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.
Twitter's Recommendation Algorithm
651–660 of 1001 posts
Re: Twitter's Recommendation Algorithm
#652Re: Twitter's Recommendation Algorithm
#653Context: 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…
Ignoring the global nature of Twitter for a moment.
Re: Twitter's Recommendation Algorithm
#654Re: Twitter's Recommendation Algorithm
#655From 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…
Re: Twitter's Recommendation Algorithm
#656Earlier 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.
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
#657From 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…
Re: Twitter's Recommendation Algorithm
#658Earlier 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
Re: Twitter's Recommendation Algorithm
#659From 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…
Re: Twitter's Recommendation Algorithm
#660From 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.