I think this is a really interesting example of how the Fediverse has so much potential that goes far beyond the visions of social media that Corporations and rightwing extremist billionaires could ever provide us.

Yes a great use of a social media platform, if structured well and locally moderated by human beings, could absolutely be useful for certain cases like this in a way that would be superbly difficult to arrange otherwise.

Individuals who suffer from problematic power dynamics, such as mistreatment or abuse from someone in power, can benefit from identifying allies, whether it is to seek advice or remedial action. Here, we use the word “ally” to refer to any individual who is sympathetic and can offer some kind of help, ranging from sharing useful information to actively navigating the situation together. Example contexts include workplace bullying [91], political persecution [57], and sexual harassment [23]. In contexts where potential allies are initially unknown, however, someone must make at least a partial disclosure of their experience for what we call ally discovery1 to succeed. But, disclosure is risky because the exact conditions under which these situations arise heighten concerns of exposure and retaliation [3, 21]. That is, one cannot know whom to trust about an issue until one discloses being affected by it; yet one cannot disclose before knowing whom to trust.

Some of the most helpful information in navigating problematic power dynamics can come from individuals who share similar traits regarding the context, e.g., women working with the same manager. How can we facilitate the flow of information in such situations, so that individuals who need it most can discover allies while maintaining one’s anonymity? We consider this problem in the context of PhD students experiencing advising challenges. Because the advising relationship is fraught with power dynamics and ripe for exploitation [14, 47, 54, 80], ally discovery is critical for enabling students to connect with peers who might offer advice or partnership toward remedial action. Yet, research shows that ally discovery within the same PhD program can be difficult [24, 27].

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Social media platforms are typically structured around a predefined network (e.g., followers) or space (e.g., group) [92]. In contrast, Moa focuses on the question: “How does each user want to design their audience per post or comment?” As a result, Moa does not implement any network model nor does it include user profile pages (REQ3).4 Furthermore, instead of letting users have access to the same content if they are in the same space, Moa’s users see different sets of posts (and comments) depending on their own and others’ consent boundaries (Section 4.2). In terms of the Form-From matrix [92], we believe Moa is closest to a Threaded Space, but consent boundaries considerably weaken both the threading and the space.

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Taking a consent-centered approach was essential to developing Moa. While consent and privacy are closely related, there is a subtle difference in emphasis. Speaking strictly from an engineering standpoint, consent boundaries are like privacy settings—they give different permissions to different sets of users. But, they differ from traditional privacy settings with respect to the categories of user-sets that can be formed, the granularity of control, and most importantly, the way the categories align with users’ own preferences for control. Below, we reflect further on how consent differs from privacy, and what consent means for system development.

To begin, privacy is a fuzzy concept—scholars disagree on how exactly to define it, though definitions cluster around themes such as the right to be left alone, restriction of access, secrecy, control over personal information, personhood, and intimacy [77]. One relevant ambiguity in the context of computing is about who provides privacy: Privacy can be conferred to a user by the user themselves, or by a privacy-protecting system.

In contrast, the essence of consent is that it is strictly user-determined. The only entity who can grant an individual’s consent is the individual herself; there is no definition of “consent” in which a system can grant itself consent to do something to a user that the user does not herself wish to allow. Thus, relying on the term “privacy” can obscure the central role of the user, as privacy encompasses multiple, often ambiguous meanings. Arguably, the reason why “user-centered privacy” is a term distinct from general privacy is because the user-centeredness of privacy is not a given. Yet, even within the usable privacy and security community, discussions of consent have been limited to the notice-and-choice paradigm [13]. Consent is not cast as a foundational principle for system design.

Based on this view, systems frequently define privacy options in advance (i.e., the system is conferring some privacy), with users only able to accept or decline them. In practice, these options are often difficult to find or understand, further limiting true user consent. For instance, many of Facebook’s privacy settings are often hidden [30].

As the feminist affirmative consent framework recommends [35],14 consent should not merely be a mechanism for relaxing privacy constraints, but a foundational principle of design: individuals affected by an action should have the agency to define exactly what their consent boundary is, in the way that they conceive as relevant to a particular context.

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Currently, Moa is maintained by the lead author in order to minimize privacy risks. Running the platform with 47 users turned out to be feasible—the lead author was diligent in moderating and seeking user feedback, and no incidents of abuse or negative feedback occurred. However, how could Moa and the concept of consent boundaries scale?

One way to scale is having multiple instances of Moa, one per organization or group, each with its own administrator, similar to Mastodon. This approach reflects an important assumption that local system admins are important for earning users’ trust, which was confirmed by our Phase 3 study. And, just as research shows that Reddit’s moderators have their own network of giving each other support [9], we imagine that there should be a support network for Moa’s administrators and moderators. This would ensure that each instance is not entirely isolated, and that when sensitive incidents happen, there would be cross-instance resources on how to resolve them.

It is possible to imagine building Moa as a larger, monolithic platform such as Facebook or Reddit, but we imagine even on such a platform, communities would have their own moderators—each trusted by their respective communities—who are empowered to provide the kind of meaningful oversight over identity that the Moa prototype included. For this study, it was easy for users to verify and understand that the moderator was also a PhD student in the same institution. At larger scale, there would need to be a process for selecting such moderators, and proving their identity to other members of the community.

In terms of consent boundaries, they are likely to be applicable to other contexts beyond PhD advising, such as among workplace employees [18, 67], survivors of sexual misbehavior by one group of people [16, 48], or potential whistleblowers at the same organization [19].

One aspect of the Moa prototype described in this paper was that there was an implicit consent boundary formed by the community of PhD students from the same institution—no user needed to consider whether their content would be revealed to non-students. Communities could be formed much like Slack’s channels around different topics, though additional mechanisms such as member-sponsored invitations to join or formal background checks would be required to ensure that community members satisfy membership requirements.

We also imagine consent boundaries could be used in general platforms such as Facebook or Reddit, likely in less sensitive contexts. Consent boundaries could help users carve out a group of people they want to interact with, without having to designate a particular space or network [92]. 6