Naseem, Usman, Dunn, Adam G., Kim, Jinman, and Khushi, Matloob (2022) Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification. In: Proceedings of the ACM Web Conference 2022. pp. 2563-2572. From: WWW '22: the ACM Web Conference 2022, 25-29 April 2022, Lyon, France.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

User-generated text on social media is a promising avenue for public health surveillance and has been actively explored for its feasibility in the early identification of depression. Existing methods in the identification of depression have shown promising results; however, these methods were all focused on treating the identification as a binary classification problem. To date, there has been little effort towards identifying users’ depression severity level and disregard the inherent ordinal nature across these fine-grain levels. This paper aims to make early identification of depression severity levels on social media data. To accomplish this, we built a new dataset based on the inherent ordinal nature over depression severity levels using clinical depression standards on Reddit posts. The posts were classified into 4 depression severity levels covering the clinical depression standards on social media. Accordingly, we reformulate the early identification of depression as an ordinal classification task over clinical depression standards such as Beck’s Depression Inventory and the Depressive Disorder Annotation scheme to identify depression severity levels. With these, we propose a hierarchical attention method optimized to factor in the increasing depression severity levels through a soft probability distribution. We experimented using two datasets (a public dataset having more than one post from each user and our built dataset with a single user post) using real-world Reddit posts that have been classified according to questionnaires built by clinical experts and demonstrated that our method outperforms state-of-the-art models. Finally, we conclude by analyzing the minimum number of posts required to identify depression severity level followed by a discussion of empirical and practical considerations of our study.

Item ID:	79233
Item Type:	Conference Item (Research - E1)
Copyright Information:	Copyright © 2022 by the Association for Computing Machinery, Inc.
Date Deposited:	06 Sep 2023 03:29
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
Downloads:	Total: 1
	More Statistics