Early Detection of Suicidal Ideation Using Bidirectional GRU and Language Models

Suicide has recently emerged as a leading cause of death worldwide, underlining the importance of effective preventative measures.Online social media posts can provide valuable insights into people who are suicidal and assist in preventing unfortunate outcomes.However, the problem lies in the complexities of such posts, as people may be hesitant to share their distressing thoughts due to different psychological and societal constraints.This study examined the utilization of Bidirectional GRU, a type of recurrent neural network that analyzes data sequences in both forward and backward directions, to improve text classification using language models.This study improves textual analysis by incorporating Bidirectional GRU (Bi-GRU) layers with popular pre-trained language models like BERT, RoBERTa, DistilBERT, DistilRoBERTa, and ELECTRA-Small.The dataset used for this research is sourced from Reddit.The BERT-BiGRU and DistilBERT-BiGRU models demonstrated notable effectiveness, achieving accuracies of 95.8% and 95.2% respectively.These models also exhibited remarkably low false negative rates, with BERT-BiGRU at 4.17% and DistilBERT-BiGRU at 2.80%.These findings show that the combination of Bi-GRU and pre-trained language models considerably improves early detection of suicidal ideation, paving the path for prompt and perhaps lifesaving interventions.