Detection of Suicidal Risk on Social Media: A Hybrid Model

--Suicidal thoughts and behaviors are increasingly recognized as a critical societal concern, highlighting the urgent need for effective tools to enable early detection of suicidal risk. In this work, we develop robust machine learning models that leverage Reddit posts to automatically classify them into four distinct levels of suicide risk severity. We frame this as a multi-class classification task and propose a RoBERT a-TF-IDF-PCA Hybrid model, integrating the deep contextual embeddings from Robustly Optimized BERT Approach (RoBERT a), a state-of-the-art deep learning transformer model, with the statistical term-weighting of TF-IDF, further compressed with PCA, to boost the accuracy and reliability of suicide risk assessment. T o address data imbalance and overfitting, we explore various data resampling techniques and data augmentation strategies to enhance model generalization. Additionally, we compare our model's performance against that of using RoBERT a only, the BERT model and other traditional machine learning classifiers. Suicidal thoughts and behaviors are increasingly becoming a significant societal concern. As of the latest estimates from World Health Organization, approximately 700,000 to 800,000 people die by suicide globally each year. In the U.S., suicide is the second leading cause of death for individuals aged 10-34 and the fourth leading cause for those aged 35-64. Suicidal thoughts can vary in severity, ranging from explicit and repetitive suicidal feelings, to actively planning suicide or engaging in self-harm behaviors like cutting or burning, and ultimately to making actual attempts through methods such as cutting, jumping, drug overdose, or using firearms.