# Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media

arXiv: https://arxiv.org/abs/2507.10008 · HTML: https://mlsanity.com/paper/2507.10008/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning](https://mlsanity.com/paper/2307.00995/) (2023)

## Built on by (most cited first, 5 of 5)

- [Suicidal Comment Tree Dataset: Enhancing Risk Assessment and Prediction Through Contextual Analysis](https://mlsanity.com/paper/2510.14395/) (2025)
- [Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations](https://mlsanity.com/paper/2602.23577/) (2026)
- [Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment](https://mlsanity.com/paper/2609.07766/) (2026)
- [Beyond Final-Token Classification: Heterogeneous Readouts for Evidence-Grounded Suicide Risk Detection](https://mlsanity.com/paper/2609.22767/) (2026)
- [Beyond Risk Prediction: Evidence Grounding and Psychosocial Factor Verification for Explainable Suicide Risk Assessment](https://mlsanity.com/paper/2610.08842/) (2026)

## Abstract (arXiv)

Suicide is a critical global health issue that requires urgent attention. Even though prior work has revealed valuable insights into detecting current suicide risk on social media, little attention has been paid to developing models that can predict subsequent suicide risk over time, limiting their ability to capture rapid fluctuations in individuals' mental state transitions. In addition, existing work ignores protective factors that play a crucial role in suicide risk prediction, focusing predominantly on risk factors alone. Protective factors such as social support and coping strategies can mitigate suicide risk by moderating the impact of risk factors. Therefore, this study proposes a novel framework for predicting subsequent suicide risk by jointly learning the dynamic influence of both risk factors and protective factors on users' suicide risk transitions. We propose a novel Protective Factor-Aware Dataset, which is built from 12 years of Reddit posts along with comprehensive annotations of suicide risk and both risk and protective factors. We also introduce a Dynamic Factors Influence Learning approach that captures the varying impact of risk and protective factors on suicide risk transitions, recognizing that suicide risk fluctuates over time according to established psychological theories. Our thorough experiments demonstrate that the proposed model significantly outperforms state-of-the-art models and large language models across three datasets. In addition, the proposed Dynamic Factors Influence Learning provides interpretable weights, helping clinicians better understand suicidal patterns and enabling more targeted intervention strategies.