Detection Of Suicidal Ideation Research Articles

Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features

Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms—such as Reddit—as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naïve Bayes, support vector machines, and random forest. The naïve Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately.

Suicide Ideation and Risk Detection from Social Media Using GPT Models

Suicide Ideation and Risk Detection from Social Media Using GPT Models

Suicidal ideation detection on social media: a review of machine learning methods

Suicidal ideation detection on social media: a review of machine learning methods

Classification of suicidal ideation severity from Twitter messages using machine learning

Depression has become a major mental health problem in Thailand and can lead to suicidal ideation. As suicidal ideation may vary in intensity and lead to suicide attempts, early detection of suicidal ideation severity should be implemented. This research presents text classification models for the prediction of suicidal ideation severity. A dataset of Twitter messages in Thai was used to develop several classification models. A web application prototype was also developed to predict suicidal ideation severity and introduce self-therapy based on Cognitive Behavioral Therapy to its users for managing negative automatic thoughts. The application prototype received satisfactory feedback during the user experience assessment. The results of this research highlight the importance and need for socio-technical systems to help with early suicidal ideation detection and early therapy in the social environment where mental health support is inadequate.

Suicidal ideation detection on social media using machine learning: A review

Suicide remains a critical global health issue, with over 800,000 deaths annually and profound impacts on individuals and their communities. The increasing role of social media platforms in expressing personal struggles has highlighted the potential of these platforms for early detection of suicidal tendencies. Advances in machine learning and natural language processing (NLP) have revolutionized this detection process, allowing for more accurate identification of suicidal ideation in social media posts. This review examines the integration of machine learning algorithms in suicide detection, including the use of multi-layered neural networks for analyzing textual, audio, and visual data. It explores various machine learning models and their performance in predicting suicide attempts, with a focus on their accuracy, precision, and recall. The review also discusses applications such as real-time monitoring systems and emergency response mechanisms, emphasizing how these technologies can enhance early intervention and support. By surveying current research and identifying challenges and future directions, this review aims to provide a comprehensive understanding of how machine learning can significantly contribute to suicide prevention efforts through effective social media monitoring.

Stacked Classification Approach using Optimized Hybrid Deep Learning Model for Early Prediction of Behaviour Changes on Social Media

Detecting signs of suicidal thoughts on social media is paramount for preventing suicides, given the platforms' role as primary outlets for emotional expression. Traditional embedding techniques focus solely on semantic analysis and lack the sentiment analysis essential for capturing emotions. This limitation poses challenges in developing high-accuracy models. Additionally, previous studies often rely on a single dataset, further constraining their effectiveness. To overcome these challenges, this study proposes an innovative approach that integrates embedding techniques such as BERT, which offers semantic and syntactic analysis of the posts, with sentiment analysis provided by VADER scores extracted from the VADER sentiment analysis tool. The identified features are then input into the proposed optimized hybrid deep learning model, specifically the Bi-GRU and Attention incorporated with Stacked or stacking Classifier (Decision Tree, Random Forest, Gradient Boost, as the base classifier and XGBoost as meta classifier), which undergoes optimization using the grid search technique to enhance detection capabilities. In evaluations, the model achieved an impressive accuracy and F1-score of 98% on the Reddit dataset and 97% on the Twitter dataset. The research evaluates the efficacy of several machine learning models, encompassing Decision Trees, Random Forests, Gradient Boosting, and XGBoost. Moreover, it examines sophisticated models like LSTM with Attention, Bi-LSTM with Attention, and Bi-GRU with Attention, augmented with word embeddings such as BERT, MUSE, and fastText, alongside the fusion of sentiment VADER score. These results emphasize the promise of a holistic strategy that combines advanced feature embedding techniques with semantic features, showcasing a notably efficient detection of suicidal ideation on social media.

A self-attention TCN-based model for suicidal ideation detection from social media posts

Early suicidal ideation detection has long been regarded as an important task that can benefit both society and individuals. In this regard, it has been shown that, very frequently, the first symptoms of this problem can be identified by analyzing the contents shared on social media. Machine learning classification models have proven promising in capturing behavioral and textual features from posts shared on social media. This study proposes a novel machine-learning model to detect the risk of suicide from social media posts, employing both natural language processing and state-of-the-art deep learning techniques. We propose an ensemble LSTM-TCN model that benefits from a self-attention mechanism to detect suicidal ideation among users of two well-known social networks, Twitter (X) and Reddit. Furthermore, we present a comprehensive analysis of the data, examining the suicidal posts both statistically and semantically, which can provide rich knowledge about suicidal ideation. Our proposed model (AL-BTCN) outperforms the compared state-of-the-art models, resulting in over 94% accuracy, recall, and F1-score. Researchers, mental health specialists, and social media service providers can all benefit from the findings of this study.

Mental disorder and suicidal ideation detection from social media using deep neural networks

Depression and suicidal ideation are global reasons for life-threatening injury and death. Mental disorders have increased especially among young people in recent years, and early detection of those cases can prevent suicide attempts. Social media platforms provide users with an anonymous space to interact with others, making them a secure environment to discuss their mental disorders. This paper proposes a solution to detect depression/suicidal ideation using natural language processing and deep learning techniques. We used Transformers and a unique model to train the proposed model and applied it to three different datasets: SuicideDetection, CEASEv2.0, and SWMH. The proposed model is evaluated using the accuracy, precision, recall, and ROC curve. The proposed model outperforms the state-of-the-art in the SuicideDetection and CEASEv2.0 datasets, achieving F1 scores of 0.97 and 0.75, respectively. However, in the SWMH data set, the proposed model is 4% points behind the state-of-the-art precision providing the F1 score of 0.68. In the real world, this project could help psychologists in the early detection of depression and suicidal ideation for a more efficient treatment. The proposed model achieves state-of-the-art performance in two of the three datasets, so they could be used to develop a screening tool that could be used by mental health professionals or individuals to assess their own risk of suicide. This could lead to early intervention and treatment, which could save lives.

Advanced deep learning and large language models for suicide ideation detection on social media

Advanced deep learning and large language models for suicide ideation detection on social media

KETCH: A Knowledge-Enhanced Transformer-Based Approach to Suicidal Ideation Detection from Social Media Content

Suicide is a major cause of death among 15- to 29-year-olds globally, claiming more than 50,000 lives in the United States in 2023 alone. Despite governmental efforts to provide support, many individuals experiencing suicidal thoughts do not seek help but are increasingly turning to social media to express their feelings. This trend offers a critical opportunity for timely detection and intervention of suicidal ideation. We develop an innovative transformer-based model for suicidal ideation detection (SID) that combines domain knowledge with dynamic embedding and lexicon-based enhancements. Our model, which is tested on social media data in two languages from different platforms, outperforms existing state-of-the-art models for SID. We have also explored its applicability to detecting depression and its practical implementation in real-world scenarios. Our research contributes significantly to the field, offering new methods for timely and proactive intervention in suicidal ideation, with potential wide-reaching effects on public health, economics, and society. Methodologically, our approach advances the integration of human expertise into AI models to enhance their effectiveness.

Suicidal Ideation Detection and Influential Keyword Extraction from Twitter using Deep Learning (SID)

INTRODUCTION: This paper focuses on building a text analytics-based solution to help the suicide prevention communities to detect suicidal signals from text data collected from online platform and take action to prevent the tragedy. OBJECTIVES: The objective of the paper is to build a suicide ideation detection (SID) model that can classify text as suicidal or non-suicidal and a keyword extractor to extracted influential keywords that are possible suicide risk factors from the suicidal text. METHODS: This paper proposed an attention-based Bi-LSTM model. An attention layer can assist the deep learning model to capture influential keywords of the model classifying decisions and hence reflects the important keywords from text which highly related to suicide risk factors or reason of suicide ideation that can be extracted from text. RESULTS: Bi-LSTM with Word2Vec embedding have the highest F1-score of 0.95. Yet, attention-based Bi-LSTM with word2vec embedding that has 0.94 F1-score can produce better accuracy when dealing with new and unseen data as it has a good fit learning curve. CONCLUSION: The absence of a systematic approach to validate and examine the keyword extracted by the attention mechanism and RAKE algorithm is a gap that needed to be resolved. The future work of this paper can focus on both systematic and standard approach for validating the accuracy of the keywords.

Prevalence of and risk factors for suicidal ideation in adolescents during the COVID-19 pandemic: a cross-sectional study.

Pandemic-related life changes may have had a deleterious impact on suicidal behaviours. Early detection of suicidal ideation and identification of subgroups at increased risk could help prevent suicide, one of the leading causes of death among adolescents worldwide. Here, we aimed to investigate the prevalence of and risk factors for suicidal ideation in adolescents using a population-based sample from Switzerland, two years into the pandemic. Between December 2021 and June 2022, adolescents aged 14 to 17 years already enrolled in a population-based cohort study (State of Geneva, Switzerland) were asked about suicidal ideation over the previous year. In addition to a regression model, we conducted a network analysis of exposures which identified direct and indirect risk factors for suicidal ideation (i.e. those connected through intermediate risk factors) using mixed graphical models. Among 492 adolescents, 14.4% (95% CI: 11.5-17.8) declared having experienced suicidal ideation over the previous year. Using network analysis, we found that high psychological distress, low self-esteem, identifying as lesbian, gay or bisexual, suffering from bullying, extensive screen time and a severe COVID-19 pandemic impact were major risk factors for suicidal ideation, with parent-adolescent relationship having the highest centrality strength in the network. Our results show that a significant proportion of adolescents experience suicidal ideation, yet these rates are comparable with pre-pandemic results. Providing psychological support is fundamental, with a focus on improving parent-adolescent relationships.

Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts.

Linguistic features, particularly the use of first-person singular pronouns (FPSPs), have been identified as potential indicators of suicidal ideation. Machine learning (ML) and natural language processing (NLP) have shown potential in suicide detection, but their clinical applicability remains underexplored. This study aimed to identify linguistic features associated with suicidal ideation and develop ML models for detection. NLP techniques were applied to clinical interview transcripts (n = 319) to extract relevant features, including four cases of FPSP (subjective, objective, dative, and possessive cases) and first-person plural pronouns (FPPPs). Logistic regression analyses were conducted for each linguistic feature, controlling for age, gender, and depression. Gradient boosting, support vector machine, random forest, decision tree, and logistic regression were trained and evaluated. Results indicated that all four cases of FPSPs were associated with depression (p < 0.05) but only the use of objective FPSPs was significantly associated with suicidal ideation (p = 0.02). Logistic regression and support vector machine models successfully detected suicidal ideation, achieving an area under the curve (AUC) of 0.57 (p < 0.05). In conclusion, FPSPs identified during clinical interviews might be a promising indicator of suicidal ideation in Chinese patients. ML algorithms might have the potential to aid clinicians in improving the detection of suicidal ideation in clinical settings.

Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts

Suicidal ideation constitutes a critical concern in mental health, adversely affecting individuals and society at large. The early detection of such ideation is vital for providing timely support to individuals and mitigating its societal impact. With social media serving as a platform for self-expression, it offers a rich source of data that can reveal early symptoms of mental health issues. This paper introduces an innovative ensemble learning method named LSTM-Attention-BiTCN, which fuses LSTM and BiTCN models with a self-attention mechanism to detect signs of suicidality in social media posts. Our LSTM-Attention-BiTCN model demonstrated superior performance in comparison to baseline models in the realm of classification and suicidal ideation detection, boasting an accuracy of 0.9405, a precision of 0.9385, a recall of 0.9424, and an F1-score of 0.9405. Our proposed model can aid healthcare professionals in recognizing suicidal tendencies among social media users accurately, thereby contributing to efforts to reduce suicide rates.

Analysis and Detection of Suicidal Ideation in Text Data Using Machine Learning

Analysis and Detection of Suicidal Ideation in Text Data Using Machine Learning

A Suicidal Ideation Detection Framework on Social Media Using Machine Learning and Genetic Algorithms

A Suicidal Ideation Detection Framework on Social Media Using Machine Learning and Genetic Algorithms

The temporality of psychological constructs, suicidal ideation and emotional dysregulation: a longitudinal study in Mexican adolescents

Background. Adolescence could be considered a period of crisis (emotional and cognitive) where the permanence of psychological discomfort is expressed in different risk factors. That is why it is necessary to evaluate the temporality of psychological constructs. Objective. Analyse, over time, the permanence of emotional dysregulation, suicidal ideation and psychological constructs (emotional disorders, perceived social support, hopelessness, impulsivity, and attitudes and beliefs). Method. Study 1 was a cross-sectional, descriptive-comparative type of 109 teenaged participants from upper secondary education who completed a Scale for the Detection of Suicidal Ideation in Young People and Difficulties in Emotion Regulation that evaluate emotional dysregulation, suicidal ideation and psychological constructs. Study 2 is a longitudinal comparative study of 85 teenaged participants of the original 109. Results. Differences were found for the psychological construct of emotional disorders, where young women obtained a higher average than young men (p &lt; .05). The psychological constructs of emotional disorders, hopelessness, attitudes and beliefs, and impulsiveness, as well as suicidal ideation and emotional dysregulation remained stable over two years. Discussion and conclusion. Emotional regulation as a psychological resource would foster flexibility, tolerance and acceptance in preventing the intensity of psychological distress.

Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones

Speech-based diaries from mobile phones can capture paralinguistic patterns that help detect mental illness symptoms such as suicidal ideation. However, previous studies have primarily evaluated machine learning models on a single dataset, making their performance unknown under distribution shifts. In this paper, we investigate the generalizability of speech-based suicidal ideation detection using mobile phones through cross-dataset experiments using four datasets with N=786 individuals experiencing major depressive disorder, auditory verbal hallucinations, persecutory thoughts, and students with suicidal thoughts. Our results show that machine and deep learning methods generalize poorly in many cases. Thus, we evaluate unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) to mitigate performance decreases owing to distribution shifts. While SSDA approaches showed superior performance, they are often ineffective, requiring large target datasets with limited labels for adversarial and contrastive training. Therefore, we propose sinusoidal similarity sub-sampling (S3), a method that selects optimal source subsets for the target domain by computing pair-wise scores using sinusoids. Compared to prior approaches, S3 does not use labeled target data or transform features. Fine-tuning using S3 improves the cross-dataset performance of deep models across the datasets, thus having implications in ubiquitous technology, mental health, and machine learning.

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study.

Assessing patients' suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have poor agreement in screening suicide risk. Patients' speech may provide more objective, language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk in depression is lacking in the literature. This study aimed to determine whether suicidal ideation can be detected via language features in clinical interviews for depression using natural language processing (NLP) and machine learning (ML). This cross-sectional study recruited 305 participants between October 2020 and May 2022 (mean age 53.0, SD 11.77 years; female: n=176, 57%), of which 197 had lifetime depression and 108 were healthy. This study was part of ongoing research on characterizing depression with a case-control design. In this study, 236 participants were nonsuicidal, while 56 and 13 had low and high suicide risks, respectively. The structured interview guide for the Hamilton Depression Rating Scale (HAMD) was adopted to assess suicide risk and depression severity. Suicide risk was clinician rated based on a suicide-related question (H11). The interviews were transcribed and the words in participants' verbal responses were translated into psychologically meaningful categories using Linguistic Inquiry and Word Count (LIWC). Ordinal logistic regression revealed significant suicide-related language features in participants' responses to the HAMD questions. Increased use of anger words when talking about work and activities posed the highest suicide risk (odds ratio [OR] 2.91, 95% CI 1.22-8.55; P=.02). Random forest models demonstrated that text analysis of the direct responses to H11 was effective in identifying individuals with high suicide risk (AUC 0.76-0.89; P<.001) and detecting suicide risk in general, including both low and high suicide risk (AUC 0.83-0.92; P<.001). More importantly, suicide risk can be detected with satisfactory performance even without patients' disclosure of suicidal ideation. Based on the response to the question on hypochondriasis, ML models were trained to identify individuals with high suicide risk (AUC 0.76; P<.001). This study examined the perspective of using NLP and ML to analyze the texts from clinical interviews for suicidality detection, which has the potential to provide more accurate and specific markers for suicidal ideation detection. The findings may pave the way for developing high-performance assessment of suicide risk for automated detection, including online chatbot-based interviews for universal screening.

Race, Rurality, and Suicidality in Children and Adolescents

IntroductionSuicide is among the leading causes of death in U.S. youth. Rural residency is a risk factor, but suicide variability by race/ethnicity is more nuanced. Early detection of suicidal ideation and intent are key components of prevention, but to the authors’ knowledge, few prior studies have examined how rurality and race interact on youth suicidality. This study examined suicidality between White non-Hispanic vs. non-White or Hispanic youth, as well as those who lived in rural vs. non-rural areas. MethodsCross-sectional analyses were conducted using data from youth age 5-17 years who had complete capture of their medical care in a Wisconsin healthcare system. Suicidality was extracted from medical records by screening for diagnoses indicative of suicidal attempt or ideation between 2017 and 2022. Race/ethnicity and rural residence were extracted from administrative records. Analyses were done in 2023. ResultsThe sample included 27,392 rural and 20,370 non-rural youth, with suicidality observed in 2% of participants. There was a significant interaction between rural residence and race/ethnicity (p = 0.015). Non-White or Hispanic youth in rural areas had the highest risk of suicidality at 75 (CI: 57, 97) per 10,000. Non-White or Hispanic youth in non-rural areas had the lowest risk of suicidality at 38 (CI: 28, 52) per 10,000. ConclusionsRacial/ethnic minority youth who lived in rural areas were more likely to experience suicidality as compared to their non-rural counterparts. Larger prospective studies are needed to identify causal elements of the rural environment that may hasten racial disparities in youth suicidality.