Why are there two spaces instead of four in this Python code, it hurts my soul
i think back in the day they copied googles python style guide
Twitter's Recommendation Algorithm
371–380 of 1001 posts
Re: Twitter's Recommendation Algorithm
#372https://twitter.com/jarokrolewski/status/1641892148084629504 > the main neural network part of @Twitter recsys algo is based on 2021 work of #SinaWeibo - Chinese clone of Twitter interesting claim
https://twitter.com/Sandeeparuchuri/status/16419015979860172...
> Part of twitter's algo Jack Dorsey, Katy Perry, Stephen Curry and Barack Obama as “testing accounts” for getting random Tweets for testingRe: Twitter's Recommendation Algorithm
#373From 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…
The author_is_elon flag doesn't surprise me, but the two political designators are somewhat shocking. I'd sure like to know what changes based on what Twitter knows about your political affiliation.
I thought it was interesting how it explicitly doesn't boost independents. So much of the two-party system is self-reinforcing.
Re: Twitter's Recommendation Algorithm
#374Earlier quoted context omitted.
NY Times, WaPo, LA Times and other major accounts too https://www.thewrap.com/ny-times-la-times-not-pay-for-twitte...
Seems dumb of them. Cost is trivial and their competition that isn’t so politically motivated will have a much further reach. The smart move would be silent on the policy change, pay, and support rival platforms as they can. Instead they will eventually pay and look like they lost.
We’re in a time where ideology trumps revenue for some companies. You know, “Get woke, go broke.”
Re: Twitter's Recommendation Algorithm
#375> Twitter has several Candidate Sources that we use to retrieve recent and relevant Tweets for a user. For each request, we attempt to extract the best 1500 Tweets from a pool of hundreds of millions through these sources. We find candidates from people you follow (In-Network) and from people you don’t follow (Out-of-Network). > Today, the For You timeline consists of 50% In-Network Tweets and 50% Out-of-Network Twee…
A year ago my account with 5700 followers got an average of 3000 impressions per post (art). Today it's only 200-500. It mentions their fanout system was replaced by something new, not sure when or if thats in the drop, but my impression count dropped around April-May last year. Clearly something decided my posts should not shown to my followers very often.
[dead]
Re: Twitter's Recommendation Algorithm
#376Earlier quoted context omitted.
LeBron doesn’t get $84 of value from Twitter? Definitely not a political statement going on there.
Like it or not but it's the twitter that gets value from celebrities. How many people are on social networks jusy so see what their fav celebrites are doing?
It obviously goes both ways. Social media is a megaphone and ego boost for celebs.
Re: Twitter's Recommendation Algorithm
#377Context: 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…
[flagged]
You missed this in your rush to display your newly acquired sarcasm101 skills:
"Skim": To read quickly or cursorily, to glance over, or to omit details in order to get the gist of something.Re: Twitter's Recommendation Algorithm
#378Earlier quoted context omitted.
Why do they exist then? No code references it, but that's Scala/JVM so many things depend on runtime initialization, so maybe some other systems do? wich ones? Is is it there to help fight impersonations? should be solved with Twitter Blue already? There was reports of people receiving notifications about Musk tweets despite not following him, so..
It's not used at run-time, it's in the repository so that the large language models that are training on the github corpus will know how special elon is, and so that the future code written for twitter by GPT-5 will take the hint and add the favoritism autonomously.
Interesting argument, and definitely worth defending; an AI that's biased by design to remember and preserve the old world order's members rule and influence
Begs the question, why make it obvious?
Re: Twitter's Recommendation Algorithm
#379Great pull request here which improves the algorithm: https://github.com/twitter/the-algorithm/pull/17
Aside from the spam PRs, there is actually one PR that fixes a bug: https://github.com/twitter/the-algorithm/pull/242/files
Re: Twitter's Recommendation Algorithm
#380From 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…
The author_is_elon flag doesn't surprise me, but the two political designators are somewhat shocking. I'd sure like to know what changes based on what Twitter knows about your political affiliation.
Exactly. I don't see NPA (no party affiliation) anywhere.