Schizophrenia Detection Using Machine Learning Approach from Social Media Content.

Yi Ji Bae,Won Hee Lee,Midan Shim

doi:10.3390/s21175924

Abstract

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

Highlights

IntroductionSchizophrenia is a severe mental illness that presents with positive (hallucinations, delusions, confused thoughts and disorganized speech) and negative (affective flattening, alogia, and avolition) symptoms [1] and language disturbance [2]
Schizophrenia is a severe mental illness that presents with positive and negative symptoms [1] and language disturbance [2]
We found a greater use of affective process words corresponding to negative emotion, anger, fear, disgust, and sadness, as well as a decreased use of words corresponding to positive emotion, joy, and anticipation in schizophrenia compared to the controls

Summary

Introduction

Schizophrenia is a severe mental illness that presents with positive (hallucinations, delusions, confused thoughts and disorganized speech) and negative (affective flattening, alogia, and avolition) symptoms [1] and language disturbance [2]. Detection and diagnosis of schizophrenia is challenging, as multiple comorbidities are associated with schizophrenia, complicating the optimal management of patients and potentially limiting positive outcomes [4]. Social media is increasingly used by those with schizophrenia for sharing mental health concerns, connecting with others who have similar mental health experiences, and searching for social support [5]. Textual contents shared on social media platforms offer new opportunities for improving our understanding of self-expressed schizophrenia at an individual and community level. Much less is known about the topics discussed in online schizophrenia communities and linguistic markers associated with individuals with schizophrenia

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