Enhancing Suicide Risk Detection with a Multisource Data Filtering and Fusion Optimization Framework (MDF-FOF)

Recently, with the popularity of social media platforms, the increase in suicide-related content has triggered great attention on automated suicide risk detection systems. This research proposes a natural language processing model based on RoBERTa, which aims to identify and classify suicide risks in social media posts. Our team propose a "Multisource data filtering and fusion optimization framework"(MDF-FOF), which effectively solves the problem of data imbalance by combining a large language model (LLM) with manual annotation to generate a balanced training sample set. In terms of model construction, we proposed a framework MDF-FOF that utilizes pre-trained RoBERTa for feature extraction and combines it with a self-built EmoBERT for soft label classification. Experimental results show that the recall rate of the proposed framework on the validation set is stable above 0.75, which is better than the existing methods and baseline models. It shows good robustness and excellent performance in tests on datasets from different sources. This study provides a reliable basic framework for multi-feature extraction of user posts in the future.