Enabling Sensitive Conversations with Consent Boundaries: Moa, a Platform for Discussing PhD Advising Relationships

2026-04-20

AI Summary

This study addresses the risk of secondary harm faced by doctoral students navigating strained advisor relationships, often exacerbated by a lack of support channels that balance privacy and relevance. To mitigate this, the authors propose Moa, a social platform designed for secure, sensitive conversations, featuring an innovative “consent boundary” mechanism. This mechanism enables users to anonymously define content visibility based on shared identities or experiences, thereby precisely reaching empathetic peer groups. Moa integrates an anonymous communication architecture, a social identity–matching algorithm, and fine-grained access control to ensure non-traceable identities while fostering effective peer support. A three-week field study with 47 real users revealed that 22.6% actively engaged with the consent boundary feature, demonstrating its efficacy in facilitating discourse around sensitive topics.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Responsible Web: Consent frameworks and practices on the web

Abstract

When an individual is harmed by someone in power, such as a workplace manager, it can help to identify allies--people who would offer sympathy, advice, or supportive action. However, ally discovery is fraught because the very people who might be most relevant--e.g., someone who reports to the same manager--might not be sympathetic and could potentially exacerbate the harm. We examine this problem in the specific context of PhD students navigating advising challenges and present a social media platform called "Moa" that brings together a number of features that we believe facilitate ally discovery. Moa's most novel element is an audience selection process that uses what we call consent boundaries, which allow users to flexibly define each post or comment's audience based on factors such as common social identity or lived experience, all while preserving anonymity--neither senders nor recipients learn each other's identities, even as the post reaches the right audience. A 3-week field study with 47 real-world users showed that the features in combination facilitated sensitive conversations about advising, with 22.6% of users using consent boundaries. We discuss both our overall "recipe" for systems for ally discovery and the benefits of a consent-centered approach to design.

Problem

• ally discovery
• consent boundaries
• sensitive conversations
• PhD advising relationships
• anonymity

Innovation

• consent boundaries
• ally discovery
• anonymous audience selection
• social identity filtering
• sensitive conversation platform