Citation

Rao G, Zhang Y, Zhang L, Cong Q, Feng Z. IEEE Access 2020; 8: 32395-32403.

Copyright

(Copyright © 2020, Institute of Electrical and Electronics Engineers)

DOI

10.1109/ACCESS.2020.2973737

PMID

unavailable

Abstract

More users suffering from depression turn to online forums to express their problems and seek help. In such forums, there is often a large volume of posts with sensitive content, indicating that the user has a risk of suicide and self-harm. Early detection of depression using appropriate deep learning models and social media data can prevent potential self-harm. However, existing depression detection models are not powerful enough to capture critical sentiment information from the large volume of posts published by each user, which makes the performance of these models not satisfying. To address this problem, we propose a hierarchical posts representations model named Multi-Gated LeakyReLU CNN (MGL-CNN) for identifying depressed individuals in online forums. The model consists of two parts: the first one is a post-level operation, which is used to learn the representation of each post of the user, and the second one is a user-level operation, which is used to obtain the overall representation of the user's emotional state. Besides, we propose another depression detection model by changing the number of gated units in the MGL-CNN, which is named Single-Gated LeakyReLU CNN (SGL-CNN). We show how to use our models to identify depressed users through a lot of posted content. Experimental results showed that our models performed better than the previous state-of-the-art models on the Reddit Self-reported Depression Diagnosis dataset, and also performed well on the Early Detection of Depression dataset. © 2013 IEEE.

Language: en

Keywords

Social networking (online); State of the art; Online forums; Detection models; Neural networks; Emotional state; Deep learning; Depression detection; Learning models; MGL-CNN; Network architecture; neural network architecture; online forums; Rhenium compounds; SGL-CNN; Social media datum