Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders

Hänsel, Katrin; Lin, Inna Wanyin; Sobolev, Michael; Muscat, Whitney; Yum-Chan, Sabrina; Choudhury, Munmun De; Kane, John M.; Birnbaum, Michael L.

doi:10.3389/fpsyt.2021.691327

Cited by 10 publications

(10 citation statements)

References 58 publications

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“… 171 Models applied to Facebook and other social platforms (e.g., Twitter and Reddit) have been successful in predicting the diagnosis of psychosis, anorexia, anxiety, and stress levels. 196 , 217 , 218 In addition to the language analysis for the user posts and comments, ML models may also process media data such as Instagram images 219 or integrate images and text to infer the user’s mental state. 219 , 220 Entries of online search also form a complementary and equally compelling dataset alongside social media activity.…”

Section: Ml-powered Technologies For Psychiatrymentioning

confidence: 99%

“… 196 , 217 , 218 In addition to the language analysis for the user posts and comments, ML models may also process media data such as Instagram images 219 or integrate images and text to infer the user’s mental state. 219 , 220 Entries of online search also form a complementary and equally compelling dataset alongside social media activity.…”

Section: Ml-powered Technologies For Psychiatrymentioning

confidence: 99%

See 1 more Smart Citation

Modern views of machine learning for precision psychiatry

Chen¹,

Kulkarni²,

Galatzer‐Levy³

et al. 2022

Patterns

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Section: Ml-powered Technologies For Psychiatrymentioning

confidence: 99%

Section: Ml-powered Technologies For Psychiatrymentioning

confidence: 99%

Modern views of machine learning for precision psychiatry

Chen¹,

Kulkarni²,

Galatzer‐Levy³

et al. 2022

Patterns

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“…In addition to the linguistic features of social media posts, the clinical utility of visual content on Instagram has also been explored. Hänsel et al [ 11 ] extracted image features such as color composition and the number of faces depicted from nearly 12000 Instagram posts from 68 individuals with schizophrenia spectrum disorders and 34 healthy controls. The study found that users with schizophrenia posted images with significantly lower saturation, colorfulness, and number of faces.…”

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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“…Note that all features were computed at the individual participant level. More details about this process can be found in Multimedia Appendix 1 [7,12,14,[24][25][26][27][28][29].…”

Section: Feature Engineeringmentioning

confidence: 99%

Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study

Nguyen

Kane

et al. 2022

JMIR Ment Health

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Background Previous research has shown the feasibility of using machine learning models trained on social media data from a single platform (eg, Facebook or Twitter) to distinguish individuals either with a diagnosis of mental illness or experiencing an adverse outcome from healthy controls. However, the performance of such models on data from novel social media platforms unseen in the training data (eg, Instagram and TikTok) has not been investigated in previous literature. Objective Our study examined the feasibility of building machine learning classifiers that can effectively predict an upcoming psychiatric hospitalization given social media data from platforms unseen in the classifiers’ training data despite the preliminary evidence on identity fragmentation on the investigated social media platforms. Methods Windowed timeline data of patients with a diagnosis of schizophrenia spectrum disorder before a known hospitalization event and healthy controls were gathered from 3 platforms: Facebook (254/268, 94.8% of participants), Twitter (51/268, 19% of participants), and Instagram (134/268, 50% of participants). We then used a 3 × 3 combinatorial binary classification design to train machine learning classifiers and evaluate their performance on testing data from all available platforms. We further compared results from models in intraplatform experiments (ie, training and testing data belonging to the same platform) to those from models in interplatform experiments (ie, training and testing data belonging to different platforms). Finally, we used Shapley Additive Explanation values to extract the top predictive features to explain and compare the underlying constructs that predict hospitalization on each platform. Results We found that models in intraplatform experiments on average achieved an F1-score of 0.72 (SD 0.07) in predicting a psychiatric hospitalization because of schizophrenia spectrum disorder, which is 68% higher than the average of models in interplatform experiments at an F1-score of 0.428 (SD 0.11). When investigating the key drivers for divergence in construct validities between models, an analysis of top features for the intraplatform models showed both low predictive feature overlap between the platforms and low pairwise rank correlation (<0.1) between the platforms’ top feature rankings. Furthermore, low average cosine similarity of data between platforms within participants in comparison with the same measurement on data within platforms between participants points to evidence of identity fragmentation of participants between platforms. Conclusions We demonstrated that models built on one platform’s data to predict critical mental health treatment outcomes such as hospitalization do not generalize to another platform. In our case, this is because different social media platforms consistently reflect different segments of participants’ identities. With the changing ecosystem of social media use among different demographic groups and as web-based identities continue to become fragmented across platforms, further research on holistic approaches to harnessing these diverse data sources is required.

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Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders

Cited by 10 publications

References 58 publications

Modern views of machine learning for precision psychiatry

Modern views of machine learning for precision psychiatry

Social media and schizophrenia: An update on clinical applications

Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study

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