Speaker 1: It's Thursday, October. I'm Oscar Ramiras from the Daily Dive
Speaker 1: podcast in Los Angeles, and this is reopening America. The
Speaker 1: Facebook papers are out and it's shedding more light on
Speaker 1: how executives their weight, various trade offs between their bottom
Speaker 1: line and impacts on public safety. In one instance, it
Speaker 1: took them years to implement a fix for an algorithm
Speaker 1: that was feeding people angry emotional content filled with misinformation.
Speaker 1: If you use the angry emoji on a post instead
Speaker 1: of the like button, it carried more weight, and then
Speaker 1: they would feed you more of the same despite the
Speaker 1: signal that you did not like it. Jeremy Merrill, Data
Speaker 1: report at the Washington Post joins us for more. Thanks
Speaker 1: for joining us, Jeremy, thanks for having me on it.
Speaker 1: Let's talk a little bit about the Facebook papers. We're
Speaker 1: learning a lot more about kind of how things were
Speaker 1: working in the background at Facebook. You know a lot
Speaker 1: of different things, internal dialogues between between workers, and how
Speaker 1: things were being portrayed out there. One of the articles
Speaker 1: that you wrote about was about this, uh, these anger emojis,
Speaker 1: these new set of emojis that popped up in seen
Speaker 1: that lets people react to comments beyond the the just
Speaker 1: the like button. Um, you know, you can laugh at something,
Speaker 1: heart something. You know, there's like a sad emoji or
Speaker 1: a sorry emoji I think they called it, uh, and
Speaker 1: then the the angry emoji also. But what happened with
Speaker 1: all of this is uh, things were waited differently, and
Speaker 1: these emojis were waited in such a way that it
Speaker 1: started promoting a lot of misinformation, a lot of things
Speaker 1: that would give you more of an emotional response. And
Speaker 1: at the time they thought that this was the way
Speaker 1: to go, but it just kind of led to all
Speaker 1: sorts of mishaps and it took them a long time
Speaker 1: to really remedy that as well. So Jeremy, help us
Speaker 1: walk through some of this. What are we seeing with it?
Speaker 1: That's exactly right. So what we're talking about here is
Speaker 1: whenever you go to Facebook dot com or open the
Speaker 1: Facebook app on your phone, you get your news feeds, right,
Speaker 1: and just just imagine all of the possible things that
Speaker 1: Facebook could show you. At the very top could be
Speaker 1: posts from your friends a couple of days ago. It
Speaker 1: could be a baby picture from someone you wrote the
Speaker 1: school bus with a high school fifteen years ago. It
Speaker 1: could be posts from a news organization or a buy
Speaker 1: nothing group in your town. There's all sorts of things
Speaker 1: and Facebook has to make a decision of what you
Speaker 1: go first and what you goes back in what should
Speaker 1: go you know three thousands which that you'll never see it,
Speaker 1: And that algorithm is sort of one of the secret
Speaker 1: sauces that that keeps people, that keeps Facebook, um sort
Speaker 1: of a life. And broadly speaking, um, there are two
Speaker 1: sets of things that make up that algorithm. One is
Speaker 1: a really complicated, really masthei for what you think of
Speaker 1: when you think of an algorithm that it's super complicated.
Speaker 1: That's meant to predict what's the likelihood that if we
Speaker 1: show this post to Jeremy, what's the likelihood that he's
Speaker 1: going to click like, what's the likelihood that he's going
Speaker 1: to click angry, what's the likelihood that he's going to
Speaker 1: write a comment? Um? So that's the first part. Are
Speaker 1: all those predictions, and the second part is really simple.
Speaker 1: It's just numbers that are picks by human beings who
Speaker 1: worked in Facebook, and those numbers are how important relatively
Speaker 1: those different predictions are. And when Facebook launched a new
Speaker 1: way of ranking your news speed, a new way of
Speaker 1: picking what stuff shows up first. Those weights were that
Speaker 1: alike is worth one point. These reaction emojis, including angry,
Speaker 1: but also all the other ones were worth five points,
Speaker 1: and comments were worth between fifteen and thirty points, and
Speaker 1: there's some other a couple other ingredients as well. But
Speaker 1: sort of the minute that this launched, someone from Facebook
Speaker 1: wrote on their internal message board and said, you know
Speaker 1: that they were worried that posts that make people react
Speaker 1: with the angry emoji might inadvertently open the door to spam, abuse, clickbait,
Speaker 1: and somebody else said, you know it's possible. Yeah, I
Speaker 1: mean it makes natural sense, right, something that angers you,
Speaker 1: something that you're like, well, you know there that's full
Speaker 1: of crap. You know, I'm gonna anger emoji that hopefully
Speaker 1: you don't get any more of it. But it was
Speaker 1: actually the opposite, because they waited it so much more
Speaker 1: than just the like or whatever. Uh, the the algorithm
Speaker 1: would actually just serve you up more. And and in
Speaker 1: that sense, you know, the worst of the platform started, uh,
Speaker 1: started shining through even more for people's news feeds. That's
Speaker 1: that's exactly right. If the algorithm learns that you're going
Speaker 1: to react annoying, it's worth five points, then it's going
Speaker 1: to show you more stuff like that, because that's what
Speaker 1: it considers. The Facebook's term was meaningful social interaction. They
Speaker 1: consider that to be a meaningful social interaction, so they're
Speaker 1: gonna try to do more of it. But gradually, um
Speaker 1: evidence started building up. People found that things that sparked
Speaker 1: a lot of angry angry reactions were disproportionately misinformation what
Speaker 1: places Book called toxicity, as well as low quality news,
Speaker 1: and people started proposing, hey, maybe this isn't a great
Speaker 1: idea to count the anger emojis so high, and they
Speaker 1: counted as exactly the same as these other these other
Speaker 1: emojis like love or care. Yeah, and that's part of
Speaker 1: what that's part of what's interesting about the Facebook papers
Speaker 1: and what we're kind of learning about how Facebook was
Speaker 1: operating is that while these experiments were going on, right
Speaker 1: how the different weights and how to serve up the
Speaker 1: algorithm best to people, the internal conversations from employees calling
Speaker 1: some of it out, saying, hey, this is a problem,
Speaker 1: this is becoming a problem. This problem is too big,
Speaker 1: and uh, you know, really not being able to remedy
Speaker 1: it right away. Uh. That's one of the other interesting
Speaker 1: aspects about it, because that's part of what is We're
Speaker 1: we're seeing these internal dialogues happening behind the scenes, now right,
Speaker 1: That's that's exactly right, And you know, to be fair
Speaker 1: to Facebook, Um, in some cases people didn't have evidence
Speaker 1: yet it was just a suspicious sort of an instinct. Hey,
Speaker 1: ranking anger so highly doesn't seem like a great idea,
Speaker 1: or it would be kind of weird, um to make
Speaker 1: distinctions between different kinds of emotions. They felt kind of
Speaker 1: uncomfortable with that, as well as raising the possibility that
Speaker 1: stuff that makes people angry angry might be important for
Speaker 1: social change. And um, but you know, eventually, this book
Speaker 1: did end up cutting the weight of this emoji all
Speaker 1: the way down to zero. They ended up making distinctions
Speaker 1: between emotions. Um, So for a while, love and care
Speaker 1: were were double alike, and stuff that they maybe go
Speaker 1: ha ha or wow was only worth the same as alike.
Speaker 1: So to their credit, they did end up making this
Speaker 1: fix eventually, But it was the case that they set
Speaker 1: these without They set the weights alto five times alike
Speaker 1: without looking into what would happen, And then once they've
Speaker 1: made that choice, they stuck with it for a couple
Speaker 1: of years until the weight of the evidence was so
Speaker 1: much that they finally had to act. Yeah, and sticking
Speaker 1: with it too, right, And you know, it's a it's
Speaker 1: a new feature on the website, so it makes sense
Speaker 1: that they want to point people to it as much
Speaker 1: as possible. So that was part of the problem too.
Speaker 1: Is that why they didn't act so quickly, is that
Speaker 1: you know, they wanted people to start using that new feature,
Speaker 1: um thinking you know, it was going to be that
Speaker 1: next great thing for them. Uh, And so that posed
Speaker 1: the problem too. And and as you mentioned once, they
Speaker 1: um did kind of start reducing the weights of these things.
Speaker 1: They found that, you know, in other parts of the
Speaker 1: algorithm also, you know, when a post would get too
Speaker 1: popular or something, and they would demote certain things. Even then,
Speaker 1: it was it was tough because uh, you know, there
Speaker 1: was no cap on how many points let's say a
Speaker 1: post could get, so um, you know, even if you
Speaker 1: demote something, I mean, it still could have a billion points, right,
Speaker 1: and it could be at the top of everybody news feed.
Speaker 1: That's exactly right. It's really difficult for us to understand
Speaker 1: how all the sort of weird mass that goes into
Speaker 1: stay going out, what goes to the top of your
Speaker 1: news feed. And some of the documents showed that even
Speaker 1: these people with access to a lot of resources that
Speaker 1: Facebook and a lot of people who can understand it offensibly,
Speaker 1: even they didn't really understand how the algorithm worked, and
Speaker 1: they were finding that, you know, the scores for the
Speaker 1: things that would appear at the top of your news
Speaker 1: speed usually are about a hundred, sorry, in the hundreds,
Speaker 1: a couple hundred, but some some of the post could
Speaker 1: get scores in the millions up to a billion. And
Speaker 1: what what you've got is a sort of battle between uh,
Speaker 1: the integrity teams, who are the ones tasked with finding
Speaker 1: stuff that's probably bad and trying to push it down
Speaker 1: in people's feeds so it doesn't go viral, and the
Speaker 1: growth teams, who are tasked with getting people to use Facebook.
Speaker 1: And so you should have had this battle where the
Speaker 1: growth team was giving these things these humongous scores, but
Speaker 1: the integrity team was limited and like the best that
Speaker 1: they could do is like cut it the score it half.
Speaker 1: So if you you know, the post number one has
Speaker 1: you know, five million as its score and post number
Speaker 1: two has eight hundred, you're trying to push post number
Speaker 1: one down because because it's probably garbage and all you
Speaker 1: can do is divide the five million and a half, well,
Speaker 1: two and a half million is still huge. So they
Speaker 1: found that they had this trouble of actually making their
Speaker 1: principled ways of trying to make the platform safer and
Speaker 1: more enjoyable for people. They weren't able to work because
Speaker 1: the math was so complicated. They were always fighting that
Speaker 1: losing battle. And you know, it's it's interesting, you know,
Speaker 1: to the point, right, it's why is Facebook serving up
Speaker 1: so much crap? Why is it serving up so much
Speaker 1: anger and divisiveness? And this is exactly why it is.
Speaker 1: The different weights that were put on things, and the
Speaker 1: math was so complicated it was hard to go back
Speaker 1: and fix it retroactively. So you know, we're learning a
Speaker 1: lot from these Facebook papers. Um, you know, Facebook choosing
Speaker 1: maximum engagement over user safety, dropping the ball in different areas.
Speaker 1: So we'll keep finding out more and uh and bring
Speaker 1: it all to you. Jeremy Merrill data reported at the
Speaker 1: Washington Post. Thank very much for joining us, Thanks for
Speaker 1: having me on. I'm Oscar Roomiers and this has been
Speaker 1: reopening America. Don't forget effort. Today's big news stories you
Speaker 1: can check me out on the Daily Dive podcast every
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