Mental illness detection through harvesting social media: a comprehensive literature review

• Published in: PeerJ. Computer science Vol. 10; p. e2296
• Main Authors: Shah, Shahid Munir, Aljawarneh, Mahmoud Mohammad, Saleem, Muhammad Aamer, Jawarneh, Mahmoud Saleh
• Format: Journal Article
• Language: English
• Published: PeerJ. Ltd 07.10.2024; PeerJ Inc
• Subjects: Computational linguistics; Data entry; Data mining; Depression detection; Depression, Mental; Detectors; Disease detection; Diseases; Early depression detection; Language processing; Machine learning; Mental illness detection; Mental illness identification; Mentally ill; Natural language interfaces; Social media; Social networks; Surveys; Users sentiments
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2296

Mental illness is a common disease that at its extremes leads to personal and societal suffering. A complicated multi-factorial disease, mental illness is influenced by a number of socioeconomic and clinical factors, including individual risk factors. Traditionally, approaches relying on personal interviews and filling out questionnaires have been employed to diagnose mental illness; however, these manual procedures have been found to be frequently prone to errors and unable to reliably identify individuals with mental illness. Fortunately, people with mental illnesses frequently express their ailments on social media, making it possible to more precisely identify mental disease by harvesting their social media posts. This study offers a thorough analysis of how to identify mental illnesses (more specifically, depression) from users’ social media data. Along with the explanation of data acquisition, preprocessing, feature extraction, and classification techniques, the most recent published literature is presented to give the readers a thorough understanding of the subject. Since, in the recent past, the majority of the relevant scientific community has focused on using machine learning (ML) and deep learning (DL) models to identify mental illness, so the review also focuses on these techniques and along with their detail, their critical analysis is presented. More than 100 DL, ML, and natural language processing (NLP) based models developed for mental illness in the recent past have been reviewed, and their technical contributions and strengths are discussed. There exist multiple review studies, however, discussing extensive recent literature along with the complete road map on how to design a mental illness detection system using social media data and ML and DL classification methods is limited. The review also includes detail on how a dataset may be acquired from social media platforms, how it is preprocessed, and features are extracted from it to employ for mental illness detection. Hence, we anticipate that this review will help readers learn more and give them a comprehensive road map for identifying mental illnesses using users’ social media data.