Date of Award
Summer 2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Dr. Sabirat Rubya
Second Advisor
Dr. Kimberly D’Anna-Hernandez
Third Advisor
Dr. Michael Zimmer
Fourth Advisor
Dr. Sabirat Rubya
Abstract
Perinatal mental health concerns, including depression, anxiety, stress, and postpartum distress, affect many women during pregnancy and the postpartum period. Although professional care and social support are important for managing these concerns, many women do not receive timely or adequate support because of stigma, limited access to resources, uncertainty about symptoms, and the difficulty of disclosing distress during a period often socially framed as joyful. As a result, women often turn to online communities, digital tools, and peer networks to seek information, reassurance, and connection, often through stories of their own experiences or by reading the experiences of others. While current perinatal mental health technologies can improve access to resources, they often fall short in supporting the emotional, relational, and situated nature of women’s experiences. This creates an opportunity to design technologies that use storytelling to make lived experiences more accessible while fostering recognition, trust, and meaningful social support. This dissertation focuses on understanding how storytelling can support social support in perinatal mental health and wellbeing, and on designing a storytelling-based system that makes peer narratives more accessible. I first explore how women seek and exchange support in online postpartum depression communities and how they perceive existing perinatal mental health technologies. Building on these formative studies, I develop peer narratives as a design direction and design MomStories, a web-based storytelling platform that organizes maternal narratives by topic, keyword, and perinatal stage. The platform includes 40 stories: 20 human-authored stories grounded in online peer-support communities and 20 AI-generated stories created from matched thematic prompts. The AI-generated stories were included to explore how generated narratives could broaden thematic coverage in MomStories while also allowing us to investigate how story source shapes perceptions of relatability, emotional resonance, and trust in perinatal peer support. I then evaluate how human-authored and AI-generated stories are perceived in this sensitive peer-support context, and examine how mothers engage with MomStories through structured story access, video-based narratives, and low-pressure participation. This work contributes to human-computer interaction and social computing by showing how storytelling can be designed as a form of low-pressure support for perinatal mental health, while also identifying design tensions around emotional safety, AI-supported content, relatability, trust, and the limits of narrative-based support.