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Social media markers to identify fathers at risk of postpartum depression: A machine learning approach

journal contribution
posted on 08.09.2020, 00:00 authored by Adrian B R Shatte, Delyse HutchinsonDelyse Hutchinson, Matthew Fuller-TyszkiewiczMatthew Fuller-Tyszkiewicz, Sam Teague
Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of “at-risk” words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers.

History

Journal

Cyberpsychology, Behavior, and Social Networking

Volume

23

Issue

9

Pagination

611 - 618

Publisher

Mary Ann Liebert, Inc. Publishers

Location

New Rochelle, N.Y.

ISSN

2152-2715

eISSN

2152-2723

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2020, Mary Ann Liebert, Inc.