Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning-Reference-Cited by-同舟云学术

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Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning

Published:2021-07-28 Issue: Volume: Page:1-16
ISSN:1381-1118
Container-title:Archives of Suicide Research
language:en
Short-container-title:Archives of Suicide Research

Author:

Jung Woojin^ORCID,Kim Donghun^ORCID,Nam Seojin^ORCID,Zhu Yongjun^ORCID

Funder

MSIT (Ministry of Science and ICT), Korea

Ministry of Education of the Republic of Korea

National Research Foundation of Korea

SKKU-SMC Future Convergence Research Program Grant

Publisher

Informa UK Limited

Subject

Psychiatry and Mental health,Clinical Psychology

Link

https://www.tandfonline.com/doi/pdf/10.1080/13811118.2021.1955783

Reference50 articles.

1. Suicide: Current Trends

2. How Henry Hellyer’s Use of Language Foretold His Suicide

3. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

4. pdp: An R Package for Constructing Partial Dependence Plots

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1. Mindsets of suicide trajectories: An Linguistic Inquiry and Word Count analysis of suicide hotline conversations;Suicide and Life-Threatening Behavior;2024-07-22

2. Suicidal Tweet Detection Using Ensemble Learning: A Multi-Model Approach for Early Intervention;2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS);2023-11-16

3. Interpretable machine learning‐based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data;Suicide and Life-Threatening Behavior;2023-03-22

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