Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea.
J Med Internet Res. 2021 Mar 8;23(3):e24870. doi: 10.2196/24870.
Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention.
We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.
Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described.
We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated.
The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.
与与医疗服务提供者面对面交流相比，社交媒体平台为精神障碍患者提供了一种更便捷、省时的沟通方式。最近，基于机器学习 (ML) 的利用大规模社交媒体数据进行心理健康探索引起了广泛关注。
我们旨在提供社交媒体中 ML 用于心理健康的文献计量分析和讨论。
从 Scopus 和 Web of Science 数据库中检索了涉及社交媒体和心理健康领域的 ML 的出版物。我们分析了出版物的分布，以衡量来源、国家、机构、作者和研究主题的生产力，并使用关键字共现网络可视化该领域的趋势。还详细描述了具有高引用量的先前研究的研究方法。
我们获得了 2015 年至 2020 年期间发表的总计 565 篇相关论文。在过去的 5 年中，出版物数量呈持续增长趋势，根据 Scopus 和 Web of Science 记录，Lecture Notes in Computer Science 和 Journal of Medical Internet Research 是两个最具生产力的来源。此外，还调查了高排名出版物中呈现的数据资源的显著方法方法。
本研究的结果突出了该研究领域的持续增长。此外，我们从高引用出版物的全面概述中检索到三个主要讨论点，为研究人员和从业者提供了新的深入方向。
