A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals

Abstract

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.

Variability in Language used on Social Media prior to Hospital Visits

“…One-fifth of the world's population uses Facebook and Twitter 21 , and people are increasingly sharing information about their health on social media sites 22 . Social media has been used to study a wide variety of health-related outcomes including depression 4 , stress 17 , and schizophrenia 23 . The benefits of studying patterns of language use as opposed to other sources of "big data" is that words are more easily interpretable, enabling studies to not only test the predictive power of social media but also to generate insights.…”

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

“…Several studies have also demonstrated that when compared with a control group, Twitter users with a selfdisclosed diagnosis of schizophrenia show unique online communication patterns (Birnbaum et al 2017a), including more frequent discussion of tobacco use (Hswen et al 2017), symptoms of depression and anxiety (Hswen et al 2018b), and suicide (Hswen et al 2018a). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala et al 2017).…”

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

“…Using social media as a resource to understand mental health is a research area that has experienced substantial growth in recent years [96], given the burden of disease associated with mental health problems and the fact that social media provides ready access to first person reports of behaviour, thoughts, and feelings. Reviewed studies covered a range of mental health topics, including predicting depression diagnosis [8], assessing suicide risk [16, 18, 24, 74-76, 98, 99], and developing a better understanding of users' experiences of eating disorders [15], schizophrenia [59,61], grief processes between gang-involved youth [58], relaxation [62], stress [63], pathological empathy [67,72], and negative emotional effects associated with campus-based mass murders [64]. Related to this, a range of metrics have been used to characterize language use associated with specific mental health conditions, with lexical diversity, readability scores, sentence complexity, negation, uncertainty, and degree of repetition, all used during the review period [23,26,27,60].…”

“…Of the thirty-one mental health-related papers reviewed (see Table 8), thirteen involved the use of Reddit data [15][16][17][18][19][20][21][22][23][24][25][26][27], ten used Twitter data [18,24,[58][59][60][61][62][63][64][65], one used Instagram [18], three used Facebook [8,18,67], six used OHC data [70][71][72][73][74][75], and one used data derived from Weibo [76], with twenty-two of the papers utilising supervised machine learning methods [8, 16, 18, 20-22, 24, 25, 58-62, 65, 67, 70-76], and twelve papers utilising unsupervised machine learning [8, 15, 18-22, 27, 59, 60, 70, 72]. The majority of the papers reported on the use of classical machine learning approaches [8, 15, 16, 18-20, 22, 24, 25, 27, 58-62, 65, 67, 71, 73-76], with a minority using modern machine learning methods [18,21,22,67,70,72].…”