Earlier quoted context omitted.
You mean owner and not owner? I think it's fair.
For what purpose? Do we know what this is used for?
Apparently it’s for analytics
Twitter's Recommendation Algorithm
381–390 of 1001 posts
Re: Twitter's Recommendation Algorithm
#382From https://github.com/twitter/the-algorithm/blob/7f90d0ca342b92... ( "author_is_elon", candidate => candidate .getOrElse(AuthorIdFeature, None).contains(candidate.getOrElse(DDGStatsElonFeature, 0L))), ( "author_is_power_user", candidate => candidate .getOrElse(AuthorIdFeature, None) .exists(candidate.getOrElse(DDGStatsVitsFeature, Set.empty[Long]).contains)), ( "author_is_democrat", candidate => candidate .getOrEls…
Well someone just asked about it in the live spaces[1] Elon is hosting and he said that should not be there. An engineer said afterwards it is just for metrics but then Elon chimed in again and said "we should get rid of it, it should be gone." [1] https://twitter.com/elonmusk/status/1641880448061120513?s=20
Doesn't necessarily mean he didn't want it there in the first place. Why else would it be there?
Re: Twitter's Recommendation Algorithm
#383I wonder what the "author_is_elon", "author_is_power_user", "author_is_democrat", and "author_is_republican" labels are for [1]. [1]: https://github.com/twitter/the-algorithm/blob/main/home-mixe...
pretty sure Elon gets a boost in the algorithm. All okay - he's the owner of a private entity and can do as he pleases.
That’s what he blew all that cash on. It’s the whole point.
Re: Twitter's Recommendation Algorithm
#384Url changed from https://github.com/twitter/the-algorithm-ml, which points to this.
Re: Twitter's Recommendation Algorithm
#385Earlier quoted context omitted.
The full list of model features in that file is interesting. I am surprised at the number of inherently redundant and colinear features, though. (e.g. has_1_image, has_2_images, has_3_images, has_4_images)
Those aren't redundant or collinear though? Maybe you are surprised they didn't encode this as an integer "num_images"? It is fairly common to one hot encode ordinal variables with only a few common/possible values this way.
True, it still seems odd to encode an explicitly ordinal variable as categorical (particularly one with a small finite range, in contrast to the follower logarithmic bucket ones), but Twitter's layout is weird enough that it could be a impactful difference in terms of engagement.
Re: Twitter's Recommendation Algorithm
#386"Today, the For You timeline consists of 50% In-Network Tweets and 50% Out-of-Network Tweets on average, though this may vary from user to user." I have spent significant effort creating a network and there you go choosing to ignore my efforts by putting in 50% of crap-I-don't-want-to-see. That is why I despise your algorithm.
"Control Panel for Twitter" plugin. You can get rid of "For You".
Re: Twitter's Recommendation Algorithm
#387Earlier quoted context omitted.
So now engineers working on the algo can ensure their launches won't lower Elon's tweet visibility. Looks like those remaining at Twitter have a knack for corporate survival.
He is the full owner of Twitter. It's his company, so nobody is going to fire him as CEO for over-promoting his tweets.
The users can and often do fire the CEO.
Re: Twitter's Recommendation Algorithm
#388Context: I teach at Princeton and study social media and recommendation systems. From a very quick skim of the repositories, this appears to be quite limited transparency. The documentation gives a decent high-level overview of how Tweet recommendation works—no surprises—and the code tracks that roadmap. Those are meaningful positive steps. But the underlying policies and models are almost entirely missing (there are…
What about these? https://huggingface.co/Twitter
Those look older to me. They all have last updated dates for October and November 2022.