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Speaker 1: There are No Girls on the Internet. As a production

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Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Todd and this

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Speaker 1: is there are no Girls on the Internet, so this

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Speaker 1: is kind of an emergency episodes, so I have to

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Speaker 1: bring in the production dream team for the first time

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Speaker 1: ever on the podcast.

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Speaker 2: We have both Joey our resident.

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Speaker 3: Fwifty Yes hello, Bridge, of course.

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Speaker 1: I'm so glad here here, and producer Mike, our resident

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Speaker 1: data analyst data scientists. What do you what is your

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Speaker 1: preferred how do you identify?

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Speaker 3: Uh, social scientists identify as a social scientist, as a

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Speaker 3: psychologist if pressed. But I'm like pretty cross disciplinary. But

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Speaker 3: data analyst is certainly correct. I have analyzed a lot

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Speaker 3: of data and I continue to. And yes, thanks for

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Speaker 3: having here, Joey. It's so nice to be on an

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Speaker 3: episode with you. It is the listeners might not realize. Yeah,

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Speaker 3: this is our first episode on air together and in

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Speaker 3: fact this first time we've ever heard each other's voices

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Speaker 3: in real time. Yeah, I say.

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Speaker 4: People keep asking for behind the scenes what's going on.

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Speaker 4: Mike and I have never actually had a conversation face

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Speaker 4: to face.

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Speaker 3: We'll only commun to kidding with each.

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Speaker 4: Other over email and over hearing each other on the podcast.

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Speaker 4: So this is a really big moment for the Tangodi universe.

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Speaker 3: Yeah, tengoty, first worlds are colliding.

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Speaker 1: Back when Taylor Swift released her latest album, we did

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Speaker 1: a few episodes talking about some of the online controversies

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Speaker 1: and conversations that that album sparked, namely the idea that

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Speaker 1: she was sort of pushing tred wife sensibilities, this was

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Speaker 1: her sort of big tread wife pivot, and then later

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Speaker 1: the idea that one of the pieces of merch from

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Speaker 1: her launch, this necklace with lightning bolts at a star

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Speaker 1: on it was actually full of coded Nazi dog whistles

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Speaker 1: and references. I remember regretting deeply that Joey, that you

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Speaker 1: were not on that episode, because as a Swifty I

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Speaker 1: am certain that you probably were plugged into that conversation.

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Speaker 2: What were your thoughts?

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Speaker 5: Oh yeah, oh yeah, you know.

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Speaker 4: I think I gotta say joe did a great job

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Speaker 4: of representing the swifty community and that episode, the choes

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Speaker 4: of the podcast are really hold in holding it together here.

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Speaker 3: But yeah, no, I think I agree.

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Speaker 4: We've talked about bridget We've talked about this off my

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Speaker 4: bazillion times listeners. I did at one point, Sun bridget

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Speaker 4: a bunch of Taylor Swift songs that you should listen to.

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Speaker 4: I don't know if she ever actually listened to them,

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Speaker 4: but I tried.

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Speaker 3: But I'll be like I guess I'll just run through my.

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Speaker 4: Swifty credentials real quick to let y'all know where I'm

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Speaker 4: coming from. I have been hesitant to call myself a

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Speaker 4: swifty in the past. I'll come and go embracing it.

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Speaker 4: I'll explain why.

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Speaker 2: But I would.

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Speaker 4: Yeah, I'm I'm a pretty big tailor swap fan. I

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Speaker 4: would call myself a swifty. I like, I'm right at

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Speaker 4: the age where like I grew up listening to her,

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Speaker 4: like Fearless was the first.

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Speaker 3: Album that I ever owned.

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Speaker 4: I've gone to three of her tours, one of which

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Speaker 4: was the Era's tour. I was at the Chicago tour

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Speaker 4: where she played the Lakes of course fitting so I

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Speaker 4: was right by like Michigan and.

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Speaker 3: Of course Bridgett.

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Speaker 2: As you know.

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Speaker 4: My other favorite thing to talk about is like fandom

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Speaker 4: culture and how fandom culture and like politics have sort

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Speaker 4: of become intertwined in the past like ten years, uh,

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Speaker 4: both of like the fandomification of politics and this like

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Speaker 4: politicization of fandom in a lot of ways. I basically

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Speaker 4: I've been an online fandom spaces my whole life. I

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Speaker 4: never was a big like I'm going to follow this musician,

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Speaker 4: like like I'm really into like following the music. I'm

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Speaker 4: never is into like following their personal life. So I

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Speaker 4: was a little bit on the outside for that. I

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Speaker 4: think I definitely know people that are more like into

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Speaker 4: like Taylor and what she's doing whatever.

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Speaker 3: I never was like that level of Sookie. Again, no

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Speaker 3: disrespect to that.

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Speaker 4: I get like we all have our like things that

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Speaker 4: we're obsessed with and I'm gonna get into later. I

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Speaker 4: think like there is sort of an unfair kind of

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Speaker 4: ridicule of Swifty is when I think it comes to

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Speaker 4: the fact that anybody you meet basically has like some

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Speaker 4: sort of fandom thing that they're really really into.

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Speaker 3: Going back to the actual question.

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Speaker 4: When this album came out, I will be real, I

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Speaker 4: didn't love it. I think like, this is the first

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Speaker 4: time I think she's released an album, and like a

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Speaker 4: lot of my friends that are Swifty, my sister's huge,

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Speaker 4: huge Swifty, and she immediately was like.

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Speaker 3: Yeah, it's not good. So I think that was interesting.

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Speaker 4: I didn't really love Tortured Pote Society either, Like I

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Speaker 4: think there were a couple of songs on in Nights

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Speaker 4: I like, but other than like I Folklore evermore, I

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Speaker 4: was a nineteen eighty nine Fearless era Dot Fearless nineteen

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Speaker 4: eighty nine reputation, like those are my favorite albums. So yeah,

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Speaker 4: I think when when this album dropped, on the one hand,

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Speaker 4: I was sort of.

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Speaker 3: Like, you know what, I didn't really like it. It's

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Speaker 3: not for me.

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Speaker 2: I'm going to move on.

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Speaker 3: I know this is going to turn into a huge

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Speaker 3: internet debate, and lo and behold, it did as I

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Speaker 3: think we're going to get into.

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Speaker 1: Yeah, I mean, you really summarized it well. And this

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Speaker 1: is what I said in the episodes that we did

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Speaker 1: about Taylor Swift after the album had just come out.

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Speaker 1: I think that there is something that happens with Taylor

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Speaker 1: Swift where she hit so many different intersection points, gender politics,

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Speaker 1: the ethics of being a billionaire, class, race, all of

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Speaker 1: these things that we know were these tensions in our society.

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Speaker 1: She sits at the intersection point of all of them.

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Speaker 1: And so in one of those episodes, I said that

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Speaker 1: I was becoming concerned and I thought the conversation was

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Speaker 1: being I don't think I used the word manipulated. I

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Speaker 1: think I said it was it felt right for manipulation.

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Speaker 1: I didn't have any proof, it was just my sense.

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Speaker 1: And part of that was because you know when a conversation,

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Speaker 1: you can just tell when a conversation feels volatile, it

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Speaker 1: feels emotional, it feels difficult to participate in in kind

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Speaker 1: of more casual ways. Those are the conversations, whether it's

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Speaker 1: Taylor Swift or something else where, I just think, ooh,

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Speaker 1: this could be easily manipulated because there's so many emotions

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Speaker 1: involved in the conversation.

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Speaker 3: Yeah, no, totally agreed.

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Speaker 4: I think Bridge I have the same feeling as you

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Speaker 4: where I think again, I listen to this album, I

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Speaker 4: sort of like this isn't for me.

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Speaker 3: I'm gonna go back to listening to folklore.

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Speaker 4: But it was like, I know there's gonna be discourse,

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Speaker 4: and immediately it was like, oh, this is going in

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Speaker 4: a bazillion different directions. I think there is a level

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Speaker 4: of like, hey, there is a conversation that we had here.

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Speaker 4: I think a lot of the boys we're getting get

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Speaker 4: into there is something to be talked about.

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Speaker 3: But yeah, it is like, I agree, and whenever Taylor.

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Speaker 4: Swift comes up, like she is just such a popular

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Speaker 4: figure and that unfortunately, like a lot of our kind

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Speaker 4: of cultural ideas about women get projected onto her just

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Speaker 4: by being like the most famous woman.

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Speaker 3: But yeah, no, one hundred percent agree.

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Speaker 1: It's very easy to make Taylor Swift kind of the

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Speaker 1: stand in for like white ladies, you know, like like

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Speaker 1: there's something about her that I think is just it's

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Speaker 1: very easy to project a lot of stuff onto her.

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Speaker 1: And so you know, when I was watching the discourse

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Speaker 1: get quite heated about Taylor Swift and this the Nazi

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Speaker 1: necklace stuff, I felt this way then and I feel

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Speaker 1: it even more strongly today that I want to be

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Speaker 1: very clear that even though I had a sense that

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Speaker 1: some of the conversation might have been being manipulated in

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Speaker 1: some way, whether by bots and authentic accounts, much of

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Speaker 1: it was real people making genuine critiques of Taylor Swift.

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Speaker 1: Like we got in the episode that we did about

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Speaker 1: the lightning bolt necklace. We got listeners weighing in in

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Speaker 1: the Spotify comments.

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Speaker 3: Thank you for that.

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Speaker 1: And it's not like those were bots, right, those were

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Speaker 1: people that I can visually see, like I know this listener,

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Speaker 1: this is a listener of the show. They are real

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Speaker 1: people engaged in real, genuine discourse because they think it

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Speaker 1: is important and so so oh, I want to make

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Speaker 1: that super clear. And I feel more than ever that

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Speaker 1: has been that reality has been confirmed. And so the

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Speaker 1: reason why we're talking about this again is because this

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Speaker 1: week a company called Goudea put out a report about

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Speaker 1: the conversation being driven about Taylor Swift online. What did

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Speaker 1: this report find, Well, here's their executive submarine. Between October

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Speaker 1: fourth and October eighteenth of twenty twenty five, the public

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Speaker 1: conversation surrounding Taylor Swift in her album The Life of

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Speaker 1: a Showgirl evolved into a highly complex narrative ecosystem. Goudea

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Speaker 1: analyzed twenty four thousand, six hundred and seventy nine posts

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Speaker 1: from eighteen thousan two hundred and thirteen users across fourteen platforms,

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Speaker 1: identifying a polarized online environment shaped by a mixture of

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Speaker 1: organic cultural discourse, symbolic reinterpretation, and targeted inauthentic activity.

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Speaker 2: A key finding of this analysis is the role that what.

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Speaker 1: They're calling inauthentic narratives played in triggering authentic engagement. The

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Speaker 1: false narrative that Taylor Swift was using Nazi symbolism did

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Speaker 1: not remain to fringe conspiratorial spaces. It successfully pulled typical

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Speaker 1: users into comparisons between Taylor Swift and Kanye West, who

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Speaker 1: we know is basically like a Nazi now is like

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Speaker 1: designing clothing lines with swastikas on them and releasing songs.

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Speaker 2: That are odes to Hitler.

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Speaker 1: Right, So, the report finds this demonstrates how a strategically

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Speaker 1: seated falsehood can convert into widespread authentic discourse, reshaping public

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Speaker 1: perception even when most users do not believe the originating claim. Additionally,

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Speaker 1: DUDEA found a significant overlap between accounts pushing the Swift

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Speaker 1: Nazi narrative and those active in a separate AstroTurf campaign

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Speaker 1: attacking Blake Lively. This overlap reveals a cross event amplification network,

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Speaker 1: one that disproportionately influences multiple celebrity driven controversies and injects

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Speaker 1: misinformation into otherwise organic conversation.

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Speaker 4: Not surmise to hear that it there's the overlap with

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Speaker 4: the Blake Lily Lively situation, And.

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Speaker 3: Two something that's also kind of is drawing up is.

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Speaker 4: Like I know We've done a story on Sangodi before

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Speaker 4: about the whole like Lena Dunham thing where there was

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Speaker 4: like a desideration about her having.

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Speaker 3: Sexually assaulted her sister. Not true, yes, very.

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Speaker 4: Much, but it was taken from like a grain of

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Speaker 4: fact and then turned into something else. I feel like

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Speaker 4: a lot of times the Taylor Swift sort of discourse

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Speaker 4: is similar, where it's like, there's things you can criticize.

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Speaker 4: I'm like, and we're gonna add to that. There's plenty

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Speaker 4: of things you can criticize about her and about her

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Speaker 4: music and about the way that she presents herself to

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Speaker 4: the world that doesn't necessarily mean like sprad wife Nazi,

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Speaker 4: you know, like it's like, yeah, from step one to

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Speaker 4: step like I don't know ten thousand something. This did

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Speaker 4: also make me think about was so this album came out?

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Speaker 3: I want to say it was the October this year, right,

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Speaker 3: it was? It was earlier this year.

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Speaker 4: I can't point so when this started, but something that

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Speaker 4: I've noticed, like within the last couple of months, primarily

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Speaker 4: looking at I don't really use Twitter anymore. Twitter is

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Speaker 4: just a nightmare of misinformation. Instagram reels for me has

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Speaker 4: gotten like I don't really, I don't know, I will

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Speaker 4: just if I'm using reels, I'm just like my list scrolling.

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Speaker 4: I've been getting so much like rage bait content that

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Speaker 4: is very like specific to topics that I'm interested in,

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Speaker 4: which has been really interesting. Something I keep going back

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Speaker 4: to is like keep getting these videos that are like

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Speaker 4: specifically about trans people that feel very like yeah, like

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Speaker 4: they're trying to create like rage bait content, and I'm like,

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Speaker 4: I can't tell if this is legit, if this is

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Speaker 4: somebody just trying to get interactions with their post, or

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Speaker 4: if like this is somebody's actual beliefs that they're putting

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Speaker 4: out there. Anyways, that's to say, I feel like I've

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Speaker 4: seen an increase of this type of content that is

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Speaker 4: specifically banking on the fact that that's how you get used.

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Speaker 4: That's how you tap into a current hot topic issue,

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Speaker 4: you say something really crazy, it gets what you use,

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Speaker 4: you get money from that. I don't know if like

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Speaker 4: the fact that I'm seeing such an increase of that

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Speaker 4: lately also a sort of hype this.

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Speaker 3: Well, you bring up a good point that there's you

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Speaker 3: know this this report focuses on accounts that are creating content,

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Speaker 3: you know, authentic human accounts and then inauthentic accounts that

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Speaker 3: maybe they're bots, maybe not. But what's left out of

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Speaker 3: this report entirely and off of the whole discourse is

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Speaker 3: the role of the platforms themselves, you know, specifically like Meta,

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Speaker 3: who is really good at targeting to our very individual interests,

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Speaker 3: and so, you know, even this report that finds that

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Speaker 3: there was a lot of coordinated inauthentic activity that wouldn't

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Speaker 3: be successful if the platforms didn't allow it to be so.

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Speaker 3: And so I think that's an important thing to keep

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Speaker 3: in mind, as well as the fact that, like, you know,

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Speaker 3: the Blake Lively connection here, I think does is a

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Speaker 3: really important part of this analysis because it it makes

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Speaker 3: it more general and it's not just a conversation about

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Speaker 3: Taylor Swift, which gets connected to so many other conversations,

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Speaker 3: but it really is about like who is controlling these

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Speaker 3: bots or these accounts rather not bots maybe bots, but

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Speaker 3: like these accounts that are not behaving like typical humans

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Speaker 3: and just really aggressively and extremely often retweeting this rage

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Speaker 3: baby content across different celebrities. I think that's a nice

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Speaker 3: feature of this study.

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Speaker 1: Yeah, I completely agree, and I this is a little

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Speaker 1: bit of a tangent.

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Speaker 3: But I think I think I've talked about this on

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Speaker 3: the podcast before, and.

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Speaker 1: It doesn't make me feel good to say it, but

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Speaker 1: I kind of got taken by what we now know

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Speaker 1: was largely an inauthentic campaign around Amber Heard. Basically at

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Speaker 1: the time, I was like a low information person when

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Speaker 1: it came to the whole Amber Heard Johnny Depp thing,

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Speaker 1: and I would be scrolling TikTok or scrolling in st

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Speaker 1: or whatever, and I would be surfaced this content that

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Speaker 1: was all rage bait, all lies, all super inflammatory, all

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Speaker 1: really to make Amber Heard look bad. And it was

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Speaker 1: content that made that led me to believe, Oh, the

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Speaker 1: popular sentiment is that everybody, it's just understood that Amber

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Speaker 1: Heard is a horrible person in lie it's on me

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Speaker 1: that I did not do more investigation and that I

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Speaker 1: was just like, Oh, I guess that's the I guess

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Speaker 1: that must be the truth. And it wasn't until actually

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Speaker 1: looking into it that I was like, wait a minute,

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Speaker 1: I am I am being manipulated about a topic that

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Speaker 1: at the time I had not been bothered to kind

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Speaker 1: of look deeply in. And I really realized, even as

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Speaker 1: somebody who thinks of myself as pretty savvy talks about

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Speaker 1: the Internet for a living. We are all very susceptible.

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Speaker 1: Like I was very susceptible. Now Luckily I didn't go

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Speaker 1: on a tirade being like Amber Heard is a liar.

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Speaker 2: I didn't.

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Speaker 3: I didn't.

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Speaker 1: It was just content I was seeing in my home

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Speaker 1: that I was just like, oh, there it is, okay,

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Speaker 1: move on.

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Speaker 3: But like none of us are immune to this, I guess,

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Speaker 3: is what I'm saying. Agreed.

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Speaker 4: I mean, I think sort of a similar thing with

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Speaker 4: like the Blake light Lively case, which I think a

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Speaker 4: lot of people did, like there was a lot of

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Speaker 4: which again going back to sometimes it's it's people where

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Speaker 4: there's like there's the grain of criticis some more starting

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Speaker 4: with and then it spirals into something else. But yeah,

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Speaker 4: I know it is Like I still to this day

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Speaker 4: we'll see like constant bashing Amber Heard, and I'm like,

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Speaker 4: what's going on? I'm like, what's the point?

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Speaker 2: Like come on?

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Speaker 4: Like yeah, I'm like there's so many bigger come.

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Speaker 1: On alone a whole deep dive expose called who trolled

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Speaker 1: Amber All? Like I feel like I feel like we

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Speaker 1: have the receipts on what happened now like give it up.

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Speaker 3: It's big of both of you to, you know, own

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Speaker 3: up to getting taken. But I bridge it to your point.

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Speaker 3: I think, you know, we're all being manipulated all the time,

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Speaker 3: Like this is what I think. One of the big

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Speaker 3: takeaways from this report is that these accounts are out there.

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Speaker 3: They are pushing narratives. Some of them might be false,

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Speaker 3: and you know, once we finally learn the truth or whatever,

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Speaker 3: we realized that like, oh, we have been taken for

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Speaker 3: a ride. But a lot of them, like Joy said,

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Speaker 3: aren't necessarily even false. Like I'm thinking about the stories

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Speaker 3: that we covered earlier this week about the student in

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Speaker 3: Oklahoma who got a zero on her paper and got

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Speaker 3: thing mad about it, like that was a true story.

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Speaker 3: Wasn't important enough that like everyone in America should be

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Speaker 3: talking about it. Probably not, and yet here we are.

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Speaker 3: That is a great point.

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Speaker 1: And I wanted to make this point from the report

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Speaker 1: really clear because it's a great segue. So the report

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Speaker 1: reads the data set reveals a multi layered structure in

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Speaker 1: which cultural commentary, political accusations, symbolic interpretation, and conspiratorial narratives intersect.

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Speaker 1: While the majority of users behave typically so we're not bots,

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Speaker 1: just we're regular people saying things that they actually believed

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Speaker 1: On the Internet. Three point seventy seven percent exhibited non

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Speaker 1: typical behavior amplifiers and accounted for twenty eight percent of

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Speaker 1: the conversation volume, suggesting coordinated influence. This narrative environment umfolded

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Speaker 1: across major platforms including Twitter, slash x, Reddit, blue Sky, TikTok,

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Speaker 1: and fringe ecosystems like four chan and Kiwi Farms, creating

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Speaker 1: a cross ecosystem narrative network capable of rapid escalation. So

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Speaker 1: just to like underline that, I believe the main takeaway

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Speaker 1: here is that while some of this was inauthentic or

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Speaker 1: bot accounts amplifying inauthentic narratives that triggered real people to

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Speaker 1: engage in real conversations and real discourse.

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Speaker 2: So again, I'll probably say this one hundred more.

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Speaker 1: Times throughout this episode, because it's a misconception about this

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Speaker 1: study that I'm really trying to squash out. The study

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Speaker 1: is not saying everybody who engaged in this conversation is

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Speaker 1: a bot that can be dismissed. What they're saying is

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Speaker 1: a small percentage of this was bot or what they

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Speaker 1: say as like atypical or non typical behavior from these accounts,

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Speaker 1: which could be bots, But the majority of the conversation

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Speaker 1: was authentic conversation from real people. And so to Joey's

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Speaker 1: point about fandoms, some of the way that real people

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Speaker 1: engaged with this kind of content that might have been

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Speaker 1: raised by nauthentic users were doing so to defend Taylor Swift,

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Speaker 1: to criticize the irrationality of this like Nazi necklace conspiracy,

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Speaker 1: and to contextualize the incident through historical conflicts with Kanye West.

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Speaker 2: And so, yeah, I would I.

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Speaker 1: Would call that like Swifty fan behavior of like like,

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Speaker 1: I like Taylor Swift.

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Speaker 2: I have seen what I believe.

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Speaker 1: To be an untrue conspiracy about her. I am going

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Speaker 1: to engage with this conspiracy content to debunk it, and

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Speaker 1: thus I am actually sort of helping a probably inauthentically

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Speaker 1: generated narrative to spread, even though I myself, I am

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Speaker 1: a real person, I'm not a bot.

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Speaker 3: Yeah, definitely, I mean it.

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Speaker 4: It's easy to get sucked into, especially as somebody who

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Speaker 4: is interested in pop culture is interested in these conversations, Like,

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Speaker 4: it's easy to get baited into those conversations. I think, like,

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Speaker 4: I don't know, like, especially when this album came out,

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Speaker 4: I had to kind of stomp.

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Speaker 3: Myself from literally there was a point.

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Speaker 4: Where I was just like, I'm just not goetna engage

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Speaker 4: in any of this because I was like, I know,

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Speaker 4: this is like pointless, Like I don't know what, Like

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Speaker 4: at the end of the day, it's arguing over something

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Speaker 4: that an album I didn't even really.

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Speaker 3: Like did you argue over it? You argue over it like.

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Speaker 4: With individuals in my life. But like, I'll be real,

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00:19:35,760 --> 00:19:38,200
Speaker 4: like there was I had made a stupid joke about

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Speaker 4: one of the songs in Tortured Poets Society when that

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Speaker 4: came out, and like I got like a bunch of

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Speaker 4: negative comments on that TikTok, and I was sort of

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Speaker 4: like I was just making fun of this fun line,

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Speaker 4: Like it wasn't that big.

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Speaker 3: Of a thing. I love Taylor Swift. I said that, like,

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Speaker 3: but yeah, no it was. I was just like, I

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Speaker 3: don't I don't know.

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Speaker 4: Maybe this is a sign that I'm like mature as

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Speaker 4: a person. That I was like, I actually need to

387
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Speaker 4: put the phone down. But that being said, I don't know.

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Speaker 4: I yeah, going back to what you were there is

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Speaker 4: something really interesting to me. Again, having spent like my

390
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Speaker 4: whole life in like online fandom spaces.

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Speaker 3: I think there also is like a weird overlap. We've

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Speaker 3: talked before on the.

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Speaker 4: Show about like I was coming of age when like

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Speaker 4: Tumbler was the big thing.

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Speaker 3: A lot of my like.

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Speaker 4: Early introduction to a lot of like political ideas was

397
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Speaker 4: also from Tumbler.

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Speaker 3: At the same time I was looking at like doctor

399
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Speaker 3: who Edits.

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Speaker 4: I think there's like because these online spaces, because so

401
00:20:35,359 --> 00:20:36,880
Speaker 4: much of this is happening online, there is there ends

402
00:20:36,920 --> 00:20:41,560
Speaker 4: up becoming this overlap with people's discussion of political issues

403
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Speaker 4: of like socio political issues whatever. So I think there

404
00:20:43,960 --> 00:20:48,120
Speaker 4: is an extent to which people like people almost sort

405
00:20:48,119 --> 00:20:51,080
Speaker 4: of see there fandom is like I need a fight

406
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Speaker 4: for this social justice, which is not.

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Speaker 3: Always a great thing.

408
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Speaker 4: Like I think I'm usually somebody who I'm in more

409
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Speaker 4: like fictional media fandom spaces, a lot of times is

410
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Speaker 4: people trying to be like I have to prove that

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Speaker 4: this piece of me.

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Speaker 3: I got in an argument.

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Speaker 4: I did get an actual argument with somebody recently who

414
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Speaker 4: was like basically we were talking about like the character

415
00:21:11,520 --> 00:21:14,679
Speaker 4: the Punisher, who is like a classics example of a

416
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Speaker 4: like you already know where this is.

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Speaker 3: Going and I was, yeah, it's a conflicting it's not

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Speaker 3: a character.

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Speaker 4: That's why he's interesting. But they were like, no, he's

420
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Speaker 4: this like leftist radical, like anti racist, and I was like, no,

421
00:21:26,680 --> 00:21:28,960
Speaker 4: he's not, Like you don't he doesn't have to have

422
00:21:29,000 --> 00:21:31,320
Speaker 4: the values that you have, Like it's okay to like

423
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Speaker 4: be a progressive person and enjoy this character who's like complicated.

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Speaker 4: I don't know, Like I think it's interesting when that

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00:21:38,840 --> 00:21:42,520
Speaker 4: ends up coming up in real people too, Like it's.

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Speaker 3: The whole Like is popstar feminist? I don't know.

427
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Speaker 4: There's there's stuff Feler Swift is on there, stuff chew SIPs.

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Speaker 4: Swift is said that it has been yes, very feminist,

429
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Speaker 4: very empowering for women. She's also I don't know, she's

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Speaker 4: a human woman who makes mistakes and like I'm not

431
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Speaker 4: looking to her to for my like political praxis. But yeah,

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Speaker 4: I know, I think with the stuff with like Kanye

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Speaker 4: and all that, I think there was sort of a

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Speaker 4: like Weed either to be like a staunch villain and

435
00:22:06,600 --> 00:22:11,000
Speaker 4: a staunch her in situation and I'm like, yeah, Kanye sucks,

436
00:22:11,040 --> 00:22:14,040
Speaker 4: Like I don't You're I'm not disagreeing with that, Like,

437
00:22:16,680 --> 00:22:18,639
Speaker 4: but like I don't know.

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Speaker 3: Celebrities.

439
00:22:21,080 --> 00:22:23,320
Speaker 4: I don't really think any of them are probably like

440
00:22:23,440 --> 00:22:24,920
Speaker 4: particularly great people.

441
00:22:25,080 --> 00:22:29,880
Speaker 3: They like just from the nature of like getting that rich,

442
00:22:29,960 --> 00:22:31,840
Speaker 3: you kind of have to be a little bit flaw

443
00:22:31,840 --> 00:22:32,239
Speaker 3: I don't know.

444
00:22:32,920 --> 00:22:34,560
Speaker 4: But yeah, no, it is like a weird, like there's

445
00:22:34,560 --> 00:22:37,359
Speaker 4: this weird sort of tendency I think lately for people.

446
00:22:37,840 --> 00:22:39,760
Speaker 3: Maybe it's just a reflection of how like messed up

447
00:22:39,800 --> 00:22:40,600
Speaker 3: the world is right now, but.

448
00:22:40,560 --> 00:22:43,280
Speaker 4: Like people really do want to like see these figures

449
00:22:43,320 --> 00:22:44,960
Speaker 4: like having all of their beliefs, and it's like, at

450
00:22:44,960 --> 00:22:46,119
Speaker 4: the end of the day, they're probably not.

451
00:22:46,440 --> 00:22:48,280
Speaker 3: Yeah, and that's always been that way.

452
00:22:48,359 --> 00:22:51,000
Speaker 1: Like I don't know, I completely agree, and I think

453
00:22:51,359 --> 00:22:53,680
Speaker 1: we'll talk about this more when we get deeper into

454
00:22:53,680 --> 00:22:57,040
Speaker 1: the report, But I do think it's created a kind

455
00:22:57,080 --> 00:23:02,879
Speaker 1: of dynamic where defending Taylor Swift is easily confused for

456
00:23:03,040 --> 00:23:05,320
Speaker 1: active for kind of activism it is.

457
00:23:05,440 --> 00:23:06,919
Speaker 4: Yeah, well it's the thing too where I think, like,

458
00:23:07,840 --> 00:23:09,760
Speaker 4: because what we were saying before about how she sort

459
00:23:09,760 --> 00:23:12,920
Speaker 4: of represents this like the idea of womanhood, the idea

460
00:23:12,960 --> 00:23:15,520
Speaker 4: of specifically white womanhood, but womanhood in general, like when

461
00:23:15,560 --> 00:23:18,440
Speaker 4: people talk about massage. Yes, she has been subject to

462
00:23:18,480 --> 00:23:21,760
Speaker 4: a lot of misogyny throughout her career. That being said,

463
00:23:23,160 --> 00:23:26,359
Speaker 4: feminism isn't just defending Taylor Swift like I don't know.

464
00:23:26,560 --> 00:23:43,800
Speaker 6: Yeah, let's take a quick break at our.

465
00:23:43,760 --> 00:23:50,080
Speaker 1: Back and this idea from the report, where you basically,

466
00:23:50,440 --> 00:23:53,320
Speaker 1: by engaging with this kind of content, real people were

467
00:23:53,359 --> 00:23:55,760
Speaker 1: boosting it and helping it to spread. This is like

468
00:23:55,920 --> 00:24:01,400
Speaker 1: part and parcel of online manipulation, campaigns of inauthentic provocation

469
00:24:01,640 --> 00:24:04,879
Speaker 1: leading to authentic user discourse as a hallmark of successful

470
00:24:05,000 --> 00:24:09,520
Speaker 1: narrative manipulation, it demonstrates how small bursts of coordinated activity

471
00:24:09,560 --> 00:24:14,520
Speaker 1: can reshape cultural perception by forcing mainstream audiences to respond

472
00:24:14,560 --> 00:24:18,320
Speaker 1: to extremized framing. Okay, so if you are a diehard

473
00:24:18,320 --> 00:24:21,439
Speaker 1: Swifty listening, you might be thinking, okay, so does this

474
00:24:21,520 --> 00:24:25,040
Speaker 1: report mean that anybody who is critical of Taylor Swift

475
00:24:25,160 --> 00:24:29,080
Speaker 1: is just a bot and whatever criticisms they raised can

476
00:24:29,160 --> 00:24:32,480
Speaker 1: just be ignored because it's bot shit. And the answer

477
00:24:32,520 --> 00:24:36,040
Speaker 1: is no, absolutely not, not even a little bit. So

478
00:24:36,200 --> 00:24:38,679
Speaker 1: we do have some criticisms of the report, which we'll

479
00:24:38,680 --> 00:24:41,280
Speaker 1: get into in a moment, but even taking it at

480
00:24:41,320 --> 00:24:44,520
Speaker 1: face value, I want to make super clear that the report,

481
00:24:44,600 --> 00:24:47,000
Speaker 1: as you just heard in that summary, does not suggest

482
00:24:47,040 --> 00:24:49,800
Speaker 1: that all or even most of the accounts driving this

483
00:24:49,880 --> 00:24:53,840
Speaker 1: kind of Taylor Swift conversation online were bots. And I

484
00:24:53,880 --> 00:24:56,840
Speaker 1: want to really underline that because one of the responses

485
00:24:56,920 --> 00:25:00,919
Speaker 1: I've seen online from this report is that the report

486
00:25:00,960 --> 00:25:03,080
Speaker 1: is saying that anybody who criticized Taylor Swift as a

487
00:25:03,119 --> 00:25:05,160
Speaker 1: bot and it can be disregarded. I've seen a lot

488
00:25:05,200 --> 00:25:08,919
Speaker 1: of black women saying I have been critical of Taylor Swift,

489
00:25:08,920 --> 00:25:09,879
Speaker 1: and I am not a bot.

490
00:25:09,920 --> 00:25:11,480
Speaker 2: I'm a real person with real issues.

491
00:25:11,800 --> 00:25:14,879
Speaker 1: I understand where that's coming from, which again we'll talk

492
00:25:14,880 --> 00:25:16,679
Speaker 1: about in a moment, but I just want to make

493
00:25:16,720 --> 00:25:19,080
Speaker 1: clear that the report is not saying this. And so

494
00:25:19,160 --> 00:25:22,080
Speaker 1: if anybody is saying, oh, well, I told you so,

495
00:25:23,040 --> 00:25:25,879
Speaker 1: all these criticisms about Taylor Swift were just coming from bots.

496
00:25:25,920 --> 00:25:29,359
Speaker 1: You were stupid for even engaging with it, that is

497
00:25:29,400 --> 00:25:33,000
Speaker 1: not correct. So in this report, they basically categorized different

498
00:25:33,040 --> 00:25:36,919
Speaker 1: buckets of Taylor Swift conversation along the lines of the

499
00:25:37,080 --> 00:25:41,639
Speaker 1: risk of those conversations being manipulated by inauthentic narratives. Topics

500
00:25:41,680 --> 00:25:43,840
Speaker 1: that we're in what they call their low risk cluster

501
00:25:44,160 --> 00:25:47,000
Speaker 1: are topics that were largely human driven and not really

502
00:25:47,080 --> 00:25:49,520
Speaker 1: driven at all by bots. So this was things like

503
00:25:49,920 --> 00:25:54,480
Speaker 1: people being uncomfortable with some of the racialized messages, I guess,

504
00:25:54,480 --> 00:25:57,360
Speaker 1: and some of the Swift's songs like Eldest Daughter and Opalite,

505
00:25:57,840 --> 00:26:01,200
Speaker 1: it was human saying that stuff, not bot side note,

506
00:26:01,240 --> 00:26:04,320
Speaker 1: I am actually not listen to any of these songs.

507
00:26:04,320 --> 00:26:06,480
Speaker 3: So I'm I I gather from the.

508
00:26:06,480 --> 00:26:09,680
Speaker 1: Discourse that a lot of it was about racialized narratives

509
00:26:09,720 --> 00:26:12,600
Speaker 1: in these songs, but I'm gonna need Joey to confirm

510
00:26:12,640 --> 00:26:16,200
Speaker 1: that for me or just icky like icky narratives in general.

511
00:26:16,640 --> 00:26:23,399
Speaker 4: Yeah, I think, I right, I think because I'll be

512
00:26:23,680 --> 00:26:28,480
Speaker 4: like of this discussion again, I didn't listen to the

513
00:26:28,520 --> 00:26:31,320
Speaker 4: album and think her oldest means she's a triad wife.

514
00:26:31,359 --> 00:26:33,720
Speaker 3: Now this means she's a racist trod wife the song

515
00:26:33,840 --> 00:26:39,439
Speaker 3: opal Light, though specifically there were a couple of I

516
00:26:39,440 --> 00:26:41,280
Speaker 3: think it was the situation where I think she just

517
00:26:43,600 --> 00:26:44,440
Speaker 3: due to being.

518
00:26:46,200 --> 00:26:48,760
Speaker 4: A white woman who has achieved this level of wealth

519
00:26:48,920 --> 00:26:52,680
Speaker 4: and you know, probably has mostly been surrounded by other

520
00:26:52,720 --> 00:26:56,520
Speaker 4: white women from similar backgrounds, maybe she just didn't like

521
00:26:56,640 --> 00:26:58,520
Speaker 4: think through it and not like I'm willing to give

522
00:26:58,520 --> 00:26:59,560
Speaker 4: her the benefit of the doubt of that.

523
00:26:59,640 --> 00:27:01,040
Speaker 3: There was a little bit like.

524
00:27:01,000 --> 00:27:03,000
Speaker 4: The Looking at the lyrics, it's sort of like this

525
00:27:03,119 --> 00:27:07,480
Speaker 4: feels icky, like there's something about it that I'm like, Oh,

526
00:27:07,800 --> 00:27:08,760
Speaker 4: I don't know about that.

527
00:27:09,400 --> 00:27:11,760
Speaker 3: I'm crediting to this again. I'm gonna give her the

528
00:27:11,760 --> 00:27:13,160
Speaker 3: benefit of the doubt.

529
00:27:13,440 --> 00:27:13,720
Speaker 6: Again.

530
00:27:13,760 --> 00:27:15,680
Speaker 4: This is celebrity I don't know in person. I don't

531
00:27:15,720 --> 00:27:17,639
Speaker 4: know what her fucking beliefs are. I don't know anything

532
00:27:17,640 --> 00:27:22,040
Speaker 4: about her. We've never met. Shocking, guys, I've never personally

533
00:27:22,080 --> 00:27:25,760
Speaker 4: met Taylor Swift. I'm willing to throw this to Just

534
00:27:25,840 --> 00:27:30,080
Speaker 4: like she made some metaphors that she thought were really artsy,

535
00:27:30,720 --> 00:27:32,840
Speaker 4: not really realizing they could be read that way, Like

536
00:27:32,920 --> 00:27:33,600
Speaker 4: this is the same way.

537
00:27:33,680 --> 00:27:35,119
Speaker 3: I like, there was a.

538
00:27:35,320 --> 00:27:37,000
Speaker 4: I'm not gonna out her from what she actually said,

539
00:27:37,000 --> 00:27:38,800
Speaker 4: but like my straight friend a couple of months ago

540
00:27:38,880 --> 00:27:40,879
Speaker 4: had like left a comment on my Instagram and the

541
00:27:40,880 --> 00:27:41,680
Speaker 4: words she used.

542
00:27:41,720 --> 00:27:43,960
Speaker 3: I was like, you cannot say that. I was like,

543
00:27:44,000 --> 00:27:45,159
Speaker 3: delete that right now.

544
00:27:45,080 --> 00:27:46,200
Speaker 7: That's not for you.

545
00:27:46,760 --> 00:27:49,600
Speaker 3: Yeah, I was like, you just called me something very transphobic.

546
00:27:50,480 --> 00:27:51,680
Speaker 3: Like I was like, I know that was that what

547
00:27:51,800 --> 00:27:55,800
Speaker 3: you met? But yeah, I'm like to say, I think

548
00:27:55,920 --> 00:27:57,040
Speaker 3: that's probably what happened.

549
00:27:57,240 --> 00:28:00,480
Speaker 4: That being said, though, I'm not gonna like criticize anybody

550
00:28:00,520 --> 00:28:04,800
Speaker 4: for being uncomfortable with that.

551
00:28:04,800 --> 00:28:05,600
Speaker 3: That is our right.

552
00:28:05,880 --> 00:28:08,000
Speaker 4: What's there's a piece of art that's in the world.

553
00:28:08,480 --> 00:28:11,080
Speaker 4: People are allowed to interpret it. However, you want something

554
00:28:11,080 --> 00:28:13,680
Speaker 4: that recently happened that I think made me sort of well,

555
00:28:13,800 --> 00:28:15,199
Speaker 4: I was on like the other side of this was

556
00:28:16,119 --> 00:28:18,080
Speaker 4: going back to this whole like having to take a

557
00:28:18,119 --> 00:28:20,679
Speaker 4: step back and be like, is this really an issue?

558
00:28:20,720 --> 00:28:23,440
Speaker 4: When the Sabrina Carpenter album came out, It's most recent

559
00:28:23,440 --> 00:28:26,560
Speaker 4: Spurita Carpenter album, I like, I'm not a big fan

560
00:28:26,600 --> 00:28:28,680
Speaker 4: of her, and I think like I had a realization

561
00:28:28,680 --> 00:28:29,960
Speaker 4: where I was like she to me is where I

562
00:28:30,080 --> 00:28:32,040
Speaker 4: was like, similar, do I think i'llow a lot of

563
00:28:32,040 --> 00:28:33,639
Speaker 4: people take the sea Taylor Swift where I was like,

564
00:28:33,760 --> 00:28:35,520
Speaker 4: m She's definitely very talented.

565
00:28:35,560 --> 00:28:37,199
Speaker 3: She's not for me, Like her music is not for me.

566
00:28:37,760 --> 00:28:39,920
Speaker 3: It's not I'm not the audience she has online.

567
00:28:39,960 --> 00:28:42,000
Speaker 4: And I think like when that album came out and

568
00:28:42,040 --> 00:28:44,440
Speaker 4: the cover drop specifically, I was sort of like, Okay,

569
00:28:45,440 --> 00:28:47,840
Speaker 4: I don't love that. There's a lot of criticisms I have,

570
00:28:48,640 --> 00:28:50,040
Speaker 4: but I can see how this can be in behoffering

571
00:28:50,080 --> 00:28:50,600
Speaker 4: to some people.

572
00:28:50,840 --> 00:28:53,680
Speaker 3: And it was. However, then I saw the conversation quickly turn.

573
00:28:53,720 --> 00:28:55,320
Speaker 4: There were like people online that were like, if you're

574
00:28:55,360 --> 00:28:59,920
Speaker 4: uncomfortable with this, your anti sex work and anti women

575
00:29:00,040 --> 00:29:01,600
Speaker 4: and an ant sid and I was like, what are

576
00:29:01,640 --> 00:29:04,280
Speaker 4: we talking about? Like I was just like, oh, that

577
00:29:04,360 --> 00:29:05,360
Speaker 4: was a weird choice.

578
00:29:05,480 --> 00:29:07,240
Speaker 3: I want to do it, but good for you.

579
00:29:07,880 --> 00:29:11,000
Speaker 1: Yeah, I mean whatever happened to just being allowed to

580
00:29:11,080 --> 00:29:13,120
Speaker 1: not like something or being like this is not for me.

581
00:29:13,320 --> 00:29:14,920
Speaker 4: I think that's my point where I'm like, I wouldn't

582
00:29:14,920 --> 00:29:16,800
Speaker 4: actually have an issue with this, Like I don't like

583
00:29:16,880 --> 00:29:19,280
Speaker 4: end discussion whatever, it's not the album's thought for me.

584
00:29:19,760 --> 00:29:21,400
Speaker 3: But I was like, well, now I feel like I have.

585
00:29:21,400 --> 00:29:24,240
Speaker 4: To come back and be like, no, guys, I'm not

586
00:29:24,480 --> 00:29:28,080
Speaker 4: secretly a misogynist or anti sex or anti sex work

587
00:29:28,160 --> 00:29:31,080
Speaker 4: or antsie. There was one particular TikTok that I saw

588
00:29:31,120 --> 00:29:32,760
Speaker 4: that was just like listing off all these things and

589
00:29:32,800 --> 00:29:33,960
Speaker 4: I was like, this.

590
00:29:33,920 --> 00:29:34,880
Speaker 3: Is a straight white girl.

591
00:29:34,920 --> 00:29:37,640
Speaker 4: Why what does this have to do with transphobia and

592
00:29:37,760 --> 00:29:38,960
Speaker 4: sex workers rights?

593
00:29:39,000 --> 00:29:41,040
Speaker 3: And I like, I.

594
00:29:41,040 --> 00:29:44,240
Speaker 1: Don't know, it was crazy, but yeah, and I think

595
00:29:44,280 --> 00:29:47,000
Speaker 1: that's kind of like what the report. It doesn't come

596
00:29:47,040 --> 00:29:49,640
Speaker 1: out and say this but sort of contextualizing it. I

597
00:29:49,680 --> 00:29:52,840
Speaker 1: feel like the temperature has just really been turned up,

598
00:29:52,920 --> 00:29:55,520
Speaker 1: and so you can't just really have a convert. It's

599
00:29:55,560 --> 00:30:00,480
Speaker 1: more difficult to have normal good faith discourse where like, oh,

600
00:30:00,520 --> 00:30:02,720
Speaker 1: this album wasn't for me, this cover wasn't for me,

601
00:30:03,200 --> 00:30:05,520
Speaker 1: like that how you miss articulated taylorists. But that's how

602
00:30:05,560 --> 00:30:07,840
Speaker 1: I would describe her, Like I don't have ill will

603
00:30:07,960 --> 00:30:11,360
Speaker 1: toward her. She's just somebody who I recognized makes music

604
00:30:11,400 --> 00:30:14,520
Speaker 1: that is not for whatever reason for me. So it's

605
00:30:14,720 --> 00:30:16,600
Speaker 1: it's not it's not an artist that I pay attention

606
00:30:16,680 --> 00:30:20,040
Speaker 1: to musically a lot. And so I think the fact

607
00:30:20,120 --> 00:30:25,480
Speaker 1: that the conversation online has gotten so volatile about these

608
00:30:25,480 --> 00:30:28,840
Speaker 1: things is exactly speaks exactly to your point, Joey.

609
00:30:29,040 --> 00:30:31,360
Speaker 3: Mike, you looks like you were gonna say something and today, Yeah, well,

610
00:30:31,400 --> 00:30:35,520
Speaker 3: I'm just curious now about the lyrics and these songs

611
00:30:35,560 --> 00:30:38,520
Speaker 3: open lay Nolla's daughter, Like I had no idea that

612
00:30:38,760 --> 00:30:43,480
Speaker 3: like this was part of the controversy. I guess that

613
00:30:43,560 --> 00:30:47,480
Speaker 3: they're like racialized lyrics in there. And I mean, it's

614
00:30:47,480 --> 00:30:50,680
Speaker 3: also interesting that those are two of the topics that

615
00:30:50,960 --> 00:30:55,480
Speaker 3: were not promoted by these inauthentic accounts, and yet I

616
00:30:55,560 --> 00:30:59,040
Speaker 3: didn't hear them, And uh, you know, that's interesting, and

617
00:30:59,080 --> 00:31:03,680
Speaker 3: it makes me wonder if perhaps the conversation about those

618
00:31:04,240 --> 00:31:08,160
Speaker 3: was in fact more nuanced than thoughtful, like Joey was describing, like,

619
00:31:08,800 --> 00:31:10,480
Speaker 3: you know, it's a piece of art and so people

620
00:31:10,520 --> 00:31:16,000
Speaker 3: can legitimately criticize it. Maybe it wasn't, you know, polarized

621
00:31:16,040 --> 00:31:18,640
Speaker 3: off the rails. Some of it probably was because we're

622
00:31:18,640 --> 00:31:22,400
Speaker 3: talking about the Internet here, but like, uh, you know,

623
00:31:22,440 --> 00:31:27,360
Speaker 3: you have to wonder if that nuance is why the

624
00:31:27,360 --> 00:31:31,680
Speaker 3: bot networks did not pick that up and instead seized

625
00:31:31,840 --> 00:31:35,719
Speaker 3: on some of these other claims. Yes, that's a really

626
00:31:35,840 --> 00:31:36,440
Speaker 3: good point.

627
00:31:36,560 --> 00:31:38,880
Speaker 1: And just to add on to that, one of the

628
00:31:38,960 --> 00:31:42,480
Speaker 1: other sort of low risk clusters that's report identified where

629
00:31:42,560 --> 00:31:45,520
Speaker 1: the conversation was pretty much entirely just real people having

630
00:31:45,520 --> 00:31:49,400
Speaker 1: real discourse in an mostly normal, chill kind of way,

631
00:31:50,040 --> 00:31:53,560
Speaker 1: was around Taylor Swift being a billionaire and the sort

632
00:31:53,600 --> 00:31:54,360
Speaker 1: of ethics of that.

633
00:31:54,560 --> 00:31:54,720
Speaker 2: Right.

634
00:31:55,120 --> 00:31:59,000
Speaker 1: That was real people raising real issues alongside real people

635
00:31:59,040 --> 00:32:04,360
Speaker 1: talking about the you know, racial imagery and some of

636
00:32:04,400 --> 00:32:07,360
Speaker 1: the issues with her songs. The report specifically says that

637
00:32:07,440 --> 00:32:11,680
Speaker 1: these topics remade stable and free from inorganic influence. So again,

638
00:32:11,840 --> 00:32:15,840
Speaker 1: if someone is trying to tell you that, oh, all

639
00:32:15,880 --> 00:32:19,560
Speaker 1: of the issues raised about Taylor Swift that was all bots.

640
00:32:19,600 --> 00:32:22,360
Speaker 1: These specifically were not raised by bots, They were raised

641
00:32:22,360 --> 00:32:26,400
Speaker 1: by real people. Then they identify the medium risk clusters.

642
00:32:26,600 --> 00:32:29,560
Speaker 1: Now this was stuff like comparisons of Taylor Swift to

643
00:32:29,640 --> 00:32:32,200
Speaker 1: Kanye West, who again is like basically a Nazi. Now,

644
00:32:32,880 --> 00:32:36,120
Speaker 1: the report found that this cluster showed the clearest transition

645
00:32:36,280 --> 00:32:42,000
Speaker 1: from inauthentic triggers to authentic community discourse. However, it was

646
00:32:42,040 --> 00:32:47,040
Speaker 1: still not bot users that were dominating this cluster of conversations,

647
00:32:47,040 --> 00:32:51,920
Speaker 1: and so other things in this bucket were conversations around

648
00:32:52,200 --> 00:32:56,120
Speaker 1: Swift and cultural appropriation, which I again I am not

649
00:32:56,200 --> 00:32:58,480
Speaker 1: a scholar of her work, but apparently that is a

650
00:32:58,480 --> 00:33:02,480
Speaker 1: topic of conversation. Her use of aa ve, a rivalry

651
00:33:02,560 --> 00:33:04,880
Speaker 1: that she might have with Beyonce, which like, are they

652
00:33:04,960 --> 00:33:07,080
Speaker 1: rivals for real? This is again a lot, a lot

653
00:33:07,120 --> 00:33:09,120
Speaker 1: of this is like new information for me that I'm

654
00:33:09,160 --> 00:33:10,080
Speaker 1: hearing for the first time.

655
00:33:10,280 --> 00:33:12,320
Speaker 3: Well, they're the two most famous women, so they must

656
00:33:12,360 --> 00:33:13,600
Speaker 3: be rivals, right, I know, right?

657
00:33:13,840 --> 00:33:16,640
Speaker 4: I bet they like each other. All women hate each other.

658
00:33:17,320 --> 00:33:18,400
Speaker 4: All women hate each other.

659
00:33:18,520 --> 00:33:22,080
Speaker 3: Bridget don't y'all know this by now? Get have two

660
00:33:22,120 --> 00:33:24,360
Speaker 3: powerful women in the same room. Yeah, no, I mean

661
00:33:24,400 --> 00:33:28,600
Speaker 3: even like reading that I personally because I've heard this

662
00:33:28,600 --> 00:33:30,440
Speaker 3: before Bram, Like, I I.

663
00:33:30,400 --> 00:33:33,760
Speaker 4: Can't think of an example of Taylor Swift using aave.

664
00:33:34,520 --> 00:33:37,520
Speaker 4: That being said, I haven't followed her entire career. Maybe

665
00:33:37,520 --> 00:33:39,200
Speaker 4: she did, Maybe I'm just missing it. But I'm like,

666
00:33:39,200 --> 00:33:41,120
Speaker 4: whenever people claim that, I know people are also mad

667
00:33:41,120 --> 00:33:44,160
Speaker 4: about like the Shake It Off music video and someone

668
00:33:44,160 --> 00:33:46,040
Speaker 4: the dancing of that. I remember watching that time too.

669
00:33:46,360 --> 00:33:49,680
Speaker 2: I remember that. I remember that Yeah, and.

670
00:33:49,840 --> 00:33:50,320
Speaker 3: Like that one too.

671
00:33:50,360 --> 00:33:52,480
Speaker 4: I was sort of like, Okay, I can hear the criticism,

672
00:33:52,520 --> 00:33:56,720
Speaker 4: but it doesn't really seem like the I don't know again,

673
00:33:56,840 --> 00:33:57,160
Speaker 4: I think.

674
00:33:59,480 --> 00:34:02,040
Speaker 3: And the r with Beyonce, like I that is something.

675
00:34:02,040 --> 00:34:04,680
Speaker 4: I'm like, I don't think I've actually, Yeah, they're the

676
00:34:04,720 --> 00:34:08,480
Speaker 4: two top artists in the country. I mean, if we

677
00:34:08,560 --> 00:34:10,239
Speaker 4: want to go back to the whole Kanye of it all,

678
00:34:10,280 --> 00:34:13,400
Speaker 4: Beyonce famously brought Healers lived on stage, yeah, two thousand

679
00:34:13,440 --> 00:34:14,640
Speaker 4: and night after all that, so I'm like, it.

680
00:34:14,600 --> 00:34:18,120
Speaker 3: Seemed like they were pretty still from where I'm standing,

681
00:34:18,280 --> 00:34:19,440
Speaker 3: but I don't know.

682
00:34:19,840 --> 00:34:21,239
Speaker 2: Uh, and they're both billionaires.

683
00:34:21,280 --> 00:34:23,040
Speaker 3: I don't think either than a sweating it like I

684
00:34:23,080 --> 00:34:23,320
Speaker 3: think this.

685
00:34:24,160 --> 00:34:27,160
Speaker 4: I'm also like I love the like negative criticism that

686
00:34:27,200 --> 00:34:27,959
Speaker 4: I have Taylor Swift.

687
00:34:28,000 --> 00:34:30,080
Speaker 3: I also have Beyonce, Like I love Beyonce too.

688
00:34:30,360 --> 00:34:32,480
Speaker 2: Oh, get me started.

689
00:34:32,480 --> 00:34:36,720
Speaker 1: It's a billionaire like I can make a whole episode, Joey.

690
00:34:37,640 --> 00:34:41,000
Speaker 1: I'm a big Beyonce fan. Beyonce and I have. This

691
00:34:41,040 --> 00:34:43,920
Speaker 1: is a side conversation. This is a conversation for another episode.

692
00:34:44,120 --> 00:34:46,840
Speaker 1: Beyonce and I have gone through a little bit of

693
00:34:46,840 --> 00:34:48,640
Speaker 1: a cooling off period.

694
00:34:49,360 --> 00:34:53,120
Speaker 3: I just think they're well, we don't even get into it.

695
00:34:53,120 --> 00:34:53,759
Speaker 2: We don't into it.

696
00:34:53,800 --> 00:34:56,200
Speaker 3: I'll just say this. I have yet to piss off

697
00:34:56,239 --> 00:34:57,919
Speaker 3: the bay Hive.

698
00:34:57,680 --> 00:35:00,800
Speaker 4: I've had a lot of other fandoms my comments section

699
00:35:01,080 --> 00:35:02,920
Speaker 4: angry at me over small remarks.

700
00:35:02,960 --> 00:35:05,320
Speaker 3: So I hope this isn't the thing that finally.

701
00:35:05,000 --> 00:35:06,120
Speaker 2: Is Yeah, we can move on.

702
00:35:06,120 --> 00:35:10,000
Speaker 1: I don't want anybody to get like bees and lemons

703
00:35:10,120 --> 00:35:13,359
Speaker 1: in our Instagram comments over anything that we're about to say.

704
00:35:13,719 --> 00:35:17,080
Speaker 3: Yeah. It does make some sense though, Like if I

705
00:35:17,280 --> 00:35:20,920
Speaker 3: was to design an authentic campaign just trying to like

706
00:35:21,719 --> 00:35:24,319
Speaker 3: gin up outrage and get a bunch of clicks online,

707
00:35:24,680 --> 00:35:26,560
Speaker 3: it makes a lot of sense to come up with

708
00:35:26,600 --> 00:35:31,000
Speaker 3: a story about Taylor Swift and Beyonce having like a

709
00:35:31,080 --> 00:35:32,879
Speaker 3: rivalry with each other, because then I get to talk

710
00:35:32,880 --> 00:35:34,359
Speaker 3: about both of them, right, and then I'm gonna get

711
00:35:34,440 --> 00:35:37,440
Speaker 3: picked up by algorithms that of people who are interested

712
00:35:37,520 --> 00:35:40,120
Speaker 3: in easier. That's a really really good point.

713
00:35:40,440 --> 00:35:43,640
Speaker 4: Yeah, you're getting you're getting people with the bmog and

714
00:35:43,680 --> 00:35:47,799
Speaker 4: their username, and you're getting people with Taylor's version and

715
00:35:47,920 --> 00:35:51,319
Speaker 4: parentheses after their username and your comments making your life

716
00:35:51,400 --> 00:35:54,880
Speaker 4: miserable for however long fay aside to pay attention to

717
00:35:54,920 --> 00:35:57,200
Speaker 4: that total.

718
00:35:57,000 --> 00:35:59,120
Speaker 1: Non sequitary question that you can cut if you need

719
00:35:59,160 --> 00:36:03,040
Speaker 1: to for time, which as a fandom expert, which fandom

720
00:36:03,239 --> 00:36:05,359
Speaker 1: do you think any It doesn't even have to be

721
00:36:05,360 --> 00:36:06,600
Speaker 1: Beyonce versus Tylor Swift?

722
00:36:07,120 --> 00:36:09,120
Speaker 3: Who which fandom is the most rabbi?

723
00:36:09,200 --> 00:36:09,719
Speaker 2: The fandom that.

724
00:36:09,680 --> 00:36:12,000
Speaker 1: You're like, I I will never piss them off. I

725
00:36:12,000 --> 00:36:14,239
Speaker 1: will never say a thing. Who you look like, you've

726
00:36:14,239 --> 00:36:15,640
Speaker 1: got an idea in your mind, your head.

727
00:36:15,680 --> 00:36:19,320
Speaker 3: Here's the thing. And you must also consider jugglers. Oh

728
00:36:19,680 --> 00:36:22,239
Speaker 3: that's a good one that I don't they do.

729
00:36:22,400 --> 00:36:26,040
Speaker 4: They're like, they're okay, maybe this is just the generational differences, but.

730
00:36:27,480 --> 00:36:30,839
Speaker 3: That's probably true. Yeah, Like your grandfather.

731
00:36:30,360 --> 00:36:34,000
Speaker 4: Is listening, actually appreciate you're you're you're bringing up something

732
00:36:34,040 --> 00:36:36,400
Speaker 4: I could spend an entire episode talking about. But with

733
00:36:36,480 --> 00:36:38,400
Speaker 4: this whole conversation, I juggle too, so they gotta keep

734
00:36:38,400 --> 00:36:40,840
Speaker 4: going back to because again I have I said this

735
00:36:40,840 --> 00:36:43,080
Speaker 4: where I was like, I have been hesitant to call

736
00:36:43,080 --> 00:36:44,520
Speaker 4: myself a swifty in the past, and I think it's

737
00:36:44,520 --> 00:36:49,040
Speaker 4: because of the reputations of swifties. Get My first like

738
00:36:49,280 --> 00:36:52,280
Speaker 4: fandom fandom that I was into as a small child

739
00:36:52,680 --> 00:36:56,840
Speaker 4: was Star Wars and you have never met crazier.

740
00:36:57,280 --> 00:37:00,080
Speaker 3: Actually I take that that. I like, here's the thing, because.

741
00:36:59,840 --> 00:37:02,919
Speaker 4: It is again it's there's something that makes swifties like.

742
00:37:04,320 --> 00:37:06,759
Speaker 3: Compared to like the bets.

743
00:37:06,360 --> 00:37:13,800
Speaker 4: Fandom beyond they beatles fans like have you ever talked

744
00:37:14,360 --> 00:37:20,560
Speaker 4: to a middle aged man about like whatever their band of.

745
00:37:20,560 --> 00:37:23,279
Speaker 3: Choice that I don't know? And then on the other side, yeah,

746
00:37:23,280 --> 00:37:26,319
Speaker 3: again like Star Wars fans like literally like ruining the

747
00:37:26,360 --> 00:37:28,319
Speaker 3: lives of like child actors.

748
00:37:28,480 --> 00:37:32,680
Speaker 4: On the other hand, I think that there is just

749
00:37:32,719 --> 00:37:35,160
Speaker 4: something about online fandom culture.

750
00:37:35,280 --> 00:37:37,080
Speaker 3: I was talking to a friend like a week.

751
00:37:36,880 --> 00:37:39,840
Speaker 4: Ago about she just got into like DC comics for

752
00:37:39,880 --> 00:37:42,480
Speaker 4: the first time, and she was like, I'm really nervous

753
00:37:42,480 --> 00:37:44,680
Speaker 4: to post anything on TikTok about this because I feel like,

754
00:37:44,680 --> 00:37:46,319
Speaker 4: people are gonna be mad that I haven't read like

755
00:37:46,440 --> 00:37:48,960
Speaker 4: X y Z things, and I'm like, you're talking about

756
00:37:49,000 --> 00:37:52,040
Speaker 4: a character that's been around since pre World War Two.

757
00:37:53,800 --> 00:37:55,719
Speaker 4: There's no way you can read all of the things.

758
00:37:55,800 --> 00:37:58,000
Speaker 4: This was a basillily different versions, Like there's just something

759
00:37:58,080 --> 00:38:00,520
Speaker 4: about fandom culture that turns people.

760
00:38:02,520 --> 00:38:03,400
Speaker 3: I love it again.

761
00:38:03,480 --> 00:38:07,680
Speaker 4: I don't love that. I love fandoms again. I will

762
00:38:07,719 --> 00:38:08,480
Speaker 4: defend fandoms.

763
00:38:08,520 --> 00:38:11,080
Speaker 3: I've my whole wall of posters.

764
00:38:10,680 --> 00:38:12,719
Speaker 4: Behind me with like there's an interview with a vampire one,

765
00:38:12,760 --> 00:38:15,440
Speaker 4: there's a DC one. There is a point where you

766
00:38:15,560 --> 00:38:17,080
Speaker 4: kind of have to be like, I'm gonna take step back.

767
00:38:17,120 --> 00:38:20,919
Speaker 4: I'm not gonna gauge the discourse anymore. Somebody just left

768
00:38:20,920 --> 00:38:22,759
Speaker 4: a very angry comment on one of my videos because

769
00:38:22,800 --> 00:38:26,240
Speaker 4: I made the mistake of saying something about how Anakin

770
00:38:26,280 --> 00:38:28,880
Speaker 4: Skywalker is a flawed, morally gray character, which you know,

771
00:38:28,960 --> 00:38:33,360
Speaker 4: I didn't think was that crazy of a of a.

772
00:38:34,080 --> 00:38:36,080
Speaker 3: I was talking about the pun track, and I was like,

773
00:38:37,760 --> 00:38:40,000
Speaker 3: that's like the whole thing of like many movies.

774
00:38:40,239 --> 00:38:42,640
Speaker 4: The most unhinged comments I ever got from fandom for

775
00:38:42,680 --> 00:38:44,839
Speaker 4: making a like offhanded joke about it was Adam Driver.

776
00:38:44,960 --> 00:38:46,040
Speaker 3: Fans like to this day.

777
00:38:46,560 --> 00:38:48,160
Speaker 4: And it was because I said that he did a

778
00:38:48,160 --> 00:38:50,759
Speaker 4: bad job. If there was like a god, this is

779
00:38:50,840 --> 00:38:52,440
Speaker 4: so old, there was like some it was like Vogue

780
00:38:52,480 --> 00:38:53,799
Speaker 4: or something. There was like a video of a bunch

781
00:38:53,840 --> 00:38:56,839
Speaker 4: of male actors reading one of the scripts from Clueless,

782
00:38:57,320 --> 00:38:59,200
Speaker 4: and Adam Driver was just like getting nothing.

783
00:38:59,239 --> 00:39:01,279
Speaker 3: And I made some men about being being like yeah,

784
00:39:01,320 --> 00:39:02,279
Speaker 3: he's doing terror and.

785
00:39:02,320 --> 00:39:05,680
Speaker 4: I had to turn off my Twitter notifications for like

786
00:39:05,680 --> 00:39:08,360
Speaker 4: twenty four hours. I was just like what, Yeah, anyway,

787
00:39:08,719 --> 00:39:11,839
Speaker 4: So that's just say I don't think there's anything particularly

788
00:39:12,080 --> 00:39:16,240
Speaker 4: like Rabbit about the about the Swifties or Beyonce fans

789
00:39:16,280 --> 00:39:16,680
Speaker 4: or whatever.

790
00:39:19,520 --> 00:39:21,960
Speaker 3: I think just because a lot of times they're young women.

791
00:39:23,640 --> 00:39:26,200
Speaker 3: I mean Beyonce, Yeah, would like Beyonce to Beyonce.

792
00:39:26,320 --> 00:39:30,760
Speaker 4: Mostly it's like women, queer people. Yeah, I think because

793
00:39:30,800 --> 00:39:34,360
Speaker 4: it's them sports fans. Oh yeah, I'm sorry.

794
00:39:34,520 --> 00:39:36,240
Speaker 3: Have you ever talked to an Eagles fan.

795
00:39:37,760 --> 00:39:38,120
Speaker 6: Like I?

796
00:39:39,040 --> 00:39:39,440
Speaker 3: I don't know.

797
00:39:39,680 --> 00:39:42,000
Speaker 4: So anyways, my point is I just think that people

798
00:39:42,040 --> 00:39:45,759
Speaker 4: are awful inherently, and there's something about fandoms that makes

799
00:39:45,840 --> 00:39:46,680
Speaker 4: us and I don't.

800
00:39:46,520 --> 00:39:48,520
Speaker 3: Know my favorite discussion of fans and now I'm going

801
00:39:48,560 --> 00:39:53,000
Speaker 3: to take us on a tangent h John Hodgman of

802
00:39:53,320 --> 00:39:58,560
Speaker 3: The Great Judicial Podcast. Judge John Hodgman once articulated the

803
00:39:58,600 --> 00:40:00,919
Speaker 3: difference between a nerd and a hipster, which I feel

804
00:40:00,960 --> 00:40:04,200
Speaker 3: are like two different types of vandoms. Like a nerd

805
00:40:04,400 --> 00:40:07,200
Speaker 3: is someone who like they both have deep arcade knowledge

806
00:40:07,280 --> 00:40:09,800
Speaker 3: of something that most people don't know about. But a

807
00:40:09,960 --> 00:40:13,000
Speaker 3: nerd is like excited to share it with you and

808
00:40:13,160 --> 00:40:15,920
Speaker 3: like bring you in and help you understand why they

809
00:40:15,960 --> 00:40:18,279
Speaker 3: get so much love and joy from this thing. And

810
00:40:18,440 --> 00:40:21,200
Speaker 3: a hipster wants to wield it like a weapon to

811
00:40:21,440 --> 00:40:23,319
Speaker 3: like hit you over the head and make you feel

812
00:40:24,000 --> 00:40:27,719
Speaker 3: like you aren't as good. You don't know about the

813
00:40:27,760 --> 00:40:31,400
Speaker 3: comic from like nineteen thirty nine where they went to

814
00:40:31,680 --> 00:40:35,799
Speaker 3: like the Negaverse or whatever. Wait, sorry, Mike, you don't

815
00:40:35,800 --> 00:40:38,400
Speaker 3: know about that comic from nineteen eighty nine. I know

816
00:40:38,480 --> 00:40:40,440
Speaker 3: about it. I'm just saying there are like other people

817
00:40:40,480 --> 00:40:42,920
Speaker 3: who like maybe don't like they're not like real fans,

818
00:40:42,960 --> 00:40:46,239
Speaker 3: you know, yeah, not like that, but yeah, exactly, But

819
00:40:46,480 --> 00:40:47,840
Speaker 3: I always think about that distinction.

820
00:40:48,200 --> 00:40:49,920
Speaker 2: I like that framework that's so.

821
00:40:50,040 --> 00:40:51,160
Speaker 3: Real though, That is exactly.

822
00:40:51,200 --> 00:40:54,919
Speaker 4: It's like they're, yeah, let's get into a bigger conversation.

823
00:40:54,600 --> 00:40:57,200
Speaker 3: To do There is just like such a pit.

824
00:40:57,440 --> 00:40:58,799
Speaker 4: Just going back to the idea of art, which again

825
00:40:58,840 --> 00:41:00,800
Speaker 4: with these songs like there could be a star that

826
00:41:00,800 --> 00:41:03,560
Speaker 4: you could put out there that like, like I've had

827
00:41:03,600 --> 00:41:05,840
Speaker 4: this app with with Going Going Mad, Taylor Slift. There

828
00:41:05,880 --> 00:41:08,080
Speaker 4: are like I like, I one song I heard that

829
00:41:08,120 --> 00:41:10,520
Speaker 4: I really like a Starcher. It's a little bit of

830
00:41:10,560 --> 00:41:13,440
Speaker 4: a corny song objectively, but it's I really like it.

831
00:41:13,480 --> 00:41:14,480
Speaker 4: It's really meaningful to me.

832
00:41:14,640 --> 00:41:17,200
Speaker 3: It was like, really, I I it's a it's a

833
00:41:17,320 --> 00:41:19,759
Speaker 3: it's a good song, and it like it meant a

834
00:41:19,800 --> 00:41:20,600
Speaker 3: lot to me at a point in.

835
00:41:20,600 --> 00:41:21,880
Speaker 4: My life where I was like going for a lot

836
00:41:21,920 --> 00:41:23,400
Speaker 4: of stuff and I like had it on Like I

837
00:41:23,480 --> 00:41:24,800
Speaker 4: was driving one time and I had it on the

838
00:41:24,880 --> 00:41:26,360
Speaker 4: car and my friend was like card with me and

839
00:41:26,520 --> 00:41:28,760
Speaker 4: they were like, oh my god, like this is stupid,

840
00:41:28,840 --> 00:41:30,800
Speaker 4: Like what are you like listening to? And I was

841
00:41:30,920 --> 00:41:33,239
Speaker 4: like all right, like I was sorry, but I had

842
00:41:33,280 --> 00:41:35,240
Speaker 4: a moment where I was just like we have different

843
00:41:35,239 --> 00:41:36,239
Speaker 4: interpretations of this.

844
00:41:36,719 --> 00:41:38,840
Speaker 3: That's okay, this isn't for you, this is for me.

845
00:41:38,920 --> 00:41:40,600
Speaker 3: I'm gonna ask you. And I was sort of like, hey,

846
00:41:40,640 --> 00:41:42,160
Speaker 3: I really like this song. Can you not make fun

847
00:41:42,200 --> 00:41:42,320
Speaker 3: of it?

848
00:41:42,400 --> 00:41:45,400
Speaker 4: And they were like, oh shit, Yeah, they also showed

849
00:41:45,440 --> 00:41:48,080
Speaker 4: me plenty of stuff that I'm like, I don't get this,

850
00:41:48,520 --> 00:41:50,279
Speaker 4: but I'm glad that it means something to you.

851
00:41:51,239 --> 00:41:52,960
Speaker 3: At the end of the day, art is supposed to

852
00:41:53,000 --> 00:41:54,920
Speaker 3: mean different things to different people, and like.

853
00:41:55,680 --> 00:41:57,400
Speaker 4: There is I think what you were saying, like the

854
00:41:57,480 --> 00:41:59,560
Speaker 4: hipster kind of side of it is like forgetting that

855
00:41:59,640 --> 00:42:01,200
Speaker 4: and being like, well, everybody has to have my point

856
00:42:01,200 --> 00:42:03,600
Speaker 4: of view, yeah, which is just some of how the

857
00:42:03,640 --> 00:42:04,240
Speaker 4: world works.

858
00:42:04,239 --> 00:42:05,880
Speaker 3: I don't know, right, it's just like a way to

859
00:42:06,040 --> 00:42:11,440
Speaker 3: exclude people and like protect your exclusive access to like

860
00:42:12,239 --> 00:42:13,239
Speaker 3: whatever this thing is.

861
00:42:17,000 --> 00:42:31,160
Speaker 7: More. After a quick break, let's get right back into it.

862
00:42:35,920 --> 00:42:37,879
Speaker 3: So I'm pretty sure. What were some of the high

863
00:42:38,000 --> 00:42:38,920
Speaker 3: risk clusters.

864
00:42:39,400 --> 00:42:42,440
Speaker 2: Well, the high risk clusters these are the clusters that.

865
00:42:42,480 --> 00:42:45,960
Speaker 1: Were heavily seated by non typical accounts and drove the

866
00:42:46,080 --> 00:42:49,920
Speaker 1: downstream effects with authentic users were things like yep, the

867
00:42:50,080 --> 00:42:57,120
Speaker 1: Nazi symbolism, conversation, conspiracy accusations, political reframing, and mega allegations,

868
00:42:57,520 --> 00:43:02,200
Speaker 1: and then a bucket that they call relationship politicization parentheses.

869
00:43:02,400 --> 00:43:03,320
Speaker 2: Kelsey NFL.

870
00:43:03,640 --> 00:43:06,600
Speaker 3: I think some of it's the tradwife the tradwife stuff. Yeah,

871
00:43:06,600 --> 00:43:10,320
Speaker 3: I assume that topic is just like anything to do

872
00:43:10,520 --> 00:43:12,720
Speaker 3: with Kelsey is just kind of rolled up and shoved

873
00:43:12,760 --> 00:43:14,640
Speaker 3: into that bucket. Yes.

874
00:43:15,200 --> 00:43:19,360
Speaker 1: So have you all seen the response to this study online,

875
00:43:19,400 --> 00:43:22,560
Speaker 1: because in my pockets of the Internet, the response has

876
00:43:22,680 --> 00:43:25,719
Speaker 1: been massive. I don't know that I've seen something kind

877
00:43:25,760 --> 00:43:28,680
Speaker 1: of instantly polarized the Internet in such a stark way

878
00:43:28,880 --> 00:43:29,600
Speaker 1: in a long time.

879
00:43:29,680 --> 00:43:32,759
Speaker 3: Have you all been paying attention to any of the response? Yeah,

880
00:43:32,880 --> 00:43:36,440
Speaker 3: I I've only seen the stuff that you showed me, Bridget,

881
00:43:36,560 --> 00:43:41,200
Speaker 3: because I am like so far offline these days. I

882
00:43:41,280 --> 00:43:44,200
Speaker 3: look at Reddit, which does not talk about any current events.

883
00:43:44,239 --> 00:43:48,200
Speaker 3: It's all like history subreddits and blue Sky, which is

884
00:43:48,280 --> 00:43:52,200
Speaker 3: its own thing like screaming about this or that, but

885
00:43:52,640 --> 00:43:53,560
Speaker 3: not Taylor Swift.

886
00:43:53,600 --> 00:43:57,000
Speaker 4: I guess I haven't just used I unrelated to this.

887
00:43:57,080 --> 00:43:58,680
Speaker 4: It's sort of been taking a break from social media

888
00:43:58,760 --> 00:44:00,960
Speaker 4: this week. However, I did and to bring this up

889
00:44:00,960 --> 00:44:03,480
Speaker 4: with a couple of people in my life over the

890
00:44:03,560 --> 00:44:05,680
Speaker 4: last twenty four hours, and I was like, oh, I'm

891
00:44:05,719 --> 00:44:07,840
Speaker 4: working out a Taylor Swist story and they immediately were like, oh,

892
00:44:07,880 --> 00:44:08,880
Speaker 4: they're only stone article.

893
00:44:09,040 --> 00:44:10,719
Speaker 7: I was like, uh yep.

894
00:44:11,560 --> 00:44:15,000
Speaker 3: So it was waking it like like I it's making

895
00:44:15,040 --> 00:44:16,120
Speaker 3: its way around. Yeah.

896
00:44:16,480 --> 00:44:18,480
Speaker 1: Yeah, And one of the things that I sort of

897
00:44:18,719 --> 00:44:21,480
Speaker 1: can't not talk about with this is the racial implications

898
00:44:21,520 --> 00:44:24,040
Speaker 1: at play, because I think a lot of the women

899
00:44:24,360 --> 00:44:27,279
Speaker 1: who were crick not all, but a lot of the

900
00:44:27,280 --> 00:44:31,839
Speaker 1: folks who are critiquing Taylor Swift are black women people

901
00:44:31,880 --> 00:44:33,640
Speaker 1: of color, and I think a lot of the people

902
00:44:33,680 --> 00:44:37,200
Speaker 1: who perhaps self identify as Swifties who were very vocal

903
00:44:37,280 --> 00:44:39,560
Speaker 1: about the fact that, you know, like, hey, anybody who's

904
00:44:39,560 --> 00:44:42,319
Speaker 1: criticizing her, this article says they were all bots, which

905
00:44:42,360 --> 00:44:45,920
Speaker 1: again is not true. I think, you know, you can

906
00:44:46,000 --> 00:44:50,040
Speaker 1: already sort of see how maybe these different types of

907
00:44:50,080 --> 00:44:52,959
Speaker 1: people are talking about Taylor Swift, but they're really talking

908
00:44:52,960 --> 00:44:55,759
Speaker 1: about other stuff race, gender, class, politics, and then the

909
00:44:55,880 --> 00:44:58,200
Speaker 1: conversation kind of becomes a stand.

910
00:44:57,920 --> 00:44:58,719
Speaker 3: In for all of this.

911
00:44:59,320 --> 00:45:03,360
Speaker 1: And I so typically saw a lot of black women saying,

912
00:45:04,000 --> 00:45:07,720
Speaker 1: you know, I am not a bot, I'm a real person.

913
00:45:08,160 --> 00:45:11,239
Speaker 1: This this was my actual critique, and I want to

914
00:45:11,280 --> 00:45:13,440
Speaker 1: make that clear. One of the things that I think

915
00:45:13,560 --> 00:45:17,560
Speaker 1: this study perhaps did not make super clear is the

916
00:45:18,160 --> 00:45:21,640
Speaker 1: authentic voices who those folks were and what they were saying.

917
00:45:21,680 --> 00:45:23,960
Speaker 1: I think that that perhaps gave a bit of credence

918
00:45:24,000 --> 00:45:25,520
Speaker 1: to people who were like, oh, you don't even need

919
00:45:25,520 --> 00:45:27,800
Speaker 1: to listen to these people there their bots anyway, And

920
00:45:27,920 --> 00:45:31,080
Speaker 1: so one of the big questions that people have been

921
00:45:31,120 --> 00:45:33,400
Speaker 1: asking is like is this study legit?

922
00:45:33,560 --> 00:45:36,560
Speaker 2: Like should it be taken at face value? What's the deal?

923
00:45:36,719 --> 00:45:39,640
Speaker 1: So Mike and I have been in conversation with the

924
00:45:39,760 --> 00:45:42,120
Speaker 1: authors of this study. We're going to have a deeper

925
00:45:42,239 --> 00:45:46,080
Speaker 1: dive episode into the methodologies that they used, but I

926
00:45:46,160 --> 00:45:48,720
Speaker 1: wanted to talk about sort of what people are saying

927
00:45:48,880 --> 00:45:50,960
Speaker 1: about this study to give you a sense before we

928
00:45:51,080 --> 00:45:53,800
Speaker 1: go into those those deeper conversations. A lot of the

929
00:45:53,840 --> 00:45:57,240
Speaker 1: folks who were genuinely critical of Tara Swift were feeling

930
00:45:57,440 --> 00:45:59,880
Speaker 1: kind of suss about this study. So I wanted to

931
00:46:00,040 --> 00:46:03,240
Speaker 1: sort of get into some of the buckets of critique

932
00:46:03,280 --> 00:46:06,680
Speaker 1: that I have seen floating around online about the study.

933
00:46:07,000 --> 00:46:09,440
Speaker 1: The first is that the methodology that the study put

934
00:46:09,480 --> 00:46:12,799
Speaker 1: together is just not a sound one. Mike, as our

935
00:46:13,200 --> 00:46:17,400
Speaker 1: resident data analyst, what can you tell us about the

936
00:46:17,440 --> 00:46:18,960
Speaker 1: methodology behind this report?

937
00:46:19,000 --> 00:46:19,840
Speaker 2: As a data scientist?

938
00:46:20,120 --> 00:46:24,120
Speaker 3: Uh So, for one thing, an analysis like this requires

939
00:46:24,320 --> 00:46:27,120
Speaker 3: tons of decisions on the part of the analyst. You know,

940
00:46:27,200 --> 00:46:29,440
Speaker 3: which posts and accounts are they going to look at,

941
00:46:29,719 --> 00:46:32,200
Speaker 3: what time period are they going to consider what are

942
00:46:32,200 --> 00:46:33,960
Speaker 3: they going to measure, how are they going to measure

943
00:46:34,000 --> 00:46:36,160
Speaker 3: that all of those decisions are going to have a

944
00:46:36,200 --> 00:46:39,560
Speaker 3: big impact on the results and the conclusions. And for

945
00:46:39,680 --> 00:46:42,160
Speaker 3: studies like this like there isn't a single standard way

946
00:46:42,280 --> 00:46:44,960
Speaker 3: to do it, because the best methodology depends on the

947
00:46:45,280 --> 00:46:48,560
Speaker 3: particular research questions that they're attempting to answer. And so

948
00:46:49,000 --> 00:46:51,640
Speaker 3: a lot of the criticism of their methodology that I've

949
00:46:51,680 --> 00:46:55,360
Speaker 3: seen is quite vague, where the person posting the criticism

950
00:46:55,640 --> 00:46:59,080
Speaker 3: isn't really being specific about which of those analytic decisions

951
00:46:59,160 --> 00:47:02,480
Speaker 3: they object to, and instead are just dismissing the whole

952
00:47:02,520 --> 00:47:05,759
Speaker 3: thing is like biased or flawed, which feels a little

953
00:47:05,840 --> 00:47:10,560
Speaker 3: lazy to me. There are definitely some thoughtful exceptions to that,

954
00:47:10,719 --> 00:47:13,920
Speaker 3: where people are raising like various specific and very valid

955
00:47:14,080 --> 00:47:15,880
Speaker 3: concerns about the study, and we are going to talk

956
00:47:15,920 --> 00:47:19,440
Speaker 3: about that, and certainly the study is not perfect, and

957
00:47:19,600 --> 00:47:23,800
Speaker 3: interpreting the results of even the best studies, though, requires

958
00:47:23,920 --> 00:47:29,920
Speaker 3: being honest about acknowledging their limitations, right, So one also

959
00:47:30,160 --> 00:47:33,680
Speaker 3: needs to consider the practical limitations of what is possible

960
00:47:33,760 --> 00:47:38,880
Speaker 3: with available resources. Analyzes are difficult and complicated and time consuming.

961
00:47:39,400 --> 00:47:42,800
Speaker 3: In this tailor swift data set the researchers examined eighteen

962
00:47:42,960 --> 00:47:46,000
Speaker 3: thousand posts. It would have been great if they'd had

963
00:47:46,040 --> 00:47:49,920
Speaker 3: a trained human expert manually review each individual post and

964
00:47:50,640 --> 00:47:52,759
Speaker 3: look at the social media history of the account that

965
00:47:52,840 --> 00:47:55,120
Speaker 3: posted it, what sort of things they talk about, who

966
00:47:55,120 --> 00:47:57,719
Speaker 3: they're connected to, and then make it a termination about

967
00:47:57,719 --> 00:48:00,680
Speaker 3: whether it's a real human or an inauthentic bought account

968
00:48:00,719 --> 00:48:03,719
Speaker 3: pretending to be an ordinary human. But a study like

969
00:48:03,800 --> 00:48:07,280
Speaker 3: that would cost thousands of dollars, maybe tens of thousands

970
00:48:07,280 --> 00:48:10,160
Speaker 3: of dollars. And you know, I personally believe that the

971
00:48:10,239 --> 00:48:15,520
Speaker 3: integrity of our online spaces and discourse is critically undervalued

972
00:48:15,640 --> 00:48:19,360
Speaker 3: and there should be much more funding to do robust

973
00:48:19,440 --> 00:48:26,680
Speaker 3: studies to examine who is manipulating our discourse. But unfortunately

974
00:48:26,840 --> 00:48:29,080
Speaker 3: that's just like not the world that we live in

975
00:48:29,239 --> 00:48:33,120
Speaker 3: right now in twenty twenty five America, and funding for

976
00:48:33,200 --> 00:48:36,560
Speaker 3: that kind of research is not abundant to say the least,

977
00:48:37,080 --> 00:48:40,000
Speaker 3: right and in fact, to my knowledge, this report from

978
00:48:40,320 --> 00:48:44,239
Speaker 3: Goudea is literally the only published analysis that provides any

979
00:48:44,400 --> 00:48:49,640
Speaker 3: data on online discourse about tailor Swift narrative during this

980
00:48:49,719 --> 00:48:52,719
Speaker 3: timeframe at all. Right, I haven't done the exhaustive search.

981
00:48:52,800 --> 00:48:54,360
Speaker 3: Maybe it's out there. I'd love to see it at

982
00:48:54,360 --> 00:48:55,960
Speaker 3: please send it if you have it. But to my

983
00:48:56,080 --> 00:48:59,200
Speaker 3: knowledge is the only thing we've got. And so given

984
00:48:59,400 --> 00:49:03,000
Speaker 3: that extreme of available data, I think it would be

985
00:49:03,160 --> 00:49:07,320
Speaker 3: foolish to completely dismiss this study because it is imperfect.

986
00:49:07,880 --> 00:49:10,719
Speaker 3: I would much rather rely on data, even if that

987
00:49:10,840 --> 00:49:14,759
Speaker 3: data is impacted by limitations and caveats, than rely one

988
00:49:14,840 --> 00:49:18,800
Speaker 3: hundred percent on vibes and my own personal experience with

989
00:49:18,880 --> 00:49:22,279
Speaker 3: the algorithm that I'm looking at. Does that make sense?

990
00:49:22,280 --> 00:49:24,480
Speaker 3: It's not really an answer I have more, but I

991
00:49:24,520 --> 00:49:27,120
Speaker 3: did want to pause there. Yeah, it does make sense.

992
00:49:27,239 --> 00:49:29,480
Speaker 1: And I should mention this too, that Mike, you and

993
00:49:29,600 --> 00:49:33,000
Speaker 1: I were able to speak to an outside expert, Molly Dwyer,

994
00:49:33,120 --> 00:49:35,719
Speaker 1: who works for a kind of similar company that does

995
00:49:35,800 --> 00:49:37,920
Speaker 1: similar kinds of reports, and we'll hear from her. We

996
00:49:38,040 --> 00:49:42,480
Speaker 1: have a fascinating conversation. Also, Joey Tumblr fan Tumblr is

997
00:49:42,520 --> 00:49:44,160
Speaker 1: what got her into this work in the first place,

998
00:49:44,200 --> 00:49:46,440
Speaker 1: which is very interesting. What's funny is that when I

999
00:49:46,560 --> 00:49:49,200
Speaker 1: was trying to figure out what expert voices I wanted

1000
00:49:49,239 --> 00:49:52,120
Speaker 1: to turn to to help put together, you know, our

1001
00:49:52,160 --> 00:49:55,480
Speaker 1: conversation about what's going on in this report, you were

1002
00:49:55,560 --> 00:49:57,799
Speaker 1: like oh, I found this person, Molly Dwyer. They'd put

1003
00:49:57,840 --> 00:50:01,560
Speaker 1: together a similar report about bot noow works impacting the

1004
00:50:01,640 --> 00:50:05,000
Speaker 1: conversation around the cracker barrel logo redesign or remember that.

1005
00:50:05,400 --> 00:50:07,800
Speaker 1: And I was like, Mike, I don't think that some

1006
00:50:08,360 --> 00:50:11,120
Speaker 1: data scientist is going to want to chime in on

1007
00:50:11,239 --> 00:50:12,920
Speaker 1: a report that she had nothing to do with.

1008
00:50:13,120 --> 00:50:15,680
Speaker 2: She's not gonna we can't ask her, but she's not gonna.

1009
00:50:15,480 --> 00:50:17,760
Speaker 1: Want to like tear apart somebody else's report.

1010
00:50:18,040 --> 00:50:20,240
Speaker 3: And boy was I wrong.

1011
00:50:21,239 --> 00:50:23,680
Speaker 1: And you were like, that is all scientists like to

1012
00:50:23,760 --> 00:50:26,759
Speaker 1: do is to not not tear into I shouldn't say that,

1013
00:50:26,840 --> 00:50:30,719
Speaker 1: but like look at and talk about the work of

1014
00:50:30,840 --> 00:50:33,360
Speaker 1: other scientists. You were like, it did not as a scientist,

1015
00:50:33,400 --> 00:50:34,800
Speaker 1: It did not surprise me at all that she was

1016
00:50:36,000 --> 00:50:38,720
Speaker 1: very happy to talk about the findings of this report.

1017
00:50:38,920 --> 00:50:41,640
Speaker 3: Absolutely one hundred percent. I mean we as scientists, we

1018
00:50:41,760 --> 00:50:47,160
Speaker 3: are trained to be skeptical. We are trained to find

1019
00:50:47,320 --> 00:50:51,719
Speaker 3: the reasons why your data does not support your conclusion, right,

1020
00:50:51,880 --> 00:50:56,920
Speaker 3: because by doing that we eliminate the bad data, and

1021
00:50:56,960 --> 00:50:59,120
Speaker 3: then we're left with like the data that does actually

1022
00:50:59,120 --> 00:51:02,840
Speaker 3: support the conclusion. And that's how we know things about

1023
00:51:02,880 --> 00:51:06,279
Speaker 3: the world, right, And so I think, you know, rather

1024
00:51:06,400 --> 00:51:08,640
Speaker 3: than a binary question of whether the methodology for this

1025
00:51:08,719 --> 00:51:12,680
Speaker 3: particular report about Taylor Swift discourse is sound or and sound,

1026
00:51:13,160 --> 00:51:16,279
Speaker 3: I think the better question is, you know, is the

1027
00:51:16,360 --> 00:51:20,560
Speaker 3: methodology sufficiently robust that we have confidence in its conclusions

1028
00:51:20,800 --> 00:51:23,000
Speaker 3: or that the conclusions are at least in the neighborhood

1029
00:51:23,080 --> 00:51:26,360
Speaker 3: of correct. And in my opinion, I think the answer

1030
00:51:26,520 --> 00:51:28,560
Speaker 3: is yes. And you know, like you said, we spoke

1031
00:51:28,719 --> 00:51:31,080
Speaker 3: with Molly Dwyer. Listeners will get to hear from her

1032
00:51:31,200 --> 00:51:34,120
Speaker 3: on Tuesday, and I really encourage them to listen because

1033
00:51:34,120 --> 00:51:35,919
Speaker 3: she really had a lot of good stuff to say.

1034
00:51:36,920 --> 00:51:39,279
Speaker 3: You know, she generally agreed. She was like, yeah, their

1035
00:51:39,280 --> 00:51:40,920
Speaker 3: approach is like pretty fine.

1036
00:51:41,280 --> 00:51:41,440
Speaker 7: You know.

1037
00:51:41,680 --> 00:51:44,440
Speaker 3: There are places to quibble, certainly, like oh, maybe I

1038
00:51:44,440 --> 00:51:47,680
Speaker 3: would have done this a little bit differently, maybe the

1039
00:51:47,960 --> 00:51:51,240
Speaker 3: exact percentages are a little off, but like the overall

1040
00:51:51,480 --> 00:51:57,919
Speaker 3: pattern of the results seem pretty well supported and also

1041
00:51:58,440 --> 00:52:02,319
Speaker 3: importantly in law with other studies that have looked at

1042
00:52:02,400 --> 00:52:07,279
Speaker 3: similar questions in different contexts, which is an important way

1043
00:52:07,440 --> 00:52:13,080
Speaker 3: for us to have confidence in analyzes when you know

1044
00:52:13,200 --> 00:52:17,800
Speaker 3: it's not possible or practical to do a perfect design

1045
00:52:17,920 --> 00:52:20,200
Speaker 3: of the study that you might want to, and so

1046
00:52:20,440 --> 00:52:22,960
Speaker 3: just briefly, there are two aspects of the approach that

1047
00:52:23,000 --> 00:52:27,000
Speaker 3: they use here that I do want to emphasize. One

1048
00:52:27,160 --> 00:52:29,440
Speaker 3: is the algorithm that they use for classifying accounts as

1049
00:52:29,440 --> 00:52:34,600
Speaker 3: either typical or atypical. Atypical sort of synonymous with like

1050
00:52:34,680 --> 00:52:36,960
Speaker 3: a body account or an automated account. And then the

1051
00:52:37,000 --> 00:52:38,840
Speaker 3: other piece of their analysis that I wanted to highlight

1052
00:52:39,040 --> 00:52:41,879
Speaker 3: was how it was longitudinal that they didn't just look

1053
00:52:41,880 --> 00:52:45,160
Speaker 3: at the mix of typical and atypical accounts posting about Swift,

1054
00:52:45,520 --> 00:52:47,839
Speaker 3: but they looked at how that mixture changed over time,

1055
00:52:48,520 --> 00:52:52,680
Speaker 3: and they found an early surge of posts from atypical

1056
00:52:52,680 --> 00:52:55,960
Speaker 3: accounts right after her album launched, which kind of helped

1057
00:52:56,120 --> 00:52:58,759
Speaker 3: set the tone for the narrative some of those high

1058
00:52:58,880 --> 00:53:05,680
Speaker 3: risk topics that you talked about earlier. Those atypical accounts

1059
00:53:05,680 --> 00:53:08,600
Speaker 3: were then later eclipsed in volume by more typical users

1060
00:53:08,640 --> 00:53:12,000
Speaker 3: as they joined the conversation and it became like a

1061
00:53:12,120 --> 00:53:18,719
Speaker 3: viral trending topic. And then later those typical humans kind

1062
00:53:18,760 --> 00:53:21,160
Speaker 3: of lost interest in this story because you can only

1063
00:53:21,280 --> 00:53:24,520
Speaker 3: talk about a lightning bolt being a piece of Nazi

1064
00:53:24,760 --> 00:53:29,560
Speaker 3: iconography for so long before it becomes boring. Those humans

1065
00:53:29,640 --> 00:53:32,920
Speaker 3: like stopped posting, and so the proportion of the unauthentic bots,

1066
00:53:34,040 --> 00:53:37,440
Speaker 3: the inauthentic accounts increased at that point is they were

1067
00:53:37,480 --> 00:53:40,759
Speaker 3: like trying to keep the conversation going. And I just

1068
00:53:40,800 --> 00:53:43,400
Speaker 3: think that that kind of temporal analysis adds a lot

1069
00:53:43,440 --> 00:53:46,960
Speaker 3: of nuance to help us understand the role of bots

1070
00:53:47,000 --> 00:53:51,520
Speaker 3: and inauthentic accounts and shaping these conversations, nuance that really

1071
00:53:51,600 --> 00:53:54,719
Speaker 3: gets lost in black and white social media posts that

1072
00:53:55,080 --> 00:53:57,680
Speaker 3: either want to claim that this report either totally vindicates

1073
00:53:57,719 --> 00:54:02,160
Speaker 3: Taylor Swift or is completely flawed and tells us nothing.

1074
00:54:04,520 --> 00:54:04,680
Speaker 7: More.

1075
00:54:04,719 --> 00:54:21,719
Speaker 1: After a quick break, let's get right back into it, Mike.

1076
00:54:21,880 --> 00:54:24,799
Speaker 1: Something that you bring up and came up in our

1077
00:54:24,840 --> 00:54:27,480
Speaker 1: conversation with Molly as well, was that I've seen this

1078
00:54:27,600 --> 00:54:31,560
Speaker 1: criticism that, oh, this isn't really like a data company.

1079
00:54:31,640 --> 00:54:35,000
Speaker 1: They're not data scientists. This is a PR firm pretending

1080
00:54:35,200 --> 00:54:39,080
Speaker 1: to do data analysis. And when I asked Molly about this,

1081
00:54:39,239 --> 00:54:41,480
Speaker 1: she had actually a very interesting answer, which was that

1082
00:54:41,880 --> 00:54:45,160
Speaker 1: it is true that companies like hers, companies like Udea

1083
00:54:46,080 --> 00:54:50,640
Speaker 1: are often working on behalf of clients or brands or celebrities,

1084
00:54:50,840 --> 00:54:53,000
Speaker 1: but that doesn't really make them PR. And she made

1085
00:54:53,040 --> 00:54:55,600
Speaker 1: this great point that we don't really kind of like

1086
00:54:55,680 --> 00:54:57,920
Speaker 1: what you were alluding to before, we don't really have

1087
00:54:58,280 --> 00:55:01,279
Speaker 1: a lot of places anymore where you can get like

1088
00:55:01,560 --> 00:55:04,200
Speaker 1: academic insights into the Internet.

1089
00:55:04,320 --> 00:55:04,440
Speaker 7: Right.

1090
00:55:04,480 --> 00:55:07,800
Speaker 1: There used to be a very robust community and field

1091
00:55:07,880 --> 00:55:11,719
Speaker 1: of study for researchers and academics of all stripes and

1092
00:55:11,760 --> 00:55:14,160
Speaker 1: all kinds to like look at API and see what

1093
00:55:14,280 --> 00:55:15,840
Speaker 1: was going on online. And a lot of that is

1094
00:55:15,880 --> 00:55:17,480
Speaker 1: really dried up and come to an end. And so

1095
00:55:17,960 --> 00:55:20,000
Speaker 1: what we do have and the absence of that is

1096
00:55:20,440 --> 00:55:23,320
Speaker 1: these private companies, and so they're not perfect.

1097
00:55:23,440 --> 00:55:25,600
Speaker 2: I'm not saying that I wish.

1098
00:55:25,480 --> 00:55:29,200
Speaker 1: That we had more robust, well funded spaces to look

1099
00:55:29,280 --> 00:55:33,480
Speaker 1: into what's happening online. However, one of the big criticisms

1100
00:55:33,480 --> 00:55:36,319
Speaker 1: that I also saw of this report was that they

1101
00:55:36,360 --> 00:55:39,320
Speaker 1: did not really make their data set transparent, and so

1102
00:55:40,040 --> 00:55:42,960
Speaker 1: I could not like replicate that. I could not say

1103
00:55:43,440 --> 00:55:45,520
Speaker 1: I want to dig into this myself and repl and

1104
00:55:45,760 --> 00:55:49,200
Speaker 1: use their data set to replicate their results to confirm them.

1105
00:55:49,320 --> 00:55:49,440
Speaker 3: Right.

1106
00:55:49,719 --> 00:55:51,759
Speaker 1: And when you and I were talking about this, Mike,

1107
00:55:52,040 --> 00:55:54,040
Speaker 1: I was like, well, I kind of get it. On

1108
00:55:54,120 --> 00:55:57,080
Speaker 1: the one hand, like this is a consequence of more

1109
00:55:57,160 --> 00:56:01,760
Speaker 1: and more private companies doing the research into what's happening

1110
00:56:01,840 --> 00:56:04,880
Speaker 1: online to tell us what's happening on the internet. However,

1111
00:56:05,080 --> 00:56:07,680
Speaker 1: these are private companies, so they might have less transparency.

1112
00:56:07,719 --> 00:56:09,120
Speaker 1: They might be able to say, oh, we were not

1113
00:56:09,200 --> 00:56:11,919
Speaker 1: going to make this public because it's a proprietary secret whatever.

1114
00:56:12,360 --> 00:56:14,960
Speaker 1: To be sure, there are private companies that do operate

1115
00:56:15,040 --> 00:56:17,560
Speaker 1: with a better level of transparency, But I do think

1116
00:56:17,680 --> 00:56:19,719
Speaker 1: that this is sort of a function of having to

1117
00:56:19,840 --> 00:56:23,360
Speaker 1: depend on the whims and the particularities of private companies

1118
00:56:23,760 --> 00:56:27,840
Speaker 1: to provide robust research into what's happening on the internet.

1119
00:56:27,880 --> 00:56:30,400
Speaker 1: Do you know what I'm saying totally? I completely agree.

1120
00:56:30,600 --> 00:56:32,960
Speaker 3: I a half percent agree that I wish they had

1121
00:56:33,080 --> 00:56:38,680
Speaker 3: released their data and made it publicly available. Like I

1122
00:56:38,800 --> 00:56:42,120
Speaker 3: can't really think of a really compelling reason not to.

1123
00:56:42,239 --> 00:56:45,280
Speaker 3: I mean, I guess again, it would be extra expense

1124
00:56:45,360 --> 00:56:49,160
Speaker 3: and time for them, So that's a concern, right. They

1125
00:56:49,200 --> 00:56:52,279
Speaker 3: would have to probably take some steps to make sure

1126
00:56:52,320 --> 00:56:55,400
Speaker 3: that there wasn't any sensitive information in there, that they

1127
00:56:55,440 --> 00:57:00,480
Speaker 3: weren't exposing people's privacy. But companies do this, you know,

1128
00:57:01,040 --> 00:57:04,040
Speaker 3: it is possible, and it's it is unfortunate that they

1129
00:57:04,200 --> 00:57:08,400
Speaker 3: didn't make it transparent, because, uh, that is one of

1130
00:57:08,560 --> 00:57:13,120
Speaker 3: in my opinion, the most valid criticisms of this study

1131
00:57:13,320 --> 00:57:16,040
Speaker 3: is that we we kind of have to take their

1132
00:57:16,080 --> 00:57:19,920
Speaker 3: word for it. But at the same time, it's like

1133
00:57:20,200 --> 00:57:25,360
Speaker 3: they don't have some sort of intense competing interest here,

1134
00:57:25,480 --> 00:57:32,400
Speaker 3: Like they're trying a company like Gudea, which you know,

1135
00:57:32,760 --> 00:57:36,960
Speaker 3: works on behalf of clients, to you know, measure narratives

1136
00:57:37,000 --> 00:57:40,400
Speaker 3: around their brands. Uh, they would put they put out

1137
00:57:40,400 --> 00:57:41,120
Speaker 3: a report like this.

1138
00:57:41,600 --> 00:57:41,720
Speaker 4: Uh.

1139
00:57:42,160 --> 00:57:44,000
Speaker 3: Maybe a little bit because they want to be a

1140
00:57:44,080 --> 00:57:46,520
Speaker 3: good actor in society, you know, we can hope that

1141
00:57:46,600 --> 00:57:48,680
Speaker 3: that's part of it, but also because they want to

1142
00:57:48,760 --> 00:57:52,040
Speaker 3: get some pressed for themselves as a company that does

1143
00:57:52,800 --> 00:57:58,880
Speaker 3: good quality analyzes and shares insightful conclusions.

1144
00:57:59,200 --> 00:57:59,320
Speaker 6: Uh.

1145
00:57:59,560 --> 00:58:02,200
Speaker 3: And so they they have a motivation to get it right,

1146
00:58:02,520 --> 00:58:05,720
Speaker 3: you know. And so the idea that they would just

1147
00:58:05,840 --> 00:58:10,960
Speaker 3: like make things up doesn't really hold a lot of water.

1148
00:58:11,200 --> 00:58:15,840
Speaker 3: And especially in this context that you described, where companies

1149
00:58:15,960 --> 00:58:19,600
Speaker 3: like Reddit and x and Meta have shut off researcher

1150
00:58:19,720 --> 00:58:22,280
Speaker 3: access to their APIs, which we used to be able

1151
00:58:22,360 --> 00:58:24,960
Speaker 3: to use to do this kind of research. Right, It

1152
00:58:25,120 --> 00:58:27,760
Speaker 3: used to be a very low barrier for a grad

1153
00:58:27,840 --> 00:58:31,200
Speaker 3: student somewhere or even just like a normal person sitting

1154
00:58:31,240 --> 00:58:33,160
Speaker 3: at their desk at home. If they had this sort

1155
00:58:33,160 --> 00:58:36,040
Speaker 3: of question, they could do this research pretty easily using

1156
00:58:36,320 --> 00:58:40,760
Speaker 3: Twitter's API. But you know, Elon Musk shut that off

1157
00:58:40,840 --> 00:58:44,560
Speaker 3: because he didn't want people poking around to publish data

1158
00:58:44,640 --> 00:58:48,320
Speaker 3: about how completely filled with bots their ecosystem is, because

1159
00:58:48,360 --> 00:58:50,520
Speaker 3: what brand is going to want to buy ads on

1160
00:58:52,120 --> 00:58:54,520
Speaker 3: a platform that's just like serving them up the bots.

1161
00:58:56,000 --> 00:58:57,920
Speaker 3: So there's that we lost the API access, and we've

1162
00:58:58,000 --> 00:59:01,000
Speaker 3: lost so much funding for or the kind of research

1163
00:59:01,120 --> 00:59:05,800
Speaker 3: that looks into this stuff that we as just like

1164
00:59:05,960 --> 00:59:09,160
Speaker 3: members of the public, we really have no choice at

1165
00:59:09,160 --> 00:59:13,600
Speaker 3: this particular moment but to make peace with the fact

1166
00:59:13,760 --> 00:59:16,080
Speaker 3: that a lot of the data that we are able

1167
00:59:16,160 --> 00:59:18,600
Speaker 3: to use to answer questions like this are going to

1168
00:59:18,680 --> 00:59:21,280
Speaker 3: come from private companies. That's just the state of things

1169
00:59:21,360 --> 00:59:24,880
Speaker 3: until we change it. Yeah, And you know, one of the.

1170
00:59:25,200 --> 00:59:27,640
Speaker 1: Other kind of buckets of criticism I've seen floating around

1171
00:59:27,680 --> 00:59:31,640
Speaker 1: the internet is, oh, Gudea, they're the AI company. They

1172
00:59:31,760 --> 00:59:35,360
Speaker 1: used AI to put this report together. To me, this

1173
00:59:35,520 --> 00:59:38,120
Speaker 1: is kind of a weird claim to debunk the whole study,

1174
00:59:38,160 --> 00:59:41,240
Speaker 1: which I just I don't think it's fair. The company's

1175
00:59:41,280 --> 00:59:44,160
Speaker 1: full name is Gudea AI, and its website says that

1176
00:59:44,200 --> 00:59:47,280
Speaker 1: it offers AI services, but it's important to note that

1177
00:59:47,760 --> 00:59:50,880
Speaker 1: just like other socialisting tools like hootswe or other tools

1178
00:59:50,920 --> 00:59:51,880
Speaker 1: that maybe you've used.

1179
00:59:52,080 --> 00:59:54,320
Speaker 2: This is machine learning and it's not what we call

1180
00:59:54,560 --> 00:59:55,320
Speaker 2: generative AI.

1181
00:59:55,560 --> 00:59:57,480
Speaker 1: So, like a quick and dirty summary of the difference

1182
00:59:57,600 --> 01:00:01,680
Speaker 1: is that machine learning has been a staple of technology

1183
01:00:01,960 --> 01:00:06,160
Speaker 1: for decades. It analyzes existing data for predictions and classifications

1184
01:00:06,200 --> 01:00:08,760
Speaker 1: and decisions, kind of like when you go on the

1185
01:00:08,840 --> 01:00:13,200
Speaker 1: Netflix the recommended, the recommended shows that's machine learning. The

1186
01:00:13,280 --> 01:00:16,600
Speaker 1: way that your Gmail knows what emails are spam and

1187
01:00:16,680 --> 01:00:20,120
Speaker 1: filters them out, that's machine learning. It operates on rules

1188
01:00:20,240 --> 01:00:25,240
Speaker 1: and patterns, while generative AI creates entirely new content through

1189
01:00:25,400 --> 01:00:28,800
Speaker 1: learning complex patterns from data sets. So think of that

1190
01:00:29,040 --> 01:00:31,720
Speaker 1: as things like Sora or CHATGPT, which is why it

1191
01:00:31,760 --> 01:00:36,000
Speaker 1: takes a lot more processing power than just a general

1192
01:00:36,120 --> 01:00:39,560
Speaker 1: machine learning. So the real difference here is creation versus

1193
01:00:39,880 --> 01:00:44,720
Speaker 1: analysis and prediction. So this company having AI as one

1194
01:00:44,760 --> 01:00:48,360
Speaker 1: of their work streams doesn't really mean anything, really, Like,

1195
01:00:48,440 --> 01:00:51,440
Speaker 1: it's not a very I don't think it's even a

1196
01:00:51,520 --> 01:00:55,240
Speaker 1: real criticism. This is kind of a tangent, But Mike,

1197
01:00:55,280 --> 01:00:57,520
Speaker 1: you and I got into a whole thing about how

1198
01:00:57,680 --> 01:01:00,600
Speaker 1: companies will put AI in their name even if they

1199
01:01:00,640 --> 01:01:04,120
Speaker 1: aren't even really AI companies as kind of a marketing term,

1200
01:01:04,280 --> 01:01:06,000
Speaker 1: even though they're not using AI the way we think

1201
01:01:06,040 --> 01:01:08,880
Speaker 1: of it, like with Chatgypt And I mean, I don't

1202
01:01:08,880 --> 01:01:10,800
Speaker 1: think it takes a genius to guess why a company

1203
01:01:10,840 --> 01:01:12,440
Speaker 1: would do that, Like why I would be like, oh,

1204
01:01:12,520 --> 01:01:17,560
Speaker 1: my company is podcasts dot AI just podcasts because it's marketing.

1205
01:01:17,680 --> 01:01:21,080
Speaker 1: It makes people look at them twice. It probably helps

1206
01:01:21,120 --> 01:01:22,760
Speaker 1: them fundraise and get more money.

1207
01:01:22,840 --> 01:01:23,640
Speaker 2: I totally get it.

1208
01:01:23,880 --> 01:01:27,120
Speaker 3: Yeah, I mean, just ask friend of the show eds

1209
01:01:27,120 --> 01:01:30,080
Speaker 3: a Trone, right, Like a huge portion of our whole

1210
01:01:30,200 --> 01:01:34,800
Speaker 3: economy is propped up by AI marketing hype. Depending on

1211
01:01:34,880 --> 01:01:37,280
Speaker 3: what sector you work in, you would be a fool

1212
01:01:37,520 --> 01:01:39,640
Speaker 3: not to put AI in your company name.

1213
01:01:40,040 --> 01:01:42,520
Speaker 1: So I would probably say I mentioned this earlier, But

1214
01:01:42,840 --> 01:01:45,720
Speaker 1: probably my biggest criticism of the study is that I

1215
01:01:45,920 --> 01:01:48,600
Speaker 1: just don't think it does a great job of really

1216
01:01:48,720 --> 01:01:54,720
Speaker 1: breaking down the authentic conversations about Taylor Swift, even though

1217
01:01:54,720 --> 01:01:56,840
Speaker 1: they make clear that the majority of the conversations that

1218
01:01:56,880 --> 01:02:00,200
Speaker 1: were happening were authentic people in not bots, just that

1219
01:02:00,920 --> 01:02:03,680
Speaker 1: a lot of the nuance was easily lost there, like

1220
01:02:03,840 --> 01:02:05,800
Speaker 1: who are these authentic people, What were the narratives that

1221
01:02:05,840 --> 01:02:07,560
Speaker 1: they actually cared about, What were the narratives how they

1222
01:02:07,640 --> 01:02:11,560
Speaker 1: How are those narratives moving alongside these inauthentic, bot driven narratives,

1223
01:02:11,680 --> 01:02:14,240
Speaker 1: Like I can kind of see how people who are

1224
01:02:14,320 --> 01:02:17,800
Speaker 1: critical of this study are now saying, oh, you know,

1225
01:02:19,080 --> 01:02:21,520
Speaker 1: the study is saying that everybody who hates Taylor Swift

1226
01:02:21,640 --> 01:02:24,080
Speaker 1: is a bot, even though the study didn't actually say that.

1227
01:02:24,480 --> 01:02:26,840
Speaker 1: I can sort of get why that's the narrative that

1228
01:02:26,960 --> 01:02:29,720
Speaker 1: is sort of spreading, because the study does not spend

1229
01:02:29,720 --> 01:02:32,960
Speaker 1: a lot of time flushing out that nuance. Again, it's

1230
01:02:33,040 --> 01:02:35,360
Speaker 1: the study itself is not in no way saying that

1231
01:02:35,440 --> 01:02:37,600
Speaker 1: everybody that was critical of Taylor Swift as a bot.

1232
01:02:37,840 --> 01:02:40,360
Speaker 1: But I get how this seems like a very limited

1233
01:02:40,480 --> 01:02:43,440
Speaker 1: view into these authentic voices, to the point where people

1234
01:02:43,480 --> 01:02:45,640
Speaker 1: are saying they feel like their voice is being erased

1235
01:02:45,720 --> 01:02:49,960
Speaker 1: by this report. And I guess I would be remiss

1236
01:02:50,160 --> 01:02:53,800
Speaker 1: to not mention the Rolling Stone of it all, because

1237
01:02:53,960 --> 01:02:57,560
Speaker 1: the findings are reported and an exclusive piece for Rolling Stone.

1238
01:02:57,920 --> 01:03:00,280
Speaker 3: Now, I don't know how this lands for the both

1239
01:03:00,320 --> 01:03:00,440
Speaker 3: of you.

1240
01:03:00,640 --> 01:03:02,760
Speaker 1: The person who wrote the piece as somebody who's been

1241
01:03:02,800 --> 01:03:05,960
Speaker 1: on the show before, Miles Clee, you know, Rolling Stone

1242
01:03:06,160 --> 01:03:11,160
Speaker 1: is a music publication, right they. I have spent my

1243
01:03:11,320 --> 01:03:14,840
Speaker 1: fair share amount of time in like celebrity media, and

1244
01:03:14,920 --> 01:03:17,439
Speaker 1: a lot of times publications like this are working hand

1245
01:03:17,520 --> 01:03:20,400
Speaker 1: in glove with celebrity pr and so it's hard for

1246
01:03:20,520 --> 01:03:23,040
Speaker 1: me to say that an outlet like Rolling Stone is

1247
01:03:23,120 --> 01:03:26,200
Speaker 1: going to be impartial when they're recording on a huge.

1248
01:03:26,080 --> 01:03:27,400
Speaker 2: Musician like Taylor Swift.

1249
01:03:27,520 --> 01:03:30,360
Speaker 1: Right, people have pointed out that Rolling Stone did a

1250
01:03:30,600 --> 01:03:33,880
Speaker 1: Taylor Swift takeover during one of her album's launches. They

1251
01:03:34,040 --> 01:03:36,680
Speaker 1: rated her album five out at five stars. I don't

1252
01:03:36,680 --> 01:03:38,680
Speaker 1: really have the answer here, but it's a question that

1253
01:03:38,720 --> 01:03:41,440
Speaker 1: I've seen come up kind of a lot. And the

1254
01:03:41,560 --> 01:03:44,520
Speaker 1: way that the article is framed for folks who don't

1255
01:03:44,560 --> 01:03:47,360
Speaker 1: know the people who write articles don't also write the

1256
01:03:47,400 --> 01:03:51,080
Speaker 1: headlines as the editor. And so Miles Clee who wrote

1257
01:03:51,160 --> 01:03:52,880
Speaker 1: wrote up the piece, it's not the person who wrote

1258
01:03:52,920 --> 01:03:57,480
Speaker 1: this headline probably, But the headline is Taylor Swift's last

1259
01:03:57,560 --> 01:04:02,400
Speaker 1: album sparked bizarre accusations of Nazi. It was a coordinated attack. Now,

1260
01:04:02,880 --> 01:04:05,160
Speaker 1: if when you get into the nitty gritty of what

1261
01:04:05,320 --> 01:04:08,200
Speaker 1: the report is saying, that's kind of a bold claim

1262
01:04:08,240 --> 01:04:11,520
Speaker 1: that the report doesn't actually make that that title is

1263
01:04:11,560 --> 01:04:16,600
Speaker 1: saying like case close smoking gun, coordinated attack in a

1264
01:04:16,680 --> 01:04:18,760
Speaker 1: way that the study when you actually read it, like

1265
01:04:19,240 --> 01:04:22,840
Speaker 1: like we've all done, doesn't actually make clear. And so

1266
01:04:24,040 --> 01:04:26,560
Speaker 1: I just you know, in our interview with Molly, who

1267
01:04:26,640 --> 01:04:29,520
Speaker 1: runs Peak Metrics, a similar kind of social listing company

1268
01:04:29,560 --> 01:04:31,880
Speaker 1: as the one that did this study, she told us that, like,

1269
01:04:32,680 --> 01:04:37,320
Speaker 1: at baseline, some percentage of Internet discourse is generated by bots.

1270
01:04:37,360 --> 01:04:39,880
Speaker 1: It's just the reality. It is, unfortunately, in this day

1271
01:04:39,880 --> 01:04:43,040
Speaker 1: and age, somewhat commonplace. So framing a report that suggests

1272
01:04:43,240 --> 01:04:47,120
Speaker 1: unusual activity around Taylor's Swift discourse as a coordinated attack

1273
01:04:47,640 --> 01:04:51,400
Speaker 1: isn't really right because like sadly, bots is just part

1274
01:04:51,640 --> 01:04:53,840
Speaker 1: of the Internet now, that's just like part of how

1275
01:04:54,040 --> 01:04:56,880
Speaker 1: online discourse happens. And so again you can sort of

1276
01:04:56,960 --> 01:04:59,520
Speaker 1: see how it sets up this narrative that Taylor Swift

1277
01:04:59,600 --> 01:05:02,600
Speaker 1: is a of something who needs to be defended, and

1278
01:05:02,760 --> 01:05:04,640
Speaker 1: she kind of is in a kind of way. But

1279
01:05:04,840 --> 01:05:08,200
Speaker 1: also what they're describing is at this point somewhat commonplace.

1280
01:05:08,440 --> 01:05:09,120
Speaker 2: Does that make sense?

1281
01:05:09,480 --> 01:05:09,720
Speaker 3: Yeah?

1282
01:05:09,960 --> 01:05:14,400
Speaker 4: I think Also so to that headline specifically saying like

1283
01:05:14,640 --> 01:05:18,439
Speaker 4: bizarre accusations of Nazism, two of things can be true

1284
01:05:18,480 --> 01:05:21,760
Speaker 4: where it's like I would agree with that that like, yeah,

1285
01:05:22,080 --> 01:05:26,840
Speaker 4: they're the high risk, like straight up accusations of Nazism,

1286
01:05:27,320 --> 01:05:31,640
Speaker 4: like she made this necklace, which again she personally didn't make.

1287
01:05:31,600 --> 01:05:34,120
Speaker 3: The necklace, but like whatever, people were mad about.

1288
01:05:33,880 --> 01:05:36,960
Speaker 4: The lightning bolts, Like yeah, on the one hand, that

1289
01:05:37,120 --> 01:05:39,560
Speaker 4: is kind of insane, that is sort of a far

1290
01:05:39,680 --> 01:05:40,240
Speaker 4: fetched claim.

1291
01:05:40,920 --> 01:05:41,880
Speaker 2: On the other hand, like.

1292
01:05:43,360 --> 01:05:49,400
Speaker 4: Saying this was like saying that the album sparked accusations

1293
01:05:49,440 --> 01:05:50,440
Speaker 4: of Nazism is like.

1294
01:05:52,320 --> 01:05:55,960
Speaker 3: Ignoring some of the real criticism of it, which again I.

1295
01:05:56,280 --> 01:05:57,160
Speaker 2: Don't know, like.

1296
01:05:58,880 --> 01:06:00,560
Speaker 3: I don't know how newsworthy some of it is.

1297
01:06:00,760 --> 01:06:04,120
Speaker 4: I don't know how like some of it being again

1298
01:06:04,320 --> 01:06:06,240
Speaker 4: Taylor still writing a song, but I was like, oh,

1299
01:06:06,480 --> 01:06:09,840
Speaker 4: I like eldest daughter, the lyrics were kind of corny.

1300
01:06:10,600 --> 01:06:11,520
Speaker 2: I didn't really like it.

1301
01:06:12,560 --> 01:06:14,280
Speaker 4: I don't think we need a whole article about that.

1302
01:06:14,480 --> 01:06:16,680
Speaker 4: Like so it's really yeah, like two things can be true.

1303
01:06:16,720 --> 01:06:19,040
Speaker 4: It can on the one hand, like there were bizarre accusations.

1304
01:06:19,080 --> 01:06:20,920
Speaker 4: There was a lot of misinformation, but.

1305
01:06:21,000 --> 01:06:26,800
Speaker 3: Saying that's all it was like yeah, and again that

1306
01:06:26,920 --> 01:06:28,280
Speaker 3: is just sort of the media atmosphere now.

1307
01:06:28,320 --> 01:06:31,920
Speaker 4: I mean, like we've done a bazillion episodes about like

1308
01:06:32,680 --> 01:06:35,600
Speaker 4: review bombing and every form of media like this just

1309
01:06:35,640 --> 01:06:37,520
Speaker 4: sort of is the reality now, which is a bad thing.

1310
01:06:37,800 --> 01:06:39,640
Speaker 4: It's a bad thing that it's happening, and we should

1311
01:06:39,640 --> 01:06:42,880
Speaker 4: be talking about it. But there is sort of a

1312
01:06:42,960 --> 01:06:45,640
Speaker 4: weird thing that happens then where it's like any criticism

1313
01:06:45,760 --> 01:06:48,600
Speaker 4: of the thing is just spots or it's review bombing,

1314
01:06:48,640 --> 01:06:49,120
Speaker 4: where it's.

1315
01:06:49,000 --> 01:06:55,280
Speaker 3: Actually rooted in XYZ. You're a misogynist, you're whatever. Sometimes

1316
01:06:55,320 --> 01:06:58,040
Speaker 3: people just don't like things. I don't know if we

1317
01:06:58,160 --> 01:07:00,160
Speaker 3: look if we really look at that title. So I

1318
01:07:00,200 --> 01:07:03,240
Speaker 3: think the word bizarre in that title is really doing

1319
01:07:03,320 --> 01:07:07,360
Speaker 3: a lot of work to like just like needle people,

1320
01:07:07,880 --> 01:07:11,120
Speaker 3: and it's almost like gaslighting, Like like Joey was saying,

1321
01:07:12,280 --> 01:07:16,120
Speaker 3: there are some like valid reasons to be concerned about

1322
01:07:16,160 --> 01:07:19,800
Speaker 3: some of the content in her album, I gather, right,

1323
01:07:19,880 --> 01:07:21,680
Speaker 3: Like I don't personally know, but that's the sense that

1324
01:07:21,800 --> 01:07:27,120
Speaker 3: I get. And so, you know, accusations of Nazism, Okay,

1325
01:07:27,200 --> 01:07:29,080
Speaker 3: maybe that's a little bit much, But like calling it

1326
01:07:29,240 --> 01:07:31,640
Speaker 3: bizarre in the headline, I have to wonder if that

1327
01:07:31,800 --> 01:07:36,360
Speaker 3: one word bizarre really was like a big catalyst of

1328
01:07:36,440 --> 01:07:41,280
Speaker 3: a lot of the backlash to this particular article.

1329
01:07:41,280 --> 01:07:43,400
Speaker 1: And when you go back and look at the bucket

1330
01:07:43,440 --> 01:07:45,560
Speaker 1: of conversation that the report says, Oh, this was not

1331
01:07:45,920 --> 01:07:49,520
Speaker 1: an authentic This was all just real listeners, real critics,

1332
01:07:49,560 --> 01:07:52,560
Speaker 1: real people, real voices, real discourse. It was all about

1333
01:07:52,640 --> 01:07:55,400
Speaker 1: exactly what you two were just talking about, like actually

1334
01:07:55,520 --> 01:07:59,040
Speaker 1: speaking to the album, the songs that the symbolism that

1335
01:07:59,080 --> 01:07:59,480
Speaker 1: it brought up.

1336
01:07:59,520 --> 01:08:01,160
Speaker 2: What are you like or not, whether you thought it

1337
01:08:01,240 --> 01:08:02,120
Speaker 2: was problematic or not.

1338
01:08:02,520 --> 01:08:06,080
Speaker 1: And I have to imagine that's not sexy people having

1339
01:08:06,600 --> 01:08:12,640
Speaker 1: authentic people having like normal discourse, whether critical or celebratory

1340
01:08:12,760 --> 01:08:14,920
Speaker 1: or somewhere in the middle. I can understand the need

1341
01:08:15,040 --> 01:08:18,760
Speaker 1: to spice that up because yeah, just normal people having

1342
01:08:18,880 --> 01:08:21,360
Speaker 1: normal conversation about their views on an album.

1343
01:08:21,479 --> 01:08:22,519
Speaker 2: No, that's not sexy.

1344
01:08:22,920 --> 01:08:25,559
Speaker 1: So yeah, I think that we've gotten to this place where,

1345
01:08:26,880 --> 01:08:29,639
Speaker 1: even when people are just trying to engage in normal

1346
01:08:29,760 --> 01:08:33,599
Speaker 1: discourse about the world, inauthentic accounts cannot help but throw

1347
01:08:33,680 --> 01:08:36,800
Speaker 1: a match into that dynamic and light it on fire

1348
01:08:36,880 --> 01:08:37,360
Speaker 1: and ignite it.

1349
01:08:37,479 --> 01:08:41,720
Speaker 3: And something about the title I think really probably turned.

1350
01:08:41,479 --> 01:08:44,559
Speaker 1: People off of the of what is in this report

1351
01:08:45,080 --> 01:08:47,560
Speaker 1: from the first blush of even looking at it. I

1352
01:08:47,600 --> 01:08:49,760
Speaker 1: don't know, I can't say, how many people actually dug

1353
01:08:49,840 --> 01:08:52,000
Speaker 1: into the report. Most people are probably reading the title

1354
01:08:52,040 --> 01:08:54,080
Speaker 1: The Rolling Stone Beets and maybe skimming it or reading it.

1355
01:08:54,400 --> 01:09:00,439
Speaker 1: And so yeah, I'm not surprised that it's Ben's sparked

1356
01:09:00,520 --> 01:09:04,360
Speaker 1: such backlash, and I guess that's kind of my So

1357
01:09:04,680 --> 01:09:08,439
Speaker 1: what here is that you know, everybody, people who listen

1358
01:09:08,479 --> 01:09:11,880
Speaker 1: to this podcast, swifties tailor Swift critics, people who don't

1359
01:09:11,920 --> 01:09:13,560
Speaker 1: care about Taylor Swift at all, just want to have

1360
01:09:13,600 --> 01:09:17,439
Speaker 1: a robust internet landscape. All of those people coming together

1361
01:09:18,200 --> 01:09:21,400
Speaker 1: really remind us that the Internet is not just bots

1362
01:09:21,479 --> 01:09:24,439
Speaker 1: and chaos. It's also real people trying to make real

1363
01:09:24,560 --> 01:09:27,400
Speaker 1: sense of culture in real time. And so I just

1364
01:09:27,439 --> 01:09:30,479
Speaker 1: think that we got to remember that. I don't think

1365
01:09:30,520 --> 01:09:34,960
Speaker 1: that this report serves to invalidate anybody's critiques or discomforts

1366
01:09:35,000 --> 01:09:36,560
Speaker 1: at the might of felt or any of that.

1367
01:09:36,720 --> 01:09:37,680
Speaker 2: I think it just shows that.

1368
01:09:38,080 --> 01:09:42,559
Speaker 1: Some conversations are more vulnerable to outside manipulation than others.

1369
01:09:42,600 --> 01:09:45,800
Speaker 1: It's just not a judgment it's on anyone. It is

1370
01:09:45,880 --> 01:09:49,400
Speaker 1: just a function and a reality of this current media

1371
01:09:49,680 --> 01:09:53,479
Speaker 1: climate that we are all kind navigating together.

1372
01:09:54,120 --> 01:10:00,040
Speaker 8: This feels like the pop culture equivalent of like, I

1373
01:10:00,160 --> 01:10:03,840
Speaker 8: never leftists online disagree with each other and they immediately

1374
01:10:03,960 --> 01:10:08,320
Speaker 8: start calling each other like CIA assets or something.

1375
01:10:09,080 --> 01:10:12,519
Speaker 3: Yes, it's just hitting me now, maybe this is another

1376
01:10:12,640 --> 01:10:13,400
Speaker 3: thing the swifties.

1377
01:10:13,479 --> 01:10:17,200
Speaker 4: Really this is where again we're all we're just living

1378
01:10:17,240 --> 01:10:20,439
Speaker 4: in a reality where everything is is we fandomized everything.

1379
01:10:20,760 --> 01:10:23,640
Speaker 3: So maybe that's it. But I at this like it

1380
01:10:23,800 --> 01:10:25,280
Speaker 3: just hit me that. I was like, why does this

1381
01:10:25,439 --> 01:10:27,559
Speaker 3: sound so familiar? And I was like, oh, like.

1382
01:10:29,080 --> 01:10:31,360
Speaker 5: This is like what I talked to like friends of

1383
01:10:31,439 --> 01:10:34,599
Speaker 5: the pod Our cool Zone media peeps about like them

1384
01:10:34,680 --> 01:10:38,400
Speaker 5: being accused of being CIA assets and it's like an

1385
01:10:38,439 --> 01:10:40,120
Speaker 5: old or not, like you just.

1386
01:10:40,160 --> 01:10:43,280
Speaker 3: Disagree with what they're saying. I don't know what tell you, dude,

1387
01:10:43,800 --> 01:10:47,000
Speaker 3: People have different idiots. That's how That's how you know

1388
01:10:47,120 --> 01:10:47,680
Speaker 3: you've made it.

1389
01:10:47,800 --> 01:10:50,639
Speaker 1: When you get accused of being like a CIA plant,

1390
01:10:50,760 --> 01:10:51,080
Speaker 1: it's like.

1391
01:10:51,840 --> 01:10:53,920
Speaker 2: You you're you've made it, You've arrived.

1392
01:10:54,320 --> 01:10:56,000
Speaker 3: I just wish we had a little bit more room

1393
01:10:56,080 --> 01:10:57,040
Speaker 3: for nuance in.

1394
01:10:57,160 --> 01:11:00,880
Speaker 4: Politics and also in just our ability to enjoy pop

1395
01:11:00,960 --> 01:11:01,760
Speaker 4: culture because at the.

1396
01:11:01,800 --> 01:11:04,000
Speaker 3: End of the day, like that's what this is about.

1397
01:11:04,120 --> 01:11:07,600
Speaker 3: This is about art that we're supposed to enjoy or

1398
01:11:07,680 --> 01:11:09,880
Speaker 3: not enjoy and just move on with your life. Listen

1399
01:11:09,960 --> 01:11:10,559
Speaker 3: to something else.

1400
01:11:10,720 --> 01:11:11,080
Speaker 7: I don't know.

1401
01:11:11,479 --> 01:11:13,920
Speaker 3: I think there's some real optimism in this report that

1402
01:11:14,000 --> 01:11:18,760
Speaker 3: we can find though that like it's actually backing up

1403
01:11:18,840 --> 01:11:22,240
Speaker 3: that like the humans do want some of that nuance.

1404
01:11:22,760 --> 01:11:28,439
Speaker 3: And you know this, one of my big takeaways from

1405
01:11:28,439 --> 01:11:33,800
Speaker 3: this report is just underscoring how much our online conversations

1406
01:11:34,040 --> 01:11:38,200
Speaker 3: are affected by bots and manipulation campaigns all the time.

1407
01:11:38,800 --> 01:11:41,719
Speaker 3: Like it's not just Taylor Swift, it's not just Blake Lively,

1408
01:11:43,160 --> 01:11:45,200
Speaker 3: it's not just politics, it's not just health. It's like

1409
01:11:45,400 --> 01:11:48,719
Speaker 3: all of it, right, and some of it's for ideological reasons,

1410
01:11:49,040 --> 01:11:50,599
Speaker 3: but I think a lot of it is just people

1411
01:11:50,680 --> 01:11:54,639
Speaker 3: trying to get engagement for monetization. We saw that when

1412
01:11:56,840 --> 01:12:00,479
Speaker 3: Twitter started revealing people's locations of accounts, and we saw like, oh,

1413
01:12:00,560 --> 01:12:04,479
Speaker 3: a lot of these like Maga accounts and also like

1414
01:12:04,960 --> 01:12:07,600
Speaker 3: super leftist accounts all seem to be based out of

1415
01:12:07,800 --> 01:12:11,960
Speaker 3: like random places in the global South, where like kind

1416
01:12:11,960 --> 01:12:14,200
Speaker 3: of it's like, maybe these people aren't ideologically invested, they're

1417
01:12:14,200 --> 01:12:20,200
Speaker 3: just trying to get engagement. And that's just like a

1418
01:12:20,360 --> 01:12:24,960
Speaker 3: feature of the Internet now. And so maybe the more

1419
01:12:25,040 --> 01:12:28,160
Speaker 3: that we acknowledge that and recognize it as a fact

1420
01:12:28,200 --> 01:12:31,640
Speaker 3: of life, it will create some space for us to

1421
01:12:31,760 --> 01:12:34,680
Speaker 3: sort of like look past it and find the other

1422
01:12:34,800 --> 01:12:39,000
Speaker 3: humans out there for some actual authentic engagement.

1423
01:12:39,439 --> 01:12:44,280
Speaker 1: Oh what a positive place to end, Joey, Mike, thank

1424
01:12:44,320 --> 01:12:46,280
Speaker 1: you for breaking this down for me. Nice to put

1425
01:12:46,360 --> 01:12:51,360
Speaker 1: YouTube in conversation at long last. Of course, Fine, that's

1426
01:12:51,400 --> 01:12:52,240
Speaker 1: what this is really about.

1427
01:12:52,280 --> 01:12:55,960
Speaker 4: Guy, episode just kind of yeah came about, but this

1428
01:12:56,160 --> 01:12:58,519
Speaker 4: is just the three of us wanted to hang out, Violin. Yeah.

1429
01:12:58,640 --> 01:12:59,800
Speaker 3: Always again.

1430
01:13:00,040 --> 01:13:02,519
Speaker 1: You will have a few more deep dives into the

1431
01:13:02,680 --> 01:13:06,960
Speaker 1: report very soon. This was just our initial let's just

1432
01:13:07,080 --> 01:13:08,800
Speaker 1: get on the mic and see what comes out.

1433
01:13:09,040 --> 01:13:09,560
Speaker 3: Feelings.

1434
01:13:09,600 --> 01:13:11,599
Speaker 1: That sounded weird because Mike is one of our producers,

1435
01:13:11,600 --> 01:13:13,920
Speaker 1: but you know what I mean. Uh yeah, thanks so

1436
01:13:14,040 --> 01:13:16,160
Speaker 1: much for listening. I will see you on the Internet.

1437
01:13:22,200 --> 01:13:24,200
Speaker 1: Got a story about an interesting thing in tech, or

1438
01:13:24,320 --> 01:13:26,160
Speaker 1: just want to say hi, You can reach us at

1439
01:13:26,160 --> 01:13:28,879
Speaker 1: Hello at tangody dot com. You can also find transcripts

1440
01:13:28,880 --> 01:13:31,360
Speaker 1: for today's episode at tengody dot com. There Are No

1441
01:13:31,439 --> 01:13:33,479
Speaker 1: Girls on the Internet was created by me Bridget Todd.

1442
01:13:33,880 --> 01:13:37,400
Speaker 1: It's a production of iHeartRadio and Unbossed creative Jonathan Stricklands.

1443
01:13:37,400 --> 01:13:40,320
Speaker 1: Our executive producer, Tari Harrison is our producer.

1444
01:13:39,960 --> 01:13:40,759
Speaker 2: And sound engineer.

1445
01:13:41,240 --> 01:13:44,439
Speaker 1: Michael Amado is our contributing producer. I'm your host, bridget Todd.

1446
01:13:45,360 --> 01:13:47,080
Speaker 1: If you want to help us grow, rate and review

1447
01:13:47,160 --> 01:13:50,720
Speaker 1: us on Apple Podcasts. For more podcasts from iHeartRadio, check

1448
01:13:50,760 --> 01:13:53,080
Speaker 1: out the iHeartRadio app, Apple podcast or wherever you get

1449
01:13:53,080 --> 01:13:53,799
Speaker 1: your podcasts.

1450
01:14:01,280 --> 01:14:04,719
Speaker 8: Oh well and well lip