Shatte, Adrian B.R., Hutchinson, Delyse M., Fuller-Tyszkiewicz, Matthew, and Teague, Samantha J. (2020) Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach. Cyberpsychology, Behavior, and Social Networking, 23 (9). pp. 611-618.

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Abstract

Postpartum depression is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of healthcare professionals late due to low awareness of symptoms and reluctance to seek help. The present study aimed to examine whether passive social media markers are effective for identifying fathers at risk of postpartum depression. We collected 67,796Reddit posts from 365 fathers, spanning a six-month period around the birth of their child. A list of ‘at risk’ words was developed in collaboration with a perinatal mental health expert. Postpartum depression 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 (SVM) classifiers using behaviour, emotion, linguistic style and discussion topics as features. The performance of these classifiers indicates that fathers at risk of postpartum depression 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.

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