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A Survey of Computational Methods for Online Mental State Assessment on Social Media

Published:17 March 2021Publication History
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Abstract

Mental state assessment by analysing user-generated content is a field that has recently attracted considerable attention. Today, many people are increasingly utilising online social media platforms to share their feelings and moods. This provides a unique opportunity for researchers and health practitioners to proactively identify linguistic markers or patterns that correlate with mental disorders such as depression, schizophrenia or suicide behaviour. This survey describes and reviews the approaches that have been proposed for mental state assessment and identification of disorders using online digital records. The presented studies are organised according to the assessment technology and the feature extraction process conducted. We also present a series of studies which explore different aspects of the language and behaviour of individuals suffering from mental disorders, and discuss various aspects related to the development of experimental frameworks. Furthermore, ethical considerations regarding the treatment of individuals’ data are outlined. The main contributions of this survey are a comprehensive analysis of the proposed approaches for online mental state assessment on social media, a structured categorisation of the methods according to their design principles, lessons learnt over the years and a discussion on possible avenues for future research.

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  1. A Survey of Computational Methods for Online Mental State Assessment on Social Media

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        cover image ACM Transactions on Computing for Healthcare
        ACM Transactions on Computing for Healthcare  Volume 2, Issue 2
        April 2021
        226 pages
        ISSN:2691-1957
        EISSN:2637-8051
        DOI:10.1145/3446675
        Issue’s Table of Contents

        Copyright © 2021 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 17 March 2021
        • Accepted: 1 November 2020
        • Revised: 1 September 2020
        • Received: 1 April 2020
        Published in health Volume 2, Issue 2

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