A large number of individuals are suffering from suicidal ideation in the world. There are a number of causes behind why an individual might suffer from suicidal ideation. As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media. Nevertheless, challenges from both data and knowledge aspects remain as obstacles, constraining the social media-based detection performance. Data implicitness and sparsity make it difficult to discover the inner true intentions of individuals based on their posts. Inspired by psychological studies, we build and unify a high-level suicide-oriented knowledge graph with deep neural networks for suicidal ideation detection on social media. We further design a two-layered attention mechanism to explicitly reason and establish key risk factors to individual's suicidal ideation. The performance study on microblog and Reddit shows that: 1) with the constructed personal knowledge graph, the social media-based suicidal ideation detection can achieve over 93% accuracy; and 2) among the six categories of personal factors, post, personality, and experience are the top-3 key indicators. Under these categories, posted text, stress level, stress duration, posted image, and ruminant thinking contribute to one's suicidal ideation detection.
Social media platforms have transformed traditional communication methods by allowing users worldwide to communicate instantly, openly, and frequently. People use social media to express their opinion and share their personal stories and struggles. Negative feelings that express hardship, thoughts of death, and self-harm are widespread in social media, especially among young generations. Therefore, using social media to detect and identify suicidal ideation will help provide proper intervention that will eventually dissuade others from self-harming and committing suicide and prevent the spread…
Institute of Psychology, Chinese Academy of Sciences（中国科学院心理研究所）；Institute of Population Research, Peking University（北京大学人口研究所）；University of Puget Sound（普吉特湾大学）
Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over …
The University of Queensland（昆士兰大学）；Aalto University（阿尔托大学）；Nanyang Technological University（南洋理工大学）
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation an…
For preventing youth suicide, social media platforms have received much attention from researchers. A few researches apply machine learning, or deep learning-based text classification approaches to classify social media posts containing suicidality risk. This paper replicated competitive social media-based suicidality detection/prediction models. We evaluated the feasibility of detecting suicidal ideation using multiple datasets and different state-of-the-art deep learning models, RNN-, CNN-, and Attention-based models. Using two suicidality evaluation datasets, we evaluated 28 combinations of…
Aalto University（阿尔托大学）；The University of Queensland（昆士兰大学）；Monash University（莫纳什大学）；Nanyang Technological University（南洋理工大学）；University of Technology Sydney（悉尼科技大学）
Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection…
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明责任人工智能与数据分析实验室）；King Abdullah University of Science and Technology（卡瓦尔大学科学与技术大学）；Washington University in St.Louis（圣路易斯华盛顿大学）；University of Macau（澳门大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
UMass-Amherst（马萨诸塞大学阿默斯特分校）；University of Rochester（罗切斯特大学）；U.S. Department of Veterans Affairs（美国退伍军人事务部）；Center of Biomedical and Health Research in Data Sciences（数据科学生物医学与健康研究中心）
Suicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors for death by suicide. Information related to patients' previous and current SA and SI are frequently documented in the electronic health record (EHR) notes. Accurate detection of such documentation may help improve surveillance and predictions of patients' suicidal behaviors and alert medical professionals for suicide prevention efforts. In this study, we first built Suicide Attempt and Ideation …
Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；New York State Psychiatric Institute（纽约州精神病学研究所）；Kent State University（肯特州立大学）
Mar Baselios College of Engineering and Technology（马尔·巴塞利奥斯工程与技术学院）
Suicidal ideation detection from social media is an evolving research with great challenges. Many of the people who have the tendency to suicide share their thoughts and opinions through social media platforms. As part of many researches it is observed that the publicly available posts from social media contain valuable criteria to effectively detect individuals with suicidal thoughts. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide. This can be achieved by identifying the sudden changes in a user behavi…
Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia Irving Medical Center（哥伦比亚大学伊万尼兹医疗中心）；New York State Psychiatric Institute（纽约州精神医学研究所）
A Big Data Analytics System for Predicting Suicidal Ideation in Real-Time Based on Social Media Streaming Data
Dams and Water Resources Research Center, University of Mosul（摩苏尔大学大坝与水资源研究中心）；Yildiz Technical University（伊兹密尔技术大学）；Centre for Industrial Software, University of Southern Denmark（南丹麦大学工业软件中心）
Suicide is a major public health crisis. With more than 20,000,000 suicide attempts each year, the early detection of suicidal intent has the potential to save hundreds of thousands of lives. Traditional mental health screening methods are time-consuming, costly, and often inaccessible to disadvantaged populations; online detection of suicidal intent using machine learning offers a viable alternative. Here we present Robin, the largest non-keyword generated suicidal corpus to date, consisting of over 1.1 million online forum postings. In addition to its unprecedented size, Robin is specially c…
Thapar Institute of Engineering & Technology（塔帕尔工程技术学院）
Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a brief period may lead to clinical depressions and the long-lasting traits of prevailing depressions can be life threatening with suicidal ideation as the possible outcome. The increasing concern towards the rise in number of suicide cases is because it is one of the leading cause of premature but preventable death. Recent studies have shown that mining soci…
Black Dog Institute（黑狗研究所）；University of New South Wales（新南威尔士大学）；Deakin University（迪肯大学）
Due to its popularity and availability, social media data may present a new way to identify individuals who are experiencing mental illness. By analysing blog content, this study aimed to investigate the associations between linguistic features and symptoms of depression, generalised anxiety, and suicidal ideation. This study utilised a longitudinal study design. Individuals who blogged were invited to participate in a study in which they completed fortnightly mental health questionnaires including the PHQ9 and GAD7 for a period of 36 weeks. Linguistic features were extracted from blog data us…
Columbia University（哥伦比亚大学）；Columbia University Data Science Institute（哥伦比亚大学数据科学研究所）
Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk. Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) that can be indicative of someone's state of mind or interest regarding mental health disorders such as r/SuicideWatch, r/Anxiety, r/depression. A challenge for previous work on suicide risk assessment has been the small amount of labeled data. We propose an empirical investigation into several classes of weakly-supervised approaches…
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière（索邦大学、巴黎脑研究所、法国国家科学研究中心、法国国家计算机与自动化研究所、法国国家健康与医学研究院、巴黎公立医院集团、皮蒂-萨尔皮特里耶医院）
Stevens Institute of Technology（史蒂文斯理工学院）；University of California San Diego（加利福尼亚大学圣迭戈分校）
We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CLPsych 2021 shared task. Additionally, we create and extract three sets of handcrafted features for suicide risk detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the…
Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Informatics and Networked Systems（信息与网络系统系）；University of Pittsburgh（匹兹堡大学）；Department of Psychiatry（精神病学系）；Tandon School of Engineering（塔能工程学院）；New York University（纽约大学）
Aalto University（阿尔托大学）；Microsoft Research（微软研究院）；University of Helsinki（赫尔辛基大学）
The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter da…
SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（大脑研究所-巴黎脑研究所-ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié Salpêtrière（皮蒂-萨尔皮特里医院）
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media
University of Pennsylvania（宾夕法尼亚大学）；School of Engineering and Applied Science（工程与应用科学学院）；New York University（纽约大学）；Center for Data Science（数据科学中心）；Georgia Institute of Technology（佐治亚理工学院）；College of Computing（计算学院）；University of California, Berkeley（加州大学伯克利分校）；Department of EECS（电子工程与科学系）；Northeastern University（东北大学）；Khoury College of Computer Science（计算机科学学院）
