Investigation of college students’ mental health status via semantic analysis of Sina microblog

Liang, Xiaohui; Gu, Siqi; Deng, Jiayuan; Gao, Zhaixing; Zhang, Zhonghua; Shen, Dong

doi:10.1007/s11859-015-1075-z

Cited by 11 publications

(10 citation statements)

References 1 publication

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“…Public health applications included: assessing the mental health of both specific and broader populations (e.g. Liang et al ., 2015; Chary et al ., 2017); monitoring mental health following an event or disaster (e.g. Glasgow et al ., 2014; 2016); and creating models of risk to improve health system delivery e.g.…”

Section: Resultsmentioning

confidence: 99%

“…diagnostic surveys and tools; n = 9). Social media data were found to be a particularly useful epidemiological resource for natural language processing and classification, including assessments of the mental health status of over 60 000 college students in China (Liang et al ., 2015) and prescription opioid misuse in an estimated sample of over 1.3 million Twitter users (Chary et al ., 2017). Social media also enables researchers to assess the impact of an incident on population mental health (e.g.…”

Section: Resultsmentioning

confidence: 99%

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Machine learning in mental health: a scoping review of methods and applications

2019

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BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine learning in mental health: a scoping review of methods and applications

2019

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“…In addition, Weibo is also a valuable data source for various types of research. Because of its anonymity, people can talk about private issues freely on Weibo, such as their physical and mental health concerns [1,2,3,4,5]. …”

Section: Introductionmentioning

confidence: 99%

An Analysis of Anxiety-Related Postings on Sina Weibo

Tian

Batterham

et al. 2017

IJERPH

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This study examines anxiety-related postings on Sina Weibo to gain insight into social networking about mental health. The themes of a random sample of anxiety-related postings (n = 1000) were assessed. The disclosure of anxiety was the most common theme. The prevalence of anxiety was higher in certain areas where the economy is stronger than others, and the people living there suffered from more stress. Users who talked about feeling anxious tended to be more active on social media during leisure hours and less active during work hours. Our findings may be developed to detect and help individuals who may suffer from anxiety disorders at a low cost.

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“…[264,265]); monitoring mental health following an event or disaster (e.g. [266,267]); and creating models of risk to improve health system delivery (e.g.…”

Section: Mental Health Application ML Technique(s) Data Typementioning

confidence: 99%

“…Public health applications typically used social media data (n=11), electronic health records (n=6), and clinical data (e.g., diagnostic surveys and tools; n=9). Social media data was found to be a particularly useful epidemiological resource, with examples including assessments of the mental health status of over 60,000 college students in China [264] and prescription opioid misuse in an estimated sample of over 1.3 million Twitter users [265].…”

Section: Mental Health Application ML Technique(s) Data Typementioning

confidence: 99%

Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

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ObjectiveThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms "big data" or "machine learning" and "mental health". Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review. ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering. Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field. BACKGROUND AND SIGNIFICANCE

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Investigation of college students’ mental health status via semantic analysis of Sina microblog

Cited by 11 publications

References 1 publication

Machine learning in mental health: a scoping review of methods and applications

Machine learning in mental health: a scoping review of methods and applications

An Analysis of Anxiety-Related Postings on Sina Weibo

Machine learning in mental health: A systematic scoping review of methods and applications

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