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

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Speaker 1: of I Heart Radio and Unboss Creative, I'm Bridget Todd

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Speaker 1: and this is there are No Girls on the Internet.

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Speaker 1: It's almost election day here in the United States, and

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Speaker 1: there is so much on the line in this election.

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Speaker 1: There are also historic numbers of women and women of

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Speaker 1: color running for elected office this cycle, and while that

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Speaker 1: is great to see, we know that these women will

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Speaker 1: face racialized, gendered attacks that their white male counterparts simply

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Speaker 1: will not have to deal with. They'll have to take

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Speaker 1: extra security concerns both online and I r L that

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Speaker 1: their male counterparts won't have to deal with. And yet

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Speaker 1: that is an extra cost burden that we know that

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Speaker 1: they'll have to shoulder just because of their identity. So

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Speaker 1: to discuss all of this, I sat down with my

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Speaker 1: good friends Samantha and Annie over at the podcast Stuff

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Speaker 1: Mom Never told you to lay out our concerns and

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Speaker 1: the upcome midterm elections and what needs to be done.

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Speaker 1: I love Halloween, but one thing that does always come

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Speaker 1: with Halloween, like every couple of years, is this tension

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Speaker 1: around voting. Is this tension that the elections are coming up.

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Speaker 1: So it's always like I'm trying to not think about it. Um,

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Speaker 1: is there anything spook here? Yes? Yes, And as we've

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Speaker 1: hinted at, that's what we're going to be talking about

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Speaker 1: a lot in here. Um and uh, Georgia, it's been

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Speaker 1: an intense mid term election for us. So yeah, yeah,

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Speaker 1: I guess let's just let's just break down some of

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Speaker 1: the things that are going on in these mid terms. Yes,

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Speaker 1: so we're when we're recording this, I don't know how

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Speaker 1: long it will be out when folks hear it, but um,

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Speaker 1: we are ten days out from the election. While when

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Speaker 1: we're courting this, and I mean I it sounds like

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Speaker 1: you kind of feel like I do, where particularly around

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Speaker 1: mid terms, it just makes me queazy because I know

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Speaker 1: that GEO TV is so different when it's not a

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Speaker 1: presidential election. Like people, it's harder to get people excited,

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Speaker 1: and especially when I know there's so much on the line.

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Speaker 1: You know, you mentioned Georgia, that's an important race. A

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Speaker 1: lot is happening, a lot is hinging on people actually

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Speaker 1: coming out, and so it's something that I just always

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Speaker 1: feel kind of like queezy about it. And for me,

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Speaker 1: I always kind of having worked in politics for most

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Speaker 1: of my adult life, or like politics adjacent. I always

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Speaker 1: kind of feel man about elections because I'm not super

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Speaker 1: invested in electoral power, like I think that there's other

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Speaker 1: ways to build power. And then around like ten days out,

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Speaker 1: I'm like, oh my god, I need to like start

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Speaker 1: GEO TVNG, I need to start like calling everyone, and

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Speaker 1: it's like it's like something happens, and so I'm sort

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Speaker 1: of waiting for that transformation to have up in And

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Speaker 1: it sounds like you two are feelings would have similarly

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Speaker 1: around the elections this time around. Yeah. So for me,

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Speaker 1: I uh recently was coming home from a trip with

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Speaker 1: my mother and she drove me past where I vote,

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Speaker 1: my polling place, and it was the first day of

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Speaker 1: early voting in Georgia and it was packed out, like

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Speaker 1: I was like, oh my god, what is happening. And

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Speaker 1: then I looked it up an early voting was happening,

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Speaker 1: and I was so it's very heartening to see that

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Speaker 1: many people turn out for a mid term. And I

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Speaker 1: waited a couple of days and when I went back,

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Speaker 1: it was still crowded and people were excited. There were

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Speaker 1: people filming. Everyone was really warm and nice, like, oh,

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Speaker 1: I'm so glad you came out to vote, which was

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Speaker 1: it was a pleasant experience for me, but it was

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Speaker 1: kind of like overshadowed the whole thing. Was you know,

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Speaker 1: there has been these crackdowns on voting rights, why are

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Speaker 1: so many people coming out so early? Which I think

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Speaker 1: is great, but it was just kind of overhanging the

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Speaker 1: whole thing. And also because it is a very divisive race,

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Speaker 1: senatorial race specifically in Georgia. I don't know, it was

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Speaker 1: it was one of those things where I was like,

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Speaker 1: I've I've been to mid terms before, like nobody was

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Speaker 1: there and I walked past that area all the time,

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Speaker 1: and so I see it and I would never see

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Speaker 1: a line, And now like every day when I walk

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Speaker 1: past it, there's a line. So that's nice, but I'm

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Speaker 1: still like, there, you just can't shake all of the

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Speaker 1: kind of negative things that might be behind why that is,

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Speaker 1: even though I'm happy to see it, if that makes sense,

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Speaker 1: right absolutely, Like as right now, we have the highest

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Speaker 1: early voter turnout in our state that we've ever had,

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Speaker 1: beating last presidential election. So it's amazing to see that

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Speaker 1: things are happening, and we know that that's great indication

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Speaker 1: of things acomb Again, yes, we have had a huge

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Speaker 1: upsurge in and UM photo suppression laws in the last

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Speaker 1: five years. Actually has always been let's just be honest,

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Speaker 1: but it's seemingly getting worse and worse, and we know, uh,

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Speaker 1: the way they have redistricted everything, and it's very, very shady.

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Speaker 1: Although you know, our current governor was the secretary of

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Speaker 1: State who controlled his own election, which says a lot

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Speaker 1: in itself, UM, And that's a big point of contention here,

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Speaker 1: as well as the fact that apparently uh Rafflesburger, that's

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Speaker 1: our current secretary of State UM, who actually stood up

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Speaker 1: to Trump at one point in time and got too

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Speaker 1: much credit in my opinion, UM for what he did.

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Speaker 1: But it's hired a former staffer of Trump for these elections.

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Speaker 1: So there's a lot of questionable things happening. So we

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Speaker 1: have a lot of concerns and a lot of hope.

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Speaker 1: Is such a mixed bag, as Annie was saying, of

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Speaker 1: what is happening here, we're seeing what's happening because the

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Speaker 1: governor's race is a pretty big deal, as the overturning

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Speaker 1: of Roe v. Wade hasn't made it a huge deal.

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Speaker 1: As we do have a six week ban in UM

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Speaker 1: Georgia currently, and then there's just talk about the fact

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Speaker 1: that they're gonna punish um those who do have abortions

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Speaker 1: as uh, murder, possibly charging them with murder, with the

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Speaker 1: fact that they're doing this whole like you know, unborn

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Speaker 1: fetus law and protecting of them as as like and

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Speaker 1: crediting them as a citizen type of conversation, which we

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Speaker 1: know is trickery and is leading way to a lot

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Speaker 1: of imprisonment and unfair treatment. And we know this is

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Speaker 1: completely targeted at marginalized individuals in our state. So there's

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Speaker 1: a lot of concerns are so much that feels like

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Speaker 1: it's being really weighty here right now, and it does.

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Speaker 1: It feels like it is life or death for so

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Speaker 1: many of us. And I'll get I'm being very local

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Speaker 1: on this moment because this is kind of where we are,

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Speaker 1: and Annie and I obviously have to concentrate so hard

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Speaker 1: in these conversations. So there is this level of doom,

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Speaker 1: which I'm wonderful conversation bringing up all the time. Apparently

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Speaker 1: there's this fear and level of doom in this election

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Speaker 1: that feels too heavy. At the same time, we are

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Speaker 1: the butt of jokes with our senator race, and it's

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Speaker 1: it's really like you want you you have to laugh,

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Speaker 1: but what to cry? As well as why why just

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Speaker 1: but why? Yeah? Oh my gosh. I if you got

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Speaker 1: if you all get me started on a hurtel Walker,

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Speaker 1: I will never I will never stop talking. Um. So

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Speaker 1: I'm not going to comment on that, boy I want to,

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Speaker 1: but truly, if you got me going, I would never stop.

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Speaker 1: I will just say that all of that is really valid.

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Speaker 1: You know. I think this feeling of it's great to

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Speaker 1: see people voting, but that like unease of life, but

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Speaker 1: what does that mean for everyone? I think that's really valid.

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Speaker 1: And just the feeling of knowing how much is on

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Speaker 1: the line, what is at stake this time around. I

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Speaker 1: think that that's I hate to say it, I think

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Speaker 1: as our elections become more and more contentious, and as

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Speaker 1: things become more and more polarized, I think that's going

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Speaker 1: to be a common sentiment. And you know, something that

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Speaker 1: kind of speaks to that is the fact that there

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Speaker 1: are more women running for statewide offices than ever before,

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Speaker 1: which you know, I think is should be good news.

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Speaker 1: Marks a record high for women running for gubernatorial and

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Speaker 1: senate races. Um, there's about sixty five women running for

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Speaker 1: governors races across the country in this cycle. UM, I

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Speaker 1: should say it is not all lefties. UM. There are

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Speaker 1: more Republicans than Democrats making those bids. This is all

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Speaker 1: according to data from the Center for American Women in Politics,

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Speaker 1: and I it's true. It's like I when there are

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Speaker 1: long lines at polling places, I'm like, oh, that's great,

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Speaker 1: like democracy and action. And that's definitely preferable to people

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Speaker 1: having elections not go their way and then trying to

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Speaker 1: force them to go their way via violent behavior like

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Speaker 1: we saw on January sits and so things feel charged.

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Speaker 1: I guess I'll put it that way. And women are

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Speaker 1: poised to make gains in state white contests, but they

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Speaker 1: remain underrepresented in key legislatures around the country. And it's

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Speaker 1: also true for black women. We're seeing more black women

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Speaker 1: and more women of color running for local office. Black

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Speaker 1: women have set record numbers for candidates and gubernatorial, Senate

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Speaker 1: and House races. It's the cycle, um. And there's a

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Speaker 1: lot of Latina and Hispanic gubernatorial and US House Senate

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Speaker 1: candidates and a record number of Asian and Pacific I

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Speaker 1: underwomen who are running for governor and so I as

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Speaker 1: frought as that can be, I think it's overall good,

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Speaker 1: you know, not just because representation matters, which it does,

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Speaker 1: but also I think that anything that gets us closer

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Speaker 1: to a representative democracy, one where there's people who actually

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Speaker 1: have the lived experience of the people that they are governing,

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Speaker 1: is a good thing. And so like that's one kind

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Speaker 1: of silver lining I guess is that particularly when I

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Speaker 1: think about issues like abortion, like you were talking about, Sam,

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Speaker 1: I I don't have any hard numbers for this, I

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Speaker 1: would suspect that women are motivate ated to run for

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Speaker 1: office because those issues have taken such a national stage recently.

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Speaker 1: I wish I could say that it's all women who

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Speaker 1: you know, want to protect abortion access. I wish I

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Speaker 1: could say that if you were a woman, that was

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Speaker 1: part of your platform, but that would not be correct. Yeah. Yeah,

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Speaker 1: and that's one thing that is very overwhelming right now

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Speaker 1: in Georgia, and I'm sure in a lot of places,

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Speaker 1: but are political ads are intense um and hard to escape.

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Speaker 1: But yeah, so it's it's kind of like what I

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Speaker 1: was saying. We have these positives. We have more women running,

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Speaker 1: we have more people of color running, we have more

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Speaker 1: black women running, But that doesn't come without these realities

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Speaker 1: that you have spoke about a lot on this show

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Speaker 1: that often get kind of swept aside and not talked about,

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Speaker 1: even though they're so important and they are impacting our

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Speaker 1: political landscape exactly. And so I think it is so

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Speaker 1: great when we support women, especially black women and women

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Speaker 1: of color, to run for office, and it just generally

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Speaker 1: be involved in civic life through things life, working as

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Speaker 1: election workers or becoming vocal advocates or activists on an issue.

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Speaker 1: But it is imperative that that support be grounded in

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Speaker 1: the reality of what we know these women will face

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Speaker 1: when they find themselves in those situations that we, you know,

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Speaker 1: champion them to to hold. Right, so we are on

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Speaker 1: the left, we're very fond of saying things like trust

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Speaker 1: black women, support black women, and that is very true.

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Speaker 1: But we can't just you know, advocate for black women

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Speaker 1: to get into these positions. We have to be honest

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Speaker 1: about what they will face when they get there and

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Speaker 1: then really do the intentional work of creating the conditions

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Speaker 1: for them to have an equal playing field to make

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Speaker 1: sure that they will thrive in those positions. Because it's

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Speaker 1: one thing to be like, oh, yes, put black women

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Speaker 1: in leadership positions, but then are you going to support

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Speaker 1: then when they face the attacks that we know they

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Speaker 1: are going to face. You know, for instance, I was

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Speaker 1: super excited when President Biden said that he was going

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Speaker 1: to pick black women to be the vice president um

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Speaker 1: and also nominated black women to be a Supreme Court justice.

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Speaker 1: But I also know that that means that those black

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Speaker 1: women would certainly face heightened attacks that their white male

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Speaker 1: was then a little bit disappointed to see that the

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Speaker 1: White House didn't really meaningfully deal with this reality, right,

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Speaker 1: Like I wondered, like, are they setting black women up

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Speaker 1: to deal with the harassment and attacks that we know

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Speaker 1: are going to be like racialized and gendered, not attacks

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Speaker 1: based on their records or their merits or their actions,

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Speaker 1: but attacks on their identity who they are. So when

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Speaker 1: these attacks happened, which we saw with both Harris and

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Speaker 1: Justice Jackson, the White House didn't really acknowledge them, and

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Speaker 1: of course the women themselves can't really talk openly about them,

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Speaker 1: so we all just kind of saw it happened, and

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Speaker 1: nobody was at the very least talking honestly about it.

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Speaker 1: Let alone working to create the conditions to combat it, right,

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Speaker 1: And that's just such a huge saying of you know,

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Speaker 1: either it's a drade on you because you're having to

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Speaker 1: deal with these attacks, or you just kind of distance

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Speaker 1: yourself from social media. And then that in itself people

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Speaker 1: can interpret in a variety of ways that are probably

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Speaker 1: not good. Um. And then people's especially younger people, seeing

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Speaker 1: that like that. There's just so many unhealthy things about

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Speaker 1: what that says, um that we're accepting um as as

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Speaker 1: something that's just like par for the course. That's how

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Speaker 1: it is. And the very unfortunate thing is this is

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Speaker 1: not just like one time or just a few times.

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Speaker 1: It's like all over the place, exactly. It's not an

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Speaker 1: isolated thing, I'm sorry to say. According to a report

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Speaker 1: from the Institute for Strategic Dialogue called Public Figures, public Rage,

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Speaker 1: candidates abuse on social media, so physically speaking, black women

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Speaker 1: and women of color are more likely to base racialize

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Speaker 1: gender to tacks than their white male counterparts on The study,

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Speaker 1: which you can find online, is super interesting. They found

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Speaker 1: the abusive messages on social media accounted for fifteen pent

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Speaker 1: of those directed at every female lawmaker that they analyze,

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Speaker 1: compared to around five to ten percent when they looked

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Speaker 1: at male candidates. A few of their keep findings or

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Speaker 1: that women of color are particularly likely to be targeted online.

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Speaker 1: Male politicians of color are not more vulnerable than their

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Speaker 1: white counterparts. They are attacked at the same rate. An

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Speaker 1: abuse towards women was more likely to be about gender

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Speaker 1: than the abuse targeting men, So abuse targeting men was

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Speaker 1: usually focused on their political stances um, while the messages

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Speaker 1: directed at women were more likely to be about their

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Speaker 1: appearance or their general competence. Interestingly enough, female Democrats received

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Speaker 1: ten times more abusive comments than their male counterparts on Facebook,

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Speaker 1: and Republican women received twice as many abusive messages as

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Speaker 1: Republican men. So it's a a bipartisan issue. It's an

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Speaker 1: issue impacting all women and women of color. You know,

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Speaker 1: it's not just women on the left whose face it.

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Speaker 1: It's all of us, right. And one of the things,

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Speaker 1: one of the points you make when you come on

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Speaker 1: here all the time, which I think is very important

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Speaker 1: and I love, um, is that a lot of times

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Speaker 1: people can distance like, oh, that's online, that's not real life. Um,

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Speaker 1: but that is not at all the case, right, Yes,

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Speaker 1: So I'm sure people are like sick of me saying

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Speaker 1: this because I've you know, say it's pretty much from

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Speaker 1: the rooftops every time I'm I am given a platform.

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Speaker 1: But I think we have this misconception that when we

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Speaker 1: talk about online harassment and abuse, we're talking about online issues.

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Speaker 1: And the research could not be clearer that even though

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Speaker 1: these attacks may start online, they do not always stay online.

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Speaker 1: And a great example that we're seeing right now is

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Speaker 1: representative Jaia Paul. A man started by sending her violent

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Speaker 1: emails and threatening messages online, showed up at side of

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Speaker 1: her house with a gun. I just had to take

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Speaker 1: a minute from recording this podcast to quick send on

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Speaker 1: a statement about the husband of Nancy Pelosi, someone who

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Speaker 1: had a just today while we're recording this, someone with

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Speaker 1: a history of violent, you know, rhetoric on social media

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Speaker 1: broke into Nancy Pelosi's house and attacked her husband with

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Speaker 1: a hammer when he found that she was not there.

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Speaker 1: And so it's so important and deeply imperative that if

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Speaker 1: we're going to take this kind of violence seriously, that

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Speaker 1: when someone reports violent threats or violent attacks online, we

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Speaker 1: then take it seriously. If we're going to prevent real

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Speaker 1: world violence from happening because the two our link. It's

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Speaker 1: just that the research could not be clear time and

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Speaker 1: time again. When somebody is the perpetrator of a mass

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Speaker 1: shooting or an incident of mass violence, you follow the

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Speaker 1: paper trail and oh, they had an online history where

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Speaker 1: they were threatening women or being aggressive or hostile towards

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Speaker 1: the women in their life online. And so we cannot

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Speaker 1: get a handle meaningfully on the abuse of women if

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Speaker 1: we do not take it seriously when it happens online,

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Speaker 1: it's just not going to happen. And so real world

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Speaker 1: violence is connected to online violence and we need to

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Speaker 1: deal with as such. But unfortunately that has not been

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Speaker 1: the case for so long. People have been so quick

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Speaker 1: to belittle it when a woman speaks off about what

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Speaker 1: she's facing online, particularly at that woman is black or

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Speaker 1: woman of color. We've talked about so much harassment and

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Speaker 1: and I know, one of the biggest platforms, and I

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Speaker 1: know we're gonna talk about this later and I'm already

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Speaker 1: addressing it. Twitter has been one of the biggest perpetrators

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Speaker 1: in allowing this type of abuse and not actually following

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Speaker 1: through in the policies they've they've already placed themselves to

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Speaker 1: try to supposedly stop these types of harassments, and then

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Speaker 1: knowing that there's even ways to actually find specific people

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Speaker 1: to target, like when you were talking about the bots,

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Speaker 1: and then just having uh pretty much hate farms created

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Speaker 1: all these types of platforms. I can't imagine what it

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Speaker 1: does to too. Yeah, it's happening to those who are

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Speaker 1: on higher profile levels. I guess it's the best way

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Speaker 1: to say it. But to be fair, like, I am

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Speaker 1: petrified as someone who kind of it's it is supposed

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Speaker 1: to be. I don't know the world, not influencer. I

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Speaker 1: don't know what a public person will say this. We'll

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Speaker 1: say public person, my name is out there, Like I'm

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Speaker 1: petrified with my five followers that I'm going to say

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Speaker 1: something wrong and or they're gonna hear someone's gonna hear

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Speaker 1: something that we do take it and say that means

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Speaker 1: we can go after her, and wondering what that would

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Speaker 1: be like because I've seen normal people, regular people getting

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Speaker 1: attacked like Holy, all they said was this one thing

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Speaker 1: and or just agreed with something and people are going

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Speaker 1: after them. Oh my god, I mean absolutely, And we're

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Speaker 1: also I wouldn't even missed to not mention that we're

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Speaker 1: having this conversation on day one of Elon Musk at

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Speaker 1: the helm at Twitter, and people keep asking like, Oh,

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Speaker 1: what do you think about that? What do you think

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Speaker 1: about that? And I can tell you that that people

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Speaker 1: who are extremists, bad actors, people who are interested in,

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Speaker 1: you know, perpetrating these the kinds of harassment campaigns that

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Speaker 1: you were just talking about, Sam, Those people are rejoicing

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Speaker 1: right Like just today, I already saw a bunch of

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Speaker 1: tweets where someone was like, I'm gonna use the end

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Speaker 1: sler as many times as i can because it's okay

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Speaker 1: now on Twitter. These are the kind of people that

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Speaker 1: Elon Musk is welcoming back to the platform. These are

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Speaker 1: the kind of people who are rejoicing because Elon Musk

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Speaker 1: is at the helm of our one of our largest

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Speaker 1: and most important communications platforms. And so you're exactly right

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Speaker 1: that I think we started this conversation talking about elected

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Speaker 1: officials people running for office. More and more we are

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Speaker 1: seeing that trickle down to just regular people. And I

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Speaker 1: want to be clear, so it is it is we

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Speaker 1: have seen this kind of online behavior keep marginalized people

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Speaker 1: from doing things like running for office, but also from

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Speaker 1: doing things like just serving their community as election officials

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Speaker 1: or poll workers from speaking up about their opinions at

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Speaker 1: school board meetings because all people who just generally in

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Speaker 1: gay in public civic life, they know that in this climate,

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Speaker 1: they are setting themselves up to be attacked. And we're

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Speaker 1: not talking about people like it's bad enough that it's

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Speaker 1: happening to women running for office, but when it's happening

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Speaker 1: to people who are just you know, everyday people. It's

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Speaker 1: not like they have a security detail. It's not like

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Speaker 1: they have money to protect themselves in the way they

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Speaker 1: would need to. And just a few examples that we're

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Speaker 1: seeing recently, educators are being attacked by extremists for being

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Speaker 1: suspected of being LGBTQ or for teaching something that these

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Speaker 1: people do not like um and these campaigns have been

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Speaker 1: incredibly effective. One of them happened right in Georgia. Cecilia Lewis.

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Speaker 1: She's a Georgia educator who was basically run out of

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Speaker 1: town by a group of parents organizing on Facebook because

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Speaker 1: they suspected her of she had not even gotten the

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Speaker 1: job yet, and they already suspected her of planning to

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Speaker 1: teach critical race theory, never mind the fact that she

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Speaker 1: wasn't that that's just complete conjecture. Uh, it just happened

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Speaker 1: to be that she is a black woman, and so

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Speaker 1: they were able to say, like, oh, she's a teacher kids,

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Speaker 1: critical race theory, even though she wasn't even an in

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Speaker 1: classroom teacher. She was just an administrator, right, And so

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Speaker 1: she had to leave town. And when she left town,

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Speaker 1: these people followed her to the next town that she

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Speaker 1: went to and ran her out of that town too.

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Speaker 1: And so these campaigns are incredibly effective. And another good

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Speaker 1: example is looking at election workers, eight percent of whom

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Speaker 1: are women. Election workers are being basically attacked and accused

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Speaker 1: of things like vote tampering if elections don't go the

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Speaker 1: way that these extremists want them to. Again, another example

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Speaker 1: from Georgia is if you watched the January six commission,

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Speaker 1: you might have seen the story of Ruby and shaf Freeman.

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Speaker 1: That story broke my heart. A black mother and her

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Speaker 1: daughter with a long storied history of serving their community

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Speaker 1: as election and poll workers like the black women in

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Speaker 1: your town who if you have any questions about voting,

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Speaker 1: and they got you. If you need help voting, they

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Speaker 1: got you. Like people who are called to serve their

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Speaker 1: community and have been for a very long time. Well,

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Speaker 1: the thanks they got for that was being horribly attacked.

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Speaker 1: When Trump lost Georgia in the election, he and Rudy

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Speaker 1: Giuliani and his other phronies baselessly, repeatedly and publicly accused

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Speaker 1: these women of vote hampering and videos of them where

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Speaker 1: they said that they were moving votes, which never happened. Um.

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Speaker 1: And this led to really terrible, frightening attacks on them

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Speaker 1: where people showed up at their home and the home

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Speaker 1: of their elderly grandmother and they had to flee for

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Speaker 1: their own safety. And again, these are regular people, not

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Speaker 1: people that have a security detail or people that maybe

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Speaker 1: have the money to scrub their personal information from the

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Speaker 1: Internet the way that you would need to if you

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Speaker 1: were facing these kinds of attacks. Yeah. Actually, I had

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Speaker 1: volunteered one time to be a just to oversee as

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Speaker 1: people are lining up, make sure everything is okay, nothing nothing,

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Speaker 1: I don't I don't talk to anybody. I just make

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Speaker 1: sure everybody's has the right to vote, almost being harassed

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Speaker 1: all these things, That's all I did. But the entire

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Speaker 1: group of people who were actually volunteering, handing out the pencils,

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Speaker 1: giving instructions, checking ideas and such. Were all black women,

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Speaker 1: every single one of them. And just this last voting

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Speaker 1: when I did my whole uh precinct was all black

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Speaker 1: but sweet too. They were so great and we had

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Speaker 1: the forty five minute line and they were so on

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Speaker 1: top of it trying to make sure to get everybody

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Speaker 1: through seamlessly, and they did a beautiful job. And they

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Speaker 1: were there like this was the I think the fifth

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Speaker 1: day of the week that they were doing this, and

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Speaker 1: they'd been there and they were not complaining, they were smiling,

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Speaker 1: making conversation and it's like people like them who are

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Speaker 1: making this happen and going on so well and so perfectly,

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Speaker 1: as if there's nothing happening. And literally the line was

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Speaker 1: smoothly going and I loved everything about that. And I

432
00:23:40,600 --> 00:23:43,840
Speaker 1: cannot imagine just because they're there, they exist, and they

433
00:23:43,880 --> 00:23:46,720
Speaker 1: decided to help their community, they're gonna be targeted and

434
00:23:46,720 --> 00:23:49,320
Speaker 1: being yelled at, and honestly, um, I know there was

435
00:23:49,400 --> 00:23:53,320
Speaker 1: conversations of people really being scared about being physically harm

436
00:23:53,359 --> 00:23:56,000
Speaker 1: because people are pushing and trying to do so many

437
00:23:56,000 --> 00:24:00,720
Speaker 1: intimidation tactics to accuse and blame somebody for or the

438
00:24:00,840 --> 00:24:04,959
Speaker 1: loss of a bully. Yeah, This is just my opinion.

439
00:24:05,160 --> 00:24:09,360
Speaker 1: Black women are the backbone of our democracy. They are

440
00:24:09,480 --> 00:24:13,320
Speaker 1: like older black woman from the South. They are the

441
00:24:13,560 --> 00:24:17,359
Speaker 1: grease of the wheels of democracy. Like I feel like

442
00:24:17,359 --> 00:24:18,880
Speaker 1: when I go into a place like that and I'm like, oh,

443
00:24:18,880 --> 00:24:21,280
Speaker 1: of course, there's like hell of black women with clipboards

444
00:24:21,280 --> 00:24:23,719
Speaker 1: in here like that, like that's the vibe. And you know,

445
00:24:23,960 --> 00:24:27,159
Speaker 1: and we know that of election workers and poll workers

446
00:24:27,160 --> 00:24:29,080
Speaker 1: are women. I would be be willing to bet at

447
00:24:29,080 --> 00:24:31,440
Speaker 1: a bunch of those are black women, especially in the South.

448
00:24:31,760 --> 00:24:34,360
Speaker 1: And I think that what we're really doing is allowing

449
00:24:34,400 --> 00:24:38,080
Speaker 1: for people who are the most marginalized in our society.

450
00:24:38,440 --> 00:24:42,119
Speaker 1: We are allowing for them to put themselves out there

451
00:24:42,200 --> 00:24:45,720
Speaker 1: to support our communities and our democracy in these ways

452
00:24:46,040 --> 00:24:47,760
Speaker 1: for not a lot of money about mind you, because

453
00:24:47,760 --> 00:24:49,920
Speaker 1: a lot of these women are volunteers, are very low paid,

454
00:24:50,400 --> 00:24:53,440
Speaker 1: and we're saying you also need to absorb these attacks

455
00:24:53,520 --> 00:24:56,320
Speaker 1: to do so when we're basically setting these women up

456
00:24:56,640 --> 00:24:59,360
Speaker 1: to be attacked and giving them no and not even

457
00:24:59,359 --> 00:25:01,400
Speaker 1: really talking about it when they are right. And so

458
00:25:02,119 --> 00:25:05,040
Speaker 1: I think it's my experience with voting has been the

459
00:25:05,080 --> 00:25:09,000
Speaker 1: exact same that these are the women who are at

460
00:25:09,000 --> 00:25:12,040
Speaker 1: this point I would say, risking a lot to make

461
00:25:12,040 --> 00:25:14,679
Speaker 1: sure that our democratic process is able to take place.

462
00:25:14,760 --> 00:25:18,280
Speaker 1: And honestly, this is probably not surprising to anybody, because

463
00:25:18,359 --> 00:25:21,800
Speaker 1: these kinds of attacks are meant exactly to keep those

464
00:25:21,880 --> 00:25:24,639
Speaker 1: kinds of people from doing what they do. It is

465
00:25:24,680 --> 00:25:27,320
Speaker 1: meant to keep them from doing their work of keeping

466
00:25:27,320 --> 00:25:29,800
Speaker 1: the wheels of democracy going. It is meant to keep

467
00:25:29,840 --> 00:25:32,280
Speaker 1: them from being engaged in civic life, because who would

468
00:25:32,320 --> 00:25:33,960
Speaker 1: want to sort of their community by working as a

469
00:25:34,000 --> 00:25:36,439
Speaker 1: poll worker or running for office if it means that

470
00:25:36,600 --> 00:25:39,920
Speaker 1: them and their families are going to be facing these

471
00:25:40,000 --> 00:25:43,000
Speaker 1: kinds of attacks. So probably not surprising to anybody that

472
00:25:43,320 --> 00:25:45,800
Speaker 1: here we are ten days out from an election and

473
00:25:45,840 --> 00:25:49,280
Speaker 1: the United States is facing a national shortage on poll workers.

474
00:25:49,560 --> 00:25:52,040
Speaker 1: Kim Wyman, who is the senior Election Security lead at

475
00:25:52,040 --> 00:25:55,399
Speaker 1: the Cybersecurity and Infrastructure Security Agency or the c i

476
00:25:55,640 --> 00:25:58,479
Speaker 1: s A, said that because of a rise in threats

477
00:25:58,480 --> 00:26:01,600
Speaker 1: against election workers, one in three election workers and poll

478
00:26:01,640 --> 00:26:05,040
Speaker 1: workers have quit their positions over fears for their safety,

479
00:26:05,359 --> 00:26:08,439
Speaker 1: and state officials are having a hard time hiring folks

480
00:26:08,440 --> 00:26:11,439
Speaker 1: for these positions because again, who would want to do

481
00:26:11,520 --> 00:26:13,960
Speaker 1: these like low paid duties? It means like, oh you

482
00:26:13,960 --> 00:26:16,560
Speaker 1: you and your family might have to flee for your safety.

483
00:26:16,720 --> 00:26:19,000
Speaker 1: This is because you wanted to do this position, helping

484
00:26:19,040 --> 00:26:22,160
Speaker 1: your community in this way. I can understand why people

485
00:26:22,200 --> 00:26:24,800
Speaker 1: are not lining up to do this work, but you

486
00:26:24,840 --> 00:26:28,680
Speaker 1: can see how big those consequences are for our democracy

487
00:26:28,720 --> 00:26:30,560
Speaker 1: and for all of us when we can't even get

488
00:26:30,600 --> 00:26:34,639
Speaker 1: people to do enact the labor that needs to happen

489
00:26:34,720 --> 00:26:39,960
Speaker 1: for our democratic process to exist. Yeah. Um, And as

490
00:26:40,000 --> 00:26:43,640
Speaker 1: you've mentioned, like we've arrived at not a great space

491
00:26:44,000 --> 00:26:48,080
Speaker 1: in our democracy, but we've had a long history, Like

492
00:26:48,119 --> 00:26:51,560
Speaker 1: we had plenty of signs of of where we could

493
00:26:51,560 --> 00:26:54,400
Speaker 1: have listened to, especially black women, and we didn't, And

494
00:26:54,480 --> 00:26:56,800
Speaker 1: this is where we are. So can you expand on

495
00:26:56,800 --> 00:26:59,920
Speaker 1: that a little bit? Yes, I believe that this entire

496
00:27:00,119 --> 00:27:02,919
Speaker 1: saying all of these fetes to our democracy is really

497
00:27:02,920 --> 00:27:06,000
Speaker 1: connected and goes back to not listening to black women

498
00:27:06,359 --> 00:27:09,280
Speaker 1: because I talked about the fact that you know, these

499
00:27:09,280 --> 00:27:13,880
Speaker 1: threats that start online become real role threats. Black women

500
00:27:13,920 --> 00:27:16,199
Speaker 1: have been saying this for a very long time, and

501
00:27:16,240 --> 00:27:19,239
Speaker 1: I feel that nobody has really listened. You know, some

502
00:27:19,320 --> 00:27:21,800
Speaker 1: of the first people to really raise the alarm about

503
00:27:21,840 --> 00:27:25,280
Speaker 1: the role that online harassment plays in our landscape, we're

504
00:27:25,320 --> 00:27:27,639
Speaker 1: black women because woman have been saying this for years,

505
00:27:27,920 --> 00:27:30,439
Speaker 1: and people and by that I mean people with power,

506
00:27:30,520 --> 00:27:33,320
Speaker 1: that people with power to do something pretty much ignored them.

507
00:27:33,359 --> 00:27:35,639
Speaker 1: And so I would then argue that not listening to

508
00:27:35,680 --> 00:27:38,879
Speaker 1: black women has consequences for all of us. A couple

509
00:27:38,880 --> 00:27:42,520
Speaker 1: of examples, Um, most people listening are probably familiar with

510
00:27:42,520 --> 00:27:44,880
Speaker 1: gamer Gate. If you are not familiar with gamer Gate,

511
00:27:45,400 --> 00:27:48,600
Speaker 1: I am jealous. But essentially it was like, if that's

512
00:27:48,600 --> 00:27:50,359
Speaker 1: something that you were like, I've never heard of that before.

513
00:27:50,800 --> 00:27:53,240
Speaker 1: I want to live the life that you are living, right,

514
00:27:54,400 --> 00:27:57,280
Speaker 1: But essentially gamer Gate was when a bunch of men

515
00:27:58,040 --> 00:28:02,080
Speaker 1: uh pretty much attacked and harra asked mostly women under

516
00:28:02,080 --> 00:28:06,760
Speaker 1: the guys of being big mad about scare quotes, ethics

517
00:28:06,840 --> 00:28:10,560
Speaker 1: in gaming journalism, and it got lots of attention rightly,

518
00:28:10,920 --> 00:28:14,200
Speaker 1: and most people like probably remember it, but those same

519
00:28:14,240 --> 00:28:18,000
Speaker 1: people probably don't know that the same folks responsible for

520
00:28:18,040 --> 00:28:21,200
Speaker 1: gamer Gate, we're using those exact same tactics against black

521
00:28:21,200 --> 00:28:24,480
Speaker 1: women before gamer Gate, women like Adria Richards, who was

522
00:28:24,520 --> 00:28:27,240
Speaker 1: targeted for racist respen online after she tweeted about a

523
00:28:27,280 --> 00:28:30,600
Speaker 1: crass joke about dongles that she overheard at a work event,

524
00:28:30,840 --> 00:28:35,119
Speaker 1: or women like Shafika Hudson who actually reported and called

525
00:28:35,160 --> 00:28:38,240
Speaker 1: out bad actors who were using BAC accounts to impersonate

526
00:28:38,240 --> 00:28:41,400
Speaker 1: black women to destabilize Internet communities. You know, these are

527
00:28:41,400 --> 00:28:44,000
Speaker 1: women who spoke up about what they were facing on

528
00:28:44,040 --> 00:28:46,560
Speaker 1: the Internet, and they were basically ignored. And I wonder

529
00:28:46,960 --> 00:28:50,360
Speaker 1: what might have happened if somebody with power had actually

530
00:28:50,600 --> 00:28:53,320
Speaker 1: listened to them and taken some action. For one, I

531
00:28:53,360 --> 00:28:56,000
Speaker 1: believe gamer Gate might have gone down very differently if

532
00:28:56,040 --> 00:29:00,400
Speaker 1: when people use these tactics against black women somebody was like, hey, wait,

533
00:29:00,560 --> 00:29:02,360
Speaker 1: this is bad. We should not allow our platform to

534
00:29:02,360 --> 00:29:05,600
Speaker 1: be used this way and made some changes. Or consider

535
00:29:05,680 --> 00:29:08,800
Speaker 1: the fact that six years after Sapi Hudson reported people

536
00:29:08,800 --> 00:29:12,760
Speaker 1: impersonating black folks on Twitter to cause chaos, white supremacist

537
00:29:12,800 --> 00:29:16,160
Speaker 1: groups used that very same tactic during the racial Uprising

538
00:29:17,240 --> 00:29:20,320
Speaker 1: to make it seem like Black Lives Matter activists were

539
00:29:20,400 --> 00:29:23,160
Speaker 1: using the Internet to call for people to like loot

540
00:29:23,240 --> 00:29:26,800
Speaker 1: homes and cause violence, and Twitter actually confirmed this. Twitter

541
00:29:26,880 --> 00:29:29,360
Speaker 1: was like, oh yeah, we can confirm now that some

542
00:29:29,440 --> 00:29:32,600
Speaker 1: of the accounts who were using our platform to call

543
00:29:32,680 --> 00:29:36,280
Speaker 1: for people to like loot and cause violence during these protests,

544
00:29:36,280 --> 00:29:39,400
Speaker 1: those were actually white supremacists pretending to be Black Lives

545
00:29:39,440 --> 00:29:42,600
Speaker 1: Matter activists, and so you know, they didn't actually explain

546
00:29:42,880 --> 00:29:46,920
Speaker 1: why they did all to prevent this from being a

547
00:29:47,000 --> 00:29:50,080
Speaker 1: tactic on a larger scale down the line, if they

548
00:29:50,160 --> 00:29:53,240
Speaker 1: knew it was a vulnerability of their platform. And when

549
00:29:53,280 --> 00:29:56,240
Speaker 1: we zoom out even further, these are the same tactics

550
00:29:56,240 --> 00:30:00,440
Speaker 1: that a Senate inquiry in would confirm we're used to

551
00:30:00,480 --> 00:30:03,800
Speaker 1: try to destabilize election, right. And so if when these

552
00:30:03,840 --> 00:30:07,360
Speaker 1: black women reported what they were seeing online, if somebody

553
00:30:07,360 --> 00:30:12,200
Speaker 1: had done something, I wonder, would bad actors who were

554
00:30:12,240 --> 00:30:15,920
Speaker 1: trying to use this tactic to destabilize our elections and

555
00:30:15,960 --> 00:30:19,520
Speaker 1: destabilize our online communities and truly cause violence and chaos,

556
00:30:20,000 --> 00:30:23,760
Speaker 1: would that have been a a viable tactic? I would

557
00:30:23,800 --> 00:30:27,000
Speaker 1: argue maybe not. But again, these are the consequences when

558
00:30:27,000 --> 00:30:29,320
Speaker 1: folks don't listen to black women and don't take them

559
00:30:29,320 --> 00:30:32,520
Speaker 1: seriously when they speak up about what they're experiencing online. Yeah,

560
00:30:32,560 --> 00:30:34,400
Speaker 1: And one of the things that annoys me the most

561
00:30:34,440 --> 00:30:38,120
Speaker 1: is when this whole idea of like that's just sort

562
00:30:38,120 --> 00:30:41,479
Speaker 1: of the price of the game. Accept it, like you know,

563
00:30:41,920 --> 00:30:44,720
Speaker 1: if you're tough, And sometimes people will try to paint

564
00:30:44,760 --> 00:30:46,960
Speaker 1: it in a very nice brush, like you're strong enough

565
00:30:47,000 --> 00:30:50,440
Speaker 1: to make it against all this harassment, all this stuff online.

566
00:30:50,520 --> 00:30:54,040
Speaker 1: You're you're so tough. But really, we shouldn't be accepting

567
00:30:54,080 --> 00:30:57,000
Speaker 1: this or expecting this in the first place. And that

568
00:30:57,120 --> 00:30:59,760
Speaker 1: is one of the things the points you're making here

569
00:30:59,840 --> 00:31:02,520
Speaker 1: is that we've normalized it to the point where I've

570
00:31:02,560 --> 00:31:06,200
Speaker 1: even heard conversations will people where people will paint it

571
00:31:06,240 --> 00:31:08,680
Speaker 1: as like, Oh, it's a good thing, she's tough, she

572
00:31:08,720 --> 00:31:13,720
Speaker 1: can survive in this environment. It shouldn't be that way. Yeah,

573
00:31:13,800 --> 00:31:17,160
Speaker 1: I don't want to have a political landscape where you

574
00:31:17,200 --> 00:31:22,600
Speaker 1: have to be able to publicly endure and withstand attacks

575
00:31:22,640 --> 00:31:26,080
Speaker 1: on you and your family, because when it's women, the

576
00:31:26,160 --> 00:31:28,640
Speaker 1: research is super clear that it's never just the woman

577
00:31:28,680 --> 00:31:32,200
Speaker 1: who was attacked. It's her, her mom, her dad, her kids,

578
00:31:32,280 --> 00:31:37,240
Speaker 1: her partner, her community, her neighbors, her friends. I don't

579
00:31:37,280 --> 00:31:40,080
Speaker 1: want a political climate that says that women have to

580
00:31:40,120 --> 00:31:43,520
Speaker 1: be able to endure these kinds of attacks on their

581
00:31:43,560 --> 00:31:47,520
Speaker 1: safety and and watch their families deal with it as well. Uh,

582
00:31:47,560 --> 00:31:50,000
Speaker 1: if they want to be elected officials, if they want

583
00:31:50,040 --> 00:31:52,480
Speaker 1: to serve their community, if they want to just take

584
00:31:52,480 --> 00:31:55,760
Speaker 1: place in civic life, that's not the kind of climate

585
00:31:55,800 --> 00:31:58,800
Speaker 1: I want we should not that's not acceptable, that's not

586
00:31:59,160 --> 00:32:15,160
Speaker 1: a norm we should be okay with, you know. And

587
00:32:15,200 --> 00:32:17,920
Speaker 1: I keep thinking about how the algorithm has changed. And

588
00:32:18,000 --> 00:32:20,640
Speaker 1: I was talking to Annie about our own algorithm, and

589
00:32:20,680 --> 00:32:22,880
Speaker 1: because we are so afraid of social media, we stay

590
00:32:22,880 --> 00:32:26,080
Speaker 1: away from it, and therefore anytime we do put anything up,

591
00:32:26,120 --> 00:32:28,240
Speaker 1: it's ignored because we don't have a lot of content

592
00:32:28,280 --> 00:32:32,000
Speaker 1: out there. And I still think about how Twitter, and

593
00:32:32,080 --> 00:32:35,360
Speaker 1: I know for all references, it's just that when we

594
00:32:35,440 --> 00:32:37,880
Speaker 1: talk about black Twitter, it is the community coming together

595
00:32:38,040 --> 00:32:40,560
Speaker 1: and having like their own space and talking and because

596
00:32:40,560 --> 00:32:44,800
Speaker 1: they've kind of become grouped and has has to become

597
00:32:44,840 --> 00:32:48,080
Speaker 1: a point where if you don't already uh follow some

598
00:32:48,120 --> 00:32:50,120
Speaker 1: of these people, or if you're not actually paying attention,

599
00:32:50,160 --> 00:32:52,720
Speaker 1: it just goes away from your feed. Kind of how

600
00:32:52,760 --> 00:32:54,960
Speaker 1: TikTok is doing that. We're just making it harder and

601
00:32:55,000 --> 00:32:58,000
Speaker 1: harder to actually see what is happening and what the

602
00:32:58,000 --> 00:33:01,440
Speaker 1: truth really is going down. Have to question, like, so

603
00:33:01,640 --> 00:33:04,800
Speaker 1: is the algorithm really a good thing in that it

604
00:33:04,840 --> 00:33:06,960
Speaker 1: does this tour where you're in your own little hole,

605
00:33:07,200 --> 00:33:10,880
Speaker 1: and where it's putting pocketing black women who are like, hey,

606
00:33:10,920 --> 00:33:13,280
Speaker 1: this bad thing is happening. We're trying to tell y'all,

607
00:33:13,360 --> 00:33:15,360
Speaker 1: but the only people who are seeing those other people

608
00:33:15,360 --> 00:33:17,720
Speaker 1: who already know. And it's like, what the hell, how

609
00:33:17,720 --> 00:33:21,280
Speaker 1: do we change that? Yeah? What an insightful question. So

610
00:33:21,360 --> 00:33:24,480
Speaker 1: this is just my opinion. This is like just representing myself.

611
00:33:24,960 --> 00:33:28,880
Speaker 1: I don't think that it is good they have a

612
00:33:28,960 --> 00:33:35,080
Speaker 1: platform like Twitter, especially be so tied to an algorithm.

613
00:33:35,280 --> 00:33:37,040
Speaker 1: I don't think it's good for all the reasons that

614
00:33:37,080 --> 00:33:40,120
Speaker 1: you just said. People they're there, voices can be siloed,

615
00:33:40,160 --> 00:33:42,800
Speaker 1: you can feel like you're talking to the choir of

616
00:33:42,800 --> 00:33:45,720
Speaker 1: people who already agree with you, and you know, I

617
00:33:45,880 --> 00:33:49,000
Speaker 1: know what you're saying. And I also think that I

618
00:33:49,080 --> 00:33:52,040
Speaker 1: don't think that platforms have shown that they're able to

619
00:33:52,160 --> 00:33:57,280
Speaker 1: be responsible with algorithmically generated content right now. Algorithms are

620
00:33:57,680 --> 00:33:59,880
Speaker 1: just it's just a fact. They are biased towards con

621
00:34:00,080 --> 00:34:04,800
Speaker 1: tent that is untrue. False information travels on Twitter, specifically

622
00:34:05,280 --> 00:34:08,680
Speaker 1: much faster and much further than correct information. They amplify

623
00:34:08,800 --> 00:34:11,680
Speaker 1: content that is extremist, and I don't mean like extremists,

624
00:34:11,680 --> 00:34:14,880
Speaker 1: like capital e extremist, although that as well, but like

625
00:34:15,680 --> 00:34:21,760
Speaker 1: content that is, you know, more extreme than not extreme.

626
00:34:21,840 --> 00:34:24,560
Speaker 1: So if I'm saying, like, I ate a piece of

627
00:34:24,560 --> 00:34:27,160
Speaker 1: toast today and it was burnt. I hate all toast

628
00:34:28,080 --> 00:34:30,240
Speaker 1: rather than like, oh, this piece of toast was burnt.

629
00:34:30,360 --> 00:34:32,920
Speaker 1: When some lose them, they're going to amplify the one

630
00:34:32,960 --> 00:34:36,319
Speaker 1: that sounds more extreme, because that's that's what gets more eyeballs. Um.

631
00:34:36,360 --> 00:34:39,719
Speaker 1: They amplify content that is polarizing, They amplify content that

632
00:34:39,840 --> 00:34:43,600
Speaker 1: is loud and aggressive. And I don't think that platforms

633
00:34:43,719 --> 00:34:46,680
Speaker 1: can be trusted to be run algorithmically because they are

634
00:34:46,680 --> 00:34:50,160
Speaker 1: going to make us all more extreme, more polarized, less informed,

635
00:34:50,160 --> 00:34:53,120
Speaker 1: and less thoughtful. I would love to see an algorithmic

636
00:34:53,239 --> 00:34:58,560
Speaker 1: model that is amplifies content that is thoughtful, honest, accurate, timely, whatever,

637
00:34:58,960 --> 00:35:00,520
Speaker 1: but I have not seen it, and so I think

638
00:35:00,520 --> 00:35:02,759
Speaker 1: platforms have shown that they can't be trusted to to

639
00:35:03,360 --> 00:35:06,600
Speaker 1: work with algorithmic models because they're just going to amplify

640
00:35:06,719 --> 00:35:10,279
Speaker 1: stuff that makes us all worse off. Right, Yeah, And

641
00:35:10,480 --> 00:35:13,799
Speaker 1: I honestly just being new to TikTok, and we've talked

642
00:35:13,800 --> 00:35:16,720
Speaker 1: about TikTok often um on the show, but it also

643
00:35:16,920 --> 00:35:19,440
Speaker 1: does the same thing with the f y P or

644
00:35:19,520 --> 00:35:22,799
Speaker 1: for you page, where it only amplifies what seems to

645
00:35:22,840 --> 00:35:26,200
Speaker 1: be uh most disturbing. I say that for my own

646
00:35:26,600 --> 00:35:29,560
Speaker 1: uh concept, because there's so much out there that the

647
00:35:29,600 --> 00:35:33,040
Speaker 1: worst shadow banning and the idea that many of these

648
00:35:33,120 --> 00:35:37,840
Speaker 1: algorithms do this that there's enough that people can complain

649
00:35:37,920 --> 00:35:40,759
Speaker 1: or make false allegations, and and their standard of what

650
00:35:41,160 --> 00:35:43,680
Speaker 1: they think is bullying and or racist is not And

651
00:35:43,719 --> 00:35:46,600
Speaker 1: apparently they have a pretty big threshold of saying it's

652
00:35:46,600 --> 00:35:49,560
Speaker 1: not right racists, We're good, um, even though it's obviously

653
00:35:49,920 --> 00:35:53,120
Speaker 1: super racist, in the fact that they seem to keep

654
00:35:53,120 --> 00:35:56,719
Speaker 1: working on that level in and allowing for things that

655
00:35:56,760 --> 00:36:00,880
Speaker 1: we know is I don't know, being threatened, being called

656
00:36:01,400 --> 00:36:04,920
Speaker 1: by a stereotype that I would consider racist, but yet

657
00:36:05,040 --> 00:36:06,480
Speaker 1: for so off that it's not that big of a deal.

658
00:36:06,480 --> 00:36:09,560
Speaker 1: They're not really threatening you. Wait. Yeah, so we have

659
00:36:09,600 --> 00:36:11,399
Speaker 1: to have a police report to actually say that we're

660
00:36:11,400 --> 00:36:14,200
Speaker 1: being threatened, and that seems to be happening often in

661
00:36:14,280 --> 00:36:16,799
Speaker 1: these platforms exactly. And this is when I said I

662
00:36:16,880 --> 00:36:19,680
Speaker 1: wasn't really a big fan of algorithm platforms. One of

663
00:36:19,719 --> 00:36:23,120
Speaker 1: the reasons is because I think that we're as humans,

664
00:36:23,239 --> 00:36:26,600
Speaker 1: we are giving the responsibility of moderating platforms more and

665
00:36:26,640 --> 00:36:31,040
Speaker 1: more to algorithms or AI and AI is smart and

666
00:36:31,160 --> 00:36:32,960
Speaker 1: good at lots of things, but there are some things

667
00:36:32,960 --> 00:36:35,560
Speaker 1: that you need a humans take on. And so Sam,

668
00:36:35,600 --> 00:36:37,200
Speaker 1: you know, you talked about how like oh, I'm being

669
00:36:37,239 --> 00:36:39,680
Speaker 1: called a racial slur or like I'm being attacked in

670
00:36:39,719 --> 00:36:43,600
Speaker 1: this way. Bad actors are so good at using dog

671
00:36:43,640 --> 00:36:48,120
Speaker 1: whistles or coded language or things to get around AI

672
00:36:48,239 --> 00:36:50,600
Speaker 1: or machine learning that is like you know, working as

673
00:36:50,640 --> 00:36:53,680
Speaker 1: a content moderator, and they know how to exploit those loopholes.

674
00:36:53,680 --> 00:36:56,279
Speaker 1: And so I completely agree, especially on TikTok, I feel

675
00:36:56,280 --> 00:36:59,640
Speaker 1: like they really need to make some changes with how

676
00:36:59,680 --> 00:37:02,680
Speaker 1: they're platform is run if that platform is going to

677
00:37:02,719 --> 00:37:05,279
Speaker 1: be safer and more inclusive. Um. I think it was

678
00:37:05,320 --> 00:37:09,080
Speaker 1: the Washington Post recently that just did a little experiment

679
00:37:09,120 --> 00:37:11,720
Speaker 1: where if you don't follow a Washington Post on TikTok,

680
00:37:11,760 --> 00:37:13,960
Speaker 1: they have a really interesting TikTok channel. They are doing

681
00:37:14,000 --> 00:37:17,600
Speaker 1: a series on TikTok where they used all these words

682
00:37:17,719 --> 00:37:21,680
Speaker 1: that they were certain we're going to get their TikTok's suppressed. Um.

683
00:37:21,719 --> 00:37:25,000
Speaker 1: So they they did a TikTok where they said racism,

684
00:37:25,120 --> 00:37:30,279
Speaker 1: black lives matter, disability, um, you know, all these other

685
00:37:30,360 --> 00:37:32,319
Speaker 1: words because they were like, if you if you use

686
00:37:32,480 --> 00:37:35,360
Speaker 1: these words in your TikTok, they are more likely to

687
00:37:35,360 --> 00:37:38,120
Speaker 1: be suppressed what they were doing an experiment and by

688
00:37:38,120 --> 00:37:40,879
Speaker 1: gott Lee it worked, you know. And so it's clear

689
00:37:40,920 --> 00:37:44,719
Speaker 1: to me that platforms are not being run one with

690
00:37:44,800 --> 00:37:48,520
Speaker 1: a thoughtful, nuanced, sensitive human who was able to understand

691
00:37:48,600 --> 00:37:51,000
Speaker 1: nuance at the helm and to just not being run

692
00:37:51,000 --> 00:37:55,799
Speaker 1: in a way that amplifies good conversation, thoughtful conversation, substitutive conversation,

693
00:37:56,120 --> 00:38:02,360
Speaker 1: and does not amplify garbage, lies, extremism, hate all that. Yeah,

694
00:38:02,400 --> 00:38:04,279
Speaker 1: and on top of that, you have again as you

695
00:38:04,320 --> 00:38:07,279
Speaker 1: talk bad players who and I've seen this a lot

696
00:38:07,280 --> 00:38:11,640
Speaker 1: more on TikTok because again on that person, but like

697
00:38:11,800 --> 00:38:14,480
Speaker 1: in the conversations that they thinking that they're doing something

698
00:38:14,520 --> 00:38:17,799
Speaker 1: good and doing that extreme call out in which they

699
00:38:17,800 --> 00:38:20,399
Speaker 1: get people fired and canceled and all of these things

700
00:38:20,400 --> 00:38:23,160
Speaker 1: and go after people on just one video. And don't

701
00:38:23,200 --> 00:38:26,000
Speaker 1: get me wrong, a lot of these I agree with

702
00:38:26,040 --> 00:38:29,040
Speaker 1: that these people who are saying really nicety crap and

703
00:38:29,080 --> 00:38:31,360
Speaker 1: being caught on camera, are caught on their phone for

704
00:38:31,440 --> 00:38:34,000
Speaker 1: saying these things should be called out. There should be repercussions.

705
00:38:34,000 --> 00:38:36,600
Speaker 1: But it's come to a point that it's entertainment almost

706
00:38:36,640 --> 00:38:39,000
Speaker 1: and I'm wondering where this is gonna lead because it's

707
00:38:39,040 --> 00:38:41,799
Speaker 1: a fairly new thing out of the last um five years.

708
00:38:41,840 --> 00:38:43,279
Speaker 1: And I say that for the both the left and

709
00:38:43,320 --> 00:38:44,919
Speaker 1: the right, that it's like this is this is getting

710
00:38:45,000 --> 00:38:50,320
Speaker 1: dangerously toxic. Dangerously toxic. That's a whole different conversation. I know. No,

711
00:38:50,600 --> 00:38:52,319
Speaker 1: it's so funny that you bring this up because I

712
00:38:52,719 --> 00:38:55,240
Speaker 1: so I recorded the first episode of Internet Hate Machine

713
00:38:55,239 --> 00:38:58,960
Speaker 1: with Sophie earlier in this week, and I haven't perhaps

714
00:38:59,120 --> 00:39:02,759
Speaker 1: unpopular opinion on this. When I first got on TikTok, specifically,

715
00:39:03,160 --> 00:39:05,640
Speaker 1: I followed a lot of creators who made content that

716
00:39:05,760 --> 00:39:08,279
Speaker 1: was like this guy is a racist and this is

717
00:39:08,320 --> 00:39:11,200
Speaker 1: where he works and I'm gonna call his employer and

718
00:39:11,200 --> 00:39:12,759
Speaker 1: get him fired. And I used to be like, yeah,

719
00:39:12,920 --> 00:39:16,680
Speaker 1: like fire that racist, like really loved it. As I have,

720
00:39:17,440 --> 00:39:20,959
Speaker 1: I guess matured and matured along with the platform, as

721
00:39:21,000 --> 00:39:24,719
Speaker 1: cathartic as that as that is, I don't think that

722
00:39:24,719 --> 00:39:29,160
Speaker 1: that is an appropriate tactic. And I think that even

723
00:39:29,239 --> 00:39:33,120
Speaker 1: if I am in agreement like yeah, this racist shouldn't

724
00:39:33,120 --> 00:39:35,719
Speaker 1: be doing this. And they're definitely exceptions to this, I'm sure,

725
00:39:36,000 --> 00:39:40,279
Speaker 1: but in general, I just know how easy it is

726
00:39:40,360 --> 00:39:45,239
Speaker 1: for bad actors and extremists to weaponize that very same tactic. Right,

727
00:39:45,360 --> 00:39:48,080
Speaker 1: We're gonna coordinate and have all these people call this

728
00:39:48,160 --> 00:39:50,520
Speaker 1: person to get them fired because we didn't like what

729
00:39:50,560 --> 00:39:53,640
Speaker 1: they tweeted. I don't think that that is a tactic

730
00:39:53,880 --> 00:39:57,000
Speaker 1: that makes us smarter, that makes us better, that makes us,

731
00:39:57,360 --> 00:40:00,279
Speaker 1: you know, healthier as a society. So even what I

732
00:40:00,280 --> 00:40:03,000
Speaker 1: see it happening with someone who ostensibly I agree with,

733
00:40:03,000 --> 00:40:06,400
Speaker 1: should you know they deserve it? I feel like I

734
00:40:06,480 --> 00:40:10,080
Speaker 1: know how easily that is a tactic to be exploited

735
00:40:10,080 --> 00:40:13,400
Speaker 1: and weaponized by bad actors. And our first episode actually

736
00:40:13,440 --> 00:40:15,239
Speaker 1: deals with this woman, ah R Richards, who I was

737
00:40:15,239 --> 00:40:18,200
Speaker 1: talking about earlier, who was one of the early targets

738
00:40:18,200 --> 00:40:21,080
Speaker 1: at that where she tweeted a picture of these two

739
00:40:21,080 --> 00:40:23,960
Speaker 1: guys at a tech conference who are making a crass

740
00:40:24,000 --> 00:40:29,360
Speaker 1: joke that she didn't like, and she got horribly mobbed,

741
00:40:29,680 --> 00:40:32,520
Speaker 1: where they coordinated on on sites like FESHND to call

742
00:40:32,600 --> 00:40:37,440
Speaker 1: her employer. They threatened her employer, and rather than supporting her,

743
00:40:37,520 --> 00:40:40,080
Speaker 1: her employer fired her and was like, Okay, yeah, you

744
00:40:40,080 --> 00:40:42,879
Speaker 1: guys want her to be fired, She's fired. And I've

745
00:40:42,960 --> 00:40:45,640
Speaker 1: just seen time and time again that that tactic is

746
00:40:45,680 --> 00:40:49,480
Speaker 1: so easily gamified and weaponized and exploited by bad actors

747
00:40:49,600 --> 00:40:52,359
Speaker 1: that I don't think anybody should be engaged in it, right,

748
00:40:52,440 --> 00:40:55,560
Speaker 1: And that's that's the other conversation that it keeps go

749
00:40:55,640 --> 00:40:57,959
Speaker 1: into my head is that the whole level of Docks

750
00:40:57,960 --> 00:40:59,920
Speaker 1: saying and which is this kind of like again, the

751
00:41:00,080 --> 00:41:02,280
Speaker 1: is the entertainment level. And this is where it's getting

752
00:41:02,280 --> 00:41:04,200
Speaker 1: really disgusting to me that I'm like, Okay, we we

753
00:41:04,239 --> 00:41:07,680
Speaker 1: have used this as a form of entertainment to see

754
00:41:07,719 --> 00:41:11,200
Speaker 1: if people can be ruined. Yes, consequences should happen, but

755
00:41:11,239 --> 00:41:14,360
Speaker 1: there's this whole new level that that's entertainment now, and

756
00:41:14,400 --> 00:41:16,840
Speaker 1: that needs to be a conversation. Like the whole gist

757
00:41:16,880 --> 00:41:19,560
Speaker 1: of the entire conversation is it becomes a small thing

758
00:41:19,719 --> 00:41:21,880
Speaker 1: that becomes a bigger and bigger and then normalized, and

759
00:41:21,880 --> 00:41:24,719
Speaker 1: then it's like we don't realize what we're doing in

760
00:41:24,760 --> 00:41:27,120
Speaker 1: that we have not only ruined people's life, but probably

761
00:41:27,200 --> 00:41:30,200
Speaker 1: a lot of innocent people's lives and attached to that

762
00:41:30,480 --> 00:41:33,080
Speaker 1: and then you see that Okay, this has been happening,

763
00:41:33,239 --> 00:41:36,000
Speaker 1: but these platforms like TikTok, which is fairly new, are

764
00:41:36,000 --> 00:41:39,920
Speaker 1: still allowing it to happen. How is this not changing exactly?

765
00:41:39,920 --> 00:41:41,839
Speaker 1: And I think we have gotten to a place where

766
00:41:41,840 --> 00:41:45,520
Speaker 1: it's it's normalized as entertainment, and it's it's not even

767
00:41:45,520 --> 00:41:48,480
Speaker 1: I mean, like we're talking about pretty serious things like

768
00:41:48,480 --> 00:41:51,680
Speaker 1: women running for office and being engaged in democratic and

769
00:41:51,719 --> 00:41:55,960
Speaker 1: civic life. Do you remever couch guy TikTok, the guy

770
00:41:56,000 --> 00:41:59,000
Speaker 1: who everybody was so convinced was cheating on his girlfriend

771
00:41:59,000 --> 00:42:01,040
Speaker 1: and it was caught on tick talk because his long

772
00:42:01,080 --> 00:42:04,680
Speaker 1: distance girlfriend films a TikTok of her surprising him and

773
00:42:04,800 --> 00:42:07,719
Speaker 1: his he's not reacting the way that maybe you might

774
00:42:07,760 --> 00:42:12,280
Speaker 1: expect somebody to react, and everybody was like overnight became

775
00:42:12,480 --> 00:42:15,640
Speaker 1: a body language expert, and like I saw people on

776
00:42:15,920 --> 00:42:18,200
Speaker 1: television news. I saw a segment where they had a

777
00:42:18,600 --> 00:42:22,839
Speaker 1: quote body language expert breaking down his body language. These

778
00:42:22,880 --> 00:42:25,759
Speaker 1: are strangers, right, And so I do think we've gotten

779
00:42:25,760 --> 00:42:29,520
Speaker 1: to this place where we are We've gotten really comfortable

780
00:42:30,040 --> 00:42:35,759
Speaker 1: making complete, like assumptions about people we do not know,

781
00:42:36,480 --> 00:42:42,080
Speaker 1: confidently going on on big platforms and airing those assumptions.

782
00:42:42,400 --> 00:42:45,520
Speaker 1: And because of the way that algorithms work, those assumptions

783
00:42:45,560 --> 00:42:49,239
Speaker 1: will get millions of people to watch them and then

784
00:42:49,440 --> 00:42:52,080
Speaker 1: reply with their own assumptions because of the of the

785
00:42:52,120 --> 00:42:55,239
Speaker 1: sort of riff and remix culture of TikTok. Then they'll say, well,

786
00:42:55,239 --> 00:42:56,840
Speaker 1: actually I think he was cheating like this and not

787
00:42:56,920 --> 00:43:00,279
Speaker 1: like that, Like it's not it's not healthy and it's

788
00:43:00,280 --> 00:43:03,200
Speaker 1: not good for our society. Oh, the couch guy was

789
00:43:03,239 --> 00:43:05,640
Speaker 1: such a meme essentially, but you know what, I've seen

790
00:43:05,680 --> 00:43:07,640
Speaker 1: something similar to that just recently on Twitter, and I

791
00:43:08,160 --> 00:43:12,839
Speaker 1: have no opinion. I Jose was like, wow, okay, gad yes, yes,

792
00:43:13,040 --> 00:43:18,920
Speaker 1: garden Lady I was like, wow, that just what just happened. Yeah,

793
00:43:19,080 --> 00:43:20,960
Speaker 1: we did an episode of There Are No Girls on

794
00:43:20,960 --> 00:43:23,680
Speaker 1: the Internet about this. So if you don't know who

795
00:43:23,680 --> 00:43:27,600
Speaker 1: garden Lady is, she is this woman who has a garden.

796
00:43:27,880 --> 00:43:30,839
Speaker 1: And she tweeted something along the lines of life I

797
00:43:30,880 --> 00:43:33,719
Speaker 1: start every morning with taking my coffee out to my

798
00:43:33,719 --> 00:43:36,080
Speaker 1: garden with my husband and if you talk for hours,

799
00:43:36,160 --> 00:43:39,480
Speaker 1: never gets old. Love him, so love love the morning.

800
00:43:39,600 --> 00:43:43,040
Speaker 1: The entire internet was like, boo, we hate it, we

801
00:43:43,120 --> 00:43:47,520
Speaker 1: hate it. Boo tomato, tomato tomato. Right, Like it went

802
00:43:47,600 --> 00:43:50,040
Speaker 1: from like you're being ablest to be and you're being

803
00:43:50,120 --> 00:43:52,600
Speaker 1: classes and there are things I'm like, I don't know

804
00:43:52,640 --> 00:43:54,719
Speaker 1: anything about this woman and then someone went into a

805
00:43:54,800 --> 00:43:57,799
Speaker 1: deep dive into her account and said, oh, she's an

806
00:43:57,840 --> 00:44:01,200
Speaker 1: awful person. Keep attacking her. And I was just what

807
00:44:01,200 --> 00:44:04,799
Speaker 1: what just happened? Yeah? And I so that that was

808
00:44:04,840 --> 00:44:06,320
Speaker 1: so interesting to me, and I feel like it's a

809
00:44:06,360 --> 00:44:11,400
Speaker 1: great example of I love social media and the Internet.

810
00:44:11,719 --> 00:44:14,240
Speaker 1: I don't think millions of people were meant to weigh

811
00:44:14,280 --> 00:44:17,840
Speaker 1: in on the morning routine of a stranger in this way.

812
00:44:18,239 --> 00:44:20,360
Speaker 1: Like I saw the same thing where people were like, actually,

813
00:44:20,440 --> 00:44:22,960
Speaker 1: she's anti vax and it's like, well, I don't think

814
00:44:22,960 --> 00:44:25,600
Speaker 1: the people who are attacking her are attacking her because

815
00:44:25,640 --> 00:44:28,520
Speaker 1: she They knew that, like she maybe posts. I mean,

816
00:44:28,560 --> 00:44:30,759
Speaker 1: I don't even I can't, you know, confirm or deny that.

817
00:44:30,800 --> 00:44:32,239
Speaker 1: But I was like, I don't know that that's why

818
00:44:32,320 --> 00:44:36,560
Speaker 1: people are attacking her, And you know, I saw probably

819
00:44:36,560 --> 00:44:40,040
Speaker 1: the wildest response to that tweet I saw was someone

820
00:44:40,120 --> 00:44:42,960
Speaker 1: being like, oh, don't you work, And she was like, Oh,

821
00:44:43,080 --> 00:44:45,440
Speaker 1: I own my own business, so I'm able to have

822
00:44:45,560 --> 00:44:49,319
Speaker 1: flexibility in the mornings. And then somebody else replied, oh,

823
00:44:49,360 --> 00:44:52,399
Speaker 1: so you're exploiting the labor of your employees and that's

824
00:44:52,400 --> 00:44:54,880
Speaker 1: why you're able to have these nice mornings terrible and

825
00:44:54,960 --> 00:44:57,840
Speaker 1: she was like, I'm the sole employee of my small business,

826
00:44:58,040 --> 00:45:00,440
Speaker 1: and so just like layers and layers of errors of

827
00:45:00,520 --> 00:45:03,680
Speaker 1: assumed bull about something they don't even know. But that's

828
00:45:03,680 --> 00:45:06,360
Speaker 1: the thing is, like it becomes in this really really

829
00:45:06,400 --> 00:45:10,400
Speaker 1: tempestuous like grounds of like, Okay, her doing a noculous

830
00:45:10,400 --> 00:45:13,480
Speaker 1: statement about how she loved this moment with her husband

831
00:45:13,719 --> 00:45:17,080
Speaker 1: has made it. Some people have weaponized it against her,

832
00:45:17,200 --> 00:45:20,400
Speaker 1: telling her she's all these things and they're like, what,

833
00:45:20,640 --> 00:45:24,440
Speaker 1: how did we get here? Why are we here? And

834
00:45:24,560 --> 00:45:29,000
Speaker 1: what is this platform? Because like like what is this platform?

835
00:45:29,160 --> 00:45:31,239
Speaker 1: Like why am I being told that I need to

836
00:45:31,280 --> 00:45:33,960
Speaker 1: care about this stranger? Why why is it start being

837
00:45:33,960 --> 00:45:36,840
Speaker 1: surfaced to me? What is going on that this is

838
00:45:36,880 --> 00:45:38,880
Speaker 1: even something that I am aware of, like the morning

839
00:45:38,960 --> 00:45:41,959
Speaker 1: routine of this person that I will probably never even meet. Yeah,

840
00:45:42,560 --> 00:45:44,759
Speaker 1: I feel like so many things. One of my big

841
00:45:44,800 --> 00:45:47,960
Speaker 1: concerns about social media and the Internet now is you know,

842
00:45:48,040 --> 00:45:51,640
Speaker 1: like internet literacy context. I feel like we've thrown contexts

843
00:45:51,640 --> 00:45:54,840
Speaker 1: out the window, but also just that kind of idea

844
00:45:55,840 --> 00:46:01,200
Speaker 1: where or this is a lighter example but of this, Oh,

845
00:46:01,239 --> 00:46:03,080
Speaker 1: I've got to have an opinion, I've gotta have a stance,

846
00:46:03,080 --> 00:46:05,080
Speaker 1: and I'm gonna say something and then you like, don't

847
00:46:05,080 --> 00:46:07,880
Speaker 1: do any other research into it, and then when you

848
00:46:07,920 --> 00:46:12,520
Speaker 1: add in like misinformation and disinformation, it just frightens me

849
00:46:12,560 --> 00:46:15,319
Speaker 1: to be honest, Like, I keep seeing all these stories about, oh,

850
00:46:15,440 --> 00:46:19,319
Speaker 1: this image was fake, and I haven't even seen the

851
00:46:19,360 --> 00:46:21,040
Speaker 1: image in question, but I know that a lot of

852
00:46:21,040 --> 00:46:23,520
Speaker 1: people saw it and they believed it, and like that

853
00:46:24,480 --> 00:46:29,880
Speaker 1: it gets my heart running for guess same. Oh my gosh.

854
00:46:30,200 --> 00:46:33,000
Speaker 1: I really resonate with your point about having to have

855
00:46:33,120 --> 00:46:35,720
Speaker 1: a take. I feel like so the way that social

856
00:46:35,760 --> 00:46:40,040
Speaker 1: media functions, both the algorithmic nature of social media platforms

857
00:46:40,040 --> 00:46:42,719
Speaker 1: and the speed by which social media platforms tend to move,

858
00:46:43,160 --> 00:46:46,560
Speaker 1: I think it incentivizes people to feel like they have

859
00:46:46,640 --> 00:46:48,239
Speaker 1: to have a take and they have to have it

860
00:46:48,320 --> 00:46:52,160
Speaker 1: right away. In reality, it's okay to not have a take.

861
00:46:52,200 --> 00:46:54,640
Speaker 1: It's okay to to be like, I don't really know

862
00:46:54,680 --> 00:46:57,800
Speaker 1: anything about this. Me putting my opinion or my voice

863
00:46:57,800 --> 00:46:59,359
Speaker 1: out there on an issue that I don't know about

864
00:46:59,800 --> 00:47:01,359
Speaker 1: is going to be helpful. It's not going to add

865
00:47:01,360 --> 00:47:03,839
Speaker 1: to the conversation. I'll just listen, right. I don't think

866
00:47:03,880 --> 00:47:07,759
Speaker 1: that social media platforms incentivize just taking a step back

867
00:47:07,840 --> 00:47:10,120
Speaker 1: or even just taking a minute to figure out what

868
00:47:10,120 --> 00:47:12,080
Speaker 1: you want to say. It's like it has to be quick,

869
00:47:12,120 --> 00:47:14,960
Speaker 1: has to be now, you have to reply, get that engagement,

870
00:47:15,280 --> 00:47:18,440
Speaker 1: and that doesn't serve anybody. I don't think that social

871
00:47:18,440 --> 00:47:24,080
Speaker 1: media platforms incentivize us to be thoughtful or you know,

872
00:47:24,840 --> 00:47:28,440
Speaker 1: our best there. I think there was like, for instance,

873
00:47:28,480 --> 00:47:31,279
Speaker 1: when the Queen died. I don't really know anything about

874
00:47:31,280 --> 00:47:33,200
Speaker 1: the monarchy. I don't really know what I don't really

875
00:47:33,520 --> 00:47:35,839
Speaker 1: I'm not invested in it, and so I'm not someone

876
00:47:35,880 --> 00:47:37,879
Speaker 1: that you would that you need to hear from on it. Right,

877
00:47:37,920 --> 00:47:40,120
Speaker 1: Like I was like, there are people who are very

878
00:47:40,160 --> 00:47:43,360
Speaker 1: invested in this. Let them have the spotlight of like

879
00:47:43,400 --> 00:47:45,560
Speaker 1: saying what they want to say. I think that it's

880
00:47:45,560 --> 00:47:48,239
Speaker 1: okay to not weigh in. It's okay to not have

881
00:47:48,280 --> 00:47:51,160
Speaker 1: a hot take. I don't think social media platforms incentivize

882
00:47:51,239 --> 00:47:54,960
Speaker 1: or encourage us to just listen sometimes. Yeah, And I

883
00:47:54,960 --> 00:47:57,719
Speaker 1: think we've seen that seep into our daily lives and

884
00:47:57,760 --> 00:48:00,160
Speaker 1: our politics, because I just see that with politicians all

885
00:48:00,160 --> 00:48:02,919
Speaker 1: the time, and then that muddies the whole conversation because

886
00:48:02,920 --> 00:48:04,520
Speaker 1: if you have this I don't know anything about this

887
00:48:04,520 --> 00:48:07,160
Speaker 1: garden lady, but if you have this whole conversation happening.

888
00:48:07,160 --> 00:48:10,799
Speaker 1: It's distracting from the very real context and information we

889
00:48:10,880 --> 00:48:18,640
Speaker 1: have about politicians that they're doing these things. So I

890
00:48:18,640 --> 00:48:22,239
Speaker 1: I think, I know that this podcast you have coming

891
00:48:22,239 --> 00:48:26,680
Speaker 1: out is so needed, um, and I'm so so excited

892
00:48:27,120 --> 00:48:29,960
Speaker 1: to hear it. Um. If you can tell us more

893
00:48:29,960 --> 00:48:33,640
Speaker 1: about it. Yeah, I mean, I feel like I've got

894
00:48:34,560 --> 00:48:40,160
Speaker 1: you all, got me worked out obviously. So I think

895
00:48:40,320 --> 00:48:42,399
Speaker 1: I think it does all relate. And I think that

896
00:48:42,600 --> 00:48:45,160
Speaker 1: what we're seeing now is these tried and true tactics

897
00:48:45,239 --> 00:48:49,440
Speaker 1: of online harassment becoming an animating and normalized feature of

898
00:48:49,440 --> 00:48:53,279
Speaker 1: our political landscape and discourse. And you know, there's this

899
00:48:53,280 --> 00:48:56,239
Speaker 1: phrase that black women are quote canaries in the cole

900
00:48:56,239 --> 00:48:58,960
Speaker 1: mine for online harms, because first it happens to us,

901
00:48:59,000 --> 00:49:01,799
Speaker 1: and then it happens to everyone, and bad actors are

902
00:49:01,840 --> 00:49:05,080
Speaker 1: just continuing to use these same tactics they were able

903
00:49:05,120 --> 00:49:08,480
Speaker 1: to perfect on black women on others. And so I

904
00:49:08,520 --> 00:49:11,520
Speaker 1: think that we need to have leadership, people with power

905
00:49:11,600 --> 00:49:15,720
Speaker 1: at you know, elected officials, policymakers and tech companies listening

906
00:49:15,800 --> 00:49:18,440
Speaker 1: to black women so that we can actually get a

907
00:49:18,560 --> 00:49:21,600
Speaker 1: handle on this. And you know, as we were sort

908
00:49:21,600 --> 00:49:23,719
Speaker 1: of talking about, I think that our Internet and our

909
00:49:23,719 --> 00:49:27,640
Speaker 1: social media platforms have become so weaponized. You know, it's

910
00:49:27,680 --> 00:49:31,240
Speaker 1: nearly impossible to have a meaningful conversation or meaningful discourse

911
00:49:31,760 --> 00:49:36,040
Speaker 1: on our communication platforms because the leaders who run those

912
00:49:36,040 --> 00:49:41,239
Speaker 1: platforms have basically incentivized and amplified lies and grifts and

913
00:49:41,320 --> 00:49:45,120
Speaker 1: scams and extremism were more polarized than ever. And grifters

914
00:49:45,640 --> 00:49:48,480
Speaker 1: know this and are basically running the show. And I

915
00:49:48,520 --> 00:49:50,880
Speaker 1: think if we're ever planning on getting a handle on this,

916
00:49:50,960 --> 00:49:53,560
Speaker 1: if we're ever planning on doing something about this, the

917
00:49:53,680 --> 00:49:57,279
Speaker 1: first step has to be talking honestly about it. And

918
00:49:57,280 --> 00:49:59,799
Speaker 1: so that's why I'm doing the podcast with cool Zone

919
00:49:59,840 --> 00:50:03,279
Speaker 1: Media called Internet Hate Machine, Uh, And I really want

920
00:50:03,280 --> 00:50:06,400
Speaker 1: to chart the history of online harassment against women have

921
00:50:06,400 --> 00:50:09,400
Speaker 1: called are specifically, and how it has led us to

922
00:50:09,480 --> 00:50:13,600
Speaker 1: this current political and social movement. And so I hope

923
00:50:13,680 --> 00:50:15,799
Speaker 1: that folks will listen. It's something that I think is

924
00:50:15,800 --> 00:50:18,000
Speaker 1: really timely right now as we go into mid terms.

925
00:50:18,239 --> 00:50:20,080
Speaker 1: I think it will be timely for a long time

926
00:50:20,120 --> 00:50:23,520
Speaker 1: as we determine what we want our political and social

927
00:50:23,600 --> 00:50:26,319
Speaker 1: landscape to look like. Right and it's important. I'm so

928
00:50:26,360 --> 00:50:28,839
Speaker 1: excited because it definitely needs to be the bigger part

929
00:50:28,840 --> 00:50:31,600
Speaker 1: of the conversation as we look at what our democracy

930
00:50:31,640 --> 00:50:34,919
Speaker 1: looks like in the future, in the near future, let's

931
00:50:35,040 --> 00:50:38,480
Speaker 1: let's say, um but not just for the United States,

932
00:50:38,480 --> 00:50:40,480
Speaker 1: but all around the world, because we were seeing similar

933
00:50:40,520 --> 00:50:43,759
Speaker 1: things happen on different timelines. And I feel like you're

934
00:50:43,760 --> 00:50:45,879
Speaker 1: going to be able to bring in such good perspective

935
00:50:46,200 --> 00:50:48,520
Speaker 1: and so showing the present, the past, and possibly the

936
00:50:48,600 --> 00:50:50,440
Speaker 1: future with all of this. So thank you and I'm

937
00:50:50,480 --> 00:50:53,439
Speaker 1: excited to listen, especially that first one. Yeah, please check

938
00:50:53,440 --> 00:50:56,640
Speaker 1: it out. Our trailer and episode zero are already lives.

939
00:50:56,719 --> 00:50:59,120
Speaker 1: You can go to Internet Hate Machine on wherever you

940
00:50:59,160 --> 00:51:02,279
Speaker 1: find your podcast, Spotify, Apple Podcasts. You know how to

941
00:51:02,280 --> 00:51:03,920
Speaker 1: find your listen. This is a podcast, so you know

942
00:51:04,040 --> 00:51:06,319
Speaker 1: how to find podcasts if you're listening to this um So.

943
00:51:06,680 --> 00:51:09,680
Speaker 1: Episode zero is already live, but our first official episode

944
00:51:09,680 --> 00:51:13,200
Speaker 1: will drop on November second, So please check it out. Yes,

945
00:51:13,520 --> 00:51:17,120
Speaker 1: go subscribe, please support it. Um it's amazing work. Can't

946
00:51:17,160 --> 00:51:19,600
Speaker 1: wait to hear it. Where else can the listeners find you?

947
00:51:19,640 --> 00:51:23,240
Speaker 1: Bridget Well, you can find me on my weekly podcast

948
00:51:23,280 --> 00:51:25,560
Speaker 1: about women in the Internet called There Are No Girls

949
00:51:25,560 --> 00:51:28,160
Speaker 1: on the Internet. You can definitely find that if you're

950
00:51:28,160 --> 00:51:30,799
Speaker 1: listening to this podcast. Um, that's not going anywhere, so

951
00:51:30,880 --> 00:51:32,560
Speaker 1: we can listen to them both. If you can find

952
00:51:32,600 --> 00:51:35,839
Speaker 1: me on Instagram at Bridget Marian d C or yes,

953
00:51:35,920 --> 00:51:38,359
Speaker 1: I'm still gonna be on Twitter elon must be damn.

954
00:51:38,560 --> 00:51:40,440
Speaker 1: You can find me on You can find me on

955
00:51:40,480 --> 00:51:44,520
Speaker 1: Twitter at Bridget Murray. Yes um, and is always such

956
00:51:44,560 --> 00:51:48,600
Speaker 1: a delight to have you, Bridget. Wishing everybody you and

957
00:51:48,680 --> 00:51:51,640
Speaker 1: everybody all the best as we enter into this final

958
00:51:51,719 --> 00:51:55,399
Speaker 1: stretch of the mid terms. And also listeners you might

959
00:51:55,400 --> 00:52:01,040
Speaker 1: have heard of cameo from Bridget's cat which was an

960
00:52:02,800 --> 00:52:07,120
Speaker 1: hear it we needed it annoyed with me even though

961
00:52:07,280 --> 00:52:09,480
Speaker 1: she doesn't have a job and like has everything and

962
00:52:09,520 --> 00:52:12,359
Speaker 1: has treated with my queen. I'm sure I would be like,

963
00:52:12,440 --> 00:52:19,040
Speaker 1: oh I have a job. Yes, Um, I cannot wait

964
00:52:19,160 --> 00:52:21,600
Speaker 1: till next time. Thank you so much again, Bridget. If

965
00:52:21,640 --> 00:52:23,560
Speaker 1: you would like to find us that you can. You

966
00:52:23,560 --> 00:52:25,799
Speaker 1: can email us at Stuff Media, Mom, Stuff at iHeart Media.

967
00:52:25,840 --> 00:52:27,480
Speaker 1: You can find us on Twitter at most of podcast

968
00:52:27,600 --> 00:52:29,400
Speaker 1: or Instagram and stuff I Never told you. Thanks as

969
00:52:29,400 --> 00:52:32,479
Speaker 1: always our super producer Chris Duda, Thank you Christina. Thanks

970
00:52:32,480 --> 00:52:34,400
Speaker 1: to you for listening stuff and till the production of

971
00:52:34,440 --> 00:52:36,120
Speaker 1: I heart Radio for a podcast. For my heart radio,

972
00:52:36,160 --> 00:52:37,920
Speaker 1: you can check out the heart radio app Apple podcast

973
00:52:37,960 --> 00:52:50,960
Speaker 1: wherever you listen to your favorite ships. So if you've

974
00:52:51,000 --> 00:52:53,040
Speaker 1: been listening to their No Girls on the Internet for

975
00:52:53,040 --> 00:52:55,600
Speaker 1: a while, you probably know that I was lucky enough

976
00:52:55,640 --> 00:52:58,720
Speaker 1: to work with the amazing team at Mozilla, the makers

977
00:52:58,719 --> 00:53:02,279
Speaker 1: of Firefox, US a podcast called I r L where

978
00:53:02,280 --> 00:53:06,759
Speaker 1: we explored the promise in perils of artificial intelligence. It

979
00:53:06,840 --> 00:53:09,759
Speaker 1: was a dream for me. And guess what, It's been

980
00:53:09,760 --> 00:53:13,319
Speaker 1: nominated for a Shorty Award and I really need your help.

981
00:53:13,520 --> 00:53:15,600
Speaker 1: Can you take a minute and vote for I r

982
00:53:15,800 --> 00:53:19,040
Speaker 1: L to win Best Use of a Podcast. It's super quick,

983
00:53:19,080 --> 00:53:22,160
Speaker 1: I promised. Just go to Tangoti dot com slash I

984
00:53:22,480 --> 00:53:25,360
Speaker 1: r L. You can vote every day, and y'all, I

985
00:53:25,360 --> 00:53:27,200
Speaker 1: don't know if this sounds bad to say, but I

986
00:53:27,280 --> 00:53:30,959
Speaker 1: kind of really want to win. So please vote. That's

987
00:53:31,000 --> 00:53:34,200
Speaker 1: Tango d T A n G O t I dot

988
00:53:34,200 --> 00:53:37,480
Speaker 1: com slash I r L and thank you. It means

989
00:53:37,520 --> 00:53:38,239
Speaker 1: so much to me.