# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 2 Related Works
While CNN is effective at capturing local patterns and features in text, such as specific phrases indicating suicidal ideations, LSTM can capture long-term dependencies in the Reddit posts. [PERSON] et al. [13] addressed the development of mapping and learning models for estimating the suicide risk severity based on forum content. The dataset consisted of r/SuicideWatch posts and was annotated by four practicing psychiatrists. The domain-specific knowledge was incorporated into the annotation process by applying the Columbia Suicide Severity Rating Scale questionnaire conventionally used by clinicians for suicide risk screening. Statistical, linguistic, and TF-IDF features were utilized to train SVM and RF. The word embeddings generated with ConceptNet were used with CNN and feed-forward neural network models. The performances of the resulting models were compared. Their results showed that CNN attained the highest performance in detecting different levels of suicidal ideations. [PERSON] et al. [24] aimed to detect suicidality in Reddit posts among opioid users. The data were collected from r/SuicideWatch, r/Depression, and opioid-related subreddits. As control data, the researchers used posts from subreddits not related to mental health. The dataset was annotated by crowdsourcing workers who were provided with annotation instructions. This study trained baseline ML models consisting of LR, RF, and SVM with TF-IDF features. Word embeddings from GloVe and FastText were used with CNN and recurrent neural networks. Their proposed CNN model outperformed other methods, demonstrating the viability of neural networks for the identification of suicidal ideations among opioid users. [PERSON] et al. [25] explored XGBoost, CNN, and bidirectional LSTM models for suicidal ideation detection. The study used a publicly available unannotated dataset comprising an equal number of r/SuicideWatch posts and control posts unrelated to mental health topics. The Word2 Vec embedding technique and TF-IDF were applied to generate vector representations of Reddit posts. The authors also performed a comparative analysis of suicidal and non-suicidal texts. The results of this study revealed that the CNN-LSTM combined model outperformed XGBoost at classifying suicidal texts. A recent trend in suicidal ideation detection is the application of pre-trained transformer models, such as Bidirectional Encoder Representations from Transformers (BERTs). BERT is pre-trained on a large corpora of general text, allowing it to gain a broad understanding of natural language. These pre-trained models are then fine-tuned for detecting suicidal ideations using task-specific Reddit datasets. Studies have consistently demonstrated that transformer-based models achieve superior performance in detecting suicidal ideations. For instance, [PERSON] et al. [26] used the UMD Reddit Suicidality Dataset to fine-tune the BERT model for detecting varying degrees of suicide risk. [PERSON] et al. [27] also utilized this dataset but implemented a different approach. They reduced the dimensionality of BERT embeddings using non-negative matrix factorization and combined them with lexicon-based and statistical features. Their results indicated that combining BERT with theoretical features led to improved performance in classifying texts into different suicide risk levels. [PERSON] et al. [28] fine-tuned multiple BERT models for the binary classification of suicidal texts and compared their performance to a LSTM baseline trained with GloVe embeddings. Their experimental results demonstrated that the BERT-based models were more effective in detecting suicidal ideation, outperforming the baseline model. As an alternative to BERT, [PERSON] et al. [16] employed the transformer-based Universal Sentence Encoder to generate sentence-level embeddings of Reddit posts.