Detecting Reddit Users with Depression Using a Hybrid Neural Network
  SBERT-CNN
Depression is a widespread mental health issue, affecting an estimated 3.8%
of the global population. It is also one of the main contributors to disability
worldwide. Recently it is becoming popular for individuals to use social media
platforms (e.g., Reddit) to express their difficulties and health issues (e.g.,
depression) and seek support from other users in online communities. It opens
great opportunities to automatically identify social media users with
depression by parsing millions of posts for potential interventions. Deep
learning methods have begun to dominate in the field of machine learning and
natural language processing (NLP) because of their ease of use, efficient
processing, and state-of-the-art results on many NLP tasks. In this work, we
propose a hybrid deep learning model which combines a pretrained sentence BERT
(SBERT) and convolutional neural network (CNN) to detect individuals with
depression with their Reddit posts. The sentence BERT is used to learn the
meaningful representation of semantic information in each post. CNN enables the
further transformation of those embeddings and the temporal identification of
behavioral patterns of users. We trained and evaluated the model performance to
identify Reddit users with depression by utilizing the Self-reported Mental
Health Diagnoses (SMHD) data. The hybrid deep learning model achieved an
accuracy of 0.86 and an F1 score of 0.86 and outperformed the state-of-the-art
documented result (F1 score of 0.79) by other machine learning models in the
literature. The results show the feasibility of the hybrid model to identify
individuals with depression. Although the hybrid model is validated to detect
depression with Reddit posts, it can be easily tuned and applied to other text
classification tasks and different clinical applications.
Ziyi Chen, Ren Yang, Sunyang Fu, Nansu Zong, Hongfang Liu, Ming Huang
# Detecting Reddit Users with Depression Using a Hybrid Neural Network

###### Abstract

Depression is a widespread mental health issue, affecting an estimated 3.8% of the global population. It is also one of the main contributors to disability worldwide. Recently it is becoming popular for individuals to use social media platforms (e.g., Reddit) to express their difficulties and health issues (e.g., depression) and seek support from other users in online communities. It opens great opportunities to automatically identify social media users with depression by parsing millions of posts for potential interventions. Deep learning methods have begun to dominate in the field of machine learning and natural language processing (NLP) because of their ease of use, efficient processing, and state-of-the-art results on many NLP tasks. In this work, we propose a hybrid deep learning model which combines a pretrained sentence BERT (SBERT) and convolutional neural network (CNN) to detect individuals with depression with their Reddit posts. The sentence BERT is used to learn the meaningful representation of semantic information in each post. CNN enables the further transformation of those embeddings and the temporal identification of behavioral patterns of users. We trained and evaluated the model performance to identify Reddit users with depression by utilizing the Self-reported Mental Health Diagnoses (SMHD) data. The hybrid deep learning model achieved an accuracy of 0.86 and an F1 score of 0.86 and outperformed the state-of-the-art documented result (F1 score of 0.79) by other machine learning models in the literature. The results show the feasibility of the hybrid model to identify individuals with depression. Although the hybrid model is validated to detect depression with Reddit posts, it can be easily tuned and applied to other text classification tasks and different clinical applications.

## I Introduction

Depression is one of the most common mental illnesses and the influence of depression on society is a serious concern for public health communities. American Psychiatric Association (APA) approximates that 6.9 percent of the population in the United States is currently affected [1], costing the United States S233 billion in 2016 [2]. The World Health Organization (WHO) estimates that depression affects around 322 million people, at a cost of approximately 2.5 trillion dollars worldwide in 2017 [3]. WHO reported that the incidence of depression increased an extra 25% in the first year of the COVID-19 pandemic, mostly due to the aspects of social lockdown and isolation and work reduction [4]. Depression characterized by a sad, empty, or irritated mood with somatic and cognitive changes can worsen the individuals' capacity to function, limiting performance in everyday duties and social life [5]. Depression is a leading cause of disability around the world [2] and depression can lead to suicide, one of the leading causes of death [6]. Moreover, it is estimated that around two-thirds of depression cases are undiagnosed. The undiagnosed and thus untreated depression have a negative impact on quality of life and workplace productivity [7]. Prevention and intervention strategies such as screening and early detection are required to reduce the impact of depression.

Recently it is becoming popular for individuals to use social media platforms (e.g., Reddit) to express their life difficulties and health issues (e.g., depression) and seek support from other users in online communities [8, 9]. The social media platforms allow the public to anonymously disclose personal information and discuss life experiences and problems (e.g., depression) that may be stigmatizing, with less fear of offline harm or consequences [10, 11]. Recently research studies also confirmed that social media is effective platforms for self-disclosure and social support seeking for mental health difficulties (e.g., depression) [10, 12]. This makes social media platforms including Reddit important resources for studying user narratives related to depression [13, 14].

Among these social media platforms, Reddit ranks the 5th most visited website in the United States, having 52 million users per day and more than 430 million active users per month [15, 16]. Reddit consists of network of communities (subreddits) with focuses on specific topics such as depression, which