Detecting Suicide Risk and Contributing Factors Using Social Media Data

Overview

Suicide remains a critical global health issue, with over 700,000 lives lost annually. Existing research has explored factors influencing suicidal thoughts, but traditional studies often rely on small-scale data sources that may overlook contextual influences. This study aims to address that gap by analyzing a large dataset of posts from Reddit communities r/SuicideWatch and r/Teenagers to detect suicidal ideation and identify associated themes. Using natural language processing and statistical methodologies, including Llama 3-8b and Mistral-7b, we fine-tuned models with manually labeled data to improve classification accuracy. Using BERTopic, key themes linked to suicidal ideation were identified: relationship struggles, academic stress, and family trauma. While non-suicidal posts also included social and academic concerns, the topics were centered around more immediate stressors rather than the long-term emotional distress issues seen in the suicidal group. Intertopic distance maps and similarity matrices revealed how family trauma impacts future relationships and academics while critical life events are associated with personal struggles and suicidal thoughts. These findings highlight the potential of NLP methodologies in analyzing large-scale social media data, offering valuable insights for informing new prevention strategies. Additionally, social media, in combination with NLP, serves as a valuable outlet for capturing genuine emotional struggles, enabling more timely and personalized mental health support compared to traditional approaches like counseling.