CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter
This work addresses suicide prevention in Iran by identifying psychiatric stressors from social media, but it is incremental as it applies existing methods to a new language domain.
The study tackled the problem of detecting psychological stressors related to suicide from Persian tweets using learning-based methods, achieving a binary classification accuracy of 0.83.
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.