Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. For example, these disorders manifest themselves somewhat frequently in texts written by nondiagnosed users in social media. However, detecting users with these conditions is not a straightforward task as they may not explicitly talk about their mental state, and if they do, contextual cues such as immediacy must be taken into account. When available, linguistic flags pointing to probable anxiety or depression could be used by medical experts to write better guidelines and treatments. In this paper, we develop a dataset designed to foster research in depression and anxiety detection in Twitter, framing the detection task as a binary tweet classification problem. We then apply state-of-the-art classification models to this dataset, providing a competitive set of baselines alongside qualitative error analysis. Our results show that language models perform reasonably well, and better than more traditional baselines. Nonetheless, there is clear room for improvement, particularly with unbalanced training sets and in cases where seemingly obvious linguistic cues (keywords) are used counter-intuitively.
Universitas Brawijaya（布拉维贾亚大学）；UIN Maulana Malik Ibrahim（毛拉纳马利克易卜拉欣国立伊斯兰大学）；Universitas Gadjah Mada（加查马达大学）；Universitas Widyagama Malang（维迪亚加玛玛琅大学）
The most common mental disorders experienced by a person in daily life are depression and anxiety. Social stigma makes people with depression and anxiety neglected by their surroundings. Therefore, they turn to social media like Twitter for support. Detecting users with potential depression and anxiety disorders through textual data is not easy because they do not explicitly discuss their mental state. It takes a model that can identify potential users who experience depression and anxiety on textual data to get treatment earlier. Text classification techniques can achieve this. One approach t…
University of Toronto（多伦多大学）；Winterlight Labs（Winterlight实验室）
Traditional screening practices for anxiety and depression pose an impediment to monitoring and treating these conditions effectively. However, recent advances in NLP and speech modelling allow textual, acoustic, and hand-crafted language-based features to jointly form the basis of future mental health screening and condition detection. Speech is a rich and readily available source of insight into an individual's cognitive state and by leveraging different aspects of speech, we can develop new digital biomarkers for depression and anxiety. To this end, we propose a multi-modal system for the s…
AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
University of Tehran（塔里斯坦大学）；University of South Carolina（南卡罗来纳大学）；University of West London（西伦敦大学）；Azad University（阿扎德大学）；Amirkabir University of Technology（阿米卡比尔技术大学）
Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case
Indian Institute of Research and Science, Bhopal（博帕尔印度研究与科学学院）；Indian Institute of Technology, Kharagpur（克勒格布尔印度理工学院）
Black Dog Institute（黑狗研究所）；University of New South Wales（新南威尔士大学）；Deakin University（迪肯大学）
Due to its popularity and availability, social media data may present a new way to identify individuals who are experiencing mental illness. By analysing blog content, this study aimed to investigate the associations between linguistic features and symptoms of depression, generalised anxiety, and suicidal ideation. This study utilised a longitudinal study design. Individuals who blogged were invited to participate in a study in which they completed fortnightly mental health questionnaires including the PHQ9 and GAD7 for a period of 36 weeks. Linguistic features were extracted from blog data us…
StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection
The George Washington University（乔治华盛顿大学）；King Saud University（沙特国王大学）；Johns Hopkins University Applied Physics Laboratory（约翰霍普金斯大学应用物理实验室）；Carnegie Mellon University（卡内基梅隆大学）
University of Exeter（埃克塞特大学）；Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所）；University of Warwick（华威大学）
Most current affect scales and sentiment analysis on written text focus on quantifying valence (sentiment) -- the most primary dimension of emotion. However, emotions are broader and more complex than valence. Distinguishing negative emotions of similar valence could be important in contexts such as mental health. This project proposes a semi-supervised machine learning model (DASentimental) to extract depression, anxiety and stress from written text. First, we trained the model to spot how sequences of recalled emotion words by $N=200$ individuals correlated with their responses to the Depres…
Mental well-being and social media have been closely related domains of study. In this research a novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed. This mixed anxiety-depressive disorder is a predominantly associated with erratic thought process, restlessness and sleeplessness. Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector <word, timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to detect the presence of anxiety indicators. Time and frequency of tweet is ana…
The Expressions of Depression and Anxiety in Chinese Psycho-counseling: Usage of First-person Singular Pronoun and Negative Emotional Words
Hangzhou Normal University（杭州师范大学）；Zhejiang University（浙江大学）；Westlake University（西湖大学）；Queen Mary University of London（伦敦玛丽女王大学）；Hangzhou Polytechnic（杭州职业技术学院）
Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages
Stanford University（斯坦福大学）；Stanford School of Medicine（斯坦福医学院）；Stanford Health Care（斯坦福医疗保健系统）；Google LLC（谷歌公司）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Veterans Affairs Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗保健系统）
Indian Institute of Technology Patna（巴特那印度理工学院）；Wright State University（莱特州立大学）
The unprecedented growth of Internet users in recent years has resulted in an abundance of unstructured information in the form of social media text. A large percentage of this population is actively engaged in health social networks to share health-related information. In this paper, we address an important and timely topic by analyzing the users' sentiments and emotions w.r.t their medical conditions. Towards this, we examine users on popular medical forums (Patient.info,dailystrength.org), where they post on important topics such as asthma, allergy, depression, and anxiety. First, we provid…
Kozminski University（科兹明斯基大学）；University of Warsaw（华沙大学）
Sentiment and lexical analyses are widely used to detect depression or anxiety disorders. It has been documented that there are significant differences in the language used by a person with emotional disorders in comparison to a healthy individual. Still, the effectiveness of these lexical approaches could be improved further because the current analysis focuses on what the social media entries are about, and not how they are written. In this study, we focus on aspects in which these short texts are similar to each other, and how they were created. We present an innovative approach to the depr…
Mental disorders such as depression and anxiety have been increasing at alarming rates in the worldwide population. Notably, the major depressive disorder has become a common problem among higher education students, aggravated, and maybe even occasioned, by the academic pressures they must face. While the reasons for this alarming situation remain unclear (although widely investigated), the student already facing this problem must receive treatment. To that, it is first necessary to screen the symptoms. The traditional way for that is relying on clinical consultations or answering questionnair…
Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis
University of Southern California（美国南加州大学）；Georgia Institute of Technology（佐治亚理工学院）；Sion Hospital（Sion医院）；Clinical Psychotherapist（临床心理治疗师）；Sophia College for Women（Sophia女子学院）；Indian Institute of Technology Bombay（印度班加罗尔理工学院）
The COVID-19 pandemic brought many challenges, from hospital-occupation management to lock-down mental-health repercussions such as anxiety or depression. In this work, we present a solution for the later problem by developing a Twitter emotion-monitor system based on a state-of-the-art natural-language processing model. The system monitors six different emotions on accounts in cities, as well as politicians and health-authorities Twitter accounts. With an anonymous use of the emotion monitor, health authorities and private health-insurance companies can develop strategies to tackle problems s…
Exploring Large Language Models for Detecting Mental Disorders
AIRI；ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院信息与系统研究信任人工智能研究中心）；FRC CSC RAS（俄罗斯科学院应用数学与计算机科学研究所）；MIPT（米哈伊尔·伊万诺维奇·普京高等经济学院）；RUDN University（俄罗斯人民友谊大学）；MBZUAI（马斯喀特大学人工智能研究所）
In this paper, we propose Evebot, an innovative, sequence to sequence (Seq2seq) based, fully generative conversational system for the diagnosis of negative emotions and prevention of depression through positively suggestive responses. The system consists of an assembly of deep-learning based models, including Bi-LSTM based model for detecting negative emotions of users and obtaining psychological counselling related corpus for training the chatbot, anti-language sequence to sequence neural network, and maximum mutual information (MMI) model. As adolescents are reluctant to show their negative …
Although yoga is a multi-component practice to hone the body and mind and be known to reduce anxiety and depression, there is still a gap in understanding people's emotional state related to yoga in social media. In this study, we investigate the causal relationship between practicing yoga and being happy by incorporating textual and temporal information of users using Granger causality. To find out causal features from the text, we measure two variables (i) Yoga activity level based on content analysis and (ii) Happiness level based on emotional state. To understand users' yoga activity, we p…
