Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models

Eli Sherman, Keith Harrigian, Carlos Aguirre, Mark Dredze


Abstract
Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention. For these models to be maximally useful, it is necessary to understand how they perform on various subgroups, especially those defined in terms of protected characteristics. In this paper we study the relationship between user demographics – focusing on gender – and depression. Considering a population of Reddit users with known genders and depression statuses, we analyze the degree to which depression predictions are subject to biases along gender lines using domain-informed classifiers. We then study our models’ parameters to gain qualitative insight into the differences in posting behavior across genders.
Anthology ID:
2021.clpsych-1.23
Volume:
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
Month:
June
Year:
2021
Address:
Online
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
217–223
Language:
URL:
https://aclanthology.org/2021.clpsych-1.23
DOI:
10.18653/v1/2021.clpsych-1.23
Bibkey:
Cite (ACL):
Eli Sherman, Keith Harrigian, Carlos Aguirre, and Mark Dredze. 2021. Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 217–223, Online. Association for Computational Linguistics.
Cite (Informal):
Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models (Sherman et al., CLPsych 2021)
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PDF:
https://aclanthology.org/2021.clpsych-1.23.pdf