Suicidal ideation detection from social media

Suicidal ideation is a global cause of life-threatening injury and, most of the time, death. Mental health issues have been rapidly increasing, and most are being avoided without adequate treatment. Due to the developments in social media platforms and the online anonymity that these platforms provide, people would like to interact more with others on social platforms. Social platforms are surveillance tools for mining social content and suicidal tendencies. The current thesis attempts to present a solution to detect depression/suicidal ideation by using state-of-the-art natural language processing (NLP) and deep learning (DL) approaches (BiLSTM, BERT Transformer). Three different novel approaches are proposed for three different datasets of textual content. The SuicideDetection dataset is a publicly available dataset which is a collection from the social platform of Reddit's subreddits ("SuicideWatch", "depression", "bipolar", "offmychest", "anxiety") in Kaggle and the SWMH dataset is a collection from only "SuicideWatch" subreddit. The CEASEv2.0 dataset is another used dataset which is a collection of 4932 suicide notes. The proposed models outperformed the latest models by 2% and 1% F1 scores on SuicideDetection and CEASEv2.0 datasets, respectively. The best models for each dataset have been analyzed and discussed in terms of performance, along with the characteristics of the datasets and limitations in the suicidal ideation classification. This performance can be measured by common metrics such as Accuracy, Precision, Recall, F1-Score, and ROC curve. As its application in the real world, this project can assist psychologists in the early identification of suicidal ideation before the suicidal person harms him/herself. The thesis also demonstrates the potential of employing DL algorithms such as transformers along with the latest word embedding techniques and NLP techniques that could improve the issue of suicidal ideation.