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I think the "quadratic at scale" concern is the one thing I failed to share from my summary thread of the differences between "shared heap" in ATProto and "message passing" in ActivityPub.

In short: if everyone fully self hosts in message passing, you send messages between just send messages to relevant recipients

In a shared heap approach, to *not* miss relevant messages, all users must receive copies of all messages (including irrelevant ones), which is quadratic if everyone fully self-hosts

This is from the "Message passing vs shared heap architectures" subsection of dustycloud.org/blog/how-decent

> A world of full self-hosting is not possible with Bluesky. In fact, it is worse than the storage requirements, because the message delivery requirements become quadratic at the scale of full decentralization: to send a message to one user is to send a message to all. Rather than writing one letter, a copy of that letter must be made and delivered to every person on earth.

dustycloud.orgHow decentralized is Bluesky really? -- Dustycloud Brainstorms

You might say "well, gossip helps with this!" or something, but it doesn't.

Bluesky *strongly emphasizes* in their documentation that they are aiming for "no missed message replies". Without directed message delivery, everyone needs to *receive* every message.

Regardless of how the messages are distributed, It's O(n^2) in best case if everyone fully self hosts and there is no directed delivery.

@cwebber I'm interested to do more big-O comparisons as well.

for a large reply thread, say a thousand actively replying users on hundreds of separate instances, the number of AP messages that need to be rapidly distributed to assemble complete reply-thread view on each instance is pretty huge, no? N^2? and then also fan-out to followers?

each also needs to fetch/render media and social cards (O(instances)). and that doesn't cover *viewers* distinct from participants/followers.

@bnewbold @cwebber "assemble complete reply-thread view"

Why is that even a goal though? On a large thread there's no way to display, let alone read, all branches of the tree. I'm usually perfectly fine reading the portion of the thread that my instance just happens to know about for random reasons (usually, on account of someone on my instance following the author of that post)

@nemobis @cwebber I plan to get in to this more in a longer response to Christine's blog post, but a design goal for atproto is to have "no compromises" compared to a centralized platform. we don't want to try and convince/educate users that they don't "need" consistent and complete views of public conversations (or accurate "counts", or low-latency notifications, etc)

@bnewbold I get it, but giant threads are broken on Twitter as well, just as they are on email. People keep adding and removing ccs. There are a million different ways of displaying the tree so everyone gets surprised by whichever display sequence Twitter happens to pick. You need to click every child of every post to surface other branches of the tree which weren't displayed for whatever reason. Notifications are haphazard. Etc.

@cwebber

@bnewbold I guess what I'm trying to say that perhaps "no missing replies ever" can be replaced by an easier goal that covers the needs of 75 % of the easiest cases and people will be happier at a lower cost.

@cwebber

@nemobis @cwebber I think this conversation really gets at a difference in approach. we (Bluesky) are trying to migrate mass numbers of users off incumbent centralized platforms into alternatives with "credible exit" and interoperation. we want to make that as seamless and low-friction as possible, and asking folks to change expectations and behavior *at the same time* cuts against that.

can see this w/ quote posts, interaction counts, recommendation feeds, etc

Nemo_bis 🌈

@bnewbold Definitely possible, and while writing the previous post I wondered whether people have vastly different experiences of what a big thread on Twitter looked like. For me it might have looked like a thread with hundreds of replies from dozens of authors branching in all directions. For others it might be, as you mentioned, mostly about celebrity posts. Maybe those have thousands of branches all attached to the same root and not so many layers? I'd love to see some analysis.

@cwebber

A quick search surfaces surprisingly little prior art. (I'm probably not looking at the right keywords.)
arxiv.org/abs/2105.11596
arxiv.org/abs/2407.06998

arXiv.orgThe Structure of Toxic Conversations on TwitterSocial media platforms promise to enable rich and vibrant conversations online; however, their potential is often hindered by antisocial behaviors. In this paper, we study the relationship between structure and toxicity in conversations on Twitter. We collect 1.18M conversations (58.5M tweets, 4.4M users) prompted by tweets that are posted by or mention major news outlets over one year and candidates who ran in the 2018 US midterm elections over four months. We analyze the conversations at the individual, dyad, and group level. At the individual level, we find that toxicity is spread across many low to moderately toxic users. At the dyad level, we observe that toxic replies are more likely to come from users who do not have any social connection nor share many common friends with the poster. At the group level, we find that toxic conversations tend to have larger, wider, and deeper reply trees, but sparser follow graphs. To test the predictive power of the conversational structure, we consider two prediction tasks. In the first prediction task, we demonstrate that the structural features can be used to predict whether the conversation will become toxic as early as the first ten replies. In the second prediction task, we show that the structural characteristics of the conversation are also predictive of whether the next reply posted by a specific user will be toxic or not. We observe that the structural and linguistic characteristics of the conversations are complementary in both prediction tasks. Our findings inform the design of healthier social media platforms and demonstrate that models based on the structural characteristics of conversations can be used to detect early signs of toxicity and potentially steer conversations in a less toxic direction.