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Twitter's Recommendation Algorithm

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Re: Twitter's Recommendation Algorithm

#451
post #31

Earlier quoted context omitted.

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.

Also how about.... everyone else in the world who is not an American voter?

Re: Twitter's Recommendation Algorithm

#452
post #156

Earlier quoted context omitted.

I know there's a joke about this regarding his ego and there's certainly some truth in that, however it's also quite believable that after a deployment he might have noticed the popularity of his tweets going down (since he no doubt checks his reach), so I can kind of understand how he might see "republicans", "democrats" and "celebrities_it_makes_sense_to_check_this_with_my_account_as_i_am_a_very_active_user" as cor…

The proper way to do that is create a pool of celebrities and monitor them. Not just the CEO's account. For very active accounts, I assume that's what the "vits" or "power user" one is for. Or, heck, "vits" might actually be what you said.

ChatGPT suggests: Vitriolic accounts.

Re: Twitter's Recommendation Algorithm

#453
post #280

Earlier quoted context omitted.

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.

This is (weirdly) common in production ML codebases written by software engineers. Like you, I have no idea why unless it's a memory optimisation (where you count 4+ as many).

Re: Twitter's Recommendation Algorithm

#454

Earlier quoted context omitted.

Anyone that has worked in social network recommendations ( raises hand ) knows that they'll be accused of being politically biased, particularly if the recommendations aren't explicitly promoting biased news sites on the Right. (e.g. The Associated Press[0] is leftist propaganda! Where's unbiased news like Gateway Pundit[1] or InfoWars[5]?!) So data scientists and engineers will get pulled in to investigate the lates…

Well, there was this: https://blog.twitter.com/en_us/topics/company/2021/rml-polit...

That doesn't say they did anything about it. Just that right wing complaints had no substance.

In fact, in just over a year from the publication of that blog post, Twitter -- as a matter of official company policy -- would be promoting the unfounded belief that Twitter engineers and scientists were actively engaging in a propaganda campaign against conservatives.

The exact opposite reaction of what I'm looking for.

https://www.npr.org/2022/12/14/1142666067/elon-musk-is-using...

Re: Twitter's Recommendation Algorithm

#455

Earlier quoted context omitted.

I thought it was interesting how it explicitly doesn't boost independents. So much of the two-party system is self-reinforcing.

The vast majority of self-proclaimed independents vote with one party just as reliably as registered members.

American's lax attitude towards cultivating more than two parties is literally killing the republic from the inside.

Re: Twitter's Recommendation Algorithm

#456

Earlier quoted context omitted.

Some more strange quirks: https://twitter.com/Ben_Cary_/status/1641893540614623258 > Twitter use to rank posts higher for those who had more followers/less people they follow > They are removing that as of today but kinda interesting that someone with 10k/10k followers would get less reach than if they had 10k followers and only followed 6k

https://twitter.com/_johnforte/status/1641900138305134594 > Twitter is also using the page rank algo that google created. Basically, if a lot of people interact with the user they create more authority in the system.

> > Twitter is also using the page rank algo that google created

Nitpick, but the PageRank algorithm was created before Google, because it was the foundation of it.

Re: Twitter's Recommendation Algorithm

#457

Earlier quoted context omitted.

I thought it was interesting how it explicitly doesn't boost independents. So much of the two-party system is self-reinforcing.

> I thought it was interesting how it explicitly doesn't boost independents. So much of the two-party system is self-reinforcing. Is it boosting? Others are claiming this code is just for metrics collection: https://news.ycombinator.com/item?id=35391896 . But on the topic of Democrats vs. Republican vs. independent; a big factor may be that "Democrat" and "Republican" are much more cohesive groups and therefore much…

Why measure something if you don’t intend to change it?

Re: Twitter's Recommendation Algorithm

#458
Well that was a giant nothing-burger. This seems to be your standard ranking stack. We find candidates based on who you follow, who they follow, who is trending, and what we think you like. We then rank them based on how likely you are to engage with them and continue to come back and give us money via our subscription service and ad views. We then try to remove spam and other negative experiences.

Where's the beef?

Re: Twitter's Recommendation Algorithm

#459

Earlier quoted context omitted.

I thought it was interesting how it explicitly doesn't boost independents. So much of the two-party system is self-reinforcing.

> I thought it was interesting how it explicitly doesn't boost independents. So much of the two-party system is self-reinforcing. Is it boosting? Others are claiming this code is just for metrics collection: https://news.ycombinator.com/item?id=35391896 . But on the topic of Democrats vs. Republican vs. independent; a big factor may be that "Democrat" and "Republican" are much more cohesive groups and therefore much…

Depends what the metrics are used for. It doesn't make sense to apply artificial boosts to metrics that are only used for internal accounting. Well, maybe if you have an egotist CEO, but that wouldn't explain the rest of the boosts. We have to assume this code has some sort of effect somewhere.

Re: Twitter's Recommendation Algorithm

#460

Great pull request here which improves the algorithm: https://github.com/twitter/the-algorithm/pull/17

That would be great (unweighting bluechecks) but they actually plan to go in the other direction: Starting April 15th non-bluechecks won't show up in the "For you" section (the algorithm timeline) at all. Unpaid users are being written completely out of the algo. https://twitter.com/elonmusk/status/1640502698549075972

it's a shame we can no longer short twitter stock
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