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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
Jihyun An
Assistant Professor
Department of Psychiatry @ Samsung Medical Center
jh85.an@samsung.com
Members - AI Researchers
Alumni
Jiwon Kang, Ph.D. (2023)
Jiwon Kang, Ph.D. (2023)
Current: NLP Researcher @ Hana Institute of Technology
Daeun Lee, Ph.D. (2024)
Daeun Lee, Ph.D. (2024)
delee12@skku.edu
Current: Post-doc @ Yale School of Medicine
Chaewon Park, M.S. (2022)
Chaewon Park, M.S. (2022)
Current: AI engineer @ RSN
Current: AI engineer @ AhnLab
Sejung Son, M.S. (2024)
Sejung Son, M.S. (2024)
maze0717@g.skku.edu
Current: AI engineer @ RSN
Hyolim Jeon, M.S. (2024)
Hyolim Jeon, M.S. (2024)
gyfla1512@g.skku.edu
Current: Data scientist @ Woori Bank
Publications
(* = (co-)corresponding author, ** = equal contributions)
Show Your Mind: Unveiling User Experience on an AI-based Mental Health Assessment System with Symptom-based Evidences
Hyunseon Won**, Migyeong Kang**, Minji Kim**, Daeun Lee, Hyein Choi, Yonghoon Kim, Daejin Choi, Minsam Ko, Jinyoung Han*
CHI EA 2025 - Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
CURE: Context- and Uncertainty-Aware Mental Disorder Detection
Migyeong Kang, Goun Choi, Hyolim Jeon, Jihyun An, Daejin Choi*, Jinyoung Han*
EMNLP 2024 - The 2024 Conference on Empirical Methods in Natural Language Processing
HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection
Juho Jung, Chaewon Kang, Jeewoo Yoon, Seungbae Kim, Jinyoung Han*
CIKM 2024 - ACM International Conference on Information and Knowledge Management (acceptance ratio = 347/1,496= 23.1%)
Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning
Daeun Lee**, Hyolim Jeon**, Sejung Son, Chaewon Park, Jihyun An, Seungbae Kim, and Jinyoung Han*
NAACL 2024 - The North American Chapter of the Association for Computational Linguistics 2024
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
The study introduces KoMOS (Korean Mental Health Dataset with Mental Disorder and Symptoms labels). The dataset contains 6,349 Q&A pairs annotated with four disorders (depression, anxiety, sleep, and eating disorders), 28 symptoms, and contextual factors (duration, frequency, age, and effects) extracted from LLMs.
With the supervision of a psychiatrist, the three trained annotators labeled 1,025 users and their 7,346 anonymized Reddit posts using the open-source text annotation tool Doccano. During annotations, we mainly consider two different label categories: (i) Diagnosis Type (e.g., MDD, BD) and (ii) BD Mood Level with a scale ranging from -3 to 3. If there is any conflict in the annotated labels across the annotators, all the annotators discuss and reach to an agreement under the supervision of the psychiatrists.
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)
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
Google Sites
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