Suicidal Post Detection on Reddit using Deep Learning Techniques

Abstract

One of the main causes of death is suicide worldwide, particularly among the young generation. The increase in suicidal posts on social media platforms such as Reddit has presented both challenges and opportunities for mental health intervention. Our work aims to use vast data is generated by individuals on Reddit by using advanced deep learning techniques to identify suicidal posts and non-suicidal posts. The dataset used is collected from the Reddit API called (PRAW) with the help of various subreddits (e.g., SuicideWatch, Anxiety, and Depression) and neutral topics (e.g., Jokes, Movies, Popular, Books). The proposed model uses long-short-term memory LSTM, bidirectional LSTM (Bi-LSTM), gated recurrent units (GRU), bidirectional GRU (Bi-GRU), and modified BERT-based transformers. The BERT-based model performed better in compared to other models with an accuracy of 98.5%, a precision of 98. 5%, and a recall of 98. 5%. These experimental results successfully verify the theoretical efficiency and adaptability of the proposed model in real-time suicidal post-detection.