Suicide Prevention
Research Background
Suicide is a severe health concern worldwide. The OECD (Organization for Economic Cooperation and Development) reported that the suicide rate of South Korea and the USA was 23.0 and 14.5 deaths per 100,000 population in 2017, which ranked 1st and 8th, respectively.
The awareness of the severity of suicide has led researchers to develop suicidality detection models using a deluge of user activity data on social media, which can help capture latent warning signs of suicide in an early stage.
Research Goal
We aims to help to establish a prevention system for early detection and immediate intervention of individuals with high-risk suicidality for clinical support using social media data. This will enable us to reduce mental health-related social costs and promote public health.
Members - Medical Experts
Jihyun An
Assistant Professor
Department of Psychiatry @ Samsung Medical Center
jh85.an@samsung.com
Members - AI Researchers
Alumni
Jiwon Kang, Ph.D. (2023)
Current: NLP Researcher @ Hana Institute of Technology
Chaewon Park, M.S. (2022)
Current: AI engineer @ RSN
Current: AI engineer @ AhnLab
Sejung Son, M.S. (2024)
maze0717@g.skku.edu
Current: AI engineer @ RSN
Publications
(* = (co-)corresponding author, ** = equal contributions)
A Dual-Prompting for Interpretable Mental Health Language Models
Hyolim Jeon**, Dongje Yoo**, Daeun Lee, Sejung Son, Seungbae Kim and Jinyoung Han*
CLPsych 2024 - The workshop on Computational Linguistics and Clinical Psychology (EACL workshop)
Detecting depression on video logs using audiovisual features
Kyungeun Min**, Jeewoo Yoon**, Migyeong Kang, Daeun Lee, Eunil Park, and Jinyoung Han*
Humanities and Social Sciences Communications 10, 788 (SSCI, JCR 2022 IF = 3.5)
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Daeun Lee, Sejung Son, Hyolim Jeon, Seungbae Kim, and Jinyoung Han*
KDD 2023 - ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Detecting Suicidality with a Contextual Graph Neural Network
Daeun Lee, Migyeong Kang, Minji Kim, and Jinyoung Han*
NAACL workshop, CLPsych 2022 - The workshop on Computational Linguistics and Clinical Psychology
D-Vlog: Multimodal Vlog Dataset for Depression Detection
Jeewoo Yoon, Chaewon Kang, Seungbae Kim, and Jinyoung Han*
AAAI 2022 - Conference on Artificial Intelligence (acceptance ratio = 1,345/9,251 = 14.5%)
Cross-Lingual Suicidal-Oriented Word Embedding Toward Suicide Prevention
Daeun Lee, Soyoung Park, Jiwon Kang, Daejin Choi, and Jinyoung Han*
EMNLP Findings 2020 - In Findings of the Association for Computational Linguistics: EMNLP
Scientific Analysis on Machine Learning for Mental Health in Social Media: A Bibliometric Study
Dataset & Code
This dataset contains the assessment of the severity of suicidality of 866 Reddit users who had posted on the r/SuicideWatch subreddit from 2008 to 2015 and their 79,569 posts uploaded to 37,083 subreddits
Suicide Dictionary (csv file) : 5.6KB
This dataset contains the assessment of the severity of suicidality of 866 Reddit users who had posted on the r/SuicideWatch subreddit from 2008 to 2015 and their 79,569 posts uploaded to 37,083 subreddits
Suicide Dictionary (csv file) : 5.6KB
Suicide-oriented Word Embedding & Suicide Dictionary for English, Chinese, and Korean (EMNLP Findings 2020)
These datasets contain the suicide-related and non-suicide-related Korean posts from Naver Cafe, and suicide-related dictionary data for generating suicide word embeddings for Chinese, English, and Korean, respectively.
Suicide Dictionary
Suicide-oriented Word embedding
Cooperation
