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

Exploring the relationship between YouTube video characteristics and a viewer's mental health traits among young adults

We investigated the relationship between individuals' mental health traits and the characteristics of YouTube videos they watch. The mental health traits considered were stress, depression, anxiety, and self-esteem, which were measured using a survey questionnaire. We considered violence shown in a video, brightness and saturation of a video as video characteristics. We utilized the viewing history log data of the participants and analyzed the videos they watched on YouTube using computer vision techniques based on deep learning algorithms. The results revealed that viewers' consumption of violent videos was positively related to stress, depression, and anxiety, but negatively related to self-esteem. Individuals with higher levels of stress, depression, or anxiety tended to view darker videos than those with lower levels of stress, depression, or anxiety.

Bipolar Disorder in Social Media: An Examination of Instagram's Role in Disseminating Accurate Information

Introduction Bipolar disorder is a chronic and recurring condition marked by fluctuations in both energy and mood that can be debilitating to individuals without treatment. While physicians clinically diagnose the condition, patients frequently seek information from alternate channels. Social media platforms, such as Instagram, have facilitated more convenient access to supplementary information about bipolar disorder. Nevertheless, there is apprehension regarding the accuracy of publicly disseminated information through these platforms. The aim of this study is to evaluate the accuracy and dependability of information about Bipolar disorder found on the social media platform, Instagram. Methodology A cross-sectional observational study was conducted by gathering data from Instagram posts linked with popular bipolar disorder hashtags. To evaluate the credibility of the chosen entries, numerical ratings were assigned to each post using the established measurement scales of the Global Quality Score and Reliability Score. Results After analyzing 196 Instagram entries about Bipolar Disorder that fulfilled inclusion criteria, the study revealed that 70.4% (n=138) of these posts were shared by individuals diagnosed with bipolar disorder. Among the content posted by these individuals, a statistically significant global quality score of 2 and a reliability score of 1 were observed. Conclusions Verified medical information of superior global quality should be shared on social media platforms by accountable parties. Individuals with further inquiries about medical knowledge should be advised to reach out to local physicians.

Precision diagnostics in children

Medical practice is transforming from a reactive to a pro-active and preventive discipline that is underpinned by precision medicine. The advances in technologies in such fields as genomics, proteomics, metabolomics, transcriptomics and artificial intelligence have resulted in a paradigm shift in our understanding of specific diseases in childhood, greatly enhanced by our ability to combine data from changes within cells to the impact of environmental and population changes. Diseases in children have been reclassified as we understand more about their genomic origin and their evolution. Genomic discoveries, additional 'omics' data and advances such as optical genome mapping have driven rapid improvements in the precision and speed of diagnoses of diseases in children and are now being incorporated into newborn screening, have improved targeted therapies in childhood and have supported the development of predictive biomarkers to assess therapeutic impact and determine prognosis in congenital and acquired diseases of childhood. New medical device technologies are facilitating data capture at a population level to support higher diagnostic accuracy and tailored therapies in children according to predicted population outcome, and digital ecosystems now tailor therapies and provide support for their specific needs. By capturing biological and environmental data as early as possible in childhood, we can understand factors that predict disease or maintain health and track changes across a more extensive longitudinal path. Data from multiple health and external sources over long-time periods starting from birth or even in the in utero environment will provide further clarity about how to sustain health and prevent or predict disease. In this respect, we will not only use data to diagnose disease, but precision diagnostics will aid the 'diagnosis of good health'. The principle of 'start early and change more' will thus underpin the value of applying a personalised medicine approach early in life.

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

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.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Social media and schizophrenia: An update on clinical applications

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.