## 1 Introduction  
Suicide constitutes a significant impediment within the realm of mental health, recognized for its severity and distinctiveness. The World Health Organization has reported that annually, upwards of 700,000 individuals grapple with suicide-related behaviors and ideation, impacting an even greater number on a psychological level [1]. In the context of the United States, the year 2021 witnessed approximately 1.7 million suicide attempts, out of which 48,183 were fatal [2], indicating that those engaging in self-harm far outnumber fatalities. This underscores the potential for intervention, as the onset of suicidal thoughts can be intercepted with timely recognition and response.  
A surge of studies has delved into the multifaceted nature of suicidality, aiming to furnish healthcare practitioners with the requisite tools for its early detection. Research by [PERSON] et al. [3] explores the long-term forecasters of suicide within clinical populations subjected to hospitalization, while [PERSON] et al. [4] scrutinize social media platforms as a means to identify signs of suicidality through user profile analysis. [PERSON] et al. [5] highlight the significance of immediate predictors of suicidal thoughts, such as emotional distress, social adversity, and disturbances in sleep. They advocate for the adoption of personal electronic devices and sophisticated computational methodologies to observe and interpret shifts in individual mood and behavioral patterns, thereby gauging suicide risk.  
The ubiquity of social media in daily life renders it a rich repository of personal disclosures [6]. The transparency and interactive nature of these platforms encourage individuals to divulge personal experiences, including those of a sensitive or distressing nature [7]. Anonymity and the absence of physical barriers further contribute to their role as a monitor for mental state evaluations. In this context, [PERSON] et al. [8] acknowledge the utility of social networks in offering valuable insights into emotional well-being and potential suicide risk, proposing that these platforms serve as ancillary channels for clinical assessment.  
In this study, we introduce a hybrid prediction model that leverages advanced neural network architectures to differentiate between suicidal and non-suicidal narratives on social platforms. The cornerstone of our study is an ensemble model that integrates Long Short-Term Memory (LSTM), self-attention, and a bidirectional architecture of Temporal Convolutional Networks (LSTM-Attention-BiTCN). This ensemble model aims to discern expressions of suicidality within social media discourse. We employ a dataset from Reddit posts to reflect users' emotional states and cognitions, utilizing a fusion of state-of-the-art deep learning architectures.  
The principal contributions of our study are as follows:  
* The development of an LSTM-Attention-BiTCN ensemble model that accurately segregates suicidal ideation within social media postings from those that are non-suicidal.
* The utilization of social media datasets from social networks such as Reddit to mirror the emotional and cognitive states of individuals.
* The demonstration of the superior performance of our proposed model in identifying suicidal expressions, surpassing the predictive capabilities of several baseline models, including K-Nearest Neighbor, Random Forest, Decision Tree, Gradient Boost, XGBoost, Logistic Regression, LSTM, Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNNs), and LSTM-CNN.  
The rest of the manuscript is structured as follows: Section 2 presents an overview of the latest studies in identifying suicidal ideation. In Section 3, the proposed methodology is described in comprehensive detail. Section 4 showcases our experimental results for the designed model. Finally, Section 5 concludes our research and proposes avenues for future research in this domain.