journal
26.0
Issue
ISSN
Citations 
SP2.0
1460-4582
2
PageRank 
References 
Authors
0.50
7
5
Name
Order
Citations
PageRank
Jianhong Luo1193.53
Jingcheng Du23016.40
Cui Tao33512.77
Hua Xu432332.99
Yaoyun Zhang59416.58