Cutting-Edge AI Technology to Recognize Signs of Suicidal Thoughts in Social Media Posts

Abstract Detecting signs of suicidal thoughts from social media poses significant challenges for researchers. Many individuals who may be at risk of suicide express their emotions and thoughts openly on these platforms, offering valuable clues for identification. However, comprehending the intricate risk factors and warning signs associated with suicide prevention remains a formidable endeavour. Automating the recognition of abrupt changes in user behavior is crucial in this context. This task leverages natural language processing techniques to scrutinize social media interactions for behavioral patterns and textual content. These insights are then processed using specialized frameworks designed to pinpoint unusual behaviors indicative of potential suicidal intentions. Cutting-edge deep learning and machine learning methodologies play a pivotal role in swiftly identifying suicidal ideation. For instance, combining RNN (Recurrent Neural Network) models with LSTM proves effective in capturing emotional nuances from user posts. Strategies such as expanding the training dataset and incorporating attention mechanisms are utilized to enhance the accuracy and efficiency of these models. This study introduces an innovative LSTM-Attention-RNN model specifically crafted for analyzing social media content to detect potential suicidal tendencies. Evaluation of this model yielded promising outcomes, achieving an accuracy of 93.7% and an F1-score of 95.8%, representing notable improvements compared to baseline models. These findings underscore the model's potential for practical implementation in suicide prevention initiatives.