Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls

Kelly, Deanna L.; Spaderna, Max; Hodzic, Vedrana; Nair, Suraj; Kitchen, Christopher; Werkheiser, Anne E.; Powell, Megan; Liu, Fang; Coppersmith, Glen; Chen, Shuo; Resnik, Philip

doi:10.1016/j.psychres.2020.113496

Cited by 8 publications

(8 citation statements)

References 44 publications

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“…Advances in computing offer new opportunities to tap large volumes of naturalistic data on social media to detect mental health issues (Coppersmith et al, 2018;Guntuku et al, 2019, Hidden for review). A growing body of literature suggests that researchers are using a number of different computational methods, which are cost effective, efficient, use low levels of human involvement, and that may help reduce the unintended consequences of undiagnosed mental illness, to analyze social media posts (Aldarwish & Ahmad, 2017;Guntuku et al, 2019;Kelly et al, 2020). These new computational analytical methods are helpful to fill "clinical whitespace"a term representing the periods between patient and clinicians interactions (Kelly et al, 2020).…”

Section: Review Of Literaturementioning

confidence: 99%

“…A growing body of literature suggests that researchers are using a number of different computational methods, which are cost effective, efficient, use low levels of human involvement, and that may help reduce the unintended consequences of undiagnosed mental illness, to analyze social media posts (Aldarwish & Ahmad, 2017;Guntuku et al, 2019;Kelly et al, 2020). These new computational analytical methods are helpful to fill "clinical whitespace"a term representing the periods between patient and clinicians interactions (Kelly et al, 2020). While these computational methods cannot and should not be equated with clinicians' diagnoses, they can provide invaluable information and insights into the early detection and manifestation of mental health conditions (Guntuku et al, 2019;Kelly et al, 2020).…”

Section: Review Of Literaturementioning

confidence: 99%

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Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter

Sinha

Larrison

Brooks

et al. 2023

Journal of Evidence-Based Social Work

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Purpose. The primary objective of this study was to identify patterns in users' naturalistic expressions on student loans on two social media platforms. The secondary objective was to examine how these patterns, sentiments, and emotions associated with student loans differ in user posts indicating mental illness.Methods. Data for this study were collected from Reddit and Twitter (2009-2020, n=85,664) using certain key terms of student loans along with first-person pronouns as a triangulating measure of posts by individuals. Unsupervised and supervised machine learning models were used to analyze the text data.Results. Results suggested 50 topics in reddit finance and 40 each in reddit mental health communities and Twitter. Statistically significant associations were found between mental illness statuses and sentiments and emotions. Posts expressing mental illness showed more negative sentiments and were more likely to express sadness and fear. Discussions. Patterns in social media discussions indicate both academic and non-academic consequences of having student debt, including users' desire to know more about their debts. Interventions should address the skill and information gaps between what is desired by the borrowers and what is offered to them in understanding and managing their debts. Conclusions.Cognitive burden created by student debts manifest itself on social media and can be used as an important marker to develop a nuanced understanding of people's expressions on a variety of socioeconomic issues. Higher volumes of negative sentiments and emotions of sadness, fear, and anger warrant immediate attention of policymakers and practitioners to reduce the cognitive burden of student debts.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter

Sinha

Larrison

Brooks

et al. 2023

Journal of Evidence-Based Social Work

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“…Kelly et al [ 4 ] performed a study that offers insight into the clinical utility of Facebook posts. Blinded clinical raters assessed eight participants with schizophrenia, seven with depression, and eight health controls using symptom severity scales, including the Brief Psychiatric Rating Scale for psychotic symptoms and the Community Assessment of Psychotic Experiences for global functioning.…”

Section: Social Mediamentioning

confidence: 99%

Social media and schizophrenia: An update on clinical applications

Fonseka¹,

Woo²

2022

WJP

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Social media has redesigned the landscape of human interaction, and data obtained through these platforms are promising for schizophrenia diagnosis and management. Recent research shows mounting evidence that machine learning analysis of social media content is capable of not only differentiating schizophrenia patients from healthy controls, but also predicting conversion to psychosis and symptom exacerbations. Novel platforms such as Horyzons show promise for improving social functioning and providing timely access to therapeutic resources. Social media is also a considerable means to assess and lessen the stigma surrounding schizophrenia. Herein, the relevant literature pertaining to social media and its clinical applications in schizophrenia over the past five years are summarized, followed by a discussion centered on user feedback to highlight future directions. Social media provides valuable contributions to a multifaceted digital phenotype that may improve schizophrenia care in the near future.

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“…A database recently collected for a collaborative observational study conducted by the University of Maryland School of Medicine and the University of Maryland College Park has been used for this study [11]. The database contains video and audio data of free response assessments administered in an interview format.…”

Section: Database and Features 21 Databasementioning

confidence: 99%

Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks

Siriwardena

Kitchen

Kelly

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g. hallucinations and delusions), using two distinct channel-delay correlation methods. We show that the schizophrenic subjects with strong positive symptoms and who are markedly ill pose complex articulatory coordination pattern in facial and speech gestures than what is observed in healthy subjects. This distinction in speech coordination pattern is used to train a multimodal convolutional neural network (CNN) which uses video and audio data during speech to distinguish schizophrenic patients with strong positive symptoms from healthy subjects. We also show that the vocal tract variables (TVs) which correspond to place of articulation and glottal source outperform the Mel-frequency Cepstral Coefficients (MFCCs) when fused with Facial Action Units (FAUs) in the proposed multimodal network. For the clinical dataset we collected, our best performing multimodal network improves the mean F1 score for detecting schizophrenia by around 18% with respect to the full vocal tract coordination (FVTC) baseline method implemented with fusing FAUs and MFCCs. CCS CONCEPTS• Computing methodologies → Neural networks; • Social and professional topics → People with disabilities.

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Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls

Cited by 8 publications

References 44 publications

Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter

Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter

Social media and schizophrenia: An update on clinical applications

Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks

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