Aims Although psychosis often occurs during adolescence, there has been little research on how the ubiquitously used Internet and social media could impact pathways to care. We examined how youth with psychotic spectrum disorders (PSD) versus non‐psychotic mood disorders (NPMD) use online resources in the early illness stages. Methods Social media use and pathways to care data were collected using a semi‐structured interview from 80 youth (PSD = 40 and NPMD = 40) aged 12–21 years within 2 years of symptom onset. Results A total of 97.5% of participants (mean age = 18.3 years) regularly used social media, spending approximately 2.6 ± 2.5 h per day online. There were 22.4% of our sample (PSD = 19.4%, NPMD = 25.0%, P = 0.56) who reported waiting to reach out for help believing that symptoms would disappear. A total of 76.5% (PSD = 67.5%, NPMD = 85.0%, P = 0.06) noticed social media habit changes during symptom emergence. Thirty per cent reported discussing their symptoms on social media (PSD = 22.5%, NPMD = 37.5%, P = 0.14). NPMD patients sought information most on how to stop symptoms (40.0% vs. 13.5%, P = 0.01), while PSD youth were more commonly interested in what caused their symptoms (21.6% vs. 15.0%, P = 0.45). More PSD patients (42.9% vs. 25.0%, P = 0.10) would prefer to receive mental health information via the Internet. Altogether, 63.6% (PSD = 64.9%, NPMD = 62.5%, P = 0.83) were amenable to clinicians proactively approaching them via social media during symptom emergence. A total of 74.3% (PSD = 78.4%, NPMD = 70.0%, P = 0.40) liked the idea of obtaining help/advice from professionals via social media. Conclusions The Internet and social media provide an unparalleled opportunity to supplement and potentially transform early intervention services, and acceptance of this approach appears to be high.

BackgroundLinguistic 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.ObjectiveThis 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.MethodsTwitter 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.ResultsSignificant 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.ConclusionsThese 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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Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words posted to Facebook in the month preceding a relapse hospitalization compared to periods of relative health, including increased usage of words belonging to the swear (p < 0.0001, Wilcoxon signed rank test), anger (p < 0.001), and death (p < 0.0001) categories, decreased usage of words belonging to work (p = 0.00579), friends (p < 0.0001), and health (p < 0.0001) categories, as well as a significantly increased use of first (p < 0.0001) and second-person (p  < 0.001) pronouns. We additionally observed a significant increase in co-tagging (p < 0.001) and friending (p < 0.0001) behaviors in the month before a relapse hospitalization. Our classifier achieved a specificity of 0.71 in predicting relapse. Results indicate that social media activity captures objective linguistic and behavioral markers of psychotic relapse in young individuals with recent onset psychosis. Machine-learning models were capable of making personalized predictions of imminent relapse hospitalizations at the patient-specific level.