{"title":"利用社交媒体上与健康相关的文本进行抑郁症研究:主题和方法","authors":"Sumei Yao, Fan Wang, Jing Chen, Quan Lu","doi":"10.1108/lht-02-2023-0076","DOIUrl":null,"url":null,"abstract":"Purpose Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods. Design/methodology/approach The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles. Findings (1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife. Practical implications The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression. Originality/value This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.","PeriodicalId":47196,"journal":{"name":"Library Hi Tech","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilizing health-related text on social media for depression research: themes and methods\",\"authors\":\"Sumei Yao, Fan Wang, Jing Chen, Quan Lu\",\"doi\":\"10.1108/lht-02-2023-0076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. 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引用次数: 0
摘要
近年来,社交媒体文本作为抑郁症研究的数据来源已成为信息管理与公共卫生之间的重要融合。本文旨在对社交媒体上的文本进行抑郁相关的研究进行梳理,特别注意研究主题和方法。作者最终选择了在Web of Science、Wiley、ACM Digital Library、EBSCO、IEEE Xplore和JMIR数据库中发表的研究文章,共57篇。(1)从编码结果来看,抑郁预测和语言特征与信息行为是两个最受欢迎的主题。病人需要的主题在过去几年中有了进展。然而,对病耻感和抗抑郁药的关注较少。(2)相对于定性方法,研究者更倾向于使用定量方法,如机器学习和统计分析。(4)从数据收集平台的分析来看,更多的研究者使用综合性社交媒体网站,如Reddit和Facebook,而不是针对抑郁症的社区,如Sunforum和Alonelylife。作者建议使用机器学习和统计分析来彻底探索与污名化和抗抑郁药相关的因素。此外,开展结合不同来源数据的混合方法研究将是有价值的。这些方法将为寻求全面了解抑郁症的决策者和制药公司提供有益的见解。这篇文章在系统地收集和研究社交媒体与抑郁症的健康相关文本的交叉点内的主题和方法方面做出了开创性的努力。
Utilizing health-related text on social media for depression research: themes and methods
Purpose Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods. Design/methodology/approach The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles. Findings (1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife. Practical implications The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression. Originality/value This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.
期刊介绍:
■Integrated library systems ■Networking ■Strategic planning ■Policy implementation across entire institutions ■Security ■Automation systems ■The role of consortia ■Resource access initiatives ■Architecture and technology ■Electronic publishing ■Library technology in specific countries ■User perspectives on technology ■How technology can help disabled library users ■Library-related web sites