Yang Xingwei, Li Guang
Information Technology Management, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON, Canada.
Smith School of Business, Queen's University, Kingston, ON, Canada.
JMIR Form Res. 2025 Jan 20;9:e60286. doi: 10.2196/60286.
Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns.
This study aimed to develop and evaluate a methodological language framework that integrates psychological patterns, contextual information, and social interactions using natural language processing and machine learning techniques. The goal was to enhance intelligent decision-making for detecting depression at the user level.
We extracted language patterns via natural language processing approaches that facilitate understanding contextual and psychological factors, such as affective patterns and personality traits linked with depression. Then, we extracted social interaction influence features. The resultant social interaction influence that users have within their online social group is derived based on users' emotions, psychological states, and context of communication extracted from status updates and the social network structure. We empirically evaluated the effectiveness of our framework by applying machine learning models to detect depression, reporting accuracy, recall, precision, and F-score using social media status updates from 1047 users along with their associated depression diagnosis questionnaire scores. These datasets also include user postings, network connections, and personality responses.
The proposed framework demonstrates accurate and effective detection of depression, improving performance compared to traditional baselines with an average improvement of 6% in accuracy and 10% in F-score. It also shows competitive performance relative to state-of-the-art models. The inclusion of social interaction features demonstrates strong performance. By using all influence features (affective influence features, contextual influence features, and personality influence features), the model achieved an accuracy of 77% and a precision of 80%. Using affective features and affective influence features also showed strong performance, achieving 81% precision and an F-score of 79%.
The developed framework offers practical applications, such as accelerating hospital diagnoses, improving prediction accuracy, facilitating timely referrals, and providing actionable insights for early interventions in mental health treatment plans.
抑郁症会对个人的思维、情绪、行为和心境产生重大影响；这种普遍存在的心理健康状况在全球范围内影响着数百万人。传统的抑郁症检测和治疗方法依赖于问卷调查和个人访谈，这可能耗时且效率低下。由于社交媒体永久性地改变了我们日常交流的模式，社交媒体帖子可以为理解个体的精神疾病提供新的视角，因为它们能对个体的语言使用和行为模式进行客观的探索。
本研究旨在开发和评估一种方法性语言框架，该框架使用自然语言处理和机器学习技术整合心理模式、上下文信息和社会互动。目标是在用户层面增强抑郁症检测的智能决策。
我们通过自然语言处理方法提取语言模式，这些方法有助于理解上下文和心理因素，如与抑郁症相关的情感模式和人格特质。然后，我们提取社会互动影响特征。用户在其在线社交群体中产生的社会互动影响是根据从状态更新和社交网络结构中提取的用户情绪、心理状态和交流上下文得出的。我们通过应用机器学习模型来检测抑郁症，并使用来自1047名用户的社交媒体状态更新及其相关的抑郁症诊断问卷分数报告准确率、召回率、精确率和F值，从而实证评估我们框架的有效性。这些数据集还包括用户帖子、网络连接和人格回答。
所提出的框架在抑郁症检测方面表现出准确且有效，与传统基线相比性能有所提高，准确率平均提高6%，F值提高10%。它相对于最先进的模型也表现出有竞争力的性能。社会互动特征的纳入显示出强大的性能。通过使用所有影响特征（情感影响特征、上下文影响特征和人格影响特征），模型的准确率达到77%，精确率达到80%。使用情感特征和情感影响特征也表现出强大的性能，精确率达到81%，F值达到79%。
所开发的框架具有实际应用价值，例如加快医院诊断、提高预测准确率、促进及时转诊以及为心理健康治疗计划的早期干预提供可操作的见解。
