## 5 Conclusions and Future Research  
In this research, we explored the potential of leveraging social media content, specifically posts from Reddit, to detect suicidal ideation using advanced deep learning techniques.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Baseline Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LSTM-CNN & 0.7623 & 0.7546 & 0.7752 & 0.7647 \\ LSTM & 0.7594 & 0.7612 & 0.7534 & 0.7573 \\ BiLSTM & 0.7610 & 0.7623 & 0.7561 & 0.7592 \\ CNN & 0.6843 & 0.6344 & 0.8649 & 0.7319 \\ LSTM-Attention-BiTCN & 0.9405 & 0.9385 & 0.9424 & 0.9405 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison performance of deep learning baseline models and the proposed model.  
We developed a novel ensemble learning approach named LSTM-Attention-BiTCN, which integrates LSTM and BiTCN models with self-attention to identify signs of suicidality in social media posts. The primary aim of this model is to aid healthcare professionals in recognizing suicidal tendencies among social media users, thereby contributing to efforts to reduce suicide rates. The LSTM-Attention-BiTCN model offers dual utility: it can be employed by social network services to gauge the likelihood of suicidal thoughts among their users, and it can also serve healthcare providers as a tool for monitoring the mental health of their patients. Based on the experimental evaluations of our model, we observed that it exhibits a high degree of accuracy in detecting suicidal ideation. It outperformed both traditional and contemporary machine learning and deep learning models. The LSTM-Attention-BiTCN model demonstrated superior performance, with an accuracy of 0.9405, a precision of 0.9385, a recall of 0.9424, and an F1-score of 0.9405.  
Looking ahead, we plan to explore a variety of deep learning techniques and their combinations to discover even more effective ensemble models. A crucial aspect of future research will be addressing out-of-vocabulary (OOV) issues, which are significant in social network post analysis. We plan to implement strategies such as subword tokenization or the FastText embedding technique to enhance model robustness against rare or unseen words. We also aim to examine different attention mechanisms to assess their impact on model performance. Future research could expand upon this work by incorporating diverse word embedding techniques and integrating various behavioral and social network profile features into the model. We also suggest analyzing multiple social media posts over different time intervals to better capture changes in behavior and mental condition. Additionally, other relevant factors such as location, application usage time, and social connections could be included to enhance the model's comprehensiveness and accuracy in detecting suicidal ideation. Finally, we propose investigating the integration of Large Language Models (LLMs) into our LSTM-Attention-BiTCN framework to improve the contextual analysis of social media posts. This integration could offer a more holistic understanding of user sentiment and behavioral patterns by leveraging the LLM's ability to analyze large datasets and generate human-like responses.  
Conceptualization, H.-S.C. and J.Y.; methodology, H.-S.C. and J.Y.; software, H.-S.C., validation, H.-S.C. and J.Y.; formal analysis, H.-S.C.; investigation, H.-S.C. and J.Y.; writing-original draft preparation, H.-S.C.; writing--review and editing, H.-S.C.; visualization, H.-S.C.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available here: [[https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch](https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch)]([https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch](https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch)), accessed on 28 August 2023.  
The authors declare no conflicts of interest.