Early identification of depression severity levels on Reddit using ordinal classification

Usman Naseem, Adam G. Dunn, Jinman Kim, Matloob Khushi

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

45 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationWWW '22
Subtitle of host publicationproceedings of the ACM Web Conference 2022
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages2563-2572
Number of pages10
ISBN (Electronic)9781450390965
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: 25 Apr 202229 Apr 2022

Conference

Conference31st ACM World Wide Web Conference, WWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period25/04/2229/04/22

Keywords

  • Depression identification
  • Social media
  • Ordinal classification

Fingerprint

Dive into the research topics of 'Early identification of depression severity levels on Reddit using ordinal classification'. Together they form a unique fingerprint.

Cite this