# Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN

arXiv: https://arxiv.org/abs/2402.02262 · HTML: https://mlsanity.com/paper/2402.02262/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping](https://mlsanity.com/paper/2010.13369/) (2020)
- [Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications](https://mlsanity.com/paper/1910.12611/) (2019)
- [MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos](https://mlsanity.com/paper/2304.05292/) (2023)
- [XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks](https://mlsanity.com/paper/2111.10854/) (2021)

## Built on by (most cited first, 2 of 2)

- [Rethinking Suicidal Ideation Detection: A Trustworthy Annotation Framework and Cross-Lingual Model Evaluation](https://mlsanity.com/paper/2507.14693/) (2025)
- [Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition](https://mlsanity.com/paper/2603.10965/) (2026)

## Abstract (arXiv)

Suicide remains a pressing global health concern, necessitating innovative approaches for early detection and intervention. This paper focuses on identifying suicidal intentions in posts from the SuicideWatch subreddit by proposing a novel deep-learning approach that utilizes the state-of-the-art RoBERTa-CNN model. The robustly Optimized BERT Pretraining Approach (RoBERTa) excels at capturing textual nuances and forming semantic relationships within the text. The remaining Convolutional Neural Network (CNN) head enhances RoBERTa's capacity to discern critical patterns from extensive datasets. To evaluate RoBERTa-CNN, we conducted experiments on the Suicide and Depression Detection dataset, yielding promising results. For instance, RoBERTa-CNN achieves a mean accuracy of 98% with a standard deviation (STD) of 0.0009. Additionally, we found that data quality significantly impacts the training of a robust model. To improve data quality, we removed noise from the text data while preserving its contextual content through either manually cleaning or utilizing the OpenAI API.