Robust suicide risk assessment on social media via deep adversarial learning

Ramit Sawhney, Harshit Joshi, Saumya Gandhi, Di Jin, Rajiv Ratn Shah

Abstract: The prevalence of social media for sharing personal thoughts makes it a viable platform for the assessment of suicide risk. However, deep learning models are not able to capture the diverse nature of linguistic choices and temporal patterns that can be exhibited by a suicidal user on social media and end up overfitting on specific cues that are not generally applicable. We propose Adversarial Suicide assessment Hierarchical Attention (ASHA), a hierarchical attention model that employs adversarial learning for improving the generalization ability of the model.