Mental Illness and Suicide Ideation Detection Using Social Media Data

• Main Author: Kirinde Gamaarachchige, Prasadith Buddhitha
• Other Authors: Inkpen, Diana
• Format: Others
• Language: en
• Published: Université d'Ottawa / University of Ottawa 2021
• Subjects: NLP
• Online Access: http://hdl.handle.net/10393/42908; http://dx.doi.org/10.20381/ruor-27125

Mental disorders and suicide have become a global public health problem. Over the years, researchers in computational linguistics have extracted features from social media data for the early detection of users susceptible to mental disorders and suicide ideation. Lack of reliable and inadequate data and the requirement of interpretability can be identified as the principal reasons for the low adoption of neural network architectures in recognizing individuals with mental disorders and suicide ideation. In recent years, a gradual increase in the use of deep neural network architectures in detecting mental disorders and suicide ideation with low false positive and false negative rates became feasible. Our research investigates the efficacy of using a shared representation to learn lower-level features mutual among mental disorders and between mental disorders and suicide ideation. In addition to discovering the shared features between users with suicidal thoughts and users who self-declared a single mental disorder, we further investigate the impact of comorbidities on suicide ideation and use two unseen datasets to investigate the generalizability of the trained models. We use data from two different social media platforms to identify if knowledge can be shared between suicide ideation and mental illness detection tasks across platforms. Through multiple experiments with different but related tasks, we demonstrate the effectiveness of multi-task learning (MTL) when predicting users with mental disorders and suicide ideation. We produce competitive results using MTL with hard parameter sharing when predicting neurotypical users, users who might have PTSD (Post-Traumatic Stress Disorder), and users with depression. The results were further improved by using auxiliary inputs such as emotion, age, and gender. To predict users with suicide ideation or mental disorders (i.e., either single or multiple disorders), we use MTL with hard and soft parameter sharing and produce state-of-the-art results predicting users with suicide ideation who require urgent attention. For similar tasks, but with data from two different social media platforms, we further improve the state-of-the-art results when predicting users with suicide ideation who require urgent attention. In addition, we managed to improve the overall performances of the models by using different auxiliary inputs.