THYMES: a framework for detecting suicidal ideation from social media posts using hyperbolic learning

Mental health concerns are a critical issue in today's digital age, posing a threat to both individual and societal well-being and making the identification of at-risk individuals crucial. Analyzing an individual's social media post history can offer insights into their mental health state and help identify the presence of suicidal ideation. However, the complexity of linguistic and temporal data, along with sparsity and time irregularities, poses a formidable challenge in machine learning. Previous methods in this domain either rely on Euclidean space for processing which does not adequately model the power-law properties of social media posts, or lose information due to the discretization of the time axis. To address these challenges, we propose a novel framework, THYMES, which leverages pre-trained encoders and a rich representation learning paradigm with hyperbolic learning to model power-law features for enhanced sequence modeling. We perform experiments on two datasets and demonstrate that THYMES outperforms previously proposed methods while maintaining classification fairness under heavy data imbalances. Additionally, we qualitatively analyze commonly misclassified samples to reveal the shortcomings of models in this domain.