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Items tagged with: ATProto
#AskFedi: are there any recent, smart articles about #ATProto and its half-baked decentralization (so far)?
My favorite piece on the subject has been @rysiek 's "Bluesky is cosplaying decentralization" rys.io/en/167.html from a year and a half ago, but significant changes have happened in the meantime... I wonder if there are any updated posts about it?
Oh and @rysiek is one of my favorite people to follow on here, so click on that Follow button if you haven't already.
BlueSky is cosplaying decentralization
Almost exactly six months after Twitter got taken over by a petulant edge lord, people seem to be done with grieving the communities this disrupted and connections they lost, and are ready, eager evenSongs on the Security of Networks
Checking in on whether #bluesky / #atproto has become any more like a communication medium, and... nope. almost unchanged since i looked at it last in June. Bluesky is a spectator platform where a small number of accounts receive most of the visibility and smaller accounts are effectively invisible. The introduction of new feed algorithms (to the degree that happened, there aren't really many that I can find in wide use) did not change that. This is a non-normative analysis: in some cases, it is good to have a medium that promotes some very small number of posts and accounts, eg. to surface singular events, etc.
From a 25h sample of the firehose...
- 600k posts, 2.4m likes, 250k boosts, 350k follows
- 40% of posts receive 0 likes, 70% receive <= 1
- accounts in the 99th percentile of likes received 44% of likes, accounts in the 95th percentile received 74%
- 40% of posts were from accounts within the top 95th percentile of accounts by likes received.
- the maximum number of likes for a post by an account not in the top 95% is 32.
The first plot below shows the cumulative sum of likes received on the y axis against each account in the sample on the x axis - this includes accounts that didnt' post during the sample (but would still have posts that could be liked, so this also shows the extreme recency bias). The second plot is a hockeystick showing the number of likes (*not* cumulative sum) received on the y axis per post on the x axis.
For background, the default algorithm only cares about likes, boosts don't matter, which is why i am calculating things by likes here - they are the primary algorithmic signal.
These are the same calculations that I did back in June, but this time i'm leaving the firehose open to do a longer sample to be able to parse momentary virality from persistent effects.
