An emotion and cognitive based analysis of mental health disorders from social media data

Uban, Ana Sabina; Chulvi, Berta; Rosso, Paolo

doi:10.1016/j.future.2021.05.032

Cited by 66 publications

(36 citation statements)

References 32 publications

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“…The proposed early intervention approach lacked discussion on the potentially hindering human factors-machine interaction with regards to a clinical psychologist needing to be resourced and trained to observe and verify findings (determining true and false positive cases) as well as providing counseling and treatment. A similar study that identified patterns of language in social media users aimed to distinguish between users diagnosed with a mental disorder and healthy users with a model of emotion evolution to assist clinicians in diagnosing patients (with depression, anorexia, and self-harm tendencies) [66].…”

Section: Web-based and Smartphone Technologiesmentioning

confidence: 99%

Human-Computer Interaction in Digital Mental Health

Balcombe

Leo

2022

Informatics

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Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.

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Section: Web-based and Smartphone Technologiesmentioning

confidence: 99%

Human-Computer Interaction in Digital Mental Health

Balcombe

Leo

2022

Informatics

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“…Overall, much work is focused on automatically identifying whether online social media users are deemed as positive mental health concerns cases. On very few occasions, the authors of such works attempt to obtain some explanations from the outcomes of the classification models to better understand why certain decisions were taken, as happened with some eRisk participants (Amini & Kosseim, 2020;Uban et al, 2021;Aragon et al, 2021). However, research devoted to understanding, measuring, visualising and providing insight on the attributes that characterise such users and differentiate them both from healthy individuals and between diverse disorders has been noticeably scarce.…”

Section: Related Workmentioning

confidence: 99%

Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

Ríssola¹,

Aliannejadi²,

Crestani³

2022

Preprint

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Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychometric attributes, emotions' co-occurrence patterns, and online behavioural traits, including social engagement and posting trends.Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary, such as biological processes. As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average. Overall, the monthly posting variance of the affected groups is higher than the control groups. Moreover, we found evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder than other less restrictive platforms. In particular, we observe on Twitter less quantifiable differences between affected and control groups compared to Reddit.

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“…The most widely analyzed conditions of such studies are depression and Post Traumatic Stress Disorder [ 34 – 38 ]. Other conditions include Bipolar Disorder, Anxiety and Social Anxiety Disorder, eating disorders, self-harm and suicide attempt [ 39 – 42 ]. Linguistic features used typically include word n-grams, sentiment, specific lexica (e.g., Linguistic Inquiry & Word Count dictionary, LIWC) and topic modelling, with other features related to social networks, emotions, cognitive styles, user activity and demographics [ 34 – 39 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

“…Other conditions include Bipolar Disorder, Anxiety and Social Anxiety Disorder, eating disorders, self-harm and suicide attempt [ 39 – 42 ]. Linguistic features used typically include word n-grams, sentiment, specific lexica (e.g., Linguistic Inquiry & Word Count dictionary, LIWC) and topic modelling, with other features related to social networks, emotions, cognitive styles, user activity and demographics [ 34 – 39 , 42 ]. Model evaluation metrics include Area Under the Curve (AUC), Precision, Accuracy of classification, and Correlation for continuous measurements.…”

Section: Introductionmentioning

confidence: 99%

Predicting subjective well-being in a high-risk sample of Russian mental health app users

Panicheva¹,

Mararitsa²,

Sorokin³

et al. 2022

EPJ Data Sci.

Self Cite

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Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and new opportunities for it, with online mental health applications gaining popularity and accumulating large and diverse user data. Nevertheless, the few existing works so far have aimed at predicting SWB, and have done so only in terms of Diener’s Satisfaction with Life Scale. None of them analyzes the scale developed by the World Health Organization, known as WHO-5 – a widely accepted tool for screening mental well-being and, specifically, for depression risk detection. Moreover, existing research is limited to English-speaking populations, and tend to use text, network and app usage types of data separately. In the current work, we cover these gaps by predicting both mentioned SWB scales on a sample of Russian mental health app users who represent a population with high risk of mental health problems. In doing so, we employ a unique combination of phone application usage data with private messaging and networking digital traces from VKontakte, the most popular social media platform in Russia. As a result, we predict Diener’s SWB scale with the state-of-the-art quality, introduce the first predictive models for WHO-5, with similar quality, and reach high accuracy in the prediction of clinically meaningful classes of the latter scale. Moreover, our feature analysis sheds light on the interrelated nature of the two studied scales: they are both characterized by negative sentiment expressed in text messages and by phone application usage in the morning hours, confirming some previous findings on subjective well-being manifestations. At the same time, SWB measured by Diener’s scale is reflected mostly in lexical features referring to social and affective interactions, while mental well-being is characterized by objective features that reflect physiological functioning, circadian rhythms and somatic conditions, thus saliently demonstrating the underlying theoretical differences between the two scales.

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An emotion and cognitive based analysis of mental health disorders from social media data

Cited by 66 publications

References 32 publications

Human-Computer Interaction in Digital Mental Health

Human-Computer Interaction in Digital Mental Health

Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

Predicting subjective well-being in a high-risk sample of Russian mental health app users

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