Characterisation of Mental Health Conditions in Social Media Using Deep Learning Techniques

Sharma, Toshita; Panchendrarajan, Rrubaa; Saxena, Akrati

doi:10.1007/978-3-031-10869-3_9

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…Once again, strong BERT performance was observed in a balanced experimental setting, therefore strengthening evidence that further research is needed before LMs could be deployed for this prediction task in more realistic, imbalanced settings. Suggestions include generating synthetic instances to create balance [106] and re-sampling [107. A review of DL approaches to mental health prediction [38] that postdates both studies [36,37] echoed the need for further work involving much larger data sets while acknowledging the impact of existing data sets that we have already highlighted [23,25]. Some of the most recent methods have harnessed generative AI, principally using GPT [98].…”

Section: Language Models and Transformersmentioning

confidence: 99%

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

Owen,

Lynham,

Smart

et al. 2024

Preprint

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BACKGROUND 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. OBJECTIVE This study examines the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of AI-driven methods, particularly Natural Language Processing (NLP), for analyzing large volumes of written text. We also detail constraints affecting research in this area. These include the dearth of high-quality public data sets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma and vulnerability of those affected by mental illness. METHODS A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by four primary areas of interest: data sets on social media and mental health, methods for predicting mental health status, longitudinal analyses on mental health, and ethical aspects on the data and analysis of mental health. Selected articles from these groups formed our narrative review. RESULTS Larger data sets where precise dates of subjects’ diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting participants 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 NLP alone. CONCLUSIONS Multimodal methods for predicting mental health status from 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. For this to be achieved, more high-quality social media data sets need to be made available and privacy concerns regarding the use of this data must be formally addressed. Also, a review of literature studying the effects of social media use on a user’s depression and anxiety is merited.

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Section: Language Models and Transformersmentioning

confidence: 99%

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

Owen,

Lynham,

Smart

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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

Owen,

Lynham,

Smart

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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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Characterisation of Mental Health Conditions in Social Media Using Deep Learning Techniques

Cited by 2 publications

References 59 publications

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

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

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

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