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Speaker 1: Trust me? Do you trust me?

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Speaker 2: Right?

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Speaker 3: Everly?

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Speaker 1: And you astrayed? Trus This is the truth, the only truth.

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Speaker 3: If anybody ever tells you to just trust them, don't

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Speaker 3: welcome to trust me. The podcast about cults, extreme belief

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Speaker 3: and manipulation from two Visible Rulers who have actually experienced it.

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Speaker 1: I Am Lola Blanc and I Am Megan Elizabeth.

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Speaker 3: Today is part one of our very informative interview with

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Speaker 3: Renee dresta professor, former research manager at Stanford Internet Observatory

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Speaker 3: and author of Invisible Rulers, The People Who Turn Lies

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Speaker 3: into Reality. In Part one this week, she is going

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Speaker 3: to talk to us about propaganda, disinformation versus misinformation, and

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Speaker 3: how social media algorithms push us all into highly individualized,

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Speaker 3: bespoke realities.

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Speaker 1: We'll discuss how she became interested in this topic when

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Speaker 1: algorithms started feeding her anti vaxer content simply because she

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Speaker 1: was interested in a healthy lifestyle, How rage bait and

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Speaker 1: other emotionally charged material spreads faster, and why social media

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Speaker 1: makes it seem like other people have more extreme views

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Speaker 1: than the majority of them actually do.

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Speaker 3: And next week we will talk about pseudo events or

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Speaker 3: nonsense controversy and not newsworthy news. How much more challenging

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Speaker 3: it's getting to discern truth from fiction and lots more,

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Speaker 3: lots more about our depressing internet lens escape.

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Speaker 1: Ah, what's real? Megan?

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Speaker 3: Yeah, exactly, that's the question that is before we ask

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Speaker 3: her this question, kind of rene, Okay, I would love

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Speaker 3: to know your equiltiest thing.

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Speaker 1: Okay, sure. Well, first, ione to start off with an apology.

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Speaker 1: I did not realize that Spotify has comments, So we're

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Speaker 1: not We don't really read a lot of the comments

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Speaker 1: can and it's not because we don't want feedback. It's

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Speaker 1: I mean some of them are you know, some people

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Speaker 1: are very and not just like I mean, like you

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Speaker 1: learn ugly, like I'm stop exposing my call them and

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Speaker 1: I kill you, you know what I mean? Like people can

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Speaker 1: get has that happened? Yeah? Yeah, yeah, why don't you

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Speaker 1: text me about that? I didn't know. Well, I mean

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Speaker 1: it's a specific one, you know what I mean? Okay, okay,

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Speaker 1: So I didn't realize that Spotify had comments, and I

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Speaker 1: was like, oh cute, some comments not so cute. People

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Speaker 1: are really upset at me because I talked about using

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Speaker 1: AI and I was like, oh, word, like they're right,

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Speaker 1: and I really didn't realize how how bad it was

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Speaker 1: for the environment.

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Speaker 3: You didn't, I thought you were. You specifically mentioned that.

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Speaker 3: That's because I started reading the comments. Oh yeah, yeah,

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Speaker 3: that's like the whole thing. That's like, Yeah, I didn't.

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Speaker 3: I didn't know the data centers are sucking the water

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Speaker 3: all of our energy.

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Speaker 1: Yeah, so I'm making higher I was. Yeah. Yeah, so

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Speaker 1: I was like a first you know, chat cheepy tear,

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Speaker 1: like much much longer ago than people would imagine. And

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Speaker 1: it just really wasn't talked about them that it was

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Speaker 1: like an energy source. I don't know why. So I

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Speaker 1: just got kind of lulled into it just being kind

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Speaker 1: of lazy, not environmentally unfriendly. So anyway, I have built

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Speaker 1: my life around being friendly to the environment. I do

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Speaker 1: all the things that I can to make my imprint

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Speaker 1: as small as possible. Not that anyone else has to

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Speaker 1: do those things, it's just what I do. It's very

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Speaker 1: important to me. Y'all are correct. I deeply apologize. Please

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Speaker 1: forgive me. I erased it all Zorah off, all of

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Speaker 1: it off gone. Wow, nice, that is more than most

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Speaker 1: people are doing. Yeah, well, please forgive me. Embarrassing take.

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Speaker 3: But I also think that the truth is that most

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Speaker 3: people are using AI in some former orders.

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Speaker 1: And that's how I justified it to myself, where it's

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Speaker 1: like I'm in a space to have like animation where

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Speaker 1: I'm like, well, if I'm not using it, other people are,

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Speaker 1: and then I'll just get lost and be way behind

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Speaker 1: like people who are just sociopathic and using it. And

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Speaker 1: it's like, right, correct, that's exactly what will happen in

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Speaker 1: every space forever because that's how the world works. And

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Speaker 1: that doesn't mean I have to keep polluting.

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Speaker 3: I also try not to use AI unless I have to.

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Speaker 3: The reality is that every corporation has integrated AI into

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Speaker 3: their operation, and you're probably going to hear an ad

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Speaker 3: for AI that we did not approve because they just

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Speaker 3: that's just what happens. Sometimes they just get they get

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Speaker 3: put on here, and I mean, you know, it is

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Speaker 3: what it is. So yeah, that's not really my cultiest thing,

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Speaker 3: but it kind of is.

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Speaker 1: I think that that is wonderful.

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Speaker 3: We should all be aspiring to use it as little

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Speaker 3: as possible. Most apps have incorporated AI into their entire functionality,

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Speaker 3: so like, I also think it's important to remember that, Like,

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Speaker 3: while it's important and good for us to all try

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Speaker 3: to do what we can on an individual level. Ultimately,

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Speaker 3: like these things are happening on a systemic level, that

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Speaker 3: corporations are spearheading to try to make more money. And

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Speaker 3: while it's good to try to amend or individual behavior,

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Speaker 3: when it's that a large of phasis stemic problem, like

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Speaker 3: we should be putting the onus on them and holding

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Speaker 3: the companies accountable, not like pointing fingers at each other

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Speaker 3: for like I needed a little help exactly, And I

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Speaker 3: think that's what I mean by like the psychopaths will

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Speaker 3: just take over. I just mean like the tech giants

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Speaker 3: are people who are so removed from reality, not everyday

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Speaker 3: users who aren't in charge of it. Even with something

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Speaker 3: like recycling, Like recycling is a scam essentially that was

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Speaker 3: marketed to us so that corporations could make more money

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Speaker 3: on plastics. Right, like these like individual behaviors, let's all

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Speaker 3: do the best we can, absolutely, but also like let's

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Speaker 3: hold people accountable. We are politicians to actually regulate that shit,

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Speaker 3: you know what I mean? Agreed, So yeah, let's let's

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Speaker 3: And also it's just like trying to understand we're all

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Speaker 3: doing our best.

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Speaker 1: Yeah, moderation, yeah exactly. All that said as an artist.

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Speaker 3: I'm obviously terrified about how these companies are stealing from

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Speaker 3: human artists to essentially make a profit for themselves, and

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Speaker 3: I don't think they should be allowed to do that.

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Speaker 3: So let's try to minimize it, and also let's try

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Speaker 3: to regulate it.

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Speaker 1: Word, what about you? What's your cultiest thing of the week?

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Speaker 3: I watched the movie, butgon, yeah, okay, and have you

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Speaker 3: seen it?

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Speaker 2: No?

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Speaker 1: Do you know what it is? Oh?

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Speaker 3: Okay, it's Yorgos Lanthemos new movie. I love Yorgoslanthemos starring

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Speaker 3: Emma Stone and Jesse always has Emma Stone in it,

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Speaker 3: and it is I guess it's a remake of another film.

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Speaker 1: Did you see the Onion article that was like I

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Speaker 1: might hang out?

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Speaker 3: It's a remake of a South Korean film called Save

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Speaker 3: the Green Planet, which I have not seen yet. But basically,

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Speaker 3: I'm not going to spoil anything, but the prem this

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Speaker 3: is all on the trailer. The premise is that Jesse

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Speaker 3: Plemmons and another guy are conspiracy theorists who kidnap Emma Stone,

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Speaker 3: who's a CEO. She's a high powered yas queen CEO.

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Speaker 3: What or yesified I should say?

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Speaker 1: And it's kind of. It feels like a.

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Speaker 3: Play, like it's all about Jesse Plemons and Emma Stones

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Speaker 3: characters like trying to outsmart each other, and like.

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Speaker 1: Okay, it's so good.

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Speaker 3: But also there's just so much interesting psychology in there,

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Speaker 3: just about like conspiracy theorists and people protecting their belief systems.

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Speaker 3: Oh my god, and somebody trying to poke holes in

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Speaker 3: those belief systems. But she's doing it with her own

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Speaker 3: need for control, you know, or am me, it's yeah,

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Speaker 3: it's really juicy. Is it in theaters or can I

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Speaker 3: watch it? It's in theaters right now? Well when at

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Speaker 3: the time of recording this episode, it won't be by

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Speaker 3: the time this episode comes out. Okay, but where do

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Speaker 3: I do I have to go to a theater to

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Speaker 3: watch it, like this weekend, let's find out?

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Speaker 1: Or can I watch it in my bad No, I'm

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Speaker 1: just kidding. I love theaters and I love the theater

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Speaker 1: experience at the movie theater. It's still in theaters. Okay, great, AMC.

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Speaker 1: So I will be going to the movie theater because

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Speaker 1: it's important to support theaters.

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Speaker 3: It's important to support film make girls when they're not Marvel.

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Speaker 3: Yeah you did it, good dog. Anyway, Yeah, I highly recommend. Well.

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Speaker 1: I'll see it this weekend and can't wait to get

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Speaker 1: my thoughts on it too, because what the heck? This

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Speaker 1: is great. I want to interview that guy you made it.

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Speaker 1: Would your ghosts come on our podcast? Your gis your ghosts?

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Speaker 1: I love you? Know who he is? Right, the Lobster?

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Speaker 2: All?

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Speaker 3: Yeah, yeah, yeah, yeah yeah, the Lobster. The favorite. The

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Speaker 3: favorite is one of my favorite movies. Honestly, the favorite

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Speaker 3: is your favorite Olivia Coleman nasty work. My favorite of

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Speaker 3: his is the Killing of a Sacred Deer.

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Speaker 1: Have you seen that movie? Yeah?

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Speaker 3: I have, so fucking good And doctor anyway, we're just

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Speaker 3: talking about Yorgas lamp Post now.

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Speaker 1: I love him. Yeah, me too. As Shall we talk

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Speaker 1: about another thing that you love, which is misinformation.

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Speaker 3: I don't love misinformation. I harden interested in propaganda and disinformation,

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Speaker 3: which she does not call misinformation. She does not use

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Speaker 3: the term mis information. And we're about to find out why.

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Speaker 1: Let's do it. Let's do it.

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Speaker 3: Welcome Renee du Resta to trust me. Thank you for

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Speaker 3: joining us today. Thanks for having me.

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Speaker 1: Was devouring your book.

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Speaker 3: This is the number one topic I'm interested in, and

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Speaker 3: so many of us are at the moment.

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Speaker 1: It is my special interest.

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Speaker 3: It is called invisible rulers, the people who turn lies

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Speaker 3: into reality. So can you start off by telling us

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Speaker 3: a little bit about your academic background and how you

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Speaker 3: came to research the things that you research, and tell

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Speaker 3: us what it is.

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Speaker 2: Yeah, sure, well thanks for saying you liked it. I

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Speaker 2: didn't have an academic background. I got into this kind

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Speaker 2: of by accident. I was a well, I mean, I

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Speaker 2: am a mom and a mom of three. And in

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Speaker 2: twenty thirteen, I had my first baby, and I moved

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Speaker 2: to California, and I was putting him on these preschool

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Speaker 2: waiting lists, and I got really involved in the vaccine conversation.

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Speaker 2: This was before COVID, so this was like the measles conversation,

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Speaker 2: not the COVID conversation.

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Speaker 1: And you know, all of social.

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Speaker 2: Media, once you have a baby, starts pushing you content.

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Speaker 2: It really realizes you've had a kid. All of a sudden,

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Speaker 2: it's no longer party pictures and your friends. It's like

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Speaker 2: mom stuff. And for me that turned into California crunchy

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Speaker 2: mom stuff. It started pushing me a lot of the

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Speaker 2: stuff around. You know, make your own baby food, do

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Speaker 2: cloth diapering, and then I did both of those things,

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Speaker 2: and so it was like, naturally, you must be an

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Speaker 2: anti vaxxer. It started pushing me in the anti vaccine

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Speaker 2: groups too, and I am not, and so I thought

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Speaker 2: that was kind of funny. You know, my background's in tech,

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Speaker 2: and so I kind of understood why it was doing it,

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Speaker 2: like I recognized it from the standpoint of like how

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Speaker 2: those recommendations work. I mostly ignored it until all of

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Speaker 2: a sudden there was actually a measles outbreak in California,

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Speaker 2: the Disneyland measles outbreak in late twenty fourteen, and I

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Speaker 2: got very involved in the sort of question of you know,

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Speaker 2: what do you.

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Speaker 1: Do about that? Right?

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Speaker 2: How do you feel like you're sending your kid to

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Speaker 2: public school, which you know I was going to be

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Speaker 2: starting that soon, and you just want to feel like

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Speaker 2: this is not a thing that you should have to

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Speaker 2: think about. In twenty fifteen, so I started writing a

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Speaker 2: lot about how effective the anti vaccine movement was at

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Speaker 2: communicating on social media. I felt like people really needed

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Speaker 2: to understand it because they still thought of it as

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Speaker 2: you know, Jenny McCarthy doing talk shows in the morning,

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Speaker 2: and it had really evolved so far past that, and

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Speaker 2: so I started actually just writing about it from the

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Speaker 2: standpoint of both a mom and then a person in

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Speaker 2: tech with a data science background who wanted to just

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Speaker 2: kind of explain how the system worked and why this

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Speaker 2: group of people was so effective as communicators and how

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Speaker 2: social media was boosting their stuff in response to them

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Speaker 2: being great communicators and the CDC and the people who

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Speaker 2: were supposed to be authoritative voices being like candidly abysmal

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Speaker 2: communicators actually, and I really felt like, you know, kind

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Speaker 2: of like chicken little maybe, like I was saying, Okay, guys,

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Speaker 2: like this guy is falling, except maybe it really is,

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Speaker 2: you know, and maybe somebody should be paying attention to that.

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Speaker 2: And so I got really really involved actually in writing

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Speaker 2: about that, and as I was doing it, I started

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Speaker 2: getting pushed other types of content, right, So I joined

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Speaker 2: some of the anti vaccine groups with a different account,

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Speaker 2: clean account that had nothing to do with my stuff,

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Speaker 2: and it started pushing me other content like pizzagate, right,

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Speaker 2: and then QAnon, And I was like, oh.

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Speaker 1: Man, it's taking you on a whole journey.

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Speaker 2: You joined this one group, and like, we're going down

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Speaker 2: a whole rabbit hole here.

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Speaker 1: Aren't we.

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Speaker 2: And so I started following that and just seeing, you know,

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Speaker 2: how how far how far down does this go?

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Speaker 1: Where does it take you next?

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Speaker 2: And that was that was what I started writing about,

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Speaker 2: and so it kind of became a career.

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Speaker 1: I had a supply chain logistics startup.

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Speaker 2: I was just in tech, but it became really something

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Speaker 2: that became kind of all consuming because I felt like

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Speaker 2: people really needed to understand just how these dynamics worked

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Speaker 2: and how this was. I called it at the time

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Speaker 2: like inadvertent algorithmic radicalization or something like that in these articles,

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Speaker 2: because we didn't have a name for it. And I

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Speaker 2: was like, here's why you you know, you think you're

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Speaker 2: joining a crunchy mom a cloth diaper and group. And

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Speaker 2: then like bam, here's the QAnon stuff that shows up

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Speaker 2: your feed. And that was how I got into it.

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Speaker 3: Wow, a fascinating context. I did not know any of

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Speaker 3: that and it totally yeah, makes sense.

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Speaker 1: I love that. That is where you went when they

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Speaker 1: tried to show you that content.

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Speaker 3: Your book kind of starts with setting up how historically

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Speaker 3: there was the rumor mil before the Internet existed, there

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Speaker 3: was just the standard traditional rumor mail amongst the.

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Speaker 1: People of the villages.

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Speaker 3: Yeah, and then separate from the rumor mail, you had propaganda,

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Speaker 3: which was when the government or influential people wanted to

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Speaker 3: get out a particular narrative. Can you talk a little

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Speaker 3: bit about what is propaganda? What defines propaganda?

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Speaker 2: So I use it in the way that it was

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Speaker 2: used maybe in its origins, which comes from like the

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Speaker 2: Catholic Church after the Protestant Reformation, realizing that they've kind

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Speaker 2: of lost control of the narrative, right, and Pope Gregory

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Speaker 2: says to the cardinals, you have to go out there

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Speaker 2: and propagate the faith. And what he means by that's

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Speaker 2: where the term comes from propaganda is this. It's this

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Speaker 2: verb form that with an imperative, you must go out

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Speaker 2: there and do this. And so it's information with an

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Speaker 2: agenda that benefits the person who is who is doing

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Speaker 2: that propagation, right, or who is requiring that propagation, So

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Speaker 2: there is some benefit to the person who is doing it.

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Speaker 1: I don't think that's.

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Speaker 2: Necessarily a pejorative, right, There is always some sort of

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Speaker 2: agenda in persuasive communication.

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Speaker 1: But I think what we're propaganda goes.

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Speaker 2: Where that term evolves is after World War Two, in particular,

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Speaker 2: it becomes, particularly in the United States, that thing that

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Speaker 2: the Nazis do, right, that thing that bad people do

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Speaker 2: to manipulate the discourse. And that's the kind of tenor

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Speaker 2: that it takes on. For most people who hear the

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Speaker 2: term right, they think of it as something that is

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Speaker 2: very inherently manipulative, something where it's persuasion, but it's persuasion

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Speaker 2: that's done with kind of a surreptitious element, kind of

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Speaker 2: a manipulative element. The audience isn't fully aware of what's happening.

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Speaker 2: And that's where I think that it has traditionally been

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Speaker 2: the purview of powerful people who have the ability to

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Speaker 2: control mass media, who have the ability to reach large

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Speaker 2: numbers of people all at once into shape a narrative.

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Speaker 2: And for most of human history, ordinary people didn't have

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Speaker 2: that power. Right. It was popes, it was leaders, it

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Speaker 2: was rulers, it was people who controlled the media establishment

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Speaker 2: who had that power. And so what I do in

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Speaker 2: the book is I kind of compare this rumor mill,

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Speaker 2: these unofficial narratives that pass from person to person, is,

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Speaker 2: you know, we share information amongst ourselves compared to this powerful,

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Speaker 2: much more top down system. And in the age of

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Speaker 2: the Internet, these two things happen in the same place

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Speaker 2: at the same time, and that power to spread messages

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Speaker 2: to reach millions of people, including with an agenda, is

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Speaker 2: something that we actually all have now.

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Speaker 1: And that's what I think is so interesting about this

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Speaker 1: moment in time.

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Speaker 3: Yeah, yeah, I love the way that you framed the

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Speaker 3: history and how these things got smashed together.

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Speaker 1: Is it just like makes it so?

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Speaker 3: I feel like I developed such a clear understanding of

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Speaker 3: how and why we have gotten to where we have

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Speaker 3: got on the Internet, which is a terrifying place. And

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Speaker 3: before the Internet, you talk a little bit about Gnome

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Speaker 3: Chomsky and Edward Hermann and this idea of manufacturing consent,

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Speaker 3: which is something I'm super interested in and don't don't worry,

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Speaker 3: I will not linger on this for too long.

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Speaker 1: But just set up for people.

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Speaker 3: Like before social media, the reason why there was this

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Speaker 3: idea of media being controlled was because the wealthy were

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Speaker 3: the ones who were able to own the systems that

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Speaker 3: produce the media basically right. And then of course there's

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Speaker 3: advertising incentives and disincentives depending on who is you know,

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Speaker 3: paying the publication to exist, and you don't want to

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Speaker 3: piss them off, so you avoid certain topics as well

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Speaker 3: as like if you have a source in the government,

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Speaker 3: if you read an article that pisses them off, then

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Speaker 3: you might lose that source. So there are all these

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Speaker 3: like different factors that would contribute prior to social media,

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Speaker 3: to the media being controlled by basically like wealthy people

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Speaker 3: to some extent, Right.

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Speaker 2: Yeah, so exactly, so, Chamski writes this book called manufacturing consent.

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Speaker 1: The phrase refers back.

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Speaker 2: To Walter Lippman and this book from theeen late nineteen twenties,

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Speaker 2: early nineteen thirties, which is where the title of my

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Speaker 2: book comes from too, because a lot of the stuff

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Speaker 2: we've known for a full century now, right, this understanding

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Speaker 2: of propaganda and influence and the role that powerful, top down.

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Speaker 1: Figures can play.

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Speaker 2: So what Chomsky says in that book and Herman his

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Speaker 2: co author, is that you have these filters, he calls them,

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Speaker 2: which are incentives that the media owners always have in

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Speaker 2: the back of their mind, maybe in the front of

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Speaker 2: their mind too, as they're thinking about how do we

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Speaker 2: shape our coverage so as not to piss off powerful people.

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Speaker 2: You might be thinking about this right now as you

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Speaker 2: look at some of the way that coverage is shaped

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Speaker 2: so as not to offend particular political administrations.

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Speaker 1: Right.

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Speaker 2: You see this very clearly outside of the United States.

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Speaker 2: Now you're starting to see it much more clearly in

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Speaker 2: the United States also. But that is the dynamic that

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Speaker 2: he writes this book about in the nineteen eighties. Manufacturing

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Speaker 2: concent was written in the nineteen eighties, so prior to

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Speaker 2: the Internet really becoming the thing that it is today,

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Speaker 2: long prior to social media. And so what I wanted

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Speaker 2: to do with the book with Invisible Rulers, which is

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Speaker 2: a reference to Edward Burnet, who was a propagandist in

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Speaker 2: World War One at the same time, who was a

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Speaker 2: contemporary of Lipmann, right, was to say, Okay, now we

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Speaker 2: have a different media ecosystem. It's not newspaper editors and

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Speaker 2: TV broadcasters sitting there thinking what are my incentives and

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Speaker 2: how did my incentives shape back coverage. It's actually more

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Speaker 2: people who are like, how does the social media companies

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Speaker 2: incentives shape what they curate for you? How does the

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Speaker 2: influencer with three million followers decide what to say?

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Speaker 1: Right?

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Speaker 2: Like, what is the incentives of this media ecosystem and

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Speaker 2: are they the same as the last one.

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Speaker 3: Another thing that I think we both found super interesting

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Speaker 3: was like you do have the companies that are know,

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Speaker 3: curating their algorithms or moderating content in a particular way,

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Speaker 3: but also there is just like an inherently human element

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Speaker 3: to this. Can you talk a little bit about two

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Speaker 3: step flow and influence? I thought it was really interesting

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Speaker 3: you mentioned a study I believe where you know, I

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Speaker 3: think traditionally we would think if something is being shown

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Speaker 3: on the news, that is going to shape opinions. But

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Speaker 3: in reality, it's been shown that people's opinions are formed

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Speaker 3: maybe more strongly or equally by their personal connections with

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Speaker 3: other people. It's people that are influencing them directly. Can

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Speaker 3: you speak to that a little bit.

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Speaker 1: Yeah.

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Speaker 2: So that's a study from the nineteen forties and what

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Speaker 2: it looked at some women indicator Illinois, right, and it

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Speaker 2: started to notice that when people talked about how they

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Speaker 2: formed political opinions. There was a presidential election that was

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Speaker 2: happening around that time. As people in the community were

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Speaker 2: talking about how they formed political opinions, what they were

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Speaker 2: saying to the researchers is that it wasn't what was

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Speaker 2: coming to them through their media consumption that was most

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Speaker 2: influential to them. It was who they were talking to

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Speaker 2: and there were a handful of women in the community

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Speaker 2: who were really really plugged into what was happening on

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Speaker 2: the media. And then they would sit there and they

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Speaker 2: would talk with their friends about it. They would talk

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Speaker 2: in their communities. So you can imagine, you know, you

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Speaker 2: would sit there, maybe you're playing cards, maybe you're at

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Speaker 2: a club, maybe you're you know, you're doing your thing

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Speaker 2: with your friends. I remember my grandmother used to come

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Speaker 2: home and I would hear all the gossip about what

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Speaker 2: she you know, about President Reagan. She was in the eighties,

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Speaker 2: and I would hear these things from her, right, because

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Speaker 2: she would just she would be out at her club

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Speaker 2: with her friends. She's an Italian immigrant, right, and she

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Speaker 2: would be talking about these things with her community, her

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Speaker 2: church club.

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Speaker 1: And she would come.

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Speaker 2: Home and she would tell me all about the things

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Speaker 2: she had heard about the American president, and you know,

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Speaker 2: and she would relate them back to me in the

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Speaker 2: context of what she heard talking about her friends talking

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Speaker 2: with their friends at church. And so this was how

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Speaker 2: this is how it happens, right, It's not necessarily what

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Speaker 2: you read in the newspaper or what you hear on television.

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Speaker 2: It's like mediated through this this second step. That's where

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Speaker 2: the two steps comes from. So the two step flow

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Speaker 2: is somebody in media says it. Your influential opinion leader

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Speaker 2: is the term that the researchers give to these influential women.

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Speaker 2: Your opinion leader says it, and then you are forming

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Speaker 2: your opinions in part based on how that opinion leader

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Speaker 2: is mediating that information for you. So you're not necessarily

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Speaker 2: hearing something on TV and magically changing your opinion. It's

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Speaker 2: much more through this process of somebody that I trust,

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Speaker 2: somebody that I like, somebody who's just like me, somebody

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Speaker 2: who's in the community is making this opinion formation.

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Speaker 1: Process happen with me.

438
00:21:33,720 --> 00:21:36,240
Speaker 2: And that's the difference between the old way of thinking

439
00:21:36,280 --> 00:21:39,879
Speaker 2: about it, which was called the hypodermic needle model, which

440
00:21:39,960 --> 00:21:42,400
Speaker 2: was you see this information and magically, as if you've

441
00:21:42,400 --> 00:21:46,360
Speaker 2: been injected, your opinion just changes, and that's not how

442
00:21:46,600 --> 00:21:49,440
Speaker 2: you actually form opinions. A lot of people think that

443
00:21:49,440 --> 00:21:52,160
Speaker 2: that's how it happens to people they don't like, right,

444
00:21:52,359 --> 00:21:55,159
Speaker 2: they think like, oh, well those people over there, you know,

445
00:21:55,720 --> 00:21:58,720
Speaker 2: they watch Fox News and then bam, they've changed their minds. Right,

446
00:21:59,600 --> 00:22:02,320
Speaker 2: But what's actually happening is much more This process of

447
00:22:02,320 --> 00:22:05,080
Speaker 2: like a culturation, Like they hear it, they talk about it,

448
00:22:05,119 --> 00:22:07,360
Speaker 2: they're talking about it with their friends. All of their

449
00:22:07,359 --> 00:22:09,760
Speaker 2: community feels this way, thinks this way, talks about it

450
00:22:09,760 --> 00:22:12,440
Speaker 2: at church, and so there's much more this cultural component.

451
00:22:12,840 --> 00:22:14,800
Speaker 2: And if you think about how you know, you hear

452
00:22:14,840 --> 00:22:17,840
Speaker 2: something crazy on the news, it doesn't magically change your mind.

453
00:22:18,320 --> 00:22:20,639
Speaker 2: So this idea that it would magically change somebody else's

454
00:22:20,680 --> 00:22:24,520
Speaker 2: mind is sort of wrong, But we tend to think that.

455
00:22:24,480 --> 00:22:26,960
Speaker 1: Way anyway, right right. I first want to say that

456
00:22:27,040 --> 00:22:29,720
Speaker 1: when you were speaking, I was imagining your grandma like clubbing.

457
00:22:29,960 --> 00:22:31,960
Speaker 1: When you were saying about the club I was like.

458
00:22:32,080 --> 00:22:35,440
Speaker 2: I was thinking, like like the bridge clubs, and that

459
00:22:35,600 --> 00:22:36,520
Speaker 2: is so sad.

460
00:22:37,160 --> 00:22:43,639
Speaker 1: Yeah, what makes somebody more influential in their community?

461
00:22:44,520 --> 00:22:46,879
Speaker 2: I think it's well, okay, so in this in the

462
00:22:46,920 --> 00:22:48,840
Speaker 2: study that they were doing, it was actually just that

463
00:22:48,840 --> 00:22:49,920
Speaker 2: they had a lot of friends.

464
00:22:50,040 --> 00:22:51,879
Speaker 1: They were just very much plugged in.

465
00:22:51,960 --> 00:22:55,399
Speaker 2: They talked to a lot of people, they were you know,

466
00:22:55,520 --> 00:22:57,439
Speaker 2: there are just certain people in a community who I

467
00:22:57,480 --> 00:23:02,240
Speaker 2: think are highly relatable, very charismatic. You really go above

468
00:23:02,240 --> 00:23:07,280
Speaker 2: and I know, I mean this is where this is

469
00:23:07,280 --> 00:23:09,640
Speaker 2: where that that very fine line when you guys reached out.

470
00:23:09,640 --> 00:23:09,840
Speaker 1: I was.

471
00:23:09,880 --> 00:23:11,840
Speaker 2: I watched a bunch of your past podcasts and your

472
00:23:11,880 --> 00:23:14,640
Speaker 2: experiences and and even just saying, you know, church groups

473
00:23:14,680 --> 00:23:17,480
Speaker 2: sometimes for some people can be like WHOA okay, But

474
00:23:17,560 --> 00:23:22,080
Speaker 2: I think it is that that question of what makes

475
00:23:22,280 --> 00:23:26,960
Speaker 2: people find other people believable, and it's that resonance. Right,

476
00:23:27,040 --> 00:23:30,440
Speaker 2: Are you a good storyteller? Do you have that ability

477
00:23:30,480 --> 00:23:34,800
Speaker 2: to draw people in? And there are you know, there

478
00:23:34,880 --> 00:23:39,880
Speaker 2: are some people who have that that combination of charisma relatability,

479
00:23:39,920 --> 00:23:42,080
Speaker 2: and they're very much out there. And you see that

480
00:23:42,119 --> 00:23:44,800
Speaker 2: in the you know, in your local community too. I imagine,

481
00:23:45,520 --> 00:23:47,040
Speaker 2: you know, I can think of certain of my neighbors

482
00:23:47,040 --> 00:23:50,680
Speaker 2: who have it. Not me, but there are people who

483
00:23:50,680 --> 00:23:54,560
Speaker 2: are just you know, in the neighborhood. They know, they

484
00:23:54,600 --> 00:23:56,800
Speaker 2: know everything, They're friends with everybody. They can tell you

485
00:23:56,840 --> 00:23:59,560
Speaker 2: what's going on in everybody's lives. They're the people who

486
00:24:00,320 --> 00:24:02,320
Speaker 2: check in on the elderly neighbors things like this, right,

487
00:24:02,400 --> 00:24:06,199
Speaker 2: we're just very deeply, deeply plugged in. And so you

488
00:24:06,240 --> 00:24:11,520
Speaker 2: had that in that offline model of socialization, maybe you know,

489
00:24:11,560 --> 00:24:13,159
Speaker 2: in the before we were all on the internet, and

490
00:24:13,160 --> 00:24:15,360
Speaker 2: then if you were to port that into the age

491
00:24:15,400 --> 00:24:17,600
Speaker 2: of social media, this is where you start to see

492
00:24:17,600 --> 00:24:20,360
Speaker 2: that figure of the influencer emerge, right, the people who

493
00:24:20,680 --> 00:24:23,359
Speaker 2: have that and do it kind of at scale with

494
00:24:23,400 --> 00:24:26,400
Speaker 2: an audience that feels like friends. But this is where

495
00:24:26,400 --> 00:24:28,239
Speaker 2: you start to get into some of those dynamics of

496
00:24:28,280 --> 00:24:29,360
Speaker 2: like para sociality.

497
00:24:29,880 --> 00:24:30,080
Speaker 1: Right.

498
00:24:30,320 --> 00:24:32,919
Speaker 3: We see people who seem like they're just like us,

499
00:24:33,000 --> 00:24:36,359
Speaker 3: and so it's sort of simulating that neighborhood vibe, except

500
00:24:36,400 --> 00:24:40,040
Speaker 3: we don't necessarily know where they're getting information or what

501
00:24:40,080 --> 00:24:42,359
Speaker 3: they're getting paid, if what they're getting or who is

502
00:24:42,359 --> 00:24:46,919
Speaker 3: paying them exactly? Can you explain the term bespoke realities

503
00:24:46,920 --> 00:24:48,440
Speaker 3: and how we are all living in them now?

504
00:24:48,760 --> 00:24:51,159
Speaker 2: So bespoke realities, I was trying to come up with

505
00:24:51,200 --> 00:24:54,840
Speaker 2: a way to describe the unique power that we all

506
00:24:54,880 --> 00:24:58,399
Speaker 2: have to just decide to pick and choose.

507
00:24:58,440 --> 00:25:00,920
Speaker 1: Almost like do you remember to choose your own adventure books?

508
00:25:02,200 --> 00:25:02,440
Speaker 2: Yeah?

509
00:25:02,520 --> 00:25:04,399
Speaker 1: Yeah, yeah, I feel like that's a lost art that

510
00:25:04,440 --> 00:25:04,920
Speaker 1: my kids.

511
00:25:05,000 --> 00:25:06,760
Speaker 2: I don't think my kids have ever seen it, choose

512
00:25:06,760 --> 00:25:09,240
Speaker 2: your own adventure book. Actually, I don't know that they

513
00:25:09,320 --> 00:25:13,160
Speaker 2: have them in the age of like Kindle, and maybe

514
00:25:13,200 --> 00:25:14,120
Speaker 2: maybe they do.

515
00:25:14,040 --> 00:25:16,080
Speaker 1: But well, video games kind of have it, you know

516
00:25:16,119 --> 00:25:16,520
Speaker 1: what I mean?

517
00:25:16,840 --> 00:25:19,359
Speaker 2: Yeah, you can kind of fork it I guess, like

518
00:25:19,520 --> 00:25:20,320
Speaker 2: just where you're going.

519
00:25:20,359 --> 00:25:22,280
Speaker 1: But I used to love those when I was a kid.

520
00:25:22,359 --> 00:25:24,200
Speaker 2: But you could also kind of like you know, dogg

521
00:25:24,240 --> 00:25:25,679
Speaker 2: your the page and go and look and see if

522
00:25:25,680 --> 00:25:27,440
Speaker 2: you're going to die on that one and decide.

523
00:25:27,119 --> 00:25:27,639
Speaker 1: To go together.

524
00:25:29,760 --> 00:25:32,639
Speaker 2: But no, it was the Uh, it's the phenomenon where

525
00:25:32,960 --> 00:25:36,879
Speaker 2: you know, you can really choose to self select into

526
00:25:37,080 --> 00:25:41,360
Speaker 2: certain media and topical universes where like this is reality

527
00:25:41,400 --> 00:25:42,879
Speaker 2: for you, Like this is what you're going to see,

528
00:25:43,280 --> 00:25:47,120
Speaker 2: and you have the power now to just say this

529
00:25:47,200 --> 00:25:50,119
Speaker 2: is what it's and it's algorithmically reinforced. Maybe we'll talk

530
00:25:50,160 --> 00:25:52,520
Speaker 2: about that. But like when I was going down my

531
00:25:52,840 --> 00:25:56,600
Speaker 2: anti vaccine rabbit hole, where I was doing it very intentionally, right,

532
00:25:56,640 --> 00:25:58,960
Speaker 2: it was it was a very conscious process. I wanted

533
00:25:59,000 --> 00:26:01,000
Speaker 2: to see what was going to happen. So I did

534
00:26:01,040 --> 00:26:03,399
Speaker 2: go join the flat Earth group, right, I did go

535
00:26:03,480 --> 00:26:06,080
Speaker 2: join the Chemtreals group, and I had a sense of

536
00:26:06,160 --> 00:26:09,919
Speaker 2: what was likely to happen. But that's because once you

537
00:26:10,080 --> 00:26:13,960
Speaker 2: join those groups, what becomes very interesting is that the

538
00:26:14,040 --> 00:26:16,520
Speaker 2: platform wants to keep you there, and so it begins

539
00:26:16,560 --> 00:26:19,520
Speaker 2: to push you more and more of that kind of content,

540
00:26:19,560 --> 00:26:21,480
Speaker 2: begins to recommend to you more and more of that

541
00:26:21,560 --> 00:26:24,320
Speaker 2: kind of content, like you've given it a signal, you've said,

542
00:26:24,359 --> 00:26:25,760
Speaker 2: I am interested in this, and it.

543
00:26:25,720 --> 00:26:26,840
Speaker 1: Says, okay, she wants that.

544
00:26:26,880 --> 00:26:28,320
Speaker 2: We're going to give her more of that, and then

545
00:26:28,320 --> 00:26:29,960
Speaker 2: we're going to show her this other thing that people

546
00:26:30,000 --> 00:26:32,680
Speaker 2: who were kind of similar to her statistically want to

547
00:26:32,680 --> 00:26:34,840
Speaker 2: see more of. Also, even if she's never typed in

548
00:26:34,880 --> 00:26:37,240
Speaker 2: that term, like I never typed in the term QAnon

549
00:26:37,359 --> 00:26:39,080
Speaker 2: when it started to push it to me, it was

550
00:26:39,240 --> 00:26:42,200
Speaker 2: entirely a push, a not a pull, right, yes, And

551
00:26:42,320 --> 00:26:44,439
Speaker 2: so you start to see this process though, where then

552
00:26:44,480 --> 00:26:46,320
Speaker 2: I did go and I joined that group, right, And

553
00:26:46,359 --> 00:26:49,680
Speaker 2: then here's more QAnon stuff, and then here's more posts

554
00:26:49,840 --> 00:26:52,920
Speaker 2: from the q and on groups in the feed because

555
00:26:52,920 --> 00:26:56,280
Speaker 2: these are highly highly active groups because people are in

556
00:26:56,320 --> 00:26:59,440
Speaker 2: there trying to figure out reality. Right, Oh, this q

557
00:26:59,680 --> 00:27:01,440
Speaker 2: drops said this thing, let's all go through it and

558
00:27:01,480 --> 00:27:03,040
Speaker 2: figure out what the hidden meaning is.

559
00:27:03,200 --> 00:27:05,480
Speaker 1: These are highly active, highly.

560
00:27:05,160 --> 00:27:09,040
Speaker 2: Engaged groups, and so the platform decides, oh, that's fantastic,

561
00:27:09,080 --> 00:27:12,240
Speaker 2: here's a highly engaged group. Once somebody's in this, they're

562
00:27:12,280 --> 00:27:16,240
Speaker 2: going to spend a lot more time on our platform.

563
00:27:15,960 --> 00:27:17,280
Speaker 1: If they keep engaging with it.

564
00:27:17,600 --> 00:27:19,960
Speaker 2: And then these become your friends, right, these become the

565
00:27:20,000 --> 00:27:22,560
Speaker 2: people that you spend all of your time with. It

566
00:27:22,600 --> 00:27:25,679
Speaker 2: starts to really supplant the other kinds of stuff that

567
00:27:25,720 --> 00:27:28,360
Speaker 2: you might have seen. You're not seeing the baby pictures,

568
00:27:28,359 --> 00:27:30,880
Speaker 2: you're not seeing the wedding pictures. You're seeing post after

569
00:27:30,920 --> 00:27:34,320
Speaker 2: post after post from this group that you're now spending

570
00:27:34,359 --> 00:27:37,080
Speaker 2: your time with, and gradually, over time, like you have

571
00:27:37,160 --> 00:27:39,240
Speaker 2: the power to say, you know what, I don't want

572
00:27:39,240 --> 00:27:41,320
Speaker 2: to see the stuff that's telling me that this is bullshit.

573
00:27:41,359 --> 00:27:43,240
Speaker 2: I just want to see this right. And so you

574
00:27:43,320 --> 00:27:45,960
Speaker 2: have the ability actually to just say, like, this is

575
00:27:45,960 --> 00:27:49,400
Speaker 2: the information environment that I am now going to self

576
00:27:49,440 --> 00:27:52,080
Speaker 2: select into more and more. And so you almost kind

577
00:27:52,119 --> 00:27:55,320
Speaker 2: of participate in that process yourself. And I'm giving you

578
00:27:55,359 --> 00:27:57,280
Speaker 2: an extreme example in light of like where we are

579
00:27:57,280 --> 00:28:00,600
Speaker 2: today on this podcast, but you can also do it

580
00:28:00,640 --> 00:28:04,560
Speaker 2: and you know, much less, much less extreme groups. You

581
00:28:04,560 --> 00:28:06,720
Speaker 2: can decide, like, you know what, I'm only going to

582
00:28:06,760 --> 00:28:09,119
Speaker 2: see left wing stuff, I'm only going to see right

583
00:28:09,119 --> 00:28:09,600
Speaker 2: wing stuff.

584
00:28:09,600 --> 00:28:11,640
Speaker 1: I'm only going to see dog videos.

585
00:28:11,800 --> 00:28:14,320
Speaker 3: You like want dog, but tail you see seventeen on

586
00:28:14,359 --> 00:28:15,280
Speaker 3: your feed the next day?

587
00:28:15,440 --> 00:28:16,640
Speaker 1: Yeah, yeah.

588
00:28:16,640 --> 00:28:19,040
Speaker 2: And so that's the process where you have the ability

589
00:28:19,080 --> 00:28:21,560
Speaker 2: to just decide like this is my information environment, now

590
00:28:21,760 --> 00:28:22,000
Speaker 2: is it?

591
00:28:22,400 --> 00:28:23,800
Speaker 1: And like this is where I'm going to go.

592
00:28:24,040 --> 00:28:25,920
Speaker 3: But most of the time it's not conscious, right, because

593
00:28:25,920 --> 00:28:28,240
Speaker 3: it's just that we are looking at something and it

594
00:28:28,400 --> 00:28:30,879
Speaker 3: notices how long we're looking at it, or we click

595
00:28:30,920 --> 00:28:34,439
Speaker 3: on it out of curiosity, and then the algorithms will

596
00:28:34,520 --> 00:28:36,399
Speaker 3: learn that that's the kind of content that keeps us

597
00:28:36,400 --> 00:28:37,960
Speaker 3: on the platform and keeps us engaged, so then it

598
00:28:38,000 --> 00:28:40,400
Speaker 3: feeds us more of it. You know, I tend to

599
00:28:40,440 --> 00:28:43,960
Speaker 3: have a very panicked news feed. I don't want that

600
00:28:44,080 --> 00:28:46,480
Speaker 3: to be my feed. It just knows that that's what

601
00:28:46,840 --> 00:28:49,120
Speaker 3: I engage with, you know, that's what will get me

602
00:28:49,160 --> 00:28:50,320
Speaker 3: to spend more time on the app.

603
00:28:50,320 --> 00:28:52,320
Speaker 1: And then I have to like be like, oh wait, stop,

604
00:28:52,320 --> 00:28:54,640
Speaker 1: stop stop. But you know, I have to force that.

605
00:28:54,600 --> 00:28:57,320
Speaker 3: Into my consciousness or I could just go on autopilot forever.

606
00:28:57,360 --> 00:28:59,920
Speaker 1: And another thing that I found interesting that you spoke

607
00:29:00,240 --> 00:29:02,400
Speaker 1: that I've never really thought about. Well, I have kind of,

608
00:29:02,400 --> 00:29:03,800
Speaker 1: but not in the way that you put it. Like

609
00:29:05,320 --> 00:29:10,480
Speaker 1: I'm very susceptible to serendipity and coincidences and stuff like this,

610
00:29:10,600 --> 00:29:15,240
Speaker 1: and these algorithms create almost like a mind reading effect

611
00:29:15,320 --> 00:29:17,760
Speaker 1: where you're like I didn't even say that out loud,

612
00:29:17,840 --> 00:29:20,800
Speaker 1: or like, wow, this is so, this is such a coincidence,

613
00:29:20,960 --> 00:29:23,760
Speaker 1: exactly what I need yes time, yeah, and then it

614
00:29:23,760 --> 00:29:27,160
Speaker 1: feels extra true. I think.

615
00:29:27,640 --> 00:29:30,960
Speaker 2: I think people also don't necessarily realize just how much

616
00:29:31,040 --> 00:29:33,400
Speaker 2: data mining is going into what they think is.

617
00:29:33,360 --> 00:29:35,160
Speaker 1: Serendipitous and personalized for them.

618
00:29:35,720 --> 00:29:40,400
Speaker 2: And this is where just the sheer amount of number

619
00:29:40,400 --> 00:29:43,920
Speaker 2: crunching that's happening behind the scenes, where you think you're

620
00:29:44,040 --> 00:29:47,120
Speaker 2: very unique, and then you realize that it's decided that

621
00:29:47,160 --> 00:29:49,160
Speaker 2: you're really just very much you know, like me living

622
00:29:49,160 --> 00:29:53,080
Speaker 2: in San Francisco doing these things. Obviously, okay, California, these behaviors,

623
00:29:53,080 --> 00:29:57,160
Speaker 2: these things naturally anti vaccine, right like, And it doesn't

624
00:29:57,200 --> 00:30:00,000
Speaker 2: mean necessarily that it is true all of the time.

625
00:30:00,200 --> 00:30:03,840
Speaker 2: It clearly isn't, but just the higher probability is there,

626
00:30:04,280 --> 00:30:06,520
Speaker 2: it is worth it for the algorithm to push it

627
00:30:06,560 --> 00:30:09,040
Speaker 2: to you. And if it doesn't feel serendip it is,

628
00:30:09,040 --> 00:30:11,000
Speaker 2: you're going to ignore it. But if you do feel

629
00:30:11,000 --> 00:30:13,200
Speaker 2: some resonance there, you're going to click. And that's what's

630
00:30:13,200 --> 00:30:15,640
Speaker 2: going to be the thing that then makes it decide

631
00:30:15,680 --> 00:30:17,680
Speaker 2: to go and do it again and again. You're not

632
00:30:17,720 --> 00:30:19,800
Speaker 2: going to notice the ones that don't hit, but the

633
00:30:19,800 --> 00:30:22,200
Speaker 2: ones that do generate some curiosity, it's going to start

634
00:30:22,200 --> 00:30:23,160
Speaker 2: that process.

635
00:30:22,960 --> 00:30:26,520
Speaker 3: Right, So we're being influenced by all of these different factors.

636
00:30:26,520 --> 00:30:27,120
Speaker 1: At the same time.

637
00:30:27,200 --> 00:30:30,040
Speaker 3: We have like people in our communities or people we

638
00:30:30,120 --> 00:30:31,840
Speaker 3: perceive to be in our communities because we have a

639
00:30:31,840 --> 00:30:36,720
Speaker 3: parasocial relationship with them. We have maybe governments or corporations

640
00:30:36,960 --> 00:30:40,000
Speaker 3: or whoever is engaging a marketing team like actively trying

641
00:30:40,040 --> 00:30:41,880
Speaker 3: to sell us stuff. And then we have the algorithm

642
00:30:42,400 --> 00:30:44,680
Speaker 3: trying to get us to stay on the app with

643
00:30:44,720 --> 00:30:47,840
Speaker 3: whatever is going to engage us the most, which of.

644
00:30:47,800 --> 00:30:49,600
Speaker 1: Course makes it very difficult.

645
00:30:49,720 --> 00:30:51,880
Speaker 3: Once you start thinking about it too much, I'm like,

646
00:30:51,960 --> 00:30:54,800
Speaker 3: do I even have my own mind? Like, how do

647
00:30:54,840 --> 00:30:57,960
Speaker 3: you even think for yourself? It seems impossible. Yeah, in

648
00:30:58,000 --> 00:31:01,040
Speaker 3: this day and age, I mean, so, why are we

649
00:31:01,240 --> 00:31:05,200
Speaker 3: so drawn to disinformation and misinformation? What is it about it?

650
00:31:05,480 --> 00:31:08,600
Speaker 2: So? First of all, I think misinformation is a tough word.

651
00:31:08,720 --> 00:31:12,040
Speaker 2: I generally don't love it. I know that a lot

652
00:31:12,080 --> 00:31:14,520
Speaker 2: of people in my field use it. It's one that

653
00:31:14,560 --> 00:31:17,200
Speaker 2: I don't like because I feel like it misdiagnoses the problem.

654
00:31:17,360 --> 00:31:20,760
Speaker 2: So it generally is like somebody who is wrong about something,

655
00:31:20,800 --> 00:31:24,160
Speaker 2: like they get a fact wrong, but misinformation implies that

656
00:31:24,360 --> 00:31:26,960
Speaker 2: if you just gave them a better fact, it would

657
00:31:27,040 --> 00:31:30,080
Speaker 2: change their mind, right, like if you know, And that's

658
00:31:30,120 --> 00:31:31,960
Speaker 2: not what's happening. Like when I was in the anti

659
00:31:32,000 --> 00:31:35,960
Speaker 2: vaccine groups, the people who are in there, they're not

660
00:31:36,080 --> 00:31:41,880
Speaker 2: in there because they haven't heard that vaccines don't cause autism, right,

661
00:31:41,960 --> 00:31:44,760
Speaker 2: or that vaccines don't cause sids. It's that they don't

662
00:31:44,760 --> 00:31:47,040
Speaker 2: believe it, and they don't believe it on such a

663
00:31:47,080 --> 00:31:50,320
Speaker 2: foundational deep level because they don't trust the people who

664
00:31:50,320 --> 00:31:52,440
Speaker 2: are giving them the accurate information.

665
00:31:52,920 --> 00:31:56,200
Speaker 1: So it's not the the information. There's no information gap there.

666
00:31:56,200 --> 00:31:57,719
Speaker 2: It's not like, oh, if we just fact checked this

667
00:31:57,760 --> 00:32:00,480
Speaker 2: one more time, we would solve that problem. It's a

668
00:32:00,520 --> 00:32:03,920
Speaker 2: real trust issue, right, It's very much an issue of trust.

669
00:32:04,080 --> 00:32:06,640
Speaker 2: It's an issue of do you believe an authority figure

670
00:32:06,800 --> 00:32:09,200
Speaker 2: or This is one of the reasons why when you

671
00:32:09,240 --> 00:32:12,120
Speaker 2: look at that progression that the algorithm sends you down,

672
00:32:12,320 --> 00:32:14,640
Speaker 2: there's something that begins to emerge from that. You have

673
00:32:14,680 --> 00:32:19,240
Speaker 2: some commonalities between anti vaccine and chemtrails and flat earth,

674
00:32:19,280 --> 00:32:21,640
Speaker 2: and that these are all roughly speaking, in the realm

675
00:32:21,640 --> 00:32:24,240
Speaker 2: of pseudoscience. But then you might ask, like, what does

676
00:32:24,280 --> 00:32:26,480
Speaker 2: pizzagate have to do with it? What does QAnon have

677
00:32:26,560 --> 00:32:28,640
Speaker 2: to do with it? And the answer is that they're too.

678
00:32:29,000 --> 00:32:32,640
Speaker 2: The sort of core component in those belief systems is

679
00:32:32,680 --> 00:32:36,960
Speaker 2: an incredibly deep distrust of government and incredibly deep distrust

680
00:32:37,040 --> 00:32:40,400
Speaker 2: of authority, a belief that people are actively lying to you,

681
00:32:40,560 --> 00:32:42,840
Speaker 2: that they're keeping the truth from you, that they've concealed

682
00:32:42,880 --> 00:32:46,080
Speaker 2: it for years, and that in order to do that,

683
00:32:46,120 --> 00:32:48,880
Speaker 2: there is like a degree of depravity in the political

684
00:32:48,880 --> 00:32:52,400
Speaker 2: elites that lets them do that. And that's where that

685
00:32:52,440 --> 00:32:56,600
Speaker 2: intersection happens. And that's where like the algorithm doesn't understand

686
00:32:57,160 --> 00:33:00,120
Speaker 2: what it's keying off of. It can't articulate that that

687
00:33:00,240 --> 00:33:03,320
Speaker 2: is the commonality between group A and group B there,

688
00:33:03,520 --> 00:33:05,560
Speaker 2: it just knows that there's a very high degree of overlap,

689
00:33:05,600 --> 00:33:07,440
Speaker 2: and so that's where it's it serves it up on

690
00:33:07,480 --> 00:33:07,920
Speaker 2: the platter.

691
00:33:08,040 --> 00:33:11,080
Speaker 1: Now it doesn't do that anymore. See how the platform

692
00:33:11,160 --> 00:33:12,000
Speaker 1: like change that.

693
00:33:12,080 --> 00:33:14,600
Speaker 2: But that was what was happening at that moment in time,

694
00:33:15,120 --> 00:33:17,520
Speaker 2: back in the twenty sixteen kind of time frame when

695
00:33:17,520 --> 00:33:19,800
Speaker 2: it was doing this twenty sixteen to twenty eighteen timeframe.

696
00:33:20,640 --> 00:33:22,800
Speaker 2: But that's what you're that's what you're starting to see happening, right.

697
00:33:22,880 --> 00:33:25,920
Speaker 2: You're seeing that that question of like what is the

698
00:33:26,720 --> 00:33:30,440
Speaker 2: what is the underlying psychology and motivation that people are

699
00:33:30,480 --> 00:33:33,720
Speaker 2: looking for that makes them not only click on this

700
00:33:33,800 --> 00:33:38,080
Speaker 2: content once, but become a sustained participant in the community.

701
00:33:38,400 --> 00:33:41,680
Speaker 2: And that's where I feel like misinformation is completely inadequate

702
00:33:42,120 --> 00:33:45,320
Speaker 2: as a frame for understanding what's happening there. Do you

703
00:33:45,400 --> 00:33:49,160
Speaker 2: have a term that you like? I actually just like propaganda.

704
00:33:47,880 --> 00:33:53,600
Speaker 2: I feel like it's the best word for the content, right,

705
00:33:53,680 --> 00:33:56,880
Speaker 2: because it does get at this question of what is

706
00:33:56,920 --> 00:34:01,120
Speaker 2: the motivation behind the creators of the content, behind the

707
00:34:01,160 --> 00:34:02,720
Speaker 2: groups that are putting it out.

708
00:34:03,760 --> 00:34:05,760
Speaker 1: They may sincerely believe.

709
00:34:05,440 --> 00:34:08,200
Speaker 2: It, right, The people who are producing that content oftentimes

710
00:34:08,200 --> 00:34:11,400
Speaker 2: generally do sincerely believe it, and so it's content that

711
00:34:11,400 --> 00:34:15,360
Speaker 2: they're producing with a particular motivation, very often a political motivation.

712
00:34:15,440 --> 00:34:15,600
Speaker 1: Right.

713
00:34:15,600 --> 00:34:18,480
Speaker 2: They want to see legislation to do the kinds of

714
00:34:18,480 --> 00:34:20,680
Speaker 2: things that you're seeing, you know, being done in health

715
00:34:20,680 --> 00:34:23,680
Speaker 2: in human services today or at a state level, very

716
00:34:23,719 --> 00:34:26,320
Speaker 2: particular pieces of legislation that they want to see introduced.

717
00:34:27,320 --> 00:34:29,799
Speaker 2: QAnon had a whole set of things that it wanted

718
00:34:29,840 --> 00:34:31,919
Speaker 2: to see happen with regard to show trials and things

719
00:34:32,000 --> 00:34:34,719
Speaker 2: like this, right, So there is a series of outcomes

720
00:34:34,760 --> 00:34:36,600
Speaker 2: that they want to see as a result of the

721
00:34:36,600 --> 00:34:39,200
Speaker 2: beliefs that they hold. And so that's why I feel

722
00:34:39,280 --> 00:34:42,200
Speaker 2: like propaganda is a perfectly adequate word, and I wish

723
00:34:42,280 --> 00:34:45,040
Speaker 2: that we didn't have thirty other words for it, right,

724
00:34:46,040 --> 00:34:50,560
Speaker 2: Disinformation I use and that's where I actually spend like

725
00:34:50,560 --> 00:34:52,480
Speaker 2: I've written a ton of papers on it. In the

726
00:34:52,520 --> 00:34:56,520
Speaker 2: context of state actors, where the question becomes from a

727
00:34:56,560 --> 00:35:01,799
Speaker 2: geopolitical perspective, how do you see nation states actually recognizing

728
00:35:01,840 --> 00:35:04,759
Speaker 2: that this dynamic happens and that they can use it

729
00:35:04,840 --> 00:35:08,879
Speaker 2: to exploit and weaken the societies of their adversaries. And

730
00:35:08,880 --> 00:35:10,839
Speaker 2: that's where when you get at questions like what does

731
00:35:10,920 --> 00:35:11,400
Speaker 2: Russia do?

732
00:35:11,480 --> 00:35:13,160
Speaker 1: What is China do? What does Irando?

733
00:35:13,480 --> 00:35:15,640
Speaker 2: You can name pretty much any country and stick it

734
00:35:15,680 --> 00:35:18,040
Speaker 2: in that sentence there, like the US does it too?

735
00:35:18,920 --> 00:35:22,480
Speaker 2: What is the process by which you can exploit this

736
00:35:22,640 --> 00:35:27,000
Speaker 2: ability to create deep distrust and to divide societies from within?

737
00:35:27,480 --> 00:35:30,759
Speaker 2: That's where I think disinformation campaigns are. That that I

738
00:35:30,760 --> 00:35:32,920
Speaker 2: think is where like the term is actually useful, where

739
00:35:32,960 --> 00:35:35,800
Speaker 2: there's a real clear intentionality.

740
00:35:35,040 --> 00:35:40,280
Speaker 3: There, right right, right, I'm so interested in what's happening

741
00:35:40,360 --> 00:35:44,040
Speaker 3: for all of us, not just conspiracy theorists. Emotionally, when

742
00:35:44,040 --> 00:35:48,120
Speaker 3: we click on something that is clickbaby or is rage baby,

743
00:35:48,280 --> 00:35:50,480
Speaker 3: or you know, like, I feel like so much of

744
00:35:50,520 --> 00:35:55,560
Speaker 3: it seems tied to identity, like I identify as somebody who

745
00:35:56,080 --> 00:35:59,279
Speaker 3: you know, feel or rather I feel special when I

746
00:35:59,360 --> 00:36:02,960
Speaker 3: know something that other people don't know, or I'm a

747
00:36:02,960 --> 00:36:07,280
Speaker 3: good person, And so I'm going to engage in justice campaigns,

748
00:36:07,760 --> 00:36:09,880
Speaker 3: whether they be rooted in reality or not.

749
00:36:11,160 --> 00:36:11,880
Speaker 1: Can you talk.

750
00:36:11,680 --> 00:36:17,399
Speaker 3: About how more extreme content we see more of it

751
00:36:17,880 --> 00:36:20,720
Speaker 3: than other content, but it's actually coming from a smaller

752
00:36:20,800 --> 00:36:22,000
Speaker 3: amount of people.

753
00:36:22,680 --> 00:36:26,920
Speaker 2: Yeah, So there's a term called majority illusion where, you know,

754
00:36:26,920 --> 00:36:29,520
Speaker 2: when we were talking about the opinion leaders who are

755
00:36:29,560 --> 00:36:33,080
Speaker 2: just very well connected and very popular. A lot of times,

756
00:36:33,120 --> 00:36:36,120
Speaker 2: if you if you form an opinion based on what

757
00:36:36,160 --> 00:36:38,760
Speaker 2: it seems like the majority of people around you believe,

758
00:36:39,480 --> 00:36:42,319
Speaker 2: and you're in a particular niche, you're going to think

759
00:36:42,400 --> 00:36:45,719
Speaker 2: that most people believe, you know, whatever, the opinion is

760
00:36:46,239 --> 00:36:49,640
Speaker 2: based on what's around you, whether or not it's actually

761
00:36:49,680 --> 00:36:51,400
Speaker 2: you know, you're not necessarily going to go look at

762
00:36:51,440 --> 00:36:51,920
Speaker 2: polling to.

763
00:36:51,920 --> 00:36:52,600
Speaker 1: Try to figure that out.

764
00:36:52,640 --> 00:36:54,800
Speaker 2: You're going to say, like, well, everybody in my community

765
00:36:54,840 --> 00:36:56,920
Speaker 2: thinks this, that must be what most people believe. So

766
00:36:56,960 --> 00:37:00,239
Speaker 2: you start to see these interesting phenomenon where people people

767
00:37:00,880 --> 00:37:04,840
Speaker 2: decide what is true or what is real based on

768
00:37:04,960 --> 00:37:08,440
Speaker 2: what surrounds them. That's what starts to seem normal. And

769
00:37:08,480 --> 00:37:12,040
Speaker 2: what's interesting on social media is this tendency towards the

770
00:37:12,160 --> 00:37:18,400
Speaker 2: extremes because social media rewards. The Atlantic had kind of

771
00:37:18,440 --> 00:37:20,520
Speaker 2: a Helen Lois. The Atlantic had a nice name for it,

772
00:37:20,560 --> 00:37:23,640
Speaker 2: like the extreme of files, right, people who they don't

773
00:37:23,760 --> 00:37:26,160
Speaker 2: just you know, you're not just expressing a political opinion.

774
00:37:26,239 --> 00:37:29,720
Speaker 2: You're taking like the most extreme version of the opinion

775
00:37:29,800 --> 00:37:33,520
Speaker 2: you possibly can. And this is why everything feels like

776
00:37:33,560 --> 00:37:37,480
Speaker 2: a caricature. There's no normal, middle of the road liberal opinion.

777
00:37:37,480 --> 00:37:41,640
Speaker 2: There's like this crazy, insane you know, like the kinds

778
00:37:41,680 --> 00:37:44,120
Speaker 2: of people, the libs of TikTok goes and like I

779
00:37:44,160 --> 00:37:46,520
Speaker 2: call it nutpicking, right, like grab some random person and

780
00:37:46,520 --> 00:37:49,279
Speaker 2: it's like this is the avatar for like this is

781
00:37:49,320 --> 00:37:50,680
Speaker 2: what liberals actually believe.

782
00:37:50,840 --> 00:37:53,880
Speaker 1: All leftists think this, yes, and you see it on

783
00:37:53,880 --> 00:37:55,719
Speaker 1: the other side too, right, And and so there's just

784
00:37:55,760 --> 00:38:00,400
Speaker 1: these like these sort of you know, kind of extreme fringe.

785
00:38:00,840 --> 00:38:02,920
Speaker 2: You'd have to really kind of delve down into the

786
00:38:02,960 --> 00:38:05,360
Speaker 2: belly of TikTok to find some of this stuff sometimes,

787
00:38:05,360 --> 00:38:06,759
Speaker 2: but they go and they find it, and they pull

788
00:38:06,800 --> 00:38:08,799
Speaker 2: it up and they're like, this is what the left

789
00:38:08,920 --> 00:38:10,360
Speaker 2: or the right actually believes.

790
00:38:10,680 --> 00:38:11,799
Speaker 1: And it moves in.

791
00:38:11,760 --> 00:38:16,480
Speaker 2: This direction where you start to see that idea of

792
00:38:17,160 --> 00:38:20,520
Speaker 2: this extreme belief becoming the thing that people believe that

793
00:38:20,600 --> 00:38:23,440
Speaker 2: a certain political group or identity group actually like.

794
00:38:23,640 --> 00:38:25,200
Speaker 1: They start to think that that is the norm.

795
00:38:25,800 --> 00:38:28,080
Speaker 2: And you can see this actually reflected in political science

796
00:38:28,080 --> 00:38:30,239
Speaker 2: surveys where they'll go and they'll pull people on, like

797
00:38:30,280 --> 00:38:33,480
Speaker 2: what do you think the majority of conservatives or liberals believe?

798
00:38:33,480 --> 00:38:35,719
Speaker 2: And they'll list a whole bunch of different opinions, and

799
00:38:35,760 --> 00:38:38,239
Speaker 2: they will have the actual polling, right, what percentage of

800
00:38:38,360 --> 00:38:40,480
Speaker 2: liberals believe in defunding the police?

801
00:38:40,560 --> 00:38:41,640
Speaker 1: Right? It's actually a very very.

802
00:38:41,480 --> 00:38:44,200
Speaker 2: Small number, but conservatives will tell you that like ninety

803
00:38:44,239 --> 00:38:46,200
Speaker 2: nine percent of liberals I just made that stat up

804
00:38:46,480 --> 00:38:48,879
Speaker 2: believe in defunding the police because that's what they think

805
00:38:48,880 --> 00:38:51,480
Speaker 2: they're seeing on Twitter, for example.

806
00:38:51,239 --> 00:38:54,080
Speaker 1: Right, it is what they're seeing on Twitter, right exactly,

807
00:38:54,160 --> 00:38:54,400
Speaker 1: you know.

808
00:38:54,800 --> 00:38:57,720
Speaker 2: And that's because you start to like, as that becomes

809
00:38:57,760 --> 00:39:01,799
Speaker 2: identified as like the liberal belief, for example, people who

810
00:39:01,880 --> 00:39:04,760
Speaker 2: don't hold that belief will actually be kind of quiet.

811
00:39:05,120 --> 00:39:06,080
Speaker 1: They will self censor.

812
00:39:06,160 --> 00:39:09,200
Speaker 2: They don't want to be seen as expressing something that

813
00:39:09,320 --> 00:39:12,719
Speaker 2: is not the correct belief or the normal belief. And

814
00:39:12,760 --> 00:39:14,319
Speaker 2: then on the other and so it actually kind of

815
00:39:14,360 --> 00:39:17,120
Speaker 2: reinforces that tendency, and so you see groups that will

816
00:39:17,120 --> 00:39:19,920
Speaker 2: tend towards the extreme. And then on social media, that

817
00:39:20,040 --> 00:39:25,160
Speaker 2: process of mocking and creating this kind of intergroup warfare

818
00:39:25,800 --> 00:39:28,480
Speaker 2: makes it even more costly to speak up and say like,

819
00:39:28,560 --> 00:39:31,880
Speaker 2: I don't actually think that right, So you see the moderates,

820
00:39:31,960 --> 00:39:33,799
Speaker 2: the people who and I don't mean moderates in like

821
00:39:33,800 --> 00:39:36,520
Speaker 2: a political identity sense, I just mean people who don't

822
00:39:36,520 --> 00:39:40,120
Speaker 2: hold extreme beliefs, actually become more and more silent. They

823
00:39:40,200 --> 00:39:42,719
Speaker 2: choose to self censor rather than to speak up. So

824
00:39:42,960 --> 00:39:45,719
Speaker 2: it actually really trends towards the extremes. And then the

825
00:39:45,800 --> 00:39:50,279
Speaker 2: algorithm also is rewarding that by surfacing high engagement, high

826
00:39:50,360 --> 00:39:53,960
Speaker 2: emotionally resonant content. So you have that process be even

827
00:39:54,040 --> 00:39:59,000
Speaker 2: more further rewarded for the influencers themselves, who can actually

828
00:39:59,040 --> 00:40:04,080
Speaker 2: make money by being those highly highly divisive content producers

829
00:40:04,120 --> 00:40:06,279
Speaker 2: as well, so there's a couple of different incentives that

830
00:40:06,320 --> 00:40:09,000
Speaker 2: intersect that move people really out into the extremes.

831
00:40:09,200 --> 00:40:11,920
Speaker 3: Yeah, with all of this stuff, it's so tempting to

832
00:40:11,960 --> 00:40:15,240
Speaker 3: be like and it's all because of the shadowy figures

833
00:40:15,239 --> 00:40:17,839
Speaker 3: at the top. But there's like, like with the manufacturing

834
00:40:17,840 --> 00:40:21,640
Speaker 3: consent propaganda model, there's a variety of filters working together

835
00:40:22,000 --> 00:40:25,239
Speaker 3: at the same time to make this happen all at once.

836
00:40:25,280 --> 00:40:28,000
Speaker 3: It's not just the one. It's not just Russia, or

837
00:40:28,280 --> 00:40:31,320
Speaker 3: it's not just whatever government. It's a lot of things happening,

838
00:40:31,320 --> 00:40:34,319
Speaker 3: including just like human tendencies.

839
00:40:34,480 --> 00:40:37,080
Speaker 1: Yeah, and I like you said somewhere in the book,

840
00:40:37,280 --> 00:40:41,360
Speaker 1: well you quoted Facebook where it said our algorithms exploit

841
00:40:41,440 --> 00:40:45,279
Speaker 1: the human brain's attraction to divisiveness, and that just like

842
00:40:45,640 --> 00:40:49,480
Speaker 1: goes to show you have a the other and we

843
00:40:49,520 --> 00:40:52,800
Speaker 1: are addicted to hating the other, and it'll take us.

844
00:40:53,320 --> 00:40:55,880
Speaker 1: I've gotten like twelve hours on my phone before just

845
00:40:55,920 --> 00:40:59,920
Speaker 1: hating other people. What you've racked up twelve hours on

846
00:41:00,120 --> 00:41:03,280
Speaker 1: my phone? Jesus Christ.

847
00:41:04,160 --> 00:41:06,480
Speaker 2: I think I think it's actually like it's an honest admission, right,

848
00:41:06,520 --> 00:41:08,719
Speaker 2: It's very easy to do. And there's a there's a

849
00:41:08,760 --> 00:41:12,160
Speaker 2: phrase and like you know, trust and safety and tech

850
00:41:12,200 --> 00:41:14,120
Speaker 2: policy in the fields that I work in, which is

851
00:41:14,120 --> 00:41:15,240
Speaker 2: like the problem with social.

852
00:41:15,080 --> 00:41:19,120
Speaker 1: Media is people, Right, that's the that's the that's like

853
00:41:19,239 --> 00:41:20,479
Speaker 1: a great joke.

854
00:41:20,600 --> 00:41:25,880
Speaker 2: But because I think there's and this is where a

855
00:41:25,920 --> 00:41:28,240
Speaker 2: lot of times I'll get, yeah, you'll get media inquiries

856
00:41:28,239 --> 00:41:30,279
Speaker 2: that wants you to talk about the algorithm right where,

857
00:41:30,320 --> 00:41:32,239
Speaker 2: which is always like one word, the algorithm, like the

858
00:41:32,280 --> 00:41:37,080
Speaker 2: only one which is there and it's real. And you know,

859
00:41:37,120 --> 00:41:40,120
Speaker 2: as we were talking about it, maybe is unreasonable to

860
00:41:40,160 --> 00:41:44,440
Speaker 2: expect people to realize why the recommender system is pushing

861
00:41:44,520 --> 00:41:46,880
Speaker 2: them that thing, right, I feel serendipitous. You know, you

862
00:41:46,920 --> 00:41:48,520
Speaker 2: know this is not a I think it's actually kind

863
00:41:48,520 --> 00:41:52,120
Speaker 2: of unreasonable to expect the average person to understand how

864
00:41:52,120 --> 00:41:53,240
Speaker 2: it works and what it's doing.

865
00:41:53,280 --> 00:41:54,560
Speaker 1: And you know, this is this is not.

866
00:41:54,480 --> 00:41:56,560
Speaker 2: You know, you shouldn't need like like a you know,

867
00:41:56,880 --> 00:41:59,040
Speaker 2: some like operating license to use it.

868
00:42:00,120 --> 00:42:02,279
Speaker 1: But you know I do. I do, like you know,

869
00:42:02,280 --> 00:42:02,520
Speaker 1: I have.

870
00:42:02,760 --> 00:42:04,759
Speaker 2: I've an eleven year old who likes YouTube a lot,

871
00:42:05,080 --> 00:42:08,600
Speaker 2: and I am always trying to explain to him what

872
00:42:08,640 --> 00:42:11,080
Speaker 2: the AutoPlay function does and why it's evil. You know,

873
00:42:13,080 --> 00:42:17,200
Speaker 2: you know, no, you do like this is not to

874
00:42:17,280 --> 00:42:18,000
Speaker 2: benefit you.

875
00:42:18,280 --> 00:42:20,520
Speaker 1: It's just to benefit it, right, this.

876
00:42:20,400 --> 00:42:22,640
Speaker 2: Is why it's showing this to you, you know, this

877
00:42:22,680 --> 00:42:25,399
Speaker 2: is why, Like, let me explain to you how much

878
00:42:25,680 --> 00:42:29,320
Speaker 2: money the streamer you are watching makes based on keeping

879
00:42:29,360 --> 00:42:31,000
Speaker 2: you there for this.

880
00:42:30,960 --> 00:42:32,120
Speaker 1: Like hour long video.

881
00:42:32,160 --> 00:42:35,640
Speaker 2: Let's talk about how many ads are interspersed in this content.

882
00:42:35,760 --> 00:42:37,600
Speaker 1: Let's go and look at the view counts, Like, let

883
00:42:37,640 --> 00:42:38,520
Speaker 1: me do the math for.

884
00:42:38,520 --> 00:42:41,279
Speaker 2: You and explain how much money. And this is not

885
00:42:41,320 --> 00:42:43,919
Speaker 2: to knock this streamer's ability to make money, like God

886
00:42:43,920 --> 00:42:46,520
Speaker 2: bless him. Like he's out there, you know, playing video games.

887
00:42:46,560 --> 00:42:48,319
Speaker 2: God knows how many hours a day. But let me

888
00:42:48,360 --> 00:42:50,680
Speaker 2: explain to you, like the math and the incentives and

889
00:42:50,719 --> 00:42:54,120
Speaker 2: the dynamics that are happening right here. And and I

890
00:42:54,160 --> 00:42:56,279
Speaker 2: think that doesn't mean that he listens to me, right,

891
00:42:56,320 --> 00:42:58,640
Speaker 2: I still fight with him and we go through all

892
00:42:58,719 --> 00:43:01,879
Speaker 2: kinds of Like turns out if you paste a URL,

893
00:43:02,000 --> 00:43:02,879
Speaker 2: moms listen to this one.

894
00:43:03,000 --> 00:43:04,120
Speaker 1: So if you paste a U.

895
00:43:04,239 --> 00:43:06,200
Speaker 2: R L from YouTube into Google docs, you can just

896
00:43:06,239 --> 00:43:07,680
Speaker 2: watch it embedded in Google Docs.

897
00:43:07,680 --> 00:43:10,759
Speaker 1: So even if you're blocking YouTube, it'll AutoPlay in the

898
00:43:10,880 --> 00:43:11,479
Speaker 1: Google doc.

899
00:43:11,600 --> 00:43:16,239
Speaker 2: Yes, yes, So basically you're trying to like appeal to

900
00:43:16,239 --> 00:43:18,080
Speaker 2: your kids better nature even as they're trying to just

901
00:43:18,080 --> 00:43:19,160
Speaker 2: find a way to get around.

902
00:43:18,920 --> 00:43:19,800
Speaker 1: The parental controls.

903
00:43:20,800 --> 00:43:23,120
Speaker 2: But these are the sorts of things where you know,

904
00:43:23,200 --> 00:43:27,480
Speaker 2: it is incredibly entertaining, It really taps into it knows

905
00:43:27,520 --> 00:43:28,640
Speaker 2: exactly what you want.

906
00:43:28,760 --> 00:43:32,840
Speaker 1: It is very attuned to you. And I think the

907
00:43:32,880 --> 00:43:34,760
Speaker 1: combination though, is of two things.

908
00:43:34,800 --> 00:43:37,120
Speaker 2: One it's helping people understand like how it works, I

909
00:43:37,120 --> 00:43:39,360
Speaker 2: think is actually very important. But then on the other side,

910
00:43:40,320 --> 00:43:43,400
Speaker 2: it is trying to create these like circuit breakers that

911
00:43:43,920 --> 00:43:46,200
Speaker 2: maybe give people a little bit more in the way

912
00:43:46,239 --> 00:43:49,479
Speaker 2: of the ability to not fall down those those rabbit

913
00:43:49,520 --> 00:43:51,360
Speaker 2: holes quite so, quite so easily.

914
00:43:51,719 --> 00:43:52,480
Speaker 1: That'd be nice.

915
00:43:52,600 --> 00:43:56,080
Speaker 3: Yeah, yeah, I will ask you more about that. But

916
00:43:56,440 --> 00:44:01,000
Speaker 3: I as somebody who's like trying to an album right

917
00:44:01,000 --> 00:44:04,520
Speaker 3: now on social media, which is a fucking nightmare. I

918
00:44:04,600 --> 00:44:07,720
Speaker 3: hate it more than anything I've ever done. But please

919
00:44:07,760 --> 00:44:11,000
Speaker 3: listen to my album. It's called crost Oh oh my god.

920
00:44:13,320 --> 00:44:15,759
Speaker 3: I notice it in myself and I've talked about it

921
00:44:15,800 --> 00:44:18,000
Speaker 3: a little bit on the podcast. Like the posts of

922
00:44:18,040 --> 00:44:20,600
Speaker 3: mine that do the best, that go viral tend to

923
00:44:20,640 --> 00:44:24,960
Speaker 3: be the more polarizing ones, and they are like my

924
00:44:25,080 --> 00:44:28,040
Speaker 3: genuine opinions, like I'm not posting anything that I don't believe.

925
00:44:28,160 --> 00:44:30,320
Speaker 3: But sometimes we'll catch myself and I'll be like, Okay,

926
00:44:30,320 --> 00:44:32,440
Speaker 3: why is it that I'm trying, Why is it that

927
00:44:32,440 --> 00:44:34,719
Speaker 3: this is what I'm posting right now? It's because I

928
00:44:34,800 --> 00:44:37,839
Speaker 3: know that, like it might make people angry, which makes

929
00:44:37,840 --> 00:44:40,520
Speaker 3: people click, and I don't want that to be my

930
00:44:40,760 --> 00:44:43,960
Speaker 3: reasoning for anything that I create, you know what I mean.

931
00:44:44,040 --> 00:44:46,520
Speaker 3: But like we live in a landscape right now where

932
00:44:46,960 --> 00:44:49,560
Speaker 3: first of all, AI has taken over everything. I mean

933
00:44:49,640 --> 00:44:53,320
Speaker 3: people in all industries, like we're in precarious financial positions.

934
00:44:53,920 --> 00:44:57,200
Speaker 3: Influencer or person who is reaching people with their art

935
00:44:57,280 --> 00:44:59,839
Speaker 3: like is still a viable option on social media, So

936
00:45:00,480 --> 00:45:04,759
Speaker 3: balancing trying to reach people with like trying not to

937
00:45:05,280 --> 00:45:09,120
Speaker 3: let that incentivization like brought your brain.

938
00:45:08,960 --> 00:45:12,400
Speaker 1: It's immune to it even if you know what's happening. Yeah,

939
00:45:12,480 --> 00:45:13,000
Speaker 1: I mean.

940
00:45:13,040 --> 00:45:15,279
Speaker 3: It's icky, but I also like I don't know how

941
00:45:15,280 --> 00:45:17,440
Speaker 3: to escape it. Like I feel like it's the entire

942
00:45:17,440 --> 00:45:19,560
Speaker 3: aquarium and I could, like someone would have to like

943
00:45:19,640 --> 00:45:22,240
Speaker 3: dump the aquarium out, you know, Yes, yeah.

944
00:45:22,120 --> 00:45:25,239
Speaker 2: No, You're absolutely right, And it's the feeling of if

945
00:45:25,280 --> 00:45:27,560
Speaker 2: you don't do it, someone else will, And that is

946
00:45:27,640 --> 00:45:29,319
Speaker 2: the issue of you know, it's.

947
00:45:29,239 --> 00:45:31,160
Speaker 1: The attention game. And I get it.

948
00:45:31,239 --> 00:45:32,800
Speaker 2: I you know, I used to be very very active

949
00:45:32,840 --> 00:45:35,960
Speaker 2: on Twitter, including as a you know, political person with

950
00:45:36,080 --> 00:45:38,520
Speaker 2: opinions in San Francisco. Right, I lived in San Francisco.

951
00:45:38,520 --> 00:45:39,960
Speaker 2: I write about this like a tiny bit in the

952
00:45:40,040 --> 00:45:41,600
Speaker 2: in the book. You see it like kind of peek

953
00:45:41,640 --> 00:45:44,160
Speaker 2: through here and there where I'm writing about, you know,

954
00:45:44,239 --> 00:45:46,319
Speaker 2: my frustrations with the school board and stuff like that,

955
00:45:46,360 --> 00:45:48,320
Speaker 2: and I'm like, all right, I know how this works.

956
00:45:48,880 --> 00:45:51,120
Speaker 1: Yeah, I bet you had some banger tweets.

957
00:45:53,239 --> 00:45:59,399
Speaker 2: Well, it's the it's the you know, morally righteous indignation

958
00:45:59,760 --> 00:46:03,560
Speaker 2: is the tone that it keys off of, and people

959
00:46:03,640 --> 00:46:05,520
Speaker 2: will respond to that unfortunately.

960
00:46:05,640 --> 00:46:06,000
Speaker 1: Yeah.

961
00:46:06,160 --> 00:46:09,239
Speaker 2: And I say unfortunately because because they then feel like

962
00:46:09,360 --> 00:46:13,200
Speaker 2: they are also in the fight, right, in the righteous fight,

963
00:46:13,440 --> 00:46:16,960
Speaker 2: and it is the thing that will get people off

964
00:46:17,000 --> 00:46:20,120
Speaker 2: the fence. And it's not bad. I mean I think

965
00:46:20,160 --> 00:46:22,560
Speaker 2: that that's what activism is. And this is the this

966
00:46:22,640 --> 00:46:24,919
Speaker 2: is the ecosystem that we live in now. And as

967
00:46:24,960 --> 00:46:26,920
Speaker 2: you know, when you have something creative that you want

968
00:46:26,960 --> 00:46:29,000
Speaker 2: to promote, there is that same dynamic of like you

969
00:46:29,160 --> 00:46:31,200
Speaker 2: made something beautiful and you want people to see it.

970
00:46:31,239 --> 00:46:34,120
Speaker 2: And so this is the ecosystem that you have to

971
00:46:34,120 --> 00:46:35,320
Speaker 2: operate with it. I mean I had to promote a

972
00:46:35,320 --> 00:46:38,680
Speaker 2: book that's even harder because there's like nothing visual, right,

973
00:46:39,800 --> 00:46:43,680
Speaker 2: screenshot the cover a couple more times, right, But no,

974
00:46:43,840 --> 00:46:46,799
Speaker 2: it's it is that question of like how do you

975
00:46:46,960 --> 00:46:51,840
Speaker 2: break through? And that is that challenge of what do

976
00:46:51,880 --> 00:46:54,560
Speaker 2: you do when people are locked into a feed that

977
00:46:54,600 --> 00:46:57,279
Speaker 2: they don't control? And so some of what I write

978
00:46:57,320 --> 00:46:59,880
Speaker 2: about from a standpoint of if you were redesigning, as

979
00:47:00,239 --> 00:47:03,279
Speaker 2: we're designing a better social media, which is part of

980
00:47:03,320 --> 00:47:05,600
Speaker 2: what I work on you. I'm a professor at Georgetown.

981
00:47:05,640 --> 00:47:07,200
Speaker 2: Part of what I study is like can you design

982
00:47:07,239 --> 00:47:07,880
Speaker 2: a better system?

983
00:47:07,920 --> 00:47:08,080
Speaker 1: Right?

984
00:47:09,360 --> 00:47:11,359
Speaker 2: One of the things whatever you think of Blue Sky,

985
00:47:11,360 --> 00:47:13,360
Speaker 2: I know it has a reputation as like lib Twitter,

986
00:47:13,440 --> 00:47:18,720
Speaker 2: and it has its own problems with vociferous, insular, not picking,

987
00:47:19,120 --> 00:47:22,080
Speaker 2: but but it has feeds where you can just kind

988
00:47:22,080 --> 00:47:24,280
Speaker 2: of like click and change from one to the next.

989
00:47:24,320 --> 00:47:27,440
Speaker 1: And I actually love that because it's a way for

990
00:47:27,640 --> 00:47:29,240
Speaker 1: users to see.

991
00:47:29,280 --> 00:47:32,120
Speaker 2: In that immediate moment, like what does it look like

992
00:47:32,160 --> 00:47:34,000
Speaker 2: if you actually do just want to have the dog

993
00:47:34,040 --> 00:47:36,200
Speaker 2: feed for a change, Like if you just want to

994
00:47:36,239 --> 00:47:37,520
Speaker 2: turn off the politics, like.

995
00:47:37,480 --> 00:47:38,319
Speaker 1: What does this look like?

996
00:47:38,960 --> 00:47:41,920
Speaker 2: And I think more than anything else when you get

997
00:47:41,960 --> 00:47:44,640
Speaker 2: people using it, and I do like interviews where I

998
00:47:44,680 --> 00:47:46,520
Speaker 2: ask people how does it make them? You know, how

999
00:47:46,520 --> 00:47:48,600
Speaker 2: does make them feel? Why do they create fees? I

1000
00:47:48,600 --> 00:47:51,000
Speaker 2: ask a lot of feed creators this question, and they

1001
00:47:51,040 --> 00:47:53,640
Speaker 2: just say, like, I wanted to create a feed that

1002
00:47:53,800 --> 00:47:57,080
Speaker 2: highlighted art, for example, is one and just so that

1003
00:47:57,120 --> 00:47:59,239
Speaker 2: I could go and use it and just see a

1004
00:47:59,239 --> 00:48:01,600
Speaker 2: bunch of art posted a bunch of politics and other things,

1005
00:48:01,600 --> 00:48:04,000
Speaker 2: and I wanted other people to have that experience. And

1006
00:48:04,040 --> 00:48:06,640
Speaker 2: when they talk about these things, it's really interesting because

1007
00:48:06,680 --> 00:48:11,239
Speaker 2: they see it as a way to give people more

1008
00:48:11,280 --> 00:48:14,960
Speaker 2: control and create an experience that doesn't have that that

1009
00:48:15,000 --> 00:48:18,080
Speaker 2: feeling where you come away just feeling exhausted and bad.

1010
00:48:18,200 --> 00:48:21,160
Speaker 1: And it also to them feels like.

1011
00:48:21,120 --> 00:48:23,440
Speaker 2: They're giving users the you know, they're giving other users

1012
00:48:23,480 --> 00:48:25,759
Speaker 2: like them the ability to just have a little bit

1013
00:48:25,760 --> 00:48:28,960
Speaker 2: more agency and control over their own experience. And this

1014
00:48:29,040 --> 00:48:31,600
Speaker 2: is the sort of thing that I think most of

1015
00:48:31,640 --> 00:48:35,120
Speaker 2: the big platforms don't do because it doesn't keep you

1016
00:48:35,160 --> 00:48:37,279
Speaker 2: on site, and it does mean that, you know, the

1017
00:48:37,719 --> 00:48:39,920
Speaker 2: people who want to push ads at you are not

1018
00:48:40,080 --> 00:48:43,680
Speaker 2: having that necessarily, that integral experience so interesting.

1019
00:48:43,840 --> 00:48:47,000
Speaker 1: It's so crazy that all of this is just for

1020
00:48:47,080 --> 00:48:52,600
Speaker 1: people to sell like plastic pieces of shit. Yeah, it's

1021
00:48:52,719 --> 00:48:54,480
Speaker 1: so insane.

1022
00:48:54,600 --> 00:48:58,120
Speaker 3: Well, and or to sell ideas. I mean, yeah, yeah,

1023
00:48:58,280 --> 00:48:59,680
Speaker 3: it's amazing. It's all to make money.

1024
00:48:59,760 --> 00:49:01,240
Speaker 1: Yeah.

1025
00:49:01,280 --> 00:49:03,640
Speaker 3: And that's where we will leave part one for now.

1026
00:49:03,719 --> 00:49:06,920
Speaker 3: Come back next week for part two for even more information.

1027
00:49:07,760 --> 00:49:11,240
Speaker 3: And Megan, I don't know, tell me a content story.

1028
00:49:11,560 --> 00:49:15,880
Speaker 1: Oh okay, well this is this is like very the

1029
00:49:16,000 --> 00:49:20,040
Speaker 1: light version of how this can go. Essentially, there was

1030
00:49:20,080 --> 00:49:23,719
Speaker 1: this horse, like a pony with rain boots on that

1031
00:49:23,880 --> 00:49:27,319
Speaker 1: was obviously AI generated, and I rode under it. This

1032
00:49:27,400 --> 00:49:31,319
Speaker 1: is my horse, gumpy and tag my friend just I

1033
00:49:31,360 --> 00:49:33,279
Speaker 1: don't know why I thought it was funny. I woke

1034
00:49:33,360 --> 00:49:35,720
Speaker 1: up the next day. I'm not really active on the internet,

1035
00:49:35,760 --> 00:49:40,319
Speaker 1: like the social media thing, thousands of comments that is

1036
00:49:40,360 --> 00:49:44,239
Speaker 1: not your horse, Yes it is her horse. No it's not.

1037
00:49:50,560 --> 00:49:57,719
Speaker 1: That's so stupid. And I was like, oh my yeah.

1038
00:49:57,760 --> 00:49:59,920
Speaker 1: So yeah, the back and forth and then somebody of

1039
00:50:00,040 --> 00:50:02,400
Speaker 1: it's like my bad, okay, maybe it's her horse, you know,

1040
00:50:02,400 --> 00:50:04,319
Speaker 1: and about other person's like no worry is like it

1041
00:50:04,360 --> 00:50:06,040
Speaker 1: happens to all of us, and I was just like

1042
00:50:06,120 --> 00:50:08,920
Speaker 1: oh my god. And again, like you know, this was

1043
00:50:08,960 --> 00:50:14,480
Speaker 1: back before I before you were yes, so yeah, I

1044
00:50:14,520 --> 00:50:17,240
Speaker 1: just I think that's, you know, a funny little example.

1045
00:50:17,280 --> 00:50:19,960
Speaker 1: But people are getting more savvy, are they.

1046
00:50:20,560 --> 00:50:24,319
Speaker 3: I have been sent like seven Sora videos this week

1047
00:50:24,360 --> 00:50:26,440
Speaker 3: from people not like noticing.

1048
00:50:26,080 --> 00:50:28,239
Speaker 1: No well the past three weeks.

1049
00:50:28,239 --> 00:50:31,040
Speaker 3: But yeah, yeah, oh my god, no, No, it's I mean,

1050
00:50:31,080 --> 00:50:33,239
Speaker 3: when you log into TikTok, like, no, I know, but

1051
00:50:33,360 --> 00:50:34,480
Speaker 3: it says Sora.

1052
00:50:34,560 --> 00:50:35,719
Speaker 1: Yeah, but not immediately.

1053
00:50:36,120 --> 00:50:38,799
Speaker 3: It usually takes like five seconds and like by the

1054
00:50:38,840 --> 00:50:41,040
Speaker 3: time we see it, like we're looking at the video.

1055
00:50:41,160 --> 00:50:43,200
Speaker 1: So it's like it's like the Gorilla.

1056
00:50:42,840 --> 00:50:45,239
Speaker 3: Experiment right right with the ball if you all don't

1057
00:50:45,280 --> 00:50:48,560
Speaker 3: remember google the Gorilla experiment. Basically, it's just an example

1058
00:50:48,600 --> 00:50:51,120
Speaker 3: of like you can be really focused on something and

1059
00:50:51,120 --> 00:50:53,719
Speaker 3: miss something else, really blatant in your face because your

1060
00:50:53,719 --> 00:50:55,279
Speaker 3: focus is it directed toward.

1061
00:50:55,040 --> 00:50:55,520
Speaker 1: The other thing.

1062
00:50:55,640 --> 00:50:58,880
Speaker 3: Sure, we've definitely talked about the Grilla experiment.

1063
00:50:59,000 --> 00:51:00,920
Speaker 1: I just want people to watch it because I was

1064
00:51:00,960 --> 00:51:03,879
Speaker 1: like really into it and was like, damn I missed it.

1065
00:51:03,880 --> 00:51:05,160
Speaker 1: It's so interesting.

1066
00:51:05,640 --> 00:51:08,839
Speaker 3: Yeah, just another example of bespokerele You know, whatever we're

1067
00:51:08,880 --> 00:51:10,120
Speaker 3: focused on is the thing we.

1068
00:51:10,120 --> 00:51:13,560
Speaker 1: See yup, yep, yep, yep. Anyway, thank you for spending

1069
00:51:13,640 --> 00:51:17,960
Speaker 1: another week with us. Make sure to rate us five stars.

1070
00:51:18,000 --> 00:51:20,759
Speaker 1: We have merched now, we have merched now, big deal.

1071
00:51:21,239 --> 00:51:23,319
Speaker 1: Uh so do all the things. And we can't wait

1072
00:51:23,360 --> 00:51:26,120
Speaker 1: to see you again next week. And as always, remember

1073
00:51:26,200 --> 00:51:29,279
Speaker 1: to follow your gut, watch out for rad flex, and

1074
00:51:29,560 --> 00:51:35,799
Speaker 1: never ever trust me. This has been an exactly right

1075
00:51:35,840 --> 00:51:39,480
Speaker 1: production hosted by me Lola Blanc and Me Megan Elizabeth.

1076
00:51:39,680 --> 00:51:42,760
Speaker 1: Our senior producer is Gee Holly. This episode was mixed

1077
00:51:42,760 --> 00:51:46,239
Speaker 1: by John Bradley. Our associate producer is Christina Chamberlain, and

1078
00:51:46,280 --> 00:51:49,360
Speaker 1: our guest booker is Patrick Kottner. Our theme song was

1079
00:51:49,360 --> 00:51:52,880
Speaker 1: composed by Holly amber Church. Trust Me as executive produced

1080
00:51:52,880 --> 00:51:56,240
Speaker 1: by Karen Kilgareth Georgia Hartstark and Danielle Kramer.

1081
00:51:56,480 --> 00:51:58,760
Speaker 3: You can find us on Instagram at trust Me podcast

1082
00:51:58,920 --> 00:52:01,040
Speaker 3: or on TikTok at trust Me Cult Podcast.

1083
00:52:01,360 --> 00:52:04,440
Speaker 1: Got your own story about cults, extreme belief, our manipulation,

1084
00:52:04,800 --> 00:52:07,800
Speaker 1: Shoot us an email at trustmepod at gmail dot com.

1085
00:52:07,880 --> 00:52:10,799
Speaker 3: Listen to trust Me on the iHeartRadio app, Apple Podcasts,

1086
00:52:10,880 --> 00:52:12,480
Speaker 3: or wherever you get your podcasts.