This work presents the systems explored as part of the CLPsych 2021 Shared Task. More specifically, this work explores the relative performance of models trained on social me- dia data for suicide risk assessment. For this task, we aim to investigate whether or not simple traditional models can outperform more complex fine-tuned deep learning mod- els. Specifically, we build and compare a range of models including simple baseline models, feature-engineered machine learning models, and lastly, fine-tuned deep learning models. We find that simple more traditional machine learning models are more suited for this task and highlight the challenges faced when trying to leverage more sophisticated deep learning models.

This work presents the systems explored as part of the CLPsych 2019 Shared Task. More specifically, this work explores the promise of deep learning systems for suicide risk assessment.

Automated depression detection is inherently a multimodal problem. Therefore, it is critical that researchers investigate fusion techniques for multimodal design. This paper presents the first-ever comprehensive study of fusion techniques for depression detection. In addition, we present novel linguistically-motivated fusion techniques, which we find outperform existing approaches.

Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.