Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach

• 成果类型: Article
• 署名作者: Zhang, Wenli; Xie, Jiaheng; Zhang, Zhu (Drew); Liu, Xiang
• 署名单位: Iowa State University; University of Delaware; University of Rhode Island; University of Delaware
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2024.2340822
• 发表日期: 2024
• 页码: 546-580
• 关键词: bayesian network; design science; big data; INFORMATION; analytics; Sentiment; people; SYSTEM; IMPACT

摘要

Depression is a pressing yet underdiagnosed issue in health management. Because depressed patients share their symptoms, life events, and treatments on digital platforms, information systems (IS) scholars resort to user-generated digital traces for depression detection. While they facilitate innovative information technology (IT) approaches to alleviate the social and economic burden of depression, most studies lack effective means to incorporate domain knowledge in depression detection systems or suffer from feature extraction difficulties. Following the design science research in IS, we propose a Deep-Knowledge-Aware Depression Detection system to detect social media users at risk of depression and explain the detection factors. We deploy extensive empirical analyses to evaluate our designed IT artifact, which shows domain knowledge greatly improves performance. Our work has significant implications for IS research in knowledge-aware machine learning, digital traces utilization, and generalizable design principles. Practically, the early detection and factor explanation from our IT artifact can assist depression management and enable large-scale assessment of the population's mental health.