mlsanityData to 14 Oct 2026

Understanding Postpartum Parents' Experiences via Two Digital Platforms

arXiv 2212.11455 ↗First posted 22 Dec 2022cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

Digital platforms, including online forums and helplines, have emerged as avenues of support for caregivers suffering from postpartum mental health distress. Understanding support seekers' experiences as shared on these platforms could provide crucial insight into caregivers' needs during this vulnerable time. In the current work, we provide a descriptive analysis of the concerns, psychological states, and motivations shared by healthy and distressed postpartum support seekers on two digital platforms, a one-on-one digital helpline and a publicly available online forum. Using a combination of human annotations, dictionary models and unsupervised techniques, we find stark differences between the experiences of distressed and healthy mothers. Distressed mothers described interpersonal problems and a lack of support, with 8.60% - 14.56% reporting severe symptoms including suicidal ideation. In contrast, the majority of healthy mothers described childcare issues, such as questions about breastfeeding or sleeping, and reported no severe mental health concerns. Across the two digital platforms, we found that distressed mothers shared similar content. However, the patterns of speech and affect shared by distressed mothers differed between the helpline vs. the online forum, suggesting the design of these platforms may shape meaningful measures of their support-seeking experiences. Our results provide new insight into the experiences of caregivers suffering from postpartum mental health distress. We conclude by discussing methodological considerations for understanding content shared by support seekers and design considerations for the next generation of support tools for postpartum parents.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 50 references that are in the map. First posted on arXiv 22 Dec 2022.

Builds on

4 of 50 references.

References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2020Pre-trained Models for Natural Language Processing: A Surveycited by 505
  4. 2021AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processingcited by 131

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.