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

“The classifier was built and evaluated by applying 10-fold cross-validation, an established technique in supervised machine learning [25].”

“Results from the Recovery After Initial Schizophrenia Episode-Early Treatment Program (RAISE-ETP) trial [31] suggest that the median duration of untreated psychosis is 74 weeks [32] and support the established hypothesis that lengthy duration of untreated psychosis (DUP) leads to worse outcomes [31,33].”

“Results from the Recovery After Initial Schizophrenia Episode-Early Treatment Program (RAISE-ETP) trial [31] suggest that the median duration of untreated psychosis is 74 weeks [32] and support the established hypothesis that lengthy duration of untreated psychosis (DUP) leads to worse outcomes [31,33].”

“Prior work identifying markers of mental illness online used similar filtering techniques based on self-reported diagnoses [22,23].”

“More than 90% of US youth use social media daily [1], placing it ahead of texting, email, and instant messaging, and they disclose considerably more about themselves online than offline [2].”

“Furthermore, classifiers designed to automatically sort individual cases into diagnostic categories have achieved success in recognizing participants with psychotic disorders from healthy controls based on linguistic differences in writing samples [15] and speech [13,18].”

“Main Findings These data contribute to a growing body of literature using language to automatically identify individuals online who may be experiencing mental illness, including depression [16,22,27], postpartum mood disorders [28], suicide [29], posttraumatic stress disorder [30], and bipolar disorder [23].”

“Main Findings These data contribute to a growing body of literature using language to automatically identify individuals online who may be experiencing mental illness, including depression [16,22,27], postpartum mood disorders [28], suicide [29], posttraumatic stress disorder [30], and bipolar disorder [23].”

“Identified benefits include developing a sense of belonging, establishing and maintaining relationships, accessing support, challenging stigma, raising awareness, and sharing experiences [4,5].”

“Language-based computational models have achieved more than 80% and 90% accuracy [19,20] in correctly identifying users with self-reported schizophrenia from healthy controls.”

“There is now clear and convincing evidence that online activity can be used to reliably monitor and predict health-related behaviors [7] ranging from the spread of the influenza virus across the United States to rates of seasonal allergies, HIV infection, cancer, smoking, and obesity [8-10].”

“Identified benefits include developing a sense of belonging, establishing and maintaining relationships, accessing support, challenging stigma, raising awareness, and sharing experiences [4,5].”

“Youth with newly diagnosed schizophrenia in particular report frequently utilizing social networking sites throughout the course of illness development and treatment, engaging in social media activity several times daily, and spending several hours per day online [6].”

“Results from the Recovery After Initial Schizophrenia Episode-Early Treatment Program (RAISE-ETP) trial [31] suggest that the median duration of untreated psychosis is 74 weeks [32] and support the established hypothesis that lengthy duration of untreated psychosis (DUP) leads to worse outcomes [31,33].”

“Comparison With Prior Work Consistent with prior trials [11-15,18,36], first-person pronouns were found to be significantly increased in the psychosis group, suggesting greater interpersonal focus.”

“Prior work in speech and text analysis has identified reliable linguistic markers associated with schizophrenia, including significant differences in word frequency, word categories, and use of self-referential pronouns [11-15].”

“Prior work in speech and text analysis has identified reliable linguistic markers associated with schizophrenia, including significant differences in word frequency, word categories, and use of self-referential pronouns [11-15].”

“There is now clear and convincing evidence that online activity can be used to reliably monitor and predict health-related behaviors [7] ranging from the spread of the influenza virus across the United States to rates of seasonal allergies, HIV infection, cancer, smoking, and obesity [8-10].”

“Additionally, these data replicate findings that biological processes, including words such as “body” and “health,” are more frequently used in psychosis [17], suggesting a greater awareness or focus on health status.”

“At the same time, there is compelling evidence to suggest that linguistic and behavioral changes manifest on the pages of social media before they are clinically detected, providing the prospect for earlier intervention [22,28,34].”

“Prior work in speech and text analysis has identified reliable linguistic markers associated with schizophrenia, including significant differences in word frequency, word categories, and use of self-referential pronouns [11-15].”

“Prior work in speech and text analysis has identified reliable linguistic markers associated with schizophrenia, including significant differences in word frequency, word categories, and use of self-referential pronouns [11-15].”

“Recently, Birnbaum et al [35] used Google AdWords to explore aspects of digital advertising most effective at engaging individuals online.”

“Furthermore, prior work has demonstrated that words that might have been automatically identified as self-disclosure such as “psychosis,” schizophrenia,” and “delusion” are often used inappropriately online [21] and may represent a major limitation to these computational models.”

“Prior work in speech and text analysis has identified reliable linguistic markers associated with schizophrenia, including significant differences in word frequency, word categories, and use of self-referential pronouns [11-15].”

“Researchers have examined social media analytics and mental health, and they identified markers in social media activity associated with worsening psychotic symptoms [57], schizophrenia [58], risk of suicidal ideation [59], and depression [60].”

“Since then, researchers have proposed a wide range of feature engineering methods and built machine-learning models for the prediction [13, 67, 69, 88, 103].”

“Since then, researchers have proposed a wide range of feature engineering methods and built machine-learning models for the prediction [13, 67, 69, 88, 103].”