Schizophrenia Detection Using Machine Learning Approach from Social Media Content
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 the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/17/5924 |
| _version_ | 1797520853410250752 |
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| author | Yi Ji Bae Midan Shim Won Hee Lee |
| author_facet | Yi Ji Bae Midan Shim Won Hee Lee |
| author_sort | Yi Ji Bae |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:04:25Z |
| format | Article |
| id | doaj.art-e5bfcec3fe1747048493622a5881c7a2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:04:25Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e5bfcec3fe1747048493622a5881c7a22023-11-22T11:14:33ZengMDPI AGSensors1424-82202021-09-012117592410.3390/s21175924Schizophrenia Detection Using Machine Learning Approach from Social Media ContentYi Ji Bae0Midan Shim1Won Hee Lee2Department of Software Convergence, Kyung Hee University, Yongin 17104, KoreaDepartment of Software Convergence, Kyung Hee University, Yongin 17104, KoreaDepartment of Software Convergence, Kyung Hee University, Yongin 17104, KoreaSchizophrenia 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.https://www.mdpi.com/1424-8220/21/17/5924social mediaRedditschizophrenianatural language processingmachine learningtopic modeling |
| spellingShingle | Yi Ji Bae Midan Shim Won Hee Lee Schizophrenia Detection Using Machine Learning Approach from Social Media Content Sensors social media schizophrenia natural language processing machine learning topic modeling |
| title | Schizophrenia Detection Using Machine Learning Approach from Social Media Content |
| title_full | Schizophrenia Detection Using Machine Learning Approach from Social Media Content |
| title_fullStr | Schizophrenia Detection Using Machine Learning Approach from Social Media Content |
| title_full_unstemmed | Schizophrenia Detection Using Machine Learning Approach from Social Media Content |
| title_short | Schizophrenia Detection Using Machine Learning Approach from Social Media Content |
| title_sort | schizophrenia detection using machine learning approach from social media content |
| topic | social media schizophrenia natural language processing machine learning topic modeling |
| url | https://www.mdpi.com/1424-8220/21/17/5924 |
| work_keys_str_mv | AT yijibae schizophreniadetectionusingmachinelearningapproachfromsocialmediacontent AT midanshim schizophreniadetectionusingmachinelearningapproachfromsocialmediacontent AT wonheelee schizophreniadetectionusingmachinelearningapproachfromsocialmediacontent |