Leveraging Online Social Content for Early Detection of Suicidal Ideation: A Multi-Modal Deep Learning Approach

The contemporary society is grappling with the alarming rise in suicide rates, exacerbated by factors such as prolonged exposure to negative emotions and challenging life events. Detecting early signs of suicidal ideation holds significant promise in addressing this crisis. In the digital age, online communication platforms have become outlets for individuals to express distress, presenting an opportunity for timely intervention. This study aims to explore online social content for the early identification of suicidal ideation, with a focus on user- generated text that contains valuable insights into people's mental states. The research begins with a thorough content analysis to extract knowledge from suicide-related text, establishing a benchmark for binary classification of suicidal ideation using various classifiers, including feature-based and deep neural networks. The urgency of early detection for prevention underscores the importance of this research. By integrating content analysis, feature engineering, and advanced deep learning methodologies, such as deep neural networks and attentive relation networks, this study aims to contribute to suicide prevention efforts. This paper advocates for the application of multi-modal deep learning techniques to identify early signs of suicidal ideation from online social content. The integration of machine learning and mental health intervention is crucial in addressing the escalating concern of suicide rates, especially among vulnerable populations like youths. Through our research, we aim to provide a valuable tool for promoting mental well-being and ultimately saving lives.