# CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter

arXiv: https://arxiv.org/abs/2403.15391 · HTML: https://mlsanity.com/paper/2403.15391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Generative and Discriminative Text Classification with Recurrent Neural Networks](https://mlsanity.com/paper/1703.01898/) (2017)
- [Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons](https://mlsanity.com/paper/1411.0778/) (2014)
- [Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users](https://mlsanity.com/paper/1411.0861/) (2014)
- [TI-Capsule: Capsule Network for Stock Exchange Prediction](https://mlsanity.com/paper/2102.07718/) (2021)
- [TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data](https://mlsanity.com/paper/2304.02094/) (2023)

## Abstract (arXiv)

Along with factors such as cancer, blood pressure, street accidents and stroke, suicide has been one of Iran main causes of death. One of the main reasons for suicide is psychological stressors. Identifying psychological stressors in an at risk population can help in the early prevention of suicidal and suicidal behaviours. In recent years, the widespread popularity and flow of real time information sharing of social media have allowed for potential early intervention in large scale and even small scale populations. However, some automated approaches to extract psychiatric stressors from Twitter have been presented, but most of this research has been for non Persian languages. This study aims to investigate the techniques of detecting psychological stress related to suicide from Persian tweets using learning based methods. The proposed capsule based approach achieved a binary classification accuracy of 0.83.