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.
University of Ottawa（渥太华大学）；Dalhousie University（达尔豪斯大学）
Reddit.com is a popular social media platform among young people. Reddit users share their stories to seek support from other users, especially during the Covid-19 pandemic. Messages posted on Reddit and their content have provided researchers with opportunity to analyze public concerns. In this study, we analyzed sentiments of COVID-related messages posted on r/Depression. Our study poses the following questions: a) What are the common topics that the Reddit users discuss? b) Can we use these topics to classify sentiments of the posts? c) What matters concern people more during the pandemic? …
NepEMO: A Multi-Label Emotion and Sentiment Analysis on Nepali Reddit with Linguistic Insights and Temporal Trends
Department of Computer Science and Application（计算机科学与应用系）；Mechi Multiple Campus, Tribhuvan University（特里布文大学梅奇多校园）；School of Computer Science（计算机科学学院）；Queensland University of Technology（昆士兰理工大学）；Department of Electronics and Computer Engineering（电子与计算机工程系）；Advanced College of Engineering and Management, Tribhuvan University（特里布文大学先进工程与管理学院）；School of Engineering and Technology（工程与技术学院）
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；University of South Carolina（南卡罗来纳大学）
Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of depression, our experiments show that DLMs coupled with process knowledge in…
National Technical University of Athens（雅典国家技术大学）；Institute for Speech and Language Processing（言语与语言处理研究所）
We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media posts and combines these representations using an attentive bidirectional GRU network. We incorporate affective information, by augmenting the text representations with features extracted from a pretrained emotion classifier. Motivated by psychological literature we propose to incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme. Our analysis indicates that moral…
Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and achieve good results with regard to the state of the art by leveraging topic analysis and learned confi…
University of Exeter（埃克塞特大学）；University of Pisa（比萨大学）；ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between conceptsin texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks …
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；Georgetown University（乔治城大学）
Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on gen…
Semantic Similarity Models for Depression Severity Estimation
Centro de Investigación en Tecnoloxías da Información e as Comunicacións (CITIC)（信息与通信技术研究中心（CITIC））；Universidade da Coruña（拉科鲁尼亚大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））；Technical University of Darmstadt（达姆施塔特工业大学）
Interdisciplinary School of Doctoral Studies, University of Bucharest（布加勒斯特大学跨学科博士学院）；PRHLT Research Center, Universitat Politècnica de València（瓦伦西亚理工大学PRHLT研究中心）；Politehnica University of Bucharest（布加勒斯特理工大学）；Faculty of Mathematics and Computer Science, University of Bucharest（布加勒斯特大学数学与计算机科学学院）
Depression detection from user-generated content on the internet has been a long-lasting topic of interest in the research community, providing valuable screening tools for psychologists. The ubiquitous use of social media platforms lays out the perfect avenue for exploring mental health manifestations in posts and interactions with other users. Current methods for depression detection from social media mainly focus on text processing, and only a few also utilize images posted by users. In this work, we propose a flexible time-enriched multimodal transformer architecture for detecting depressi…
Instituto Politécnico Nacional（国立理工学院）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）
Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individuals' ph…
RWTH Aachen University（亚琛工业大学）；University of Amsterdam（阿姆斯特丹大学）
In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on exp…
The University of Adelaide（阿德莱德大学）；University of Moratuwa（莫拉图瓦大学）；University of Colombo School of Computing（科伦坡大学计算学院）；University of Colombo（科伦坡大学）
People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person's choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent), consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psycholog…
