arXiv:2606. 07714v1 Announce Type: cross Abstract: Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent psychologically meaningful risk factors.
arXiv:2606. 28334v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) and social media data have led to growing optimism about the ability to detect suicide risk at scale.
A new language‑processing tool has been developed to estimate suicide risk from natural language. By analyzing text, the tool can identify individuals at the highest risk. This capability could enable faster and more targeted interventions.
The paper presents a system for the IEEE BigData 2026 Cup on Explainable Suicide Risk Assessment on Social Media. It tackles three tasks—risk-level classification, evidence phrase extraction, and multi-label factor identification—using Qwen2.5-Instruct models adapted with quantized low-rank adaptation (QLoRA) and an answer-masked causal language-model objective. The final system achieved a composite score of 0.7738, with 0.8089 on Task 1 and 0.6919 on Task 2, demonstrating that task‑specific training and tailored aggregation improve performance across the three tasks.
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
arXiv:2609.07766v1 Announce Type: cross Abstract: Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidenc...
arXiv:2606. 19637v1 Announce Type: cross Abstract: Clinical NLP increasingly relies on electronic health record (EHR) data to detect suicidal behaviors, treating clinical documentation as more reliable ground truth than social media.
The paper reviews how large language models are applied in mental health, covering areas such as social media analysis, clinical conversational agents, therapy support tools, prompt engineering, and multimodal learning. It synthesizes interdisciplinary studies that use social media posts, electronic medical records, and multimodal inputs to detect depression, assess suicide risk, provide personalized therapy, and generate psychoeducational content. The review also discusses advances in model interpretability, annotation strategies, multimodal fusion techniques, and highlights ethical, sociotechnical, and regulatory challenges while proposing frameworks for safe, equitable, and accountable deployment.
arXiv:2609.22767v1 Announce Type: cross Abstract: The IEEE BigData Cup benchmark combines three prediction problems with different output structures: ordinal suicide-risk classification, multi-label...
arXiv:2512. 06227v3 Announce Type: replace-cross Abstract: Real-world indicators play an important role in many Natural Language Processing (NLP) applications, such as life events for mental health analysis and risky behaviours for online safety, yet labelling such information is often costly and/or difficult due to its multi-label and dynamic nature.
The paper introduces Label Semantic Expansion (LSE), a method that enriches sparse label representations by adding descriptive topic words grounded in a corpus. It proposes a Label-Guided Neural Topic Model (LGNTM) that learns label-aligned topics, integrates lexical and document semantics, and maintains consistency between topic and label structures. Experiments show that LSE and LGNTM improve label-topic alignment, label expansion, topic quality, and downstream classification performance.
MonoTM is an interpretable topic modeling framework that separates the estimation of document–topic mixtures from the generation of topic descriptors. It uses sparse autoencoders to extract dense, interpretable features for mixture estimation, then learns topic descriptors from a distinct set of corpus‑grounded semantic features. This approach preserves global topic structure while providing more meaningful, semantic‑unit descriptors than traditional top‑word lists.
arXiv:2608. 15691v1 Announce Type: cross Abstract: Health misinformation circulating during pandemics can gain traction rapidly, creating harmful narratives that compete with public health guidance.