Predicting mental illness using social media posts and comments

Mohsin Kamal, Saif Ur Rehman khan, Shahid Hussain, Anam Nasir, Khurram Aslam, Subhan Tariq, Mian Farhan Ullah

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

—From the last decade, a significant increase of social media implications could be observed in the context of e-health. The medical experts are using the patient’s post and their feedbacks on social media platforms to diagnose their infectious diseases. However, there are only few studies who have leveraged the capabilities of machine learning (ML) algorithms to classify the patient’s mental disorders such as Schizophrenia, Autism, and Obsessive-compulsive disorder (OCD) and Post-traumatic stress disorder (PTSD). Moreover, these studies are limited to large number of posts and relevant comments which could be considered as a threat for their effectiveness of their proposed methods. In contrast, this issue is addressed by proposing a novel ML methodology to classify the patient’s mental illness on the basis of their posts (along with their relevant comments) shared on the well-known social media platform “Reddit”. The proposed methodology is exploit by leveraging the capabilities of widely-used classifier namely “XGBoost” for accurate classification of data into four mental disorder classes (Schizophrenia, Autism, OCD and PTSD). Subsequently, the performance of the proposed methodology is compared with the existing state of the art classifiers such as Naïve Bayes and Support vector machine whose performance have been reported by the research community in the target domain. The experimental result indicates the effectiveness of the proposed methodology to classify the patient data more effectively as compared to the state of the art classifiers. 68% accuracy was achieved, indicating the efficacy of the proposed model.

Original languageEnglish (US)
Pages (from-to)607-613
Number of pages7
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Issue number12
DOIs
StatePublished - Dec 1 2020

All Science Journal Classification (ASJC) codes

  • General Computer Science

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