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.
Highlights
Mental health problem remains a major challenge affecting modern people’s health
We introduce two hierarchical neural network models with gated units and convolutional networks for fulfilling depression detection task, which are named Multi-Gated LeakyReLU Convolutional Neural Networks (CNNs) (MGL-CNN) and Single-Gated LeakyReLU CNN (SGL-CNN)
THE PROPOSED METHOD We proposed two novel hierarchical depression detection models named MGL-CNN and SGL-CNN for identifying depressed individuals in online forums
Summary
Mental health problem remains a major challenge affecting modern people’s health. People’s mental health condition is closely related to their quality of life. G. Rao et al.: MGL-CNN: Hierarchical Posts Representations Model for Identifying Depressed Individuals in Online Forums media data for early detection of depression tasks has become an effective means. Detection of depression on social media is a continuous process of data accumulation, and only when there is sufficient evidence can a certain degree of depression risk be identified. This requires collecting a large number of posts with strong temporal associations and long time spans. We propose two general hierarchical posts representations models for identifying depressed users on large-scale datasets, including two essential parts: the post-level operation and the user-level operation.
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