X's algorithm was changed in mid-July 2024 to boost right-leaning accounts
mastodon.social
mastodon.social
I am not surprised that Trump and Republicans immediately garnered more attention as a consequence, rightfully or otherwise.
[1]: https://en.wikipedia.org/wiki/Attempted_assassination_of_Don...
I can't judge how legitimate this particular report is. From personal experience with an account I haven't used in double digit years and deleted recently, my feed was exclusively Musk tweets without it being my choice. So anecdotally I have every reason to believe that Musk inflates his own... everything, probably.
Using your company to artificially inflate your own importance somehow strikes me as extremely pathetic. It's like getting an employee to stuff your crotch to make things look (but not be!) bigger.
You'd struggle to convince me the number of Premium registrations wasn't heavily biased to the Right.
July 13 was the day of the assassination attempt...
"Science"
The amount of bullshit that cropped up my feed immediately was nuts ( calling it right wing might be fitting).
I logged onto my old account afterwards and everything was normal again.
Just my personal experience with it
https://github.com/twitter/the-algorithm-ml - Last commit: April 6th, 2023
Either the algorithm hasn't been updated for over a year (unlikely) or the public version is abandoned.
First, while the code was open sourced, the configs and data that determines its behavior wasn't. Any researcher that has some knowledge of the matter will tell you this.
Second, that code was last updated in July 2023 [1]. News feed algorithms are updated regularly, especially around the time of US elections [2].
Don't get me wrong I don't like singular influence like this either, but what's the legality and how do we draw the like between this and any other attempts to sway public opinion by tuning content?
I guess what I am trying to say is measuring bias sounds like a long and profitable academic career in itself. That is, hard to define, vague methodology, and uncertain results.
This was further exacerbated because the linked study seemingly ignores what happened on the day specified to demonstrate their narrative.
Essentially, the linked study placed the cart before the horse.
The study doesn't offer any conclusion, but it analyses some data and offers a potential interpretation. Other interpretations can be provided and discussed. The post was heading for the front page. Personally, I was very interested in the discussion.
Was any HN guideline broken?
Basically arguing that correlation is causation and placing the cart before the horse. This isn't good grounds for insightful or even useful conversations and this was already breaking the guidelines by being a political subject matter about celebrities.
Second, the speculation you're writing about is an interpretation of the new data analysis.
Third, the authors don't argue that correlation is causation. If you still think they do, then please quote the sentence where they do that.
Honestly, it looks like this thread is being taken down because some people don't like it, including you, which goes against HN guidelines.
I have a feeling that the research on its own would not have generated the discussion that the toot did, because the toot really just prompted people to give their own impression without discussing the actual research. Sometimes I flag stuff just because it's flame bait, maybe the submission didn't break guidelines but the comments will be snarky and shallow and not conducive to building the community culture. And I rely on the democratic aspect of HN, if my flag is frivolous so be it, no harm done.
I find it interesting to reflect on the fact that -- despite being raised on a diet of science fiction scenarios about dystopias -- my generation built these machines. In hindsight, it's blindingly obvious that algorithms would be manipulated by the wealthy and powerful to service their agendas. Of _course_ they would, every incentive was plainly there from the start. But we figured it wouldn't be a problem because it was us who built them, and we weren't the baddies.
I say this not to point the finger at other people. I worked on a major social media system -- and I was one of those who defended the system as it was being created. And we all should have known better.