## 2 Literature Review  
The pervasive nature of social media has not only provided a platform for communication and social interaction but has also become a medium through which expressions of mental distress, including suicidality, are frequently manifested. This has led to a series of scholarly investigations aimed at harnessing social media's expansive reach to preemptively identify and mitigate the risks/associated with suicide.  
For example, [PERSON] et al. [9] devised an advanced knowledge graph complemented by deep learning methodologies to monitor and assess the presence of suicidal expressions on digital platforms. This system employs a dual-layer attention mechanism, which highlights key predictors and explicit reasoning behind individuals' suicidal tendencies. Analyzing content from Reddit and other microblogging services, their approach pinpointed personality traits, user-generated content, and personal experiences as primary domains from a total of six, which could potentially signal suicidal ideations through analysis of text, imagery, emotional distress, and experiences of depression.  
Another study by [PERSON] et al. [10] incorporated fastText embedding coupled with an XGBoost classifier to sift through data effectively. They introduced a pioneering framework that segregates content that indicates suicidal thoughts from that which suggests depression. This model utilized the machine learning classifier XGBoost for refined categorization, the TF-IDF vector for highlighting term significance, and fastText embedding for nuanced contextual understanding.  
[PERSON] et al. [11] explored a weakly supervised methodology to extract current indicators of suicidal ideation from unstructured notes within Electronic Health Records (EHRs). Leveraging a rule-based natural language processing approach, they annotated training and validation notes, which then served as a foundation for training Logistic Regression, Support Vector Machine, and CNN models. This approach, designed for comprehensive analysis of clinical documentation, has significant implications for concentrated suicide prevention strategies within clinical information systems.  
[PERSON] et al. [12] implemented a decision model that addresses the imbalanced representation across varying age groups and demographic backgrounds. They crafted an ensemble model integrating Support Vector Machine with K-Nearest Neighbor algorithms,which possesses an inherent explainability and is complemented by mechanisms for correcting inaccuracies due to mislabeled data. This model demonstrated a notable classification accuracy rate of 98.05%, distinguishing between states of depression and suicidal ideation.  
[PERSON] et al. [13] conducted a thorough investigation into suicide ideation using a combination of n-gram analysis, an LSTM-CNN model, and additional machine learning techniques. They curated a dataset from the r/SuicideWatch subreddit featuring both suicidal and non-suicidal posts. Employing 10-fold cross-validation to refine the hyperparameters, their model integrates the analytical strengths of CNNs and LSTMs to process textual data. The model architecture utilizes an embedding layer for vector representation, a dropout layer to prevent overfitting, an LSTM layer for feature extraction, followed by a pooling layer, a flattening step, and a SoftMax function. Evaluation metrics, including F1 score, recall, accuracy, and precision, were applied to gauge the performance of the baseline model within this deep learning classification framework.