AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.

Publication date: Nov 15, 2024

Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence-driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness. A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review. Larger datasets with precise dates of participants’ diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone. Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user’s depression and anxiety is merited.

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Concepts Keywords
Achievable anxiety
Google depression
Media mental health
Schizophrenia narrative review
Therapy natural language processing
schizophrenia
social media

Semantics

Type Source Name
disease IDO quality
disease MESH COVID-19 pandemic
disease MESH health status
disease IDO intervention
disease MESH privacy
disease MESH mental illness
disease MESH schizophrenia
disease MESH depression
disease MESH anxiety
disease MESH anxiety disorders
disease MESH major depressive disorder
disease MESH neurocognitive disorders
disease MESH dementia
drug DRUGBANK Gold
drug DRUGBANK Methionine
disease IDO process
disease MESH bipolar disorder
disease MESH seasonal affective disorder
disease MESH autism
disease MESH eating disorders
disease MESH PTSD
disease MESH panic attacks
disease MESH suicide
disease IDO history
disease MESH attention deficit hyperactivity disorder
disease MESH obsessive compulsive disorder
disease IDO symptom
disease MESH suicidal ideation
disease IDO algorithm
drug DRUGBANK Isoxaflutole
disease MESH confusion
disease MESH Emergency
disease MESH Depersonalization
disease MESH relapse
disease MESH loneliness
disease MESH attempted suicide
disease MESH psychosis
disease MESH tumor
pathway REACTOME Release
drug DRUGBANK Serine
drug DRUGBANK Coenzyme M
disease IDO site
drug DRUGBANK Bismuth subgallate
drug DRUGBANK Trestolone
drug DRUGBANK Carboxyamidotriazole
drug DRUGBANK Naltrexone
drug DRUGBANK Troleandomycin
disease MESH postpartum depression
disease MESH pathological gambling
disease MESH comorbidity
drug DRUGBANK Alpha-1-proteinase inhibitor
drug DRUGBANK Saquinavir
disease IDO replication
disease MESH Anosognosia
drug DRUGBANK Pentaerythritol tetranitrate
pathway REACTOME Reproduction

Original Article

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