Detection of suicidal ideation in written communication

Sammanfattning: Suicide remains a global cause of mortality, presenting challenges in detection and prevention despite known warning signs. This work aimed to improve personal security management by leveraging machine learning advancements to identify suicidal ideation in written communications. Using a design science approach, six machine learning models based on the RoBERTa model were developed with different hyperparameter values. These models were trained on a well-balanced dataset comprising 1,114 instances of suicide letters and social media posts. The model achieving the highest accuracy (0.919) and F1 score (0.919) during training was evaluated on a dataset consisting of posts from the subreddits r/terraluna and r/Terra_Luna_crypto. These posts were published during a period when the cryptocurrency Terra Luna experienced a crash, leading to reported cases of alleged suicides. The fine-tuned model demonstrated a reasonably high accuracy (0.841) and weighted F1 score (0.913) when tested on this real-world dataset. Additionally, a smaller test was conducted on selected posts (34 posts) from this dataset containing mentions of specific words. The model achieved an accuracy of 0.852, and a weighted F1 score of 0.887 when classifying these posts. There exist a considerable potential for further research and development in this field. By expanding and improving the dataset used in this project, incorporating additional features and contextual information, the accuracy and practicality of the model in real-life situations can be greatly enhanced. The ultimate objective is to create a resilient system that genuinely assists in the prevention of suicide. The results of this work offer hope and optimism for a future where advanced technology, combined with human compassion, addresses one of the most pressing public health issues of our time.