School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia.
BMC Psychol. 2024 Feb 29;12(1):105. doi: 10.1186/s40359-024-01603-1.
There has been an increasing number of papers which explore the representation of mental health on social media using various social media platforms and methodologies. It is timely to review methodologies employed in this growing body of research in order to understand their strengths and weaknesses. This systematic literature review provides a comprehensive overview and evaluation of the methods used to investigate the representation of mental ill-health on social media, shedding light on the current state of this field. Seven databases were searched with keywords related to social media, mental health, and aspects of representation (e.g., trivialisation or stigma). Of the 36 studies which met inclusion criteria, the most frequently selected social media platforms for data collection were Twitter (n = 22, 61.1%), Sina Weibo (n = 5, 13.9%) and YouTube (n = 4, 11.1%). The vast majority of studies analysed social media data using manual content analysis (n = 24, 66.7%), with limited studies employing more contemporary data analysis techniques, such as machine learning (n = 5, 13.9%). Few studies analysed visual data (n = 7, 19.4%). To enable a more complete understanding of mental ill-health representation on social media, further research is needed focussing on popular and influential image and video-based platforms, moving beyond text-based data like Twitter. Future research in this field should also employ a combination of both manual and computer-assisted approaches for analysis.
越来越多的论文探讨了使用各种社交媒体平台和方法在社交媒体上呈现心理健康的问题。及时审查这一不断发展的研究领域中使用的方法，以了解其优缺点是很重要的。本系统文献综述全面概述和评估了用于研究社交媒体上心理健康不良表现的方法，揭示了该领域的现状。使用与社交媒体、心理健康和表现方面（例如，淡化或污名化）相关的关键字搜索了七个数据库。在符合纳入标准的 36 项研究中，最常用于数据收集的社交媒体平台是 Twitter（n=22，61.1%）、新浪微博（n=5，13.9%）和 YouTube（n=4，11.1%）。绝大多数研究使用手动内容分析（n=24，66.7%）分析社交媒体数据，只有少数研究采用了更现代的数据分析技术，如机器学习（n=5，13.9%）。很少有研究分析视觉数据（n=7，19.4%）。为了更全面地了解社交媒体上心理健康不良的表现，需要进一步研究关注流行和有影响力的图像和视频为基础的平台，超越像 Twitter 这样的基于文本的数据。该领域的未来研究还应同时采用手动和计算机辅助分析方法。
