@inproceedings{0402d78be3ea4e59bd863cda8c44f155,
title = "Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models",
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 domaininformed classifiers. We then study our models' parameters to gain qualitative insight into the differences in posting behavior across genders.",
author = "Eli Sherman and Keith Harrigian and Carlos Aguirre and Mark Dredze",
note = "Funding Information: The first author was sponsored by a Google PhD Fellowship. Publisher Copyright: {\textcopyright}2021 Association for Computational Linguistics.; 7th Workshop on Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 ; Conference date: 11-06-2021",
year = "2021",
language = "English (US)",
series = "Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021",
publisher = "Association for Computational Linguistics (ACL)",
pages = "217--223",
editor = "Nazli Goharian and Philip Resnik and Andrew Yates and Molly Ireland and Kate Niederhoffer and Rebecca Resnik",
booktitle = "Computational Linguistics and Clinical Psychology",
address = "United States",
}