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      Social media and schizophrenia: An update on clinical applications

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          Abstract

          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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          Most cited references38

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          A machine learning approach to predicting psychosis using semantic density and latent content analysis

          Subtle features in people’s everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis in 40 participants of the North American Prodrome Longitudinal Study. We demonstrate how the linguistic marker of semantic density can be obtained using the mathematical method of vector unpacking, a technique that decomposes the meaning of a sentence into its core ideas. We also demonstrate how the latent semantic content of an individual’s speech can be extracted by contrasting it with the contents of conversations generated on social media, here 30,000 contributors to Reddit. The results revealed that conversion to psychosis is signaled by low semantic density and talk about voices and sounds. When combined, these two variables were able to predict the conversion with 93% accuracy in the training and 90% accuracy in the holdout datasets. The results point to a larger project in which automated analyses of language are used to forecast a broad range of mental disorders well in advance of their emergence.
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            A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals

            Background Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclosures. Objective This study aims to move from noisy self-reports of schizophrenia on social media to more accurate identification of diagnoses by exploring a human-machine partnered approach, wherein computational linguistic analysis of shared content is combined with clinical appraisals. Methods Twitter timeline data, extracted from 671 users with self-disclosed diagnoses of schizophrenia, was appraised for authenticity by expert clinicians. Data from disclosures deemed true were used to build a classifier aiming to distinguish users with schizophrenia from healthy controls. Results from the classifier were compared to expert appraisals on new, unseen Twitter users. Results Significant linguistic differences were identified in the schizophrenia group including greater use of interpersonal pronouns (P<.001), decreased emphasis on friendship (P<.001), and greater emphasis on biological processes (P<.001). The resulting classifier distinguished users with disclosures of schizophrenia deemed genuine from control users with a mean accuracy of 88% using linguistic data alone. Compared to clinicians on new, unseen users, the classifier’s precision, recall, and accuracy measures were 0.27, 0.77, and 0.59, respectively. Conclusions These data reinforce the need for ongoing collaborations integrating expertise from multiple fields to strengthen our ability to accurately identify and effectively engage individuals with mental illness online. These collaborations are crucial to overcome some of mental illnesses’ biggest challenges by using digital technology.
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              Measuring attitudes towards mental health using social media: investigating stigma and trivialisation

              Background There are numerous campaigns targeting mental health stigma. However, evaluating how effective these are in changing perceptions is complex. Social media may be used to assess stigma levels and highlight new trends. This study uses a social media platform, Twitter, to investigate stigmatising and trivialising attitudes across a range of mental and physical health conditions. Methods Tweets (i.e. messages) associated with five mental and five physical health conditions were collected in ten 72-h windows over a 50-day period using automated software. A random selection of tweets per condition was considered for the analyses. Tweets were categorised according to their topic and presence of stigmatising and trivialising attitudes. Qualitative thematic analysis was performed on all stigmatising and trivialising tweets. Results A total of 1,059,258 tweets were collected, and from this sample 1300 tweets per condition were randomly selected for analysis. Overall, mental health conditions were found to be more stigmatised (12.9%) and trivialised (14.3%) compared to physical conditions (8.1 and 6.8%, respectively). Amongst mental health conditions the most stigmatised condition was schizophrenia (41%) while the most trivialised was obsessive compulsive disorder (33%). Conclusions Our findings show that mental health stigma is common on social media. Trivialisation is also common, suggesting that while society may be more open to discussing mental health problems, care should be taken to ensure this is done appropriately. This study further demonstrates the potential for social media to be used to measure the general public’s attitudes towards mental health conditions.
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                Author and article information

                Contributors
                Journal
                World J Psychiatry
                WJP
                World Journal of Psychiatry
                Baishideng Publishing Group Inc
                2220-3206
                19 July 2022
                19 July 2022
                : 12
                : 7
                : 897-903
                Affiliations
                Harvard South Shore–Psychiatry Residency Program, Veteran Affairs Boston Healthcare System, Brockton, MA 02301, United States
                Chinese American Health Promotion Program, Department of Psychiatry and Biobehavioral Sciences, Olive View-University of California, Los Angeles Medical Center, Sylmar, CA 91104, United States. bkpwoo@ 123456gmail.com
                Author notes

                Author contributions: Fonseka LN and Woo BKP both performed the collection of data and contributed to the manuscript drafting; all authors have read and approve the final manuscript.

                Corresponding author: Benjamin K P Woo, MD, Professor, Chinese American Health Promotion Program, Department of Psychiatry and Biobehavioral Sciences, Olive View-University of California, Los Angeles Medical Center, 14445 Olive View Drive, Sylmar, CA 91104, United States. bkpwoo@ 123456gmail.com

                Article
                jWJP.v12.i7.pg897
                10.5498/wjp.v12.i7.897
                9331455
                36051600
                d05a29a8-3c53-4865-a954-fd0e0c4e747d
                ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.

                This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.

                History
                : 23 February 2022
                : 18 May 2022
                : 18 June 2022
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                social media,schizophrenia,digital phenotype,facebook,youtube,instagram

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