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Speaker 1: Hey, this is Annie and Samantha and welcome to stuff

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Speaker 1: I've never told your protection of I Heart Radio Today listeners.

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Speaker 1: We are so excited we have such such a great

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Speaker 1: episode for you, and we have such a wonderful guest,

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Speaker 1: Um bridget Todd, past host and host of There Are

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Speaker 1: No Girls on the Internet, the podcast, which you should

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Speaker 1: absolutely check out, is here joining us today. Hello, I'm

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Speaker 1: so excited to be here hang out with you guys

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Speaker 1: during the quarantine. Oh, we're so excited to have you.

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Speaker 1: And we're also excited because this is the first of

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Speaker 1: a regularly occurring segment about women about the Internet. Um,

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Speaker 1: would you like to describe kind of what you're envisioning

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Speaker 1: for this this segment. Yeah, what I'm envisioning is, you know,

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Speaker 1: I'm the kind of person who is online all day

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Speaker 1: every day on Twitter, looking at memes and all of that,

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Speaker 1: and from all of that time, useless time spent online

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Speaker 1: stories about women, about marginalized voices about you know, how

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Speaker 1: we are showing up online always occur, and so I

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Speaker 1: thought the sminty listenership might appreciate going into those rabbit

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Speaker 1: holes with me and talking about some of the ways

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Speaker 1: that our stories show up or don't show up on

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Speaker 1: the internet. Yes, yes, Um, the listeners know. I'm a

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Speaker 1: big fan of a good internet rabbit hole. I like

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Speaker 1: to spotlight a rabbit hole to day because you never

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Speaker 1: know what you'll find. Um. And I've been like racking

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Speaker 1: my brains trying to think of a clever name for

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Speaker 1: this segment. So if you have any ideas on that,

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Speaker 1: Bridget or any listeners have any ideas, just just I'm

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Speaker 1: all ears. I love a good title for things. Yeah,

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Speaker 1: I wanted to. I want to sprow much to the listeners.

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Speaker 1: I bet listeners probably have some good some good ideas.

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Speaker 1: I hadn't thought about that though. Oh yeah, we love

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Speaker 1: good title. Alliteration has even better soteration. Can't be that yes, yes, yes, yes,

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Speaker 1: um And I keep saying segment, which isn't totally accurate.

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Speaker 1: This is like a series, um, and for today as

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Speaker 1: we are in the United States where in this election

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Speaker 1: it feels like season is not accurate to say, but

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Speaker 1: that's what most people call it. Um. We're talking about

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Speaker 1: disinformation and misinformation and election season and women and how

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Speaker 1: all of that interacts correct exactly. Yeah, I was partly

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Speaker 1: inspired by the episode that y'all did on Q and

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Speaker 1: on and women. Um, I think that we often assume

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Speaker 1: that when things happen online, you know, the deep all

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Speaker 1: assumption is that the person online is a white male,

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Speaker 1: and so as we know that is not true, we

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Speaker 1: often kind of leave out this very critical and growing

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Speaker 1: section of people who are having these important experiences online.

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Speaker 1: And so I was really happy to see that y'all

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Speaker 1: spotlighted women and Q and on and that very strange

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Speaker 1: intersection there. Um and yeah, I was just really inspired

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Speaker 1: by the work that y'all have been doing. Thank you. Yeah,

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Speaker 1: that's another interesting aspect too, because, as you say, a

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Speaker 1: lot of the default assumptions are this internet users white male,

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Speaker 1: and even when you think of Q and on, you

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Speaker 1: think white male. Exactly, it is a growing phenomenon that

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Speaker 1: more and more women are becoming involved in Q and

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Speaker 1: on and and being involved online Q and on, which is, yeah,

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Speaker 1: you're not hearing too much about that, but we really

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Speaker 1: should be looking into why that's going on exactly. That's

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Speaker 1: what we were talking about with the major candidate that

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Speaker 1: that's running for the U S. House of Representative here

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Speaker 1: in joy Orgia, Marjorie green Um, who is a Q

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Speaker 1: and non advocate and has used that as part of

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Speaker 1: our platform and is winning and is winning and is

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Speaker 1: most likely going to be elected because she's not being contested.

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Speaker 1: The one man who was contesting her in the Democratic

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Speaker 1: Party dropped out because of unknown threats. So that's says

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Speaker 1: a lot in itself. And that's kind of one of

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Speaker 1: the things that I love that we're talking about this

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Speaker 1: because not only are they playing and I say day

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Speaker 1: as a broader day of Q and on and people

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Speaker 1: of conspiracy theory, which originated as possibly a joke to

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Speaker 1: see what they can fish fish out into this fear

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Speaker 1: of motherhood and children, and it's kind of just opened

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Speaker 1: up this huge world of oh my god, what is

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Speaker 1: happening and why are we being so misled and why

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Speaker 1: does it seem to be specifically targeting women in this misinformation? Yeah,

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Speaker 1: it's it's These are all great points. I think that

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Speaker 1: you know, you brought up this connection between like like

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Speaker 1: Q and on and like fear of motherhood. I think

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Speaker 1: that it really goes down to the fact that women

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Speaker 1: often are marginalized or not supported, they're not heard, and

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Speaker 1: so we have to take these issues that are very

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Speaker 1: real issues in our life to the Internet. And I

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Speaker 1: think that that can make us vulnerable in these like,

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Speaker 1: very specific ways. Combine that with the fact that people

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Speaker 1: assume that Internet users are all men. The way, the

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Speaker 1: very specific ways that women are are being targeted and

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Speaker 1: are vulnerable for this kind of exploitation just go unexamined,

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Speaker 1: untalked about. And I think there's also an assumption that

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Speaker 1: quote unquote women's spaces online, you know, uh, places like Pinterest,

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Speaker 1: or places like Facebook groups for moms, or like anti

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Speaker 1: vax Facebook groups, things like that. There's this assumption that

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Speaker 1: there's nothing worth investigating going on on these groups, but

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Speaker 1: we know that it allows them to be hotbeds for

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Speaker 1: disinformation and misinformation, which is actually really harmful. Yes, um,

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Speaker 1: And actually, before we get you deep into this conversation,

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Speaker 1: would you mind explaining the difference between disinformation and misinformation. Absolutely,

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Speaker 1: it's a question I get a lot. So misinformation is

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Speaker 1: misleading or incorrect information that leads to somebody being misled.

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Speaker 1: They might not actually have you know, bad intent, or

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Speaker 1: they're not. They may not they may not necessarily be

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Speaker 1: like spreading bad or inaccurate information on a purpose, but

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Speaker 1: they are leading somebody to be misled. Disinformation, on the

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Speaker 1: other hand, is nefarious, intentional spread of inaccurate information. And

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Speaker 1: so a good way that you can think about it

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Speaker 1: is misinformation is misleading. Disinformation is a damn lie. Right.

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Speaker 1: This is like someone is lying to you on purpose

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Speaker 1: to cause some kind of problem. Yeah, and we've seen

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Speaker 1: a lot of that unfortunately during this election cycle. Um.

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Speaker 1: And also it's been interesting seeing um kind of social

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Speaker 1: media companies struggling to deal with this, uh and hearing

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Speaker 1: a lot about that in the news of you know,

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Speaker 1: flagging this tweet as false or whatever. Um, what what

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Speaker 1: in your experience as someone who is on the internet

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Speaker 1: a lot, what kind of disinformation strategies are you seeing

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Speaker 1: so many? Right? So a big one that people are

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Speaker 1: probably kind of familiar with is people will impersonate marginalized people,

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Speaker 1: oftentimes black women, uh to just honestly so discord and

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Speaker 1: and and confusion. Right, So that's one, Um, when it

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Speaker 1: comes to women candidates really playing into biases, so you know,

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Speaker 1: spreading falsehoods about women candidates being you know, um, shrill

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Speaker 1: or unstrustworthy or incompetent. We know these things are not

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Speaker 1: grounded in fact, but these are messages that really take

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Speaker 1: off on social media. And I can really have a

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Speaker 1: lot of a lot of a big impact. UM, things

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Speaker 1: like cheap fakes. You know, we people are pretty familiar

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Speaker 1: with deep fakes, these videos that are you know, not

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Speaker 1: real or manipulated in some way, but we also have

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Speaker 1: sheep fakes, where a video is taken out of context

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Speaker 1: and spread on social media, you know, saying as if like, oh,

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Speaker 1: so and so I said this thing, but actually it's

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Speaker 1: a it's an out of contact snippets. That's a cheap fake. Um.

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Speaker 1: But all of these different ways that are really meant

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Speaker 1: to create confusion in voters and really meant to have people,

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Speaker 1: particularly women and people of color, just think, well, the

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Speaker 1: system is rigged. The system is so confusing, it's so

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Speaker 1: chaotic that I'm just going to check out of the

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Speaker 1: democratic process altogether. It's not worth it, right Yeah. Um.

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Speaker 1: And you know, uh, we've talked a lot about um

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Speaker 1: on this show. Those words that you kind of use

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Speaker 1: that we see leveled at female politicians, and how ambition

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Speaker 1: is like my favorite one, UM, where like a man

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Speaker 1: being ambitious is great, but a woman being ambitious Wait

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Speaker 1: a minute now, um, And then I remember the episode

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Speaker 1: you and I did bridget where about reporters in India

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Speaker 1: getting slut shamed and just like waking up to an

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Speaker 1: inbox full of these very disturbing images of of your

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Speaker 1: face on some other woman's body and some like horrific

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Speaker 1: act or like violence being perpetrated against you, and sharing

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Speaker 1: those images with your family, with the world, with everybody,

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Speaker 1: and just how like even me thinking about that, it

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Speaker 1: makes my just tense up, like that's it's so awful,

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Speaker 1: And um, I was just wondering if you you could

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Speaker 1: speak to sort of this gendered attack, engendered disinformation. Absolutely.

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Speaker 1: There was actually a recent study by Lucida di Meco,

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Speaker 1: who was a senior expert and advocate and a writer

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Speaker 1: around women's leadership in gender equality. The study is called

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Speaker 1: She Persisted, and it really shows the ways that information

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Speaker 1: is so gendered. And so that's one of the reasons

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Speaker 1: why it's so important to talk about it through this lens.

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Speaker 1: You know, social media can really be a double edged

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Speaker 1: swords or women in politics. On the one hand, it

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Speaker 1: really allows women to have this platform to cut through

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Speaker 1: chatter and have direct communication with the public, but also

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Speaker 1: that is where we get these incredibly biased, gendered, sexist attacks.

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Speaker 1: So the thing that you talked about with the journalists

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Speaker 1: in India sensitive images, whether they're like sometimes doctored, sometimes

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Speaker 1: not real. That is a common occurrence for women running

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Speaker 1: for office or women in politics in general. And I

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Speaker 1: would even taken a step further and say women in public,

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Speaker 1: women who speak their mind or have an opinion in

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Speaker 1: the public, right, Like, there is nothing people love to

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Speaker 1: pile on more than a woman who has an opinion

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Speaker 1: in public. And oftentimes the way that these attacks are

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Speaker 1: framed are very gendered. You know, research actually shows that men,

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Speaker 1: even men of color, when compared to their female colleagues

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Speaker 1: of color, don't get the same kind of attacks, right,

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Speaker 1: And so they're highly racialized and highly gendered, right. And

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Speaker 1: I think it's really interesting when we talk about women

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Speaker 1: of color being really ridiculed when we look at Kamala

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Speaker 1: Harris and her even debate style, the fact that the

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Speaker 1: matter was that she was holding back, but even with

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Speaker 1: our holding back and making a few faces still made

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Speaker 1: her a target as being overly uh disingenuous, untrustworthy and

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Speaker 1: according to Trump, a monster like without how do we

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Speaker 1: actually talk about this kind of level of misogyny slashed racism,

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Speaker 1: especially when we're in this type of debate was especially

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Speaker 1: in this type of election. Well, how do we address that,

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Speaker 1: how do we make sure we call it out as

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Speaker 1: we see it? Well, that's a great question. Um. One

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Speaker 1: of the organizations I work with, Ultra Violet, actually created

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Speaker 1: a media guide. UM. You can find it if you

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Speaker 1: just google Ultra Violet Media Guide. I'm sure you can

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Speaker 1: find it, UM to help us spot and talk about

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Speaker 1: these incredibly racialized gendered attacks on not just Kamala Harris,

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Speaker 1: all women of color. Right, they could often be very subtle,

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Speaker 1: things like calling Kamala Harris quote aggressive. We know that's

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Speaker 1: a dog whistle for Black women who speak their mind

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Speaker 1: are very assertive in their speech style. So sometimes they

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Speaker 1: can be really subtle. Sometimes they can be really obvious

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Speaker 1: and outright, like Trump calling Kamala Harris a monster, you

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Speaker 1: know after her debate style. We know that these are

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Speaker 1: just you know, especially when you think about Trump, he

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Speaker 1: has a pattern of lashing out at black women when

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Speaker 1: he feels attacked, and so this is nothing new from him.

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Speaker 1: But I also think the media really plays a role

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Speaker 1: in shaping how we talk about and think about these

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Speaker 1: kinds of sexist gender attacks. And so one of the

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Speaker 1: things that we've really been working on with Ultra Violet

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Speaker 1: is trying to have a little bit of a media

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Speaker 1: accountability and say, you know, if Trump says something that

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Speaker 1: is a racist sexist attack on a black woman in politics,

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Speaker 1: don't put don't repeat that in the headline so that

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Speaker 1: it goes all over social because evidence shows that the

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Speaker 1: more people see something, even if it's not true, they

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Speaker 1: believe it. And so if Trump perpetuates a racist sexist

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Speaker 1: attack on a black woman, if a news article puts

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Speaker 1: that attack in the headline and it just goes all

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Speaker 1: over social media, people will start to believe that, right

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Speaker 1: even if we all know it's it's b s. And

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Speaker 1: so really making sure that we understand and know how

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Speaker 1: to spot these attacks and that we're able to hold

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Speaker 1: the media accountable when they, you know, unwittingly perhaps you know,

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Speaker 1: perpetuate it. Yeah. Yeah, And I know last time you

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Speaker 1: were here, um, you you spoke about how in Umen

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Speaker 1: there there were these Russian actors. There was a Senate

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Speaker 1: inquiry that found that these Russian actors were behind a

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Speaker 1: lot of um disinformation attacks that were specifically targeting black people,

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Speaker 1: um to keep them from voting in ten And is

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Speaker 1: that our our foreign countries normally behind disinformation. That's a

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Speaker 1: great question. So in that case in sixteen, you know,

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Speaker 1: the Senate inquiry did confirm that these were likely Russian assets.

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Speaker 1: But I almost find that focusing on foreign entities and

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Speaker 1: foreign and bad actors makes us let our guard down

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Speaker 1: for the reality, which is that a lot of these

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Speaker 1: attacks and campaigns are actually domestic. They're they're you know,

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Speaker 1: homegrown attacks on women and people of color, and so

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Speaker 1: I think it's really important that we say, like, yes,

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Speaker 1: we have documented evidence that these things happen in but

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Speaker 1: also in it's not just foreign actors, it's people right

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Speaker 1: here in the United States who are trying to so

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Speaker 1: chaos and distrust right And I've noticed the surge of

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Speaker 1: accounts coming up with stock photos of black men or

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Speaker 1: black people who are saying and it's all the same

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Speaker 1: tweet essentially saying that they were no longer going to

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Speaker 1: be voting for Democrats because they're longal trust the system

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Speaker 1: and now we're going to be Republicans and have for

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Speaker 1: the first time, you know, voted as Republican. What is

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Speaker 1: the surge and is this kind of like the lazier

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Speaker 1: form of the boy because it's obvious stock photos when

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Speaker 1: you start like looking at it, but then it's kind

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Speaker 1: of like, wait, why why even put that effort. Yeah,

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Speaker 1: I mean it's almost kind of funny when you look

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Speaker 1: at it. I actually did an episode with this woman,

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Speaker 1: Shafika Hudson, who you know, she started the hashtag your

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Speaker 1: slip is showing to call out some of these fake

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Speaker 1: accounts posing as black people. And what's funny is that

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Speaker 1: when you actually at first glance, they're like, oh, this

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Speaker 1: is a suspiciously good looking black person who is leaving

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Speaker 1: the Democratic Party after years, Okay, But then when you

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Speaker 1: actually keep looking, you think like, oh, well, this the

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Speaker 1: way that he's phrase this sounds like this other tweet

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Speaker 1: I saw, or um, oh, the way that he's using

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Speaker 1: you know, he's the way that he's using like African

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Speaker 1: American vernacular English just seems a little bit off. Honestly,

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Speaker 1: if you if you dig into them and like look

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Speaker 1: at them enough, I think that they have these little tells.

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Speaker 1: But yeah, I think, you know, creating this impression that

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Speaker 1: there is a mass exodus of black people from the

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Speaker 1: Democratic Party by using these fake accounts. I could see

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Speaker 1: how it would, you know, just be so confusing and

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Speaker 1: like I means, like you see these tweets and you're like,

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Speaker 1: what's going on? I could spec the ultimate goal is

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Speaker 1: to get people to just be like, the political process

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Speaker 1: doesn't make sense to me. It's so confusing. I'm just

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Speaker 1: like not even gonna be involved. And I can see

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Speaker 1: how that would actually, um, you know, lead to that.

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Speaker 1: What's funny is that in somebody sent me a link

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Speaker 1: to a Twitter account that used my picture from LinkedIn,

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Speaker 1: like an old head shot, and this person was saying

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Speaker 1: all of these very strange things about Hillary Clinton, and

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Speaker 1: you know, I I flagged the account and it was

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Speaker 1: taken down, but I never quite figured out what was

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Speaker 1: going on. It just was a very weird situation. And

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Speaker 1: this was way before we knew or I knew any

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Speaker 1: of this, and you know, I never I was just like,

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Speaker 1: what is this? Who would who would take my LinkedIn picture,

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Speaker 1: go through the trouble of making a Twitter account and

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Speaker 1: then have these like incredibly like I don't know how

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Speaker 1: to put it, but incredibly fake bad like obviously a

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Speaker 1: non black person trying to pretend to be a black

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Speaker 1: woman kind of tweets Like who would do this? Now?

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Speaker 1: I know? Right? Well? And on top of that, as

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Speaker 1: we were reading, we know um Candice Owens, who has

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Speaker 1: been a big proponent for Trump talking about black people

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Speaker 1: for Trump, she actually created a blexit event Like that

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Speaker 1: feels like a whole new level of trolling. And on

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Speaker 1: top of that, admitting like allegedly paying a few of

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Speaker 1: the attendees to even come to this event. What kind

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Speaker 1: of level is that? And I have to say, you know,

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Speaker 1: I live in Washington, d C. This happened, This event

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Speaker 1: happened at the White House. I was so saying, like

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Speaker 1: all the things you just said, yes, yes, yes, But

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Speaker 1: also I was so you a raised but not that

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Speaker 1: that was happening, because I'm thinking, you know, there's still

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Speaker 1: a pandemic. You have a group of black people who

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Speaker 1: are you know, disproportionately impacted by COVID. You group him

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Speaker 1: on the White House lawn. Who knows maths? Who knows? Right? Like?

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Speaker 1: Who knows? I was furious to see that. Literally, I

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Speaker 1: remember thinking, from what I understand, people got their lodging

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Speaker 1: and meals paid for by attending the event. The idea

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Speaker 1: of these people being like unleashed on my city and

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Speaker 1: going into restaurants, I was. I was furious, curious, as

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Speaker 1: you should be, because us irritating as well. But speaking

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Speaker 1: of Candis Owens, I didn't want to kind of talk

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Speaker 1: about her whole play because it again coming back to

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Speaker 1: Kamala Harris and just anyone in person of color in

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Speaker 1: general coming out attacking Kamala for not being black enough,

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Speaker 1: not being Indian enough, not being able to qualify as

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Speaker 1: this type of person and able to speak and say yes,

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Speaker 1: I am a person who was making kind of headway

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Speaker 1: and trying to in leadership. Why do you think that

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Speaker 1: this type of rhetoric is not only harmful but can

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Speaker 1: work in some cases in deterring people to vote for her. Well,

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Speaker 1: I think that we just really don't understand how to

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Speaker 1: talk about race and ethnicity and our heritages. I really

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Speaker 1: don't like Candice Owen, but you know, she tweeted, I

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Speaker 1: am so excited that we get to watch Kamala Harris,

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Speaker 1: who swore into Congress as a quote Indian American, now

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Speaker 1: plays a black woman card all the way to November. Right,

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Speaker 1: Like I have to say, if you were someone who

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Speaker 1: was hell bent on misrepresenting people's identity to to weaponize

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Speaker 1: it against them, that's effective, right, And so I think

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Speaker 1: that she really I think that people like Owen really

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Speaker 1: play into the fact that people don't understand you know,

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Speaker 1: people's identities in this country. The idea that Kamala Harris,

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Speaker 1: you know, her identity can contain multiple things, that's how

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Speaker 1: identities work. We don't have a lot of I think

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Speaker 1: that people don't really necessarily understand how to talk about that,

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Speaker 1: and so it leaves the issue very vulnerable to be

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Speaker 1: exploited by people who you know, want to weaponize someone's

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Speaker 1: actually like someone's identity, like who they are. You know,

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Speaker 1: Kamala Harris has been even before she was in the

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Speaker 1: National spotlight, has been so clear about the ways her

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Speaker 1: Indian American heritage and her you know, African American heritage

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Speaker 1: have really made her the person that she is, you know,

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Speaker 1: grounded her in her identity. And that's so beautiful and

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Speaker 1: so rich, and honestly, it's such an exuppression of I

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Speaker 1: would say, the platonic ideal of America that someone could

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Speaker 1: have different identities and those identities could enrich them and

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Speaker 1: become part of who they are and their legacy. To

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Speaker 1: tear that down and then try to reframe that to

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Speaker 1: weaponize it against somebody I think is so like vile,

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Speaker 1: but Unfortunately it's effective. Mhmm. Yeah. And and speaking of

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Speaker 1: Kamala Harris, Um, I I know that when forever ago, um,

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Speaker 1: we saw these attacks on Hillary Clinton. Um, I remember being,

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Speaker 1: oh my gosh, back when you could go out and

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Speaker 1: do things. I was at a rally and somebody had

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Speaker 1: a sign for Hillary Clinton and this was a college

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Speaker 1: in Atlanta that said, like, go back to the kitchen. Um.

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Speaker 1: So how would you say, like the difference, what differences

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Speaker 1: do you see in these attacks on Hillary Clinton and

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Speaker 1: the attacks on Kamala Harris. Yeah, I would say, you know,

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Speaker 1: I remember those attacks on on Senator Clinton. You know,

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Speaker 1: oh gosh, I mean it seems like forever ago now.

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Speaker 1: But you know, um that so many gendered attacks, Like

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Speaker 1: do you remember when she had pneumonia and people acted

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Speaker 1: like she was you know one ft in the Graine

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Speaker 1: she needed to quit immediately, quit campaigning, quit everything because

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Speaker 1: she was about to die exactly. I mean, yeah, people,

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Speaker 1: it's just so simple. People just don't like women, right,

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Speaker 1: And so definitely she we all saw it. She had

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Speaker 1: these incredibly gendered attacks on her character. But I think

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Speaker 1: when it comes to you know, Kamala Harris, I think

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Speaker 1: whenever you occupy intersecting identities. So if you're a woman

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Speaker 1: and you're also black or you know, whatever intersection intersecting

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Speaker 1: identities you have, that always makes attacks more salient, right,

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Speaker 1: because people are exploiting your identity to to create this

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Speaker 1: sort of layered attack. And so she's not just being

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Speaker 1: attacked because she's a person of color. She's not just

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Speaker 1: being attacked because she's a woman. She's being attacked because

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Speaker 1: she's a woman of color. And it has the added

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Speaker 1: problem of the fact that if we don't really know

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Speaker 1: how to talk about race, and so these attacks often

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Speaker 1: go overlooked. It's almost kind of like a gas lighting, right,

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Speaker 1: that she could be the recipient of these gendered racist attacks,

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Speaker 1: but that because we don't know how to talk about race,

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Speaker 1: we would pretend that they're not happening right where we're

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Speaker 1: not talking about them. And so I think that that

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Speaker 1: it's always going to be you know, it's hard for women,

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Speaker 1: but I think that for women of color it will

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Speaker 1: always be disproportionately more difficult. Yeah, And I it's strange

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Speaker 1: because seen does feel so long ago, but it also

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Speaker 1: feels like the specter that's just like hanging over and

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Speaker 1: it's I've seen arguments still to this day of people

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Speaker 1: being so hurt and understandably so from that election. Um.

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Speaker 1: And and that was, at least in my case, one

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Speaker 1: of the first instances I got. I was familiar with

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Speaker 1: disinformation and especially on on online social media. Um. What

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Speaker 1: as we are entering people are voting now I voted, Um,

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Speaker 1: what strategies? How do you think it will be different?

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Speaker 1: How do you think this information will be different in

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Speaker 1: or what what you think what do you expect? Um

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Speaker 1: is going to happen in the realm of disinformation and

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Speaker 1: this election? And can I add to this too, because

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Speaker 1: now we're also seeing not only disinformation from outside people,

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Speaker 1: we're seeing it directly from the source of our president

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Speaker 1: at this point in time, that he's retweeting things in disinformation,

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Speaker 1: real disinformation that are harmful to many people out there,

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Speaker 1: and we see that as constant. He's getting on the

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Speaker 1: damn phone with people and going off rants with disinformation

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Speaker 1: that is being heard by instead of social media just

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Speaker 1: being by the forties fifty year old boomers like the

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Speaker 1: senior citizens who may truly believe what he is saying

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Speaker 1: and ranting about. Can you talk about that as well,

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Speaker 1: and how that is affecting this year's election as opposed

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Speaker 1: to what was before. Yeah, I mean, Sam, your point

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Speaker 1: is so accurate. With Trump in the White House, it

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Speaker 1: is like a disinformation super spreader of like like event

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Speaker 1: every day. You know, there is just a study I

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Speaker 1: can't remember the exact study, but I can find it

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Speaker 1: um that said that Trump is the number one spreader

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Speaker 1: of COVID disinformation and misinformation, right, And so think about that,

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Speaker 1: something that could impact someone's health, something that can like

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Speaker 1: physically harm communities. He is the number one person who

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Speaker 1: is spreading it, right, things about false cures, things about

428
00:25:25,200 --> 00:25:28,280
Speaker 1: you know, like when he when he talked about how

429
00:25:28,880 --> 00:25:34,000
Speaker 1: like like just like completely incorrect, harmful information. And so

430
00:25:34,320 --> 00:25:36,119
Speaker 1: I think what's different this year is that we do

431
00:25:36,200 --> 00:25:39,000
Speaker 1: have someone who is has proven time and time and

432
00:25:39,000 --> 00:25:44,160
Speaker 1: time again that he is not above spreading disinformation and misinformation,

433
00:25:44,200 --> 00:25:46,880
Speaker 1: no matter how harmful it is. And so I think

434
00:25:46,920 --> 00:25:49,520
Speaker 1: that is a real difference. You Know, one of the

435
00:25:49,560 --> 00:25:52,439
Speaker 1: things I've seen in my research on disinformation is the

436
00:25:52,480 --> 00:25:56,000
Speaker 1: way that when Trump tweets something, it can get picked

437
00:25:56,080 --> 00:26:01,280
Speaker 1: up on so quickly and spread so fast, right, And

438
00:26:01,320 --> 00:26:04,080
Speaker 1: so I think that's a real difference that we are

439
00:26:04,080 --> 00:26:06,320
Speaker 1: going to have to contend with in that we have

440
00:26:06,440 --> 00:26:08,720
Speaker 1: someone who is so powerful and has such a huge

441
00:26:08,760 --> 00:26:11,399
Speaker 1: megaphone and has so many people's ears, like you said,

442
00:26:11,840 --> 00:26:15,199
Speaker 1: you know, spreading this information that could really get somebody

443
00:26:15,280 --> 00:26:18,040
Speaker 1: hurt and really impacts our democracy. Um, I think I

444
00:26:18,040 --> 00:26:21,480
Speaker 1: would say another thing that we are seeing a lot of,

445
00:26:22,440 --> 00:26:26,320
Speaker 1: I think it's gotten worse is bad actors willingness to

446
00:26:26,440 --> 00:26:32,040
Speaker 1: exploit um existing fractures in our communities. And so you know,

447
00:26:32,359 --> 00:26:34,199
Speaker 1: I did an episode of tap of my show there

448
00:26:34,200 --> 00:26:36,919
Speaker 1: are no girls on the Internet about Vanessa Yen, the

449
00:26:37,040 --> 00:26:41,040
Speaker 1: soldier who was killed. And you know that was during

450
00:26:41,160 --> 00:26:44,040
Speaker 1: that happened during earlier in the summer when we had

451
00:26:44,080 --> 00:26:47,680
Speaker 1: all of these uprisings for you know, black lives, folks

452
00:26:47,680 --> 00:26:50,600
Speaker 1: like Brianna Taylor and George Floyd, and we saw bad

453
00:26:50,640 --> 00:26:54,879
Speaker 1: actors online putting these messages on huge Facebook pages with

454
00:26:55,119 --> 00:26:57,760
Speaker 1: lots and lots of lots of reach saying things like

455
00:26:58,320 --> 00:27:02,760
Speaker 1: why should the Latine community support the black community when quote,

456
00:27:02,800 --> 00:27:05,320
Speaker 1: they killed one of our own, right, And so everybody

457
00:27:05,359 --> 00:27:09,919
Speaker 1: knows that marginalized communities are stronger when we work together.

458
00:27:10,240 --> 00:27:13,920
Speaker 1: But it's very it's very savvy how they exploit these

459
00:27:13,960 --> 00:27:19,280
Speaker 1: existing fractures in communities to continue to cause chaos and

460
00:27:19,359 --> 00:27:23,400
Speaker 1: spread confusion, right, and so I think that we've seen

461
00:27:23,440 --> 00:27:26,960
Speaker 1: a lot more willingness of bad actors to exploit really

462
00:27:27,000 --> 00:27:31,800
Speaker 1: sensitive topics, topics that take nuance or thoughtfulness to discuss publicly,

463
00:27:32,119 --> 00:27:36,200
Speaker 1: and just collapsing them into you know, us against them,

464
00:27:36,320 --> 00:27:39,120
Speaker 1: or this is why we shouldn't vote Democrat, or these

465
00:27:39,119 --> 00:27:41,760
Speaker 1: things that divide us instead of unite us because we're

466
00:27:41,760 --> 00:27:44,760
Speaker 1: so much stronger when we're united. We have some more

467
00:27:44,800 --> 00:27:46,520
Speaker 1: for you listeners, but first we have a quick break

468
00:27:46,520 --> 00:28:02,639
Speaker 1: for word from our sponsor, and we're back. Thank you sponsored.

469
00:28:03,240 --> 00:28:06,800
Speaker 1: As you're talking about the issue with the UM one

470
00:28:06,880 --> 00:28:10,000
Speaker 1: marginalized group pitted against another in order to break down

471
00:28:10,000 --> 00:28:13,480
Speaker 1: and divide, I just recently read an article talking about

472
00:28:13,520 --> 00:28:17,160
Speaker 1: how men in the Latin's community are there's a big

473
00:28:17,240 --> 00:28:20,159
Speaker 1: chunk of them that are voting for Trump because the

474
00:28:20,200 --> 00:28:22,520
Speaker 1: appeal of his matismo, as they say, is like that

475
00:28:22,640 --> 00:28:27,040
Speaker 1: the manliness of his appeal has actually spoken to this

476
00:28:27,119 --> 00:28:30,280
Speaker 1: group of people. And I find it very interesting because

477
00:28:30,280 --> 00:28:33,080
Speaker 1: we see that played a time and time again as

478
00:28:33,080 --> 00:28:36,480
Speaker 1: where he's exploited that I'm a man's man in order

479
00:28:36,560 --> 00:28:38,840
Speaker 1: to get these votes and to get this ladder vote

480
00:28:38,920 --> 00:28:40,720
Speaker 1: and even to the point that we have groups that

481
00:28:40,800 --> 00:28:43,840
Speaker 1: are talking about make women great again. Have you seen this? No,

482
00:28:45,320 --> 00:28:48,240
Speaker 1: So it's this whole campaign where they are there's a

483
00:28:48,240 --> 00:28:51,360
Speaker 1: group of men, right wing men who are going around

484
00:28:51,640 --> 00:28:55,160
Speaker 1: trying to take money for women to come to conventions

485
00:28:55,280 --> 00:28:59,200
Speaker 1: to be taught to be womanly, feminine and back into

486
00:28:59,320 --> 00:29:01,760
Speaker 1: the original role of what women should be. But it's

487
00:29:01,800 --> 00:29:04,320
Speaker 1: kind of they've taken this huge art on being who

488
00:29:04,400 --> 00:29:06,760
Speaker 1: who they are and playing on it because of this

489
00:29:06,840 --> 00:29:10,479
Speaker 1: new found need to have this conversation about we need

490
00:29:10,520 --> 00:29:12,960
Speaker 1: to be more manly, but community and Trump is getting

491
00:29:13,040 --> 00:29:15,520
Speaker 1: us to be manly again. Have you seen this as

492
00:29:15,520 --> 00:29:18,600
Speaker 1: a part of like a push in this disinformation? Is

493
00:29:18,600 --> 00:29:23,320
Speaker 1: this the manly machismo kind of rhetoric? Oh? First of all,

494
00:29:23,360 --> 00:29:26,120
Speaker 1: I just have to I have to ask, like, what

495
00:29:26,160 --> 00:29:29,080
Speaker 1: the hell kind of things do you think they're teaching

496
00:29:29,240 --> 00:29:34,520
Speaker 1: women in these trainings? Like I, oh my gosh, it's

497
00:29:34,560 --> 00:29:38,400
Speaker 1: like how much money was like ten tho dollars It

498
00:29:38,480 --> 00:29:40,959
Speaker 1: was at the Florida convention ten dollars and then they

499
00:29:40,960 --> 00:29:44,000
Speaker 1: gave this marked huge mark down price and it's just

500
00:29:44,040 --> 00:29:47,200
Speaker 1: like ten men talking to women about being feminine. Oh

501
00:29:47,240 --> 00:29:49,880
Speaker 1: my god, if I think if I could like where,

502
00:29:50,000 --> 00:29:52,440
Speaker 1: like dress up in some sort of disguise and go

503
00:29:52,520 --> 00:29:54,640
Speaker 1: to one of these with like a camera, Oh my god,

504
00:29:54,680 --> 00:29:58,040
Speaker 1: look out. Um. It's such a good question, though. I

505
00:29:58,080 --> 00:29:59,960
Speaker 1: think that, you know, you make such a good point.

506
00:30:00,040 --> 00:30:04,920
Speaker 1: I think that it really comes down to Trump's willingness

507
00:30:05,000 --> 00:30:09,520
Speaker 1: and effectiveness at exploiting these biases that exist in our society,

508
00:30:09,600 --> 00:30:13,600
Speaker 1: so biases around women, misconceptions of of like what it

509
00:30:13,680 --> 00:30:15,920
Speaker 1: is to be like a manly man or a womanly woman.

510
00:30:16,000 --> 00:30:18,440
Speaker 1: You know, I think that Trump is very effective at

511
00:30:18,480 --> 00:30:20,880
Speaker 1: exploiting these things. And the reason why they work is

512
00:30:20,920 --> 00:30:26,400
Speaker 1: because these all these old school stereotypical notions of what

513
00:30:26,440 --> 00:30:27,920
Speaker 1: it means to be a woman, what it means to

514
00:30:28,000 --> 00:30:29,880
Speaker 1: be a man, all of these sort of traditional ideas,

515
00:30:29,960 --> 00:30:33,440
Speaker 1: they have been baked into our society for so long.

516
00:30:33,480 --> 00:30:35,280
Speaker 1: I mean you you all do such a good job

517
00:30:35,280 --> 00:30:37,400
Speaker 1: of breaking them down every week, right. We we know

518
00:30:37,520 --> 00:30:41,160
Speaker 1: that these things have staying power, and that's why they're

519
00:30:41,200 --> 00:30:44,640
Speaker 1: so effective for people like Trump and these this group

520
00:30:44,680 --> 00:30:48,200
Speaker 1: of whoever who is who was like charging ten thousand

521
00:30:48,240 --> 00:30:50,880
Speaker 1: dollars also talk about that price point that is a

522
00:30:50,880 --> 00:30:56,320
Speaker 1: lot of money A lot of money, and I'm like,

523
00:30:56,320 --> 00:30:59,280
Speaker 1: who the hell is gonna pay this? What? I know,

524
00:30:59,440 --> 00:31:03,160
Speaker 1: I'm curious. I would love to get a syllabus for

525
00:31:03,800 --> 00:31:05,960
Speaker 1: this court. Find that link for you because we talked

526
00:31:05,960 --> 00:31:11,640
Speaker 1: about web page and it's yeah, it's incredible. Um. But yeah,

527
00:31:11,640 --> 00:31:14,560
Speaker 1: it's also this whole level of him coming after suburban wives.

528
00:31:14,640 --> 00:31:19,400
Speaker 1: Literally at the recent rally, he is begging suburban women

529
00:31:19,560 --> 00:31:22,160
Speaker 1: please vote for me, please like me. I'm trying to

530
00:31:22,280 --> 00:31:26,680
Speaker 1: make your community safe again. Yeah. I think it really

531
00:31:27,000 --> 00:31:30,720
Speaker 1: just goes back to his willingness to try to exploit

532
00:31:30,840 --> 00:31:34,920
Speaker 1: these very traditional ideas of what women want. I'm not

533
00:31:34,960 --> 00:31:36,600
Speaker 1: gonna sit here and say that I think that Trump

534
00:31:36,640 --> 00:31:39,400
Speaker 1: has any kind of a good read on what women want,

535
00:31:39,480 --> 00:31:42,120
Speaker 1: but I think it's like his him trying to him

536
00:31:42,160 --> 00:31:45,760
Speaker 1: assuming that he does, and trying to trying to exploit that.

537
00:31:45,800 --> 00:31:48,760
Speaker 1: You know, it really makes me think about how in

538
00:31:48,840 --> 00:31:53,720
Speaker 1: earlier waves of the right and things like that organizing online,

539
00:31:53,960 --> 00:31:55,880
Speaker 1: but we saw the ways that they weren't kind of

540
00:31:55,920 --> 00:31:59,520
Speaker 1: appealing to manly men who were sick of these like

541
00:31:59,600 --> 00:32:03,560
Speaker 1: soy boy, Beta's right. It's really it really, I mean

542
00:32:03,920 --> 00:32:08,240
Speaker 1: gender the jet like gender norms are on mother, that's

543
00:32:08,240 --> 00:32:11,360
Speaker 1: all I can say, right, it really really we are

544
00:32:11,560 --> 00:32:16,320
Speaker 1: so our society is, it's so baked into our society

545
00:32:16,360 --> 00:32:19,800
Speaker 1: in these toxic, harmful ways that watching them play out

546
00:32:20,440 --> 00:32:22,720
Speaker 1: on you know, the election stage or in politics or

547
00:32:22,760 --> 00:32:24,800
Speaker 1: online in these ways, it's just so it's just truly

548
00:32:24,840 --> 00:32:27,480
Speaker 1: so bizarre. You know. On top of that, as we're

549
00:32:27,480 --> 00:32:30,200
Speaker 1: sitting here talking about it, I have kind of named

550
00:32:30,240 --> 00:32:33,080
Speaker 1: off every person that he has been begging to notice him,

551
00:32:33,120 --> 00:32:37,000
Speaker 1: whether it's using um, token black people in order to

552
00:32:37,040 --> 00:32:39,280
Speaker 1: give them into line and not doing the blex it

553
00:32:39,520 --> 00:32:43,360
Speaker 1: or uh, appealing to suburban women. But the one group

554
00:32:43,400 --> 00:32:46,360
Speaker 1: of people that he's really not talked to or actually

555
00:32:46,440 --> 00:32:51,040
Speaker 1: even notice are typically marginalized women women of color, specifically

556
00:32:51,040 --> 00:32:54,120
Speaker 1: black women. Oh of course, right, if you look at

557
00:32:54,120 --> 00:32:56,720
Speaker 1: the numbers of Black women who voted for Trump, we

558
00:32:56,840 --> 00:32:59,320
Speaker 1: knew better. Like black women were not falling for it,

559
00:33:00,240 --> 00:33:02,160
Speaker 1: and I mean they always It's almost like a cliche

560
00:33:02,200 --> 00:33:04,240
Speaker 1: at this point when we talk about politics, like listen

561
00:33:04,240 --> 00:33:06,640
Speaker 1: to black women, Black women, we do, we do know

562
00:33:06,680 --> 00:33:09,200
Speaker 1: what's up, right, but like whatever he was selling we

563
00:33:09,200 --> 00:33:11,920
Speaker 1: were not buying. We see him attack Black women are

564
00:33:11,960 --> 00:33:17,120
Speaker 1: sisters like Kamala Harris, like White House correspondent you, Michel Sendor,

565
00:33:17,400 --> 00:33:20,080
Speaker 1: you know, like April Ryan. We see him attack our

566
00:33:20,120 --> 00:33:23,320
Speaker 1: sisters every day. There's this we're not, we're not, We're

567
00:33:23,320 --> 00:33:25,560
Speaker 1: not gonna do it, Like there's nothing We're not buying

568
00:33:25,560 --> 00:33:28,280
Speaker 1: what he's selling. You know. It is funny that it's

569
00:33:28,320 --> 00:33:31,320
Speaker 1: like he's he's not. He realizes like, I'm not gonna

570
00:33:31,360 --> 00:33:33,040
Speaker 1: win with them. It may I may as well just

571
00:33:33,080 --> 00:33:36,960
Speaker 1: cut my bosses. You really, like literally just like those

572
00:33:37,080 --> 00:33:40,400
Speaker 1: hands of like never mind next. It's really interesting when

573
00:33:40,440 --> 00:33:43,800
Speaker 1: you really focus on his his goals and you're like, oh,

574
00:33:44,040 --> 00:33:48,160
Speaker 1: we see who you're afraid of exactly. I do want

575
00:33:48,160 --> 00:33:50,560
Speaker 1: to add in here to one idea. I was reading

576
00:33:50,600 --> 00:33:53,040
Speaker 1: about this a couple of years ago and it's just

577
00:33:53,040 --> 00:33:55,239
Speaker 1: really stuck with me. Is um. If you look at

578
00:33:55,280 --> 00:33:57,240
Speaker 1: the here in the United States, the Republican Party and

579
00:33:57,240 --> 00:34:01,080
Speaker 1: the Democratic Party, there's almost been a masculinization and a

580
00:34:01,160 --> 00:34:06,160
Speaker 1: feminization of like masculization of the Republican Party, feminization of

581
00:34:06,200 --> 00:34:11,560
Speaker 1: the Democratic Party, and just seeing that play out, and

582
00:34:11,560 --> 00:34:14,640
Speaker 1: and the Republican Party really really leaning into that because

583
00:34:14,640 --> 00:34:18,160
Speaker 1: in our society, masculine is better, right, be aggressive, be rude,

584
00:34:18,680 --> 00:34:20,640
Speaker 1: tell women that they belong in the kitchen or whatever,

585
00:34:20,960 --> 00:34:24,400
Speaker 1: because you're speaking your mind and that's okay, Um, But

586
00:34:24,480 --> 00:34:28,120
Speaker 1: that is something that I've been thinking about and I

587
00:34:28,160 --> 00:34:32,279
Speaker 1: feel like I am witnessing. Yeah. I mean, just look

588
00:34:32,320 --> 00:34:34,520
Speaker 1: at the way that we talked about masks at the

589
00:34:34,560 --> 00:34:39,040
Speaker 1: debate between Trump and Biden, Right, Biden wore a mask

590
00:34:39,320 --> 00:34:42,160
Speaker 1: his the people he was with war masks. I think

591
00:34:42,160 --> 00:34:45,080
Speaker 1: it was a Republican commentator, Tommy larn Or. I don't

592
00:34:45,080 --> 00:34:47,480
Speaker 1: know if that's her name, however you say it, um,

593
00:34:47,520 --> 00:34:51,000
Speaker 1: who tweeted something around among lines of nice mask, Biden

594
00:34:51,200 --> 00:34:54,880
Speaker 1: may as well carry a purse? What? And this was

595
00:34:55,120 --> 00:35:00,120
Speaker 1: after Trump and them tested positive, right, Like, do do

596
00:35:00,360 --> 00:35:04,360
Speaker 1: some of these Republicans think that you can be manlier

597
00:35:04,560 --> 00:35:06,799
Speaker 1: than a virus, like you can be you can sort

598
00:35:06,800 --> 00:35:11,560
Speaker 1: of out masculine contains in what do they think? This?

599
00:35:11,680 --> 00:35:16,760
Speaker 1: This this equation with people on the left with like

600
00:35:16,760 --> 00:35:20,319
Speaker 1: like feminization. So if you are wearing a mask because

601
00:35:20,320 --> 00:35:22,240
Speaker 1: you want to protect you and your and your people

602
00:35:22,280 --> 00:35:24,880
Speaker 1: around you, that's you know, you may as well be

603
00:35:24,920 --> 00:35:28,400
Speaker 1: carrying a purse, right, you know, this idea that being

604
00:35:28,440 --> 00:35:30,799
Speaker 1: I mean, they've they've even shown that one of the

605
00:35:30,840 --> 00:35:34,120
Speaker 1: big problems when it comes to covid is that men

606
00:35:34,440 --> 00:35:36,719
Speaker 1: don't feel manly when they were a mask, so they

607
00:35:36,760 --> 00:35:40,960
Speaker 1: don't And just think about how how how sick that is,

608
00:35:41,000 --> 00:35:46,760
Speaker 1: how how toxic that traditional gender roles when when when,

609
00:35:46,760 --> 00:35:49,880
Speaker 1: when like taken to this level, how harmful it is

610
00:35:49,920 --> 00:35:53,080
Speaker 1: that people are willing to get sick and make other sick.

611
00:35:53,400 --> 00:35:57,200
Speaker 1: Just too conformed these rigid ideas of gender. Is it

612
00:35:57,400 --> 00:35:59,279
Speaker 1: really is so sad, like gender is a hell of

613
00:35:59,280 --> 00:36:02,120
Speaker 1: a drug. Really, I mean, it really is kind of

614
00:36:02,120 --> 00:36:04,680
Speaker 1: funny because they did make this a masculine thing, to

615
00:36:04,719 --> 00:36:08,319
Speaker 1: the point that when Trump was second came back, they

616
00:36:08,320 --> 00:36:10,560
Speaker 1: were like, he beat it, He's a real man, He's

617
00:36:10,640 --> 00:36:12,960
Speaker 1: outdid this, and he's too strong for that, like literally

618
00:36:13,120 --> 00:36:16,480
Speaker 1: putting on these things as if he fought in a

619
00:36:16,560 --> 00:36:19,719
Speaker 1: wrestling match and beat a world champion, Like it was

620
00:36:19,840 --> 00:36:22,640
Speaker 1: that level. And that's what people were pushing, even to

621
00:36:22,760 --> 00:36:24,920
Speaker 1: the point that he's talking about being immune and of

622
00:36:25,000 --> 00:36:29,000
Speaker 1: course spreading more disinformation, causing this whole conversation about want

623
00:36:29,000 --> 00:36:31,600
Speaker 1: to kiss people, which is a whole different weirdness that

624
00:36:31,760 --> 00:36:35,360
Speaker 1: I was like, what is happening? Um? But all of

625
00:36:35,440 --> 00:36:37,640
Speaker 1: that to say is like it has become this fine

626
00:36:37,680 --> 00:36:41,640
Speaker 1: line and it started at the very beginning of the virus,

627
00:36:41,760 --> 00:36:43,919
Speaker 1: Like at the very beginning when they're like, this could

628
00:36:43,960 --> 00:36:47,000
Speaker 1: be preventative, let's try this, it became a is a

629
00:36:47,320 --> 00:36:49,759
Speaker 1: is a female versus male thing. It's a feminine versus

630
00:36:49,800 --> 00:36:52,920
Speaker 1: a masculine thing. And it's played even deeper as more

631
00:36:52,920 --> 00:36:55,520
Speaker 1: and more, over two hundred and what fifteen thousand people

632
00:36:55,560 --> 00:36:57,880
Speaker 1: have died in our country, and it's kind of like,

633
00:36:58,239 --> 00:37:02,279
Speaker 1: what is happening in this point as Well's the fact

634
00:37:02,320 --> 00:37:05,279
Speaker 1: that the majority of people who are really affected is

635
00:37:05,360 --> 00:37:09,080
Speaker 1: the black community, is the elderly community, are those with morbidities?

636
00:37:09,239 --> 00:37:12,279
Speaker 1: And why are we seeing that as a week? Yeah,

637
00:37:12,400 --> 00:37:15,360
Speaker 1: I I I'm so glad you brought this up. I

638
00:37:15,640 --> 00:37:19,359
Speaker 1: hate that so much. I think that there's so much

639
00:37:19,400 --> 00:37:21,759
Speaker 1: wrong with this equating. You know, so if if the

640
00:37:21,840 --> 00:37:25,800
Speaker 1: if you follow that logic that Trump is strong because

641
00:37:25,840 --> 00:37:29,279
Speaker 1: he was able to quote unquote beat COVID, you know,

642
00:37:29,600 --> 00:37:32,719
Speaker 1: so whatever you know, does that mean that the over

643
00:37:32,880 --> 00:37:35,799
Speaker 1: two hundred thousand people that we have lost were weak?

644
00:37:36,239 --> 00:37:38,160
Speaker 1: And I also think it really plays into these dis

645
00:37:38,200 --> 00:37:43,120
Speaker 1: ablest trope around sickness, around healthy bodies. You know, I

646
00:37:43,280 --> 00:37:45,920
Speaker 1: really I hate the way that we talk about it.

647
00:37:46,080 --> 00:37:48,239
Speaker 1: I truly do. And I think we saw it at

648
00:37:48,280 --> 00:37:51,120
Speaker 1: the beginning of the pandemic when the party line or

649
00:37:51,160 --> 00:37:55,719
Speaker 1: the standard line was only the sick and weak will die,

650
00:37:55,960 --> 00:38:00,160
Speaker 1: and there resist sort of implication of you know, a

651
00:38:00,600 --> 00:38:03,800
Speaker 1: they're expendable and be that there was a sort of

652
00:38:03,960 --> 00:38:06,480
Speaker 1: unsaid vibe at least that I felt like I saw

653
00:38:06,880 --> 00:38:09,680
Speaker 1: where it's like, wouldn't we be better off without them anyway?

654
00:38:09,880 --> 00:38:11,520
Speaker 1: You know? And I just think that like it really

655
00:38:11,680 --> 00:38:18,480
Speaker 1: played into these incredibly disturbing ablest tropes around. You know,

656
00:38:19,120 --> 00:38:22,520
Speaker 1: whose lives are valuable and whose aren't, and whose are

657
00:38:22,560 --> 00:38:25,160
Speaker 1: expendable and who's aren't Just because there's somebody who is

658
00:38:25,560 --> 00:38:30,080
Speaker 1: older or somebody who is, you know, chronically ill, Your

659
00:38:30,200 --> 00:38:33,719
Speaker 1: life matters. You deserve to live a fulfilling and enriching life.

660
00:38:34,160 --> 00:38:37,920
Speaker 1: You're not expendable in society people talking about like their grandparents.

661
00:38:38,560 --> 00:38:42,120
Speaker 1: Just because your your grandparents is elderly doesn't mean that

662
00:38:42,719 --> 00:38:44,880
Speaker 1: they can just die and it's fine. I really, I

663
00:38:45,000 --> 00:38:47,440
Speaker 1: really have a big problem with the way that the

664
00:38:47,520 --> 00:38:50,320
Speaker 1: way that that was framed early on, and certainly the

665
00:38:50,400 --> 00:38:53,200
Speaker 1: way that Trump talking about COVID as you beat it

666
00:38:53,360 --> 00:38:55,239
Speaker 1: like as if it was our wrestling match the way

667
00:38:55,320 --> 00:38:57,200
Speaker 1: that that kind of played into it, I really really

668
00:38:57,280 --> 00:39:01,640
Speaker 1: hate it. Right, and then again him touting this whole

669
00:39:02,000 --> 00:39:05,040
Speaker 1: herd immunity, which the World Health Organization just was like, please,

670
00:39:05,080 --> 00:39:08,560
Speaker 1: this is unethical and immoral to play pretend like this

671
00:39:08,719 --> 00:39:11,240
Speaker 1: is okay, because what you're saying in the herd immunity

672
00:39:11,320 --> 00:39:14,560
Speaker 1: mentality is people should die and they will be okay.

673
00:39:15,040 --> 00:39:18,440
Speaker 1: Like that's literally giving permission to kill off people because

674
00:39:18,480 --> 00:39:21,320
Speaker 1: they're not quote unquote strong enough, when in actuality, we

675
00:39:21,400 --> 00:39:23,920
Speaker 1: don't know who is affected exactly why. I mean, we

676
00:39:24,040 --> 00:39:26,080
Speaker 1: obviously know that some of the people who have been

677
00:39:26,080 --> 00:39:28,800
Speaker 1: affected with common not a lot of people who have

678
00:39:28,880 --> 00:39:31,960
Speaker 1: been affected have common abilities and have died. Doesn't mean

679
00:39:32,000 --> 00:39:34,040
Speaker 1: they should have died, doesn't mean they would have been

680
00:39:34,080 --> 00:39:35,880
Speaker 1: dead without that. This is kind of the argument that

681
00:39:35,960 --> 00:39:37,560
Speaker 1: I've had that to have with people. I'm like, yeah,

682
00:39:37,600 --> 00:39:40,040
Speaker 1: but tell me this, because the same argument as like, well,

683
00:39:40,320 --> 00:39:43,720
Speaker 1: everybody's dying of COVID nowadays, even if it's not COVID,

684
00:39:43,760 --> 00:39:45,360
Speaker 1: they're just putting it on death sort of thicket. And

685
00:39:45,400 --> 00:39:48,000
Speaker 1: I'm like, but tell me this. If it wasn't for COVID,

686
00:39:48,000 --> 00:39:52,200
Speaker 1: would they be alive right now? Well? Yeah, okay, then oh,

687
00:39:52,640 --> 00:39:56,719
Speaker 1: it's it's this whole mentality. Bless you for for entertaining

688
00:39:56,760 --> 00:40:01,880
Speaker 1: these conversations and trying to inform the mass lots of

689
00:40:01,960 --> 00:40:07,480
Speaker 1: my family, to be honest, but anywhere, Yeah, that's just

690
00:40:07,560 --> 00:40:09,719
Speaker 1: such a whole big thing. But it kind of comes

691
00:40:09,760 --> 00:40:13,880
Speaker 1: down to once again this whole like masculine versus feminine

692
00:40:14,280 --> 00:40:16,600
Speaker 1: point of view and why we continue to have to

693
00:40:16,680 --> 00:40:19,640
Speaker 1: have a fight about what's wrong was right? And we know,

694
00:40:20,640 --> 00:40:24,239
Speaker 1: let's talk about the current Supreme Court hearing in confirmation

695
00:40:24,760 --> 00:40:26,760
Speaker 1: is the fact that there is a lot at stake,

696
00:40:27,239 --> 00:40:34,200
Speaker 1: especially for marginalized people, most likely for a can we

697
00:40:34,280 --> 00:40:37,239
Speaker 1: talk about this whole like they're trying to make her

698
00:40:37,320 --> 00:40:40,279
Speaker 1: a hero because she is a mom who has adopted

699
00:40:40,520 --> 00:40:43,920
Speaker 1: black children, which I want to vomit, but just go

700
00:40:44,040 --> 00:40:46,160
Speaker 1: ahead and at that they're because caause that's nothing more

701
00:40:46,200 --> 00:40:50,399
Speaker 1: infuriating than tokenizing children because the family may have done

702
00:40:50,440 --> 00:40:53,600
Speaker 1: something that is just find responsible and it's that something

703
00:40:53,640 --> 00:40:55,759
Speaker 1: you're called to do. And if you find yourself in

704
00:40:55,840 --> 00:40:58,800
Speaker 1: a good place that you can adopt these wonderful children

705
00:40:58,880 --> 00:41:01,360
Speaker 1: that need homes, it's great. But we also know that

706
00:41:01,520 --> 00:41:03,640
Speaker 1: a lot of times it comes with white savior complex,

707
00:41:03,920 --> 00:41:07,120
Speaker 1: which has a lot of implications and a lot of

708
00:41:08,120 --> 00:41:11,239
Speaker 1: trauma behind this, and now we're seeing it played as

709
00:41:11,320 --> 00:41:14,040
Speaker 1: her plant being the hero and they're touting her as

710
00:41:14,080 --> 00:41:17,000
Speaker 1: a new RBG. Can you we talk about how social

711
00:41:17,040 --> 00:41:19,640
Speaker 1: media is kind of helping in this rhetoric. Yeah, it's

712
00:41:19,640 --> 00:41:21,759
Speaker 1: funny that you say this so um in my in

713
00:41:21,840 --> 00:41:24,320
Speaker 1: one of my day jobs, I'm I'm working with feminist

714
00:41:24,360 --> 00:41:27,759
Speaker 1: groups and women's groups to track this and and you know,

715
00:41:28,200 --> 00:41:30,680
Speaker 1: keep an eye on how messages about her are being

716
00:41:30,719 --> 00:41:32,960
Speaker 1: framed online. And you're exactly right that she is being

717
00:41:33,080 --> 00:41:36,799
Speaker 1: framed as this feminist hero. And listen same to your

718
00:41:36,840 --> 00:41:39,600
Speaker 1: point about the way that she has been framed as

719
00:41:39,680 --> 00:41:44,959
Speaker 1: a hero for adopting black children. And it's just one

720
00:41:45,280 --> 00:41:49,080
Speaker 1: proximity to blackness does not make someone anti racist or

721
00:41:49,120 --> 00:41:51,319
Speaker 1: being so saying like I have an adopted black daughter,

722
00:41:51,680 --> 00:41:54,920
Speaker 1: I have a black you know spouse, I have black friends. Obviously,

723
00:41:55,000 --> 00:41:58,040
Speaker 1: that doesn't make somebody anti racist. And to what really

724
00:41:58,080 --> 00:42:00,960
Speaker 1: makes me sad is that that thinking, the thinking that

725
00:42:01,080 --> 00:42:04,400
Speaker 1: she is a hero for adopting black children. It really

726
00:42:05,040 --> 00:42:09,239
Speaker 1: just it really leaves out the adopted kids. You know.

727
00:42:09,320 --> 00:42:10,759
Speaker 1: I just really hate the way that when we have

728
00:42:10,880 --> 00:42:14,680
Speaker 1: these conversations. They often sent her the white person who

729
00:42:14,800 --> 00:42:19,680
Speaker 1: adopted children, you know, their story, you know, as a hero,

730
00:42:20,080 --> 00:42:22,920
Speaker 1: or their story as someone who you know did this

731
00:42:23,120 --> 00:42:25,320
Speaker 1: great thing, and it really just lead doesn't leave a

732
00:42:25,360 --> 00:42:28,360
Speaker 1: lot of room for the agency of the kids. And

733
00:42:28,440 --> 00:42:30,440
Speaker 1: I think that like most people who I know who

734
00:42:30,520 --> 00:42:33,560
Speaker 1: have adopted children for them, they say, you know, oh,

735
00:42:33,640 --> 00:42:35,400
Speaker 1: don't tell me that my kids are so lucky that

736
00:42:35,440 --> 00:42:37,520
Speaker 1: I adopted them. We are the lucky ones that we

737
00:42:37,640 --> 00:42:40,520
Speaker 1: adopted our kids. And I think that most people who

738
00:42:40,960 --> 00:42:44,960
Speaker 1: have adopted kids really want to center their kids and

739
00:42:45,120 --> 00:42:46,480
Speaker 1: not make it seem like there's some kind of a

740
00:42:46,560 --> 00:42:50,879
Speaker 1: hero for what they did, right. I think it's fun.

741
00:42:51,000 --> 00:42:53,440
Speaker 1: I found it interesting because one of the first things

742
00:42:53,880 --> 00:42:57,120
Speaker 1: I saw about her was of course a misleading not misleading.

743
00:42:57,200 --> 00:42:59,319
Speaker 1: It was misleading because they posted a picture of her

744
00:42:59,360 --> 00:43:05,279
Speaker 1: sister instead of her, and they're black children, which I

745
00:43:05,320 --> 00:43:08,400
Speaker 1: found hilarious. We can't even find the right Plami family.

746
00:43:08,880 --> 00:43:12,480
Speaker 1: I mean, come on, um, But the first thing they

747
00:43:12,520 --> 00:43:15,040
Speaker 1: wanted to tell was, well, obviously she can't be racist

748
00:43:15,080 --> 00:43:18,360
Speaker 1: because she has two black children. Obviously she uh is

749
00:43:18,360 --> 00:43:21,799
Speaker 1: anti choice because she believes in adoption, and of course

750
00:43:21,960 --> 00:43:24,520
Speaker 1: I am adopted as well. And I get told that

751
00:43:24,600 --> 00:43:26,840
Speaker 1: often to my face, that I should be anti choice

752
00:43:27,000 --> 00:43:30,560
Speaker 1: because I was, you know, saved, because I wasn't aborted.

753
00:43:30,640 --> 00:43:32,000
Speaker 1: And I'm like, well, no, there's a whole lot of

754
00:43:32,040 --> 00:43:34,520
Speaker 1: other complications to that story, and we won't we won't

755
00:43:34,560 --> 00:43:37,759
Speaker 1: go there. Um, but being told that you should be

756
00:43:37,840 --> 00:43:40,920
Speaker 1: grateful and having these things, but honestly putting this on

757
00:43:41,080 --> 00:43:44,680
Speaker 1: a platform and making her the idealist. But we know

758
00:43:45,200 --> 00:43:48,319
Speaker 1: that the white couple that actually drove the children off

759
00:43:48,360 --> 00:43:50,560
Speaker 1: the cliff and killed themselves and the children with them.

760
00:43:50,600 --> 00:43:53,279
Speaker 1: We see things like that which doesn't always happen. I

761
00:43:53,400 --> 00:43:56,640
Speaker 1: talked about a family recently who are YouTube families adopted

762
00:43:56,680 --> 00:43:59,240
Speaker 1: an Asian child who was autistic and then gave them away.

763
00:44:00,000 --> 00:44:02,080
Speaker 1: Of that, I remember that, And what's what's a lot

764
00:44:02,120 --> 00:44:05,239
Speaker 1: about that story is that when they were trying to

765
00:44:05,360 --> 00:44:09,160
Speaker 1: adopt their child, Huxley, they they gave him a new name, um,

766
00:44:09,239 --> 00:44:12,160
Speaker 1: but they chose was Huxley. They were before that, they

767
00:44:12,200 --> 00:44:16,040
Speaker 1: were on message boards asking people, we're looking to adopt

768
00:44:16,160 --> 00:44:21,120
Speaker 1: someone from another country who has disabilities. But as they

769
00:44:21,200 --> 00:44:24,080
Speaker 1: put it, like disabilities that are easier to manage, you know,

770
00:44:24,360 --> 00:44:26,160
Speaker 1: like they were it seemed as if they were trying

771
00:44:26,239 --> 00:44:29,719
Speaker 1: to gear up personlf give. They were hand picking, you know,

772
00:44:30,000 --> 00:44:32,319
Speaker 1: their child for what seems like we want to get

773
00:44:32,480 --> 00:44:34,200
Speaker 1: we want to get the attention that comes with this,

774
00:44:34,280 --> 00:44:35,680
Speaker 1: but we don't want it to be that hard. Like

775
00:44:36,320 --> 00:44:38,920
Speaker 1: I really that story. Not to go off on a tangent,

776
00:44:39,000 --> 00:44:40,839
Speaker 1: but that story just broke my heart. It really broke

777
00:44:40,920 --> 00:44:43,560
Speaker 1: my heart when people say things like this to your

778
00:44:43,600 --> 00:44:47,239
Speaker 1: face about how you must feel about abortion because you're

779
00:44:47,239 --> 00:44:49,920
Speaker 1: someone who was adopted. What like what is that like?

780
00:44:50,120 --> 00:44:54,759
Speaker 1: Like what is your response? Oh girl, we this is

781
00:44:54,800 --> 00:44:58,520
Speaker 1: a whole big conversation. But a lot of mean, a

782
00:44:58,680 --> 00:45:00,880
Speaker 1: lot of this has to do with the anxiety of

783
00:45:00,960 --> 00:45:03,480
Speaker 1: being having an imposter syndrome and being told this is

784
00:45:03,680 --> 00:45:05,520
Speaker 1: what I should feel, and when I am against it

785
00:45:05,680 --> 00:45:08,000
Speaker 1: or when I don't feel that way, having to backtrack

786
00:45:08,080 --> 00:45:11,759
Speaker 1: and explain myself even more because I feel like I

787
00:45:11,840 --> 00:45:15,280
Speaker 1: have to justify something that shouldn't have to be automatically justified.

788
00:45:15,560 --> 00:45:18,160
Speaker 1: That makes sense. Um, And so yeah, this is just

789
00:45:18,280 --> 00:45:20,759
Speaker 1: a whole narrative that's placed on me that I'm like

790
00:45:21,120 --> 00:45:24,080
Speaker 1: grew up with saying, yes, I'm so grateful, I'm so grateful.

791
00:45:24,160 --> 00:45:26,560
Speaker 1: Don't say anything bad, don't say anything bad and then

792
00:45:26,680 --> 00:45:29,880
Speaker 1: waking up as an adult be like that sucked. Some

793
00:45:30,040 --> 00:45:32,960
Speaker 1: of these things are really bad and having to acknowledge that.

794
00:45:33,080 --> 00:45:34,600
Speaker 1: And that's kind of the thing is like when you

795
00:45:34,680 --> 00:45:38,680
Speaker 1: sit here in the background and she's not necessarily she

796
00:45:38,760 --> 00:45:41,000
Speaker 1: because I can't say she did it, but whoever is

797
00:45:41,080 --> 00:45:44,839
Speaker 1: trying to push her forward as a saint, it's using

798
00:45:44,920 --> 00:45:47,600
Speaker 1: her children as the narrative, as as subjects, as tokens

799
00:45:47,920 --> 00:45:52,600
Speaker 1: for her hers. It's an incredibly uh distraught type of

800
00:45:52,680 --> 00:45:54,600
Speaker 1: moment of like, what the hell are you doing? You're

801
00:45:54,680 --> 00:45:57,000
Speaker 1: using these kids without knowing what the hell is happening

802
00:45:57,440 --> 00:46:00,560
Speaker 1: behind behind the scenes. And kids aren't ups, you know,

803
00:46:00,800 --> 00:46:04,239
Speaker 1: kids aren't props to to further some rhetorical message or

804
00:46:04,840 --> 00:46:08,120
Speaker 1: you know idea. It's just it's so like I said,

805
00:46:08,120 --> 00:46:10,120
Speaker 1: I just feel I feel bad for kids who have

806
00:46:10,280 --> 00:46:15,120
Speaker 1: to whose stories get reduced in public in this way.

807
00:46:15,239 --> 00:46:18,399
Speaker 1: And I can't even imagine what that feels like. Right well,

808
00:46:18,400 --> 00:46:20,759
Speaker 1: I mean, just now that I've gone about Tangent, just

809
00:46:20,880 --> 00:46:23,759
Speaker 1: to go back, it's kind of incredible to see how

810
00:46:24,080 --> 00:46:26,480
Speaker 1: social media has been pushing her this way. Even the

811
00:46:26,560 --> 00:46:30,200
Speaker 1: White House uh tweets have been pushing her in this way.

812
00:46:30,280 --> 00:46:33,160
Speaker 1: Of trying to a make a CB happen as if

813
00:46:33,239 --> 00:46:36,320
Speaker 1: she is the new uh, you know, Ruth Bader, and

814
00:46:36,360 --> 00:46:39,760
Speaker 1: I'm like, no, stop it and be trying to hide

815
00:46:39,880 --> 00:46:43,719
Speaker 1: any of her past stuff, including the several conversations and

816
00:46:43,800 --> 00:46:45,879
Speaker 1: seminars that she was a part of that was very

817
00:46:45,920 --> 00:46:48,960
Speaker 1: anti choice. And it's kind of interesting to see how

818
00:46:49,360 --> 00:46:52,120
Speaker 1: the social media narrative has really dwindled it down to

819
00:46:52,320 --> 00:46:55,120
Speaker 1: making her the hero. Oh yes, oh yes, And I

820
00:46:55,200 --> 00:46:58,480
Speaker 1: think that you know, I like if you like. So

821
00:46:58,560 --> 00:47:00,640
Speaker 1: the hearing is going on today right now as we're

822
00:47:00,640 --> 00:47:02,680
Speaker 1: as we're recording this, and I checked into it earlier,

823
00:47:02,760 --> 00:47:05,759
Speaker 1: and the conversation on social media one is really being

824
00:47:05,800 --> 00:47:08,480
Speaker 1: dominated by folks on the right who were doing exactly that,

825
00:47:08,600 --> 00:47:11,719
Speaker 1: And so I do think that they are just kind

826
00:47:11,719 --> 00:47:14,359
Speaker 1: of hitting this message, like like you know, the idea

827
00:47:14,360 --> 00:47:17,160
Speaker 1: that if you say something a million times it kind

828
00:47:17,160 --> 00:47:19,560
Speaker 1: of makes it seem true. They're just continuing to hit

829
00:47:19,640 --> 00:47:21,719
Speaker 1: this message like she is, like she is the new

830
00:47:21,800 --> 00:47:24,719
Speaker 1: feminist hero. Isn't it great that we that we get to,

831
00:47:24,920 --> 00:47:27,960
Speaker 1: you know, have this new feminist hero. Um. Something that

832
00:47:28,040 --> 00:47:31,279
Speaker 1: someone said when RBG died is that so many of

833
00:47:31,360 --> 00:47:35,719
Speaker 1: the thing the specific policies and things that RBG gave us,

834
00:47:35,840 --> 00:47:37,960
Speaker 1: you know, things that have made not just the lives

835
00:47:38,000 --> 00:47:42,279
Speaker 1: of women, but everybody better. It's like RBG opened the

836
00:47:42,360 --> 00:47:44,239
Speaker 1: door and now the person who was trying to take

837
00:47:44,280 --> 00:47:46,920
Speaker 1: her place is shutting that door for so many of us.

838
00:47:46,960 --> 00:47:49,440
Speaker 1: And it's just it's it's like twisting the knife. Man.

839
00:47:49,520 --> 00:47:52,040
Speaker 1: I don't know, it's so it's so hard. It's been,

840
00:47:52,200 --> 00:47:55,320
Speaker 1: it's been a rough it's been. I mean not to

841
00:47:55,400 --> 00:48:01,040
Speaker 1: be too dramatic, but when RBG died, it was the

842
00:48:01,200 --> 00:48:03,880
Speaker 1: cherry on like it felt like the sherry on a

843
00:48:04,200 --> 00:48:09,120
Speaker 1: sandwich of a year. That's the one I can say it.

844
00:48:10,440 --> 00:48:12,120
Speaker 1: We have a little bit more for you listeners, but

845
00:48:12,200 --> 00:48:13,919
Speaker 1: first we have one more quick break for a word

846
00:48:13,920 --> 00:48:31,200
Speaker 1: from our sponsor and her back. Thank you sponsor. What

847
00:48:31,360 --> 00:48:34,880
Speaker 1: are some things that we can do to stop some

848
00:48:35,040 --> 00:48:39,080
Speaker 1: of this misinformation from being spread right now? Well, the

849
00:48:39,160 --> 00:48:42,160
Speaker 1: most important thing to think about is, you know, there's

850
00:48:42,160 --> 00:48:45,240
Speaker 1: so much disinformation and misinformation out there. The most important

851
00:48:45,280 --> 00:48:47,560
Speaker 1: thing that all of us, anybody listening right now, can

852
00:48:47,600 --> 00:48:51,360
Speaker 1: do is to become a trusted source of accurate and

853
00:48:51,560 --> 00:48:54,880
Speaker 1: positive information. Right so, you know, make sure that the

854
00:48:54,880 --> 00:48:57,839
Speaker 1: people in your community have the accurate information they need

855
00:48:57,920 --> 00:49:00,560
Speaker 1: to vote, make sure that they understand stand what they're

856
00:49:00,640 --> 00:49:02,880
Speaker 1: voting or who they're voting for. Make sure that they

857
00:49:02,960 --> 00:49:06,520
Speaker 1: have that information and kind of become an influencer in

858
00:49:06,600 --> 00:49:08,400
Speaker 1: your own little pocket of the internet. Even if you

859
00:49:08,480 --> 00:49:12,560
Speaker 1: only have five ten followers, somebody on your timeline, whether

860
00:49:12,600 --> 00:49:15,520
Speaker 1: it's your family member, your friend, probably looks to you

861
00:49:15,640 --> 00:49:17,920
Speaker 1: and thinks, this person knows what they're talking about. I

862
00:49:18,120 --> 00:49:21,680
Speaker 1: can count on this person to provide accurate information. So

863
00:49:21,840 --> 00:49:26,440
Speaker 1: focus on, you know, becoming that person in your online community,

864
00:49:26,480 --> 00:49:29,400
Speaker 1: and really that is a good, the most important thing

865
00:49:29,480 --> 00:49:32,520
Speaker 1: we can all do to inoculate our communities against disinformation.

866
00:49:33,160 --> 00:49:36,239
Speaker 1: Another important thing to note is you don't want to

867
00:49:36,480 --> 00:49:40,680
Speaker 1: inadvertently help disinformation or misinformation spread. I see this all

868
00:49:40,719 --> 00:49:42,360
Speaker 1: the time on social media because of the way that

869
00:49:42,400 --> 00:49:46,239
Speaker 1: social media works. It's algorithm based. People. You know, we'll

870
00:49:46,280 --> 00:49:49,280
Speaker 1: see a tweet that's wrong or misleading, and they'll retweet

871
00:49:49,320 --> 00:49:52,759
Speaker 1: it to to debunk it. But because the algorithm is

872
00:49:52,840 --> 00:49:54,759
Speaker 1: just like pushing it into more people's feed so you're

873
00:49:54,760 --> 00:49:58,520
Speaker 1: actually helping it grow. So never retweet, never share, even

874
00:49:58,560 --> 00:50:00,040
Speaker 1: if it's to debunk it, because you're only go to

875
00:50:00,080 --> 00:50:04,440
Speaker 1: make it grow, So instead focus on just posting positive

876
00:50:04,520 --> 00:50:08,040
Speaker 1: information and active information about how people can vote and

877
00:50:08,080 --> 00:50:10,279
Speaker 1: make sure that folks know, make sure that people have

878
00:50:10,360 --> 00:50:13,279
Speaker 1: the information they need to vote in these new these

879
00:50:13,680 --> 00:50:15,520
Speaker 1: different ways that we're doing it now, vote by mail,

880
00:50:15,560 --> 00:50:17,680
Speaker 1: all of that, make sure that they have the information

881
00:50:17,760 --> 00:50:20,320
Speaker 1: they need to do it safely, and just focus on

882
00:50:20,400 --> 00:50:25,080
Speaker 1: putting that message out. Those are great tips. Um. What

883
00:50:25,760 --> 00:50:27,560
Speaker 1: are there any resources you want to shout out or

884
00:50:27,560 --> 00:50:29,480
Speaker 1: any groups you want to shout out who are particularly

885
00:50:29,560 --> 00:50:32,960
Speaker 1: fighting this information right now? Well, Ultra Violet, a group

886
00:50:33,000 --> 00:50:35,560
Speaker 1: that I work with, is really um doing a lot

887
00:50:35,600 --> 00:50:37,800
Speaker 1: of work to make sure that just people, you know,

888
00:50:38,040 --> 00:50:41,080
Speaker 1: everyday people have the information they need to you know,

889
00:50:41,280 --> 00:50:44,480
Speaker 1: not make disinformation spread. Also, groups like the Pan America

890
00:50:44,520 --> 00:50:47,400
Speaker 1: Foundation are putting together guides. Uh, Sam, you mentioned that

891
00:50:47,440 --> 00:50:50,279
Speaker 1: you often have conversations with your family if you're someone

892
00:50:50,320 --> 00:50:52,960
Speaker 1: who's listening and you're like, yes, I know this, but

893
00:50:53,200 --> 00:50:57,279
Speaker 1: my wild uncle posts wild things in the group chat

894
00:50:57,400 --> 00:51:00,720
Speaker 1: or on the WhatsApp group or on Facebook. Pen America

895
00:51:00,760 --> 00:51:03,000
Speaker 1: Foundation has a great guide about how to talk to

896
00:51:03,440 --> 00:51:06,600
Speaker 1: the people in your community about their use or spread

897
00:51:06,719 --> 00:51:09,160
Speaker 1: of disinformation or misinformation. Because nobody wants to be the

898
00:51:09,200 --> 00:51:11,960
Speaker 1: person that's like getting into a long asked Facebook back

899
00:51:11,960 --> 00:51:13,600
Speaker 1: and forth with your uncle that you're gonna see it

900
00:51:13,680 --> 00:51:15,960
Speaker 1: Thanksgiving and it's gonna get awkward and everybody's gonna be

901
00:51:15,960 --> 00:51:18,280
Speaker 1: seeing it, like nobody wants that. There are better ways

902
00:51:18,320 --> 00:51:20,439
Speaker 1: to do it, So definitely check out Pan America's guide.

903
00:51:22,480 --> 00:51:27,879
Speaker 1: I love it. Um are there? What do you think

904
00:51:28,000 --> 00:51:30,560
Speaker 1: like social media companies should be doing to to deal

905
00:51:30,640 --> 00:51:33,040
Speaker 1: with all of this? Oh, my gosh, I mean they've

906
00:51:33,040 --> 00:51:36,080
Speaker 1: pretty much done nothing, So I think they could start

907
00:51:36,120 --> 00:51:39,840
Speaker 1: by doing something. You know, I think we really have

908
00:51:40,040 --> 00:51:43,880
Speaker 1: to have a real reckoning around the ways that social

909
00:51:43,960 --> 00:51:48,279
Speaker 1: media platforms like Facebook and Twitter have allowed disinformation to

910
00:51:48,400 --> 00:51:52,200
Speaker 1: run rampant and really cause chaos and our democracy. So

911
00:51:52,520 --> 00:51:54,200
Speaker 1: at the very base level, I would love to see,

912
00:51:54,560 --> 00:51:58,040
Speaker 1: you know, Facebook be more transparent about their policies and

913
00:51:58,120 --> 00:52:01,200
Speaker 1: how they are enforced enforced their existing policies that are

914
00:52:01,239 --> 00:52:03,520
Speaker 1: on the book, you know, every now and then when

915
00:52:03,560 --> 00:52:07,000
Speaker 1: they have some sort of big pr disaster like um,

916
00:52:07,280 --> 00:52:09,640
Speaker 1: that kid out in Kenosha who shot those two protesters

917
00:52:09,680 --> 00:52:12,759
Speaker 1: and used Facebook events to organize you know, come to

918
00:52:12,920 --> 00:52:17,280
Speaker 1: arms are you know bare arms rally? Uh? That event

919
00:52:17,640 --> 00:52:20,800
Speaker 1: went against their policies. So you know, it's difficult to

920
00:52:20,840 --> 00:52:22,839
Speaker 1: trust Facebook when they come and say like, oh, we're

921
00:52:22,880 --> 00:52:24,359
Speaker 1: gonna have a new policy, we are to crack down

922
00:52:24,400 --> 00:52:27,360
Speaker 1: on this, or that they should be enforcing their existing

923
00:52:27,520 --> 00:52:31,239
Speaker 1: policies and you know, having more transparency around how those

924
00:52:31,280 --> 00:52:35,880
Speaker 1: policies are enforced. So that's the very baseline thing they

925
00:52:35,960 --> 00:52:40,360
Speaker 1: can do to help prevent the spread of harmful disinformation

926
00:52:40,400 --> 00:52:46,719
Speaker 1: and misinformation and also violence on their platforms. Yeah, yeah, UM,

927
00:52:46,880 --> 00:52:51,920
Speaker 1: and I hope that. Um, I hope that happens because

928
00:52:51,960 --> 00:52:53,400
Speaker 1: I know all of us are being exposed to this

929
00:52:53,520 --> 00:52:55,520
Speaker 1: more and more, and we have people we care about

930
00:52:55,520 --> 00:52:58,680
Speaker 1: being supposed to be more and more. UM. And I

931
00:52:58,760 --> 00:53:01,040
Speaker 1: hope that next time you're on the show, Bridget our

932
00:53:01,160 --> 00:53:06,960
Speaker 1: conversation is more positive, an upbeat. I don't know where

933
00:53:07,000 --> 00:53:12,520
Speaker 1: it will be in a month from now, but fingers cross,

934
00:53:12,600 --> 00:53:15,279
Speaker 1: fingers crossed. I hope so too. I hope so too.

935
00:53:16,440 --> 00:53:20,000
Speaker 1: Oh my gosh, UM, So thank you so much for

936
00:53:20,280 --> 00:53:25,279
Speaker 1: being here, um and having this conversation, much needed conversation. UM.

937
00:53:25,560 --> 00:53:28,840
Speaker 1: So looking forward to having you on the show in

938
00:53:28,920 --> 00:53:31,640
Speaker 1: the future. Um. Where can the listeners find you in

939
00:53:31,719 --> 00:53:34,520
Speaker 1: the meantime, Well, you can listen to and subscribe to

940
00:53:34,640 --> 00:53:36,680
Speaker 1: my podcast. There are no girls on the internet. On

941
00:53:36,760 --> 00:53:39,560
Speaker 1: I Heart Radio, this very network. We don't just have

942
00:53:39,920 --> 00:53:43,080
Speaker 1: enraging conversations some some of them are fun and interesting

943
00:53:43,160 --> 00:53:46,880
Speaker 1: and insightful or touching. They are not just enraging, I

944
00:53:47,000 --> 00:53:49,440
Speaker 1: promise um and you can follow me on Twitter at

945
00:53:49,520 --> 00:53:52,000
Speaker 1: at bridget Marie or on Instagram at bridget Marie and

946
00:53:52,080 --> 00:53:56,239
Speaker 1: d C awesome and definitely do that listeners if you're

947
00:53:56,280 --> 00:53:58,960
Speaker 1: not doing it already. If you would like to contact us,

948
00:53:59,000 --> 00:54:01,880
Speaker 1: you can or email Stuff Media mom Stuff at iHeart

949
00:54:01,920 --> 00:54:03,560
Speaker 1: media dot com. You can find us on Instagram at

950
00:54:03,560 --> 00:54:05,040
Speaker 1: stuff I Never Told You, are on Twitter at mom

951
00:54:05,080 --> 00:54:07,920
Speaker 1: Stuff Podcast. Thanks as always to our super producer Andrew

952
00:54:07,960 --> 00:54:11,400
Speaker 1: Howard Andrew, and thanks to you for listening Stuff I

953
00:54:11,480 --> 00:54:13,239
Speaker 1: Never Told You, the production of iHeart Radio. For more

954
00:54:13,280 --> 00:54:15,399
Speaker 1: podcasts from my heart Radio is the iHeart Radio app,

955
00:54:15,400 --> 00:54:17,680
Speaker 1: Apple podcast, or wherever you listen to your favorite shows.