Methodologies for Monitoring Mental Health on Twitter: Systematic Review.
Methodologies for Monitoring Mental Health on Twitter: Systematic Review.
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DOI：
10.2196/42734
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发表时间：
2023-05-08
影响因子：
7.4
通讯作者：
Haworth, M. A.
中科院分区：
文献类型：
--
作者：
Cara, Nina H. Di;Maggio, Valerio;Davis, Oliver S. P.;Haworth, M. A.
使用社交媒体数据来预测心理健康结果有可能允许持续监测心理健康和福祉，并提供及时的信息，以补充传统的临床评估。然而，从心理健康和机器学习的角度来看，用于为此目的创建模型的方法必须具有高质量。由于其数据的可访问性，Twitter一直是社交媒体的热门选择，但访问大数据集并不能保证获得强大的结果。本研究旨在回顾目前文献中用于从Twitter数据预测心理健康结果的方法，重点关注潜在心理健康数据的质量和使用的机器学习方法。使用与精神健康障碍、算法和社交媒体相关的关键词，在6个数据库中进行了系统检索。共筛选了2759条记录，其中164篇（5.94%）论文进行了分析。收集了有关数据采集、预处理、模型创建和验证方法的信息，以及有关可复制性和伦理考虑的信息。回顾的164项研究使用了119个主要数据集。另有8个数据集的描述不够详细，无法纳入，6.1%（10/164）的论文根本没有描述其数据集。在这119个数据集中，只有16个（13.4%）可以访问有关社交媒体用户心理健康障碍的真实数据（即已知特征）。其他86.6%（103/119）的数据集通过搜索关键字或短语收集数据，这些关键字或短语可能不能代表精神健康障碍患者使用Twitter的模式。分类标签的精神健康障碍注释是可变的，57.1%（68/119）的数据集在该注释上没有基础事实或临床输入。尽管焦虑是一种常见的心理健康障碍，但很少受到关注。共享高质量的地面实况数据集对于开发具有临床和研究实用性的可信算法至关重要。鼓励跨学科和背景的进一步合作，以更好地了解什么类型的预测将有助于支持管理和识别心理健康障碍。为这一领域的研究人员和更广泛的研究界提出了一系列建议，目的是提高未来产出的质量和效用。
The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results. This study aims to review the current methodologies used in the literature for predicting mental health outcomes from Twitter data, with a focus on the quality of the underlying mental health data and the machine learning methods used. A systematic search was performed across 6 databases, using keywords related to mental health disorders, algorithms, and social media. In total, 2759 records were screened, of which 164 (5.94%) papers were analyzed. Information about methodologies for data acquisition, preprocessing, model creation, and validation was collected, as well as information about replicability and ethical considerations. The 164 studies reviewed used 119 primary data sets. There were an additional 8 data sets identified that were not described in enough detail to include, and 6.1% (10/164) of the papers did not describe their data sets at all. Of these 119 data sets, only 16 (13.4%) had access to ground truth data (ie, known characteristics) about the mental health disorders of social media users. The other 86.6% (103/119) of data sets collected data by searching keywords or phrases, which may not be representative of patterns of Twitter use for those with mental health disorders. The annotation of mental health disorders for classification labels was variable, and 57.1% (68/119) of the data sets had no ground truth or clinical input on this annotation. Despite being a common mental health disorder, anxiety received little attention. The sharing of high-quality ground truth data sets is crucial for the development of trustworthy algorithms that have clinical and research utility. Further collaboration across disciplines and contexts is encouraged to better understand what types of predictions will be useful in supporting the management and identification of mental health disorders. A series of recommendations for researchers in this field and for the wider research community are made, with the aim of enhancing the quality and utility of future outputs.
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影响因子：
7.4
作者：
Birnbaum, Michael L.;Ernala, Sindhu Kiranmai;Kane, John M.
通讯作者：
Kane, John M.
DOI：
10.1007/s42979-021-00958-1
发表时间：
2022
期刊：
SN computer science
影响因子：
--
作者：
Babu NV;Kanaga EGM
通讯作者：
Kanaga EGM
影响因子：
3.9
作者：
Bechini, Alessio;Bondielli, Alessandro;Renda, Alessandro
通讯作者：
Renda, Alessandro
影响因子：
5.2
作者：
Braithwaite SR;Giraud-Carrier C;West J;Barnes MD;Hanson CL
通讯作者：
Hanson CL
影响因子：
5.3
作者：
Castillo-Sánchez G;Marques G;Dorronzoro E;Rivera-Romero O;Franco-Martín M;De la Torre-Díez I
通讯作者：
De la Torre-Díez I