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Twitter's Recommendation Algorithm

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Re: Twitter's Recommendation Algorithm

#651

Earlier quoted context omitted.

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.

IIRC Some states you cannot vote outside your declared party (Washington)

Re: Twitter's Recommendation Algorithm

#652

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 that twitter thinks the U.S is the world

Twitter thinks Twitter is the world.

Re: Twitter's Recommendation Algorithm

#653

Context: 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…

Is it valid to focus tracking a Dem/Rep split when that split is an exclusionary design for many Americans? Or is it not exclusionary in your belief? I'm curious of a social science perspective.

Ignoring the global nature of Twitter for a moment.

Re: Twitter's Recommendation Algorithm

#655
post #11

From 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…

It’s April Fools’ Day where I live.

Re: Twitter's Recommendation Algorithm

#656

Earlier quoted context omitted.

> can only be a net positive compared to the past alternatives This seems to be unsubstantiated. Are you really claiming that selective disclosure is always superior to complete lack of transparency?

The degree to which it is selective has yet to be determined. Are you claiming total ignorance is superior to partial revelation? I think we would all do ourselves better to go live on a desert island and abandon everything about modern life. A shovel might be useful to bury our heads while we're there.

> Are you claiming total ignorance is superior to partial revelation?

I am claiming that this is at least sometimes true, yes. Not always, but sometimes.

You're the one claiming that partial revelation is always, without exception, superior to total ignorance. That seems unlikely. Propoganda is often partial revelation, are you saying it is always better to receive only propoganda than to receive no information at all?

Re: Twitter's Recommendation Algorithm

#657
post #11

From 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…

Only used for metrics, apparently. [0] /** * These author ID lists are used purely for metrics collection. We track how often we are * serving Tweets from these authors and how often their tweets are being impressed by users. * This helps us validate in our A/B experimentation platform that we do not ship changes * that negatively impacts one group over others. */ [0]: https://github.com/twitter/the-algorithm/blob/7f…

So many unnecessarily cynical takes here. Let's say you were in charge of a large legacy system that some segment of customers complain about it not working for them as well as other segments. How would you know whether their complaints are valid unless you measured it? You have to know first. So measure it.

Re: Twitter's Recommendation Algorithm

#658

Earlier quoted context omitted.

I expect they're tracking the red team/blue team metrics because of the political shitstorm that's been the GOP's assertions they're being silenced by The Algorithm.

The fallacy of false equivalence systematized in code. Now one side can spew as much disinfo and incitement to violence as it likes, and any algorithm change that prevents this shit from getting amplified will be rejected as bias. BSaaS = Both Sides as a Service

This shouldn't really be a surprise to anyone. It was reported years ago that Twitter was unable to cut down on hate speech because the automated systems they developed triggered too many [debatably false] positives on Republican politicians and that was bad for the company's reputation. If Twitter wanted to prevent future code changes from impacting that approach, there needed to be something like this in the code or tests.

Re: Twitter's Recommendation Algorithm

#659
post #11

From 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…

If you buy a company for $44B and take it private, I for one say you should get your own flag.

Re: Twitter's Recommendation Algorithm

#660
post #31
post #11

From 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.

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