# Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

arXiv: https://arxiv.org/abs/2104.04140 · HTML: https://mlsanity.com/paper/2104.04140/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 95 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications](https://mlsanity.com/paper/1910.12611/) (2019)
- [Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?](https://mlsanity.com/paper/2010.08660/) (2020)
- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017)
- [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015)
- [Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning](https://mlsanity.com/paper/1912.00512/) (2019)
- [Learning to Skim Text](https://mlsanity.com/paper/1704.06877/) (2017)
- [ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge](https://mlsanity.com/paper/1704.03560/) (2017)
- [Conversation Modeling on Reddit using a Graph-Structured LSTM](https://mlsanity.com/paper/1704.02080/) (2017)
- [Unsupervised Detection of Sub-events in Large Scale Disasters](https://mlsanity.com/paper/1912.13332/) (2019)
- [A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care](https://mlsanity.com/paper/1808.03265/) (2018)
- [Comparing Suicide Risk Insights derived from Clinical and Social Media data](https://mlsanity.com/paper/2012.11393/) (2020)

## Built on by (most cited first, 15 of 15)

- [ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance](https://mlsanity.com/paper/2305.08010/) (2023)
- [A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework](https://mlsanity.com/paper/2203.11856/) (2022)
- [Process Knowledge-infused Learning for Suicidality Assessment on Social Media](https://mlsanity.com/paper/2204.12560/) (2022)
- [Process Knowledge-infused Learning for Clinician-friendly Explanations](https://mlsanity.com/paper/2306.09824/) (2023)
- [SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques](https://mlsanity.com/paper/2410.16322/) (2024)
- [Semantic Similarity Models for Depression Severity Estimation](https://mlsanity.com/paper/2211.07624/) (2022)
- [WellXplain: Wellness Concept Extraction and Classification in Reddit Posts for Mental Health Analysis](https://mlsanity.com/paper/2308.13710/) (2023)
- [Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities](https://mlsanity.com/paper/2110.15269/) (2021)
- [Mental Disorder Classification via Temporal Representation of Text](https://mlsanity.com/paper/2406.15470/) (2024)
- [Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support](https://mlsanity.com/paper/2604.18302/) (2026)
- [Integrating Supervised Extractive and Generative Language Models for Suicide Risk Evidence Summarization](https://mlsanity.com/paper/2403.15478/) (2024)
- [IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being](https://mlsanity.com/paper/2406.13791/) (2024)
- [Detection of Suicidal Risk on Social Media: A Hybrid Model](https://mlsanity.com/paper/2505.23797/) (2025)
- [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)
- [Explainable Suicide Risk Assessment on Social Media with Multi-Task QLoRA](https://mlsanity.com/paper/2610.00610/) (2026)

## Abstract (arXiv)

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.