Predicting acute suicidal ideation on Instagram using ensemble machine learning models-Reference-Cited by-同舟云学术

Predicting acute suicidal ideation on Instagram using ensemble machine learning models

Published:2021-09 Issue: Volume:25 Page:100424
ISSN:2214-7829
Container-title:Internet Interventions
language:en
Short-container-title:Internet Interventions

Author:

Lekkas Damien^ORCID,Klein Robert J.^ORCID,Jacobson Nicholas C.^ORCID

Funder

National Institute on Drug Abuse

Publisher

Elsevier BV

Subject

Health Informatics

Reference57 articles.

1. Social Media Data for Online Adolescent Suicide Risk Identification: Considerations for Integration Within Platforms, Clinics, and Schools;Adrian,2018

2. Detecting suicidal ideation on forums: proof-of-concept study;Aladağ;J. Med. Internet Res.,2018

3. Short-term prediction of suicidal thoughts and behaviors in adolescents: can recent developments in technology and computational science provide a breakthrough?;Allen;J. Affect. Disord.,2019

4. Machine learning and semantic sentiment analysis based algorithms for suicide sentiment prediction in social networks;Birjali;Procedia Comput. Sci.,2017

5. Validating machine learning algorithms for twitter data against established measures of suicidality;Braithwaite;JMIR Ment. Health,2016

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