相关论文: Learning Models for Suicide Prediction from Social…

Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states.…

Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evaluates 36 studies that apply machine learning (ML) and deep…

Mental health challenges and cyberbullying are increasingly prevalent in digital spaces, necessitating scalable and interpretable detection systems. This paper introduces a unified multiclass classification framework for detecting ten…

The early detection of mental health disorders from social media text is critical for enabling timely support, risk assessment, and referral to appropriate resources. This work introduces multiMentalRoBERTa, a fine-tuned RoBERTa model…

In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain…

Suicide prevention remains a critical public health challenge. While online platforms such as Reddit's r/SuicideWatch have historically provided spaces for individuals to express suicidal thoughts and seek community support, the advent of…

Identifying physiological and behavioral markers for mental health conditions is a longstanding challenge in psychiatry. Depression and suicidal ideation, in particular, lack objective biomarkers, with screening and diagnosis primarily…

Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polarization and disinformation. Here,…

Seven out of ten people with bipolar disorder are initially misdiagnosed and thirty percent of individuals with bipolar disorder will commit suicide. Identifying the early phases of the disorder is one of the key components for reducing the…

Mental health is a critical global public health issue, and psychological support hotlines play a pivotal role in providing mental health assistance and identifying suicide risks at an early stage. However, the emotional expressions…

In recent work, we identified and studied a small cohort of Twitter users whose pregnancies with birth defect outcomes could be observed via their publicly available tweets. Exploiting social media's large-scale potential to complement the…

As awareness of mental health issues grows, online counseling support services are becoming increasingly prevalent worldwide. Detecting whether users express suicidal ideation in text-based counseling services is crucial for identifying and…

Death by suicide is the seventh leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI applications in image and voice processing, has created a promising opportunity to revolutionize suicide…

This paper investigates the possibility of creating a machine learning tool that automatically determines the state of mind and emotion of an individual through a questionnaire, without the aid of a human expert. The state of mind and…

Early detection of depression from online social media posts holds promise for providing timely mental health interventions. In this work, we present a high-quality, expert-annotated dataset of 1,017 social media posts labeled with…

This review underscores the critical need for effective strategies to identify and support individuals with suicidal ideation, exploiting technological innovations in ML and DL to further suicide prevention efforts. The study details the…

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI…

The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of collected Twitter data, models were developed for classifying…

This study investigates the use of Large Language Models (LLMs) for improved depression detection from users social media data. Through the use of fine-tuned GPT 3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier…

We propose a method that integrates supervised extractive and generative language models for providing supporting evidence of suicide risk in the CLPsych 2024 shared task. Our approach comprises three steps. Initially, we construct a…