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Speaker 1: Welcome to Tech Stuff, a production from my Heart Radio.

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Speaker 1: Hey there, and welcome to tech Stuff. I'm your host,

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Speaker 1: Jonathan Strickland. I'm an executive producer with our Heart Radio

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Speaker 1: and I love all things tech and I had to

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Speaker 1: rush through that intro. Ladies, gentlemen, everybody. I have to

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Speaker 1: talk to my good friend Bridget, host of There Are

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Speaker 1: No Girls on the Internet, who has joined the show

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Speaker 1: today to talk about a very serious topic. But before

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Speaker 1: we get into that, Bridget, it is so nice to

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Speaker 1: have you on the show. It's so nice to be here.

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Speaker 1: I'm so excited. You know, we connect every week just

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Speaker 1: via phone, and it's nice to be connecting in people's

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Speaker 1: ear buzz in podcast land. Yeah. So, Bridget has been

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Speaker 1: hosting an incredible show, There Are No Girls on the Internet.

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Speaker 1: If you haven't heard it already, you absolutely need to

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Speaker 1: seek it out. And specifically, I wanted to bring her

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Speaker 1: on the show to talk about a season two theme

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Speaker 1: that you've really done a lot of hard work on, Disinformed,

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Speaker 1: because we're going to be talking about misinformation, disinformation and

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Speaker 1: the promotion of that through algorithms. But Bridge on one,

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Speaker 1: in your own words, why don't you talk a bit

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Speaker 1: about there are no girls on the Internet in general

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Speaker 1: and disinformed particular. Yeah, so There Are No Girls on

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Speaker 1: the Internet was really born from this idea that we

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Speaker 1: know that marginalized people, underrepresented people, women, communities of color,

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Speaker 1: other folks from underrepresented backgrounds have had such a huge

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Speaker 1: impact on technology and honestly, just what makes the Internet

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Speaker 1: a fun, great place? Like how many times is there

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Speaker 1: hilarious hashtag or a hilarious vine or a hilarious video

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Speaker 1: on TikTok that goes viral that you're seeing everywhere and

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Speaker 1: the person behind it is a woman or a person

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Speaker 1: of color. And so I really wanted to carve out

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Speaker 1: a space where we could really highlight and sell brain

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Speaker 1: and amplify all the amazing impacts that people from underrepresented

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Speaker 1: backgrounds have had on the Internet. And so that's why

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Speaker 1: I started There Are No Girls on the Internet. It's

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Speaker 1: been a really fun journey to really talk about some

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Speaker 1: of the ways that underrepresenting folks either show up or

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Speaker 1: don't show up online. And from making that show, one

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Speaker 1: of the things that came up for me again and

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Speaker 1: again was the role that disinformation and distorted media stories,

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Speaker 1: fake news that you know, um things like online harassment radicalization.

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Speaker 1: How these same underrepresenting people who are showing up, making up,

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Speaker 1: making great content online, making the Internet such a fun place,

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Speaker 1: those same people are really at the forefront of a

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Speaker 1: lot of this stuff, right, You know, when it comes

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Speaker 1: to disinformation, communities of color are the most impacted, women

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Speaker 1: are the most impacted. But we are the same ones

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Speaker 1: who are doing a lot of the research and a

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Speaker 1: lot of the work to fight back. And I was

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Speaker 1: really fascinated by that. I was fascinated by the human

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Speaker 1: center of of disinformation. I think with a lot of tech,

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Speaker 1: it can be hard to remember that it's really about people.

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Speaker 1: You know, that people looked up at the other end

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Speaker 1: of screens or the other end of phones, and disinformation

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Speaker 1: was a situation where it really was important to meet

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Speaker 1: a center those people and to make sure that their

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Speaker 1: stories were at the forefront. You bring up a point

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Speaker 1: that I think is good for us to lay out

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Speaker 1: right at the very beginning, because while we're going to

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Speaker 1: be talking about social media platforms, specifically platforms like Facebook, YouTube, Twitter,

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Speaker 1: those sort of things, and to another extent, some of

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Speaker 1: the more uh extreme social networks that are out there

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Speaker 1: that are heavily promoting things like radicalization within the confines

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Speaker 1: of those own communities. While we're going to talk about that,

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Speaker 1: we have to remember that's one piece of a very big,

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Speaker 1: very complicated puzzle. UH. The the pathway to extremism or

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Speaker 1: radicalization is not a one lane highway where everybody goes

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Speaker 1: the exact same way. There's usually a lot of different

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Speaker 1: factors at play, and if we're being completely honest, we

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Speaker 1: still don't really have a full understanding of what weighted

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Speaker 1: UH factors are the most important, right like obviously being

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Speaker 1: in person with groups that are UH proselytizing extremist views

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Speaker 1: and are reinforcing when new people come into the community

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Speaker 1: that those extremist views are legitimate and and reinforcing. Whenever

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Speaker 1: someone expresses an extremist view, that's clearly very important, and

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Speaker 1: when it happens in a real space meet space, UH,

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Speaker 1: it can have a huge impact. But we also know

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Speaker 1: that online interactions also can play a big part, especially

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Speaker 1: as we have embraced them more wholeheartedly in the last decade,

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Speaker 1: and clearly in a situation like we are in now

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Speaker 1: where all of a lot of us are at home

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Speaker 1: and the only interactions were mostly having with other people

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Speaker 1: are online we're seeing that online radicalization is in fact

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Speaker 1: a factor as well, but to what extent it is

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Speaker 1: as effective or ineffective as others. That's something that's under

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Speaker 1: academic debate. So we're gonna have this discussion, but we

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Speaker 1: do want to make it clear that there are no

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Speaker 1: absolutes that we know of yet. These are all things

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Speaker 1: that are under study and consideration. It's more that we're

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Speaker 1: trying to kind of take stock of what's going on

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Speaker 1: and what elements could be playing a part in the

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Speaker 1: in the process of someone encountering extremist views and the

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Speaker 1: possibility of getting wrapped up in that um. So with

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Speaker 1: that in mind, let's talk about social media. One thing

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Speaker 1: I noticed I decided to do a quick search bridget

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Speaker 1: before we jumped on this call, and I found a

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Speaker 1: Pew Research survey from a couple of years ago that

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Speaker 1: was looking at, uh, what percentage of people were getting

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Speaker 1: some or most of their news through social networking sites,

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Speaker 1: primarily Facebook, And at this point it's around adults get

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Speaker 1: either some of their news through social media or practically

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Speaker 1: all of their news through social media. So obviously a

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Speaker 1: social platform plays a big part in the way we

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Speaker 1: access information that we then tried to incorporate into our lives. Yeah,

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Speaker 1: I think so. I know that study. I'm glad that

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Speaker 1: you grounded it in our conversation in that study. You know,

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Speaker 1: as someone who tracks and and talks and thinks a

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Speaker 1: lot about the way that platforms operate in the role

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Speaker 1: they have in our society. For a long time, platforms

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Speaker 1: like Facebook would say that they were just neutral plat forms,

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Speaker 1: you know, they did not need to take any kind

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Speaker 1: of editorial strategy because they're just neutral platforms. And I

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Speaker 1: think that Pew study that adults get some some aspect

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Speaker 1: of their news from from these platforms really shows how

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Speaker 1: that's not always the case. You know, you have to

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Speaker 1: I think that tech leaders should be thinking about their

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Speaker 1: platform policies based on the role they actually play in

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Speaker 1: their users lives. And if most users are saying, hey,

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Speaker 1: I use your platforms to get my news, then maybe

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Speaker 1: you do need to have a little bit of editorial strategy, right,

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Speaker 1: Maybe you do need to have policies in place to

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Speaker 1: make sure that it's that it's being used in safe

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Speaker 1: and responsible ways. I'm glad that we have moved away

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Speaker 1: from that thinking that's like, oh, it is not my

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Speaker 1: fire on a platform. It is not my responsibility to

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Speaker 1: have any kind of policing or policies around what is

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Speaker 1: on that platform, which I think was the conversation for

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Speaker 1: so long. Yeah, I think the interesting thing to me

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Speaker 1: is that, um, you will hear a lot of debate

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Speaker 1: in in the in America about Section to thirty, which

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Speaker 1: is a specific piece of legislation that provides online platforms

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Speaker 1: some legal immunity from the sort of stuff that their

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Speaker 1: users might post to those platforms, saying that they are

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Speaker 1: not responsible for the things that their users share. And

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Speaker 1: the hope of the people who actually drafted that legislation

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Speaker 1: was that it was going to be a two pronged thing.

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Speaker 1: It would allow platforms to establish themselves, because otherwise they

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Speaker 1: would constantly be under legal threat and nothing would ever

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Speaker 1: gain any traction, because this legislation was proposed in the

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Speaker 1: nineties when the Worldwide Web was very young, And the

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Speaker 1: second part was that they were really hoping it was

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Speaker 1: going to lead to uh to moderation practices on these platforms,

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Speaker 1: that the platforms would moderate the material that was being

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Speaker 1: posted to them without fear of legal action against them

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Speaker 1: for doing so, that they would be given that freedom.

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Speaker 1: And the problem or one of the problems is that

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Speaker 1: a lot of these platforms embraced the first part where

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Speaker 1: they're not held responsible for the stuff that people post

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Speaker 1: to them, but they didn't go so hard on the

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Speaker 1: second part, where they actually moderate the material. And we've

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Speaker 1: seen that time and again because we've seen it on Facebook,

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Speaker 1: we've seen it a lot on YouTube. I mean, the

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Speaker 1: the whole uh controversy a couple of years ago about

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Speaker 1: the the various bizarre and sometimes very disturbing videos that

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Speaker 1: were showing up on YouTube's children oriented service shows that, Uh,

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Speaker 1: they weren't really looking at it from that perspective for

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Speaker 1: multiple reasons. And we'll get into some of those a

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Speaker 1: little bit later, because it does tie into the algorithm

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Speaker 1: side too. Uh. The other part Pew study that I

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Speaker 1: saw that was interesting and disheartening is a separate study

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Speaker 1: found that people who gained most of their news from

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Speaker 1: social media of sites were in general less engaged and

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Speaker 1: less knowledgeable about the subject matter than those who were

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Speaker 1: consuming it from multiple sources. So if you were someone who, yeah,

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Speaker 1: you've got some of your news from social platforms, but

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Speaker 1: you also got some of your news from journals or

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Speaker 1: newspapers or television, or radio or whatever. You generally had

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Speaker 1: a better understanding of subject matter. And this to me

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Speaker 1: almost sounds like the problem of people reading a headline,

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Speaker 1: never clicking through to read and digest the content, and

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Speaker 1: basing their entire response on the headline and maybe comments

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Speaker 1: that are placed underneath an article that's been posted to Facebook,

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Speaker 1: let's say, which is really upsetting as someone who has

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Speaker 1: an English background, I mean especially and for us, like

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Speaker 1: we've worked in media, we know there's a special skill

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Speaker 1: in creating headlines that doesn't necessarily relate back to the

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Speaker 1: content of the piece that it's attached to, or it's

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Speaker 1: it's some minor element that's elevated in order to gain attention.

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Speaker 1: So this, to me is another issue. And this is

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Speaker 1: before we even get into purposeful misinformation. This can just

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Speaker 1: be a misunderstanding because you didn't take the time to

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Speaker 1: read the full material. Oh absolutely, I can. I'm I

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Speaker 1: can say so much about this. Um So, I got

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Speaker 1: my One of the places that I've worked in my

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Speaker 1: life was MSNBC. I was on their digital team and

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Speaker 1: so our job was essentially framing stories on social media

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Speaker 1: in ways that we're going to get them clicks, and

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Speaker 1: you know, attention online, and so we also had access

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Speaker 1: to metrics, so we could actually see how people were

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Speaker 1: engaging with this content. And the amount of times that

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Speaker 1: people share stories on Facebook that we can see on

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Speaker 1: the back end that they have not actually clicked in

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Speaker 1: to read would frighten you. But it was our job

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Speaker 1: to kind of explore that, and so we learned that,

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Speaker 1: you know, oh, people like to share articles where it

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Speaker 1: seems like they're trying to indicate something about themselves, like

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Speaker 1: this article makes them look thoughtful or um interesting or

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Speaker 1: unique or well read but really informed, um, and so

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Speaker 1: they want to project that to the best of their followers,

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Speaker 1: so they share it. But those same stories are the

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Speaker 1: ones that people aren't clicking in to read. And so

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Speaker 1: it really is not surprising to me that people who

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Speaker 1: get their media and news from social media primarily are

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Speaker 1: less informed and less engaged, because I think these media

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Speaker 1: ecosystems really prioritize that. I think that they built a

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Speaker 1: media landscape that prioritizes sharing without reading thoughtfully right, like

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Speaker 1: everything happened so quickly on the Internet, I feel like

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Speaker 1: that is what's really the priority. And I also think

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Speaker 1: just our our media landscape has really changed I grew

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Speaker 1: up in a household where we got the newspaper. We've

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Speaker 1: got multiple newspapers in my home and we have to,

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Speaker 1: you know, note the ways that are Our journalism industry

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Speaker 1: has really been you know, um decimated. And so it's

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Speaker 1: not surprising to me that as small local papers that

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Speaker 1: are putting out local, relevant, timely information right on your doorstep,

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Speaker 1: as those begin to shut their doors, more and more

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Speaker 1: people are turning to social media, which is in turn

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Speaker 1: and making them less informed. That this is not a

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Speaker 1: surprise to me. Right, I have a great example to

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Speaker 1: give you a bridget It's a very recent one and

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Speaker 1: I think it's one you'll appreciate, which is that, uh,

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Speaker 1: as I record this, I think we're like a day

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Speaker 1: out from when a trailer for a new Mortal Kombat

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Speaker 1: movie came out and over on Jezebel, a writer wrote

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Speaker 1: an article with the title, why isn't chun Lee in

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Speaker 1: the Mortal Kombat movie? And for those who aren't schooled

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Speaker 1: in Mortal Kombat, chun Ley is not a character from

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Speaker 1: Mortal Kombat. She's a character from Street Fighter, different different franchise, Right,

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Speaker 1: But that was the headline. If you at the article,

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Speaker 1: there were hints in the article that this was a joke,

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Speaker 1: that there's a point where she they say, if in

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Speaker 1: the ultimate street Fight, chun Lee needs to be there,

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Speaker 1: indicating yes, the author knows that chun Lee doesn't live

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Speaker 1: in Mortal Kombat world, that chun Ley lives in street

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Speaker 1: Fighter world. But it was almost like a like a

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Speaker 1: social experiment, and of course Twitter went nuts and everybody

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Speaker 1: started slamming this article, saying, look, how stupid this person is.

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Speaker 1: They don't even know that chun Ley is not in

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Speaker 1: Mortal Kombat. Which made me say, all this is telling

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Speaker 1: me is which of you guys failed to read the article,

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Speaker 1: Because if you had read the article, you would have

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Speaker 1: seen it was a joke, you would have recognized it

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Speaker 1: as satire, and you would know that that this was

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Speaker 1: a wind up the whole time. You're really just falling

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Speaker 1: into the trap that the writer set at this point.

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Speaker 1: And it's just the perfect microcosm of this this tendency.

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Speaker 1: And I think that po listing links to two news

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Speaker 1: articles has almost become a shortcut for a comment. It's

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Speaker 1: almost like a comment or a gift or an emoji. Right,

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Speaker 1: It's a way for someone to say, here is my thought,

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Speaker 1: on that, uh in headline form, even though you may

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Speaker 1: not have clicked through to read the actual article underneath,

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Speaker 1: or you're just you're looking for something that supports whatever

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Speaker 1: position you're taking on any particular topic, whether it's something

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Speaker 1: lighthearted in pop culture or it's something really serious and

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Speaker 1: it has to do with like politics or health. And

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Speaker 1: I see that a lot too, where people are clearly

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Speaker 1: grabbing articles. They're just doing the down in dirty Google

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Speaker 1: search to find something that looks like it supports their

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Speaker 1: position and uses that in place. So again, this is

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Speaker 1: all before you even get too purposeful misinformation or disinformation.

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Speaker 1: It may be that the sources that you pull from

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Speaker 1: our misinformation, but it may be that they're totally yet

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Speaker 1: you just don't know the context for it. So you're

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Speaker 1: just you're just throwing things at the wall to see

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Speaker 1: what sticks. And uh So we're doing a lot of

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Speaker 1: the work ourselves is part of it, Like we have

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Speaker 1: created a a kind of culture that supports the sharing

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Speaker 1: of misinformation. But the flip side of that coin is

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Speaker 1: these platforms are optimized for the sharing of misinformation and

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Speaker 1: the elevation of misinformation. And that's really the part that

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Speaker 1: we can look at more critically and say in what

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Speaker 1: ways is this happening? And uh and and how culpable

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Speaker 1: are these platforms for that process? Uh? Keeping in mind

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Speaker 1: that in most cases, I think we would argue that

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Speaker 1: ultimately there are other people not connected to the platforms

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Speaker 1: who are generating the content that's getting elevated there, but

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Speaker 1: the platforms are benefiting from that. Um. So let's talk

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Speaker 1: a little bit about that and and what is going

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Speaker 1: on with these platforms and why we want to even

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Speaker 1: talk about algorithms. Uh So, just so you guys out there, no,

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Speaker 1: and algorithm is essentially just a list of instructions. It's

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Speaker 1: a you can think of it like a program. Doesn't

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Speaker 1: have to be a program, but it's essentially a list

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Speaker 1: of instructions that guides some sort of process. And in

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Speaker 1: the case of social networks, essentially what we talk about

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Speaker 1: when we say algorithm is it's the bit of that

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Speaker 1: platform that decides what content you see in what order

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Speaker 1: you see it. So for YouTube, it could be the

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Speaker 1: recommendation engine where you're watching one video and then you've

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Speaker 1: got a whole bunch of other videos recommended along the side.

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Speaker 1: So for me, it's all, uh, cute animal videos. I

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Speaker 1: wish I could lie and say it wasn't. I'm a

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Speaker 1: punk rock kind of guy. But it's all kitten and

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Speaker 1: puppy videos sometimes a sloth um and Facebook. It's it's

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Speaker 1: which posts you see in which order. You know, everyone

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Speaker 1: gets frustrated that they're not seeing it in a reverse

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Speaker 1: chronological order. Well, that's by design, and if as users

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Speaker 1: we think of it as it's the algorithm that shows

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Speaker 1: us what is potentially next on our docket. For the platforms,

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Speaker 1: algorithms exist solely because the platform wants to keep people

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Speaker 1: on it for as long as possible, because that's how

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Speaker 1: the platforms make money. It's through advertising, Uh, it's through

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Speaker 1: the the commoditization of people's personal information that they generate

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Speaker 1: while they're on that platform. And the longer you're on it,

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Speaker 1: the more the platform makes So the algorithms are are

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Speaker 1: I would argue a moral they don't really care what

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Speaker 1: is they're serving up as long as it keeps you there,

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Speaker 1: and that I think is the core of the problem.

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Speaker 1: Absolutely a thousand percent correct. I think that tech platforms

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Speaker 1: need to be held accountable for the ways that their

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Speaker 1: algorithms have really just take an advantage of a lot

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Speaker 1: of human nature is not so great tendencies, right humans. Listen,

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Speaker 1: we all know humans are terrible, right. We are lazy.

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Speaker 1: We make rash, snap decisions, myself very much included. We

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Speaker 1: will smash that share button, smash that like button, smash

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Speaker 1: that comment button on an article that frankly, we have

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Speaker 1: not read, and we're just putting our uninformed opinion out there.

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Speaker 1: I am guilty of all of this. Right, Platforms Essentially,

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Speaker 1: algorithms have rewarded that kind of behavior, that kind of

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Speaker 1: quick behavior, snap decision making, and created this this media

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Speaker 1: landscape where the citizenry is less informed, less engaged, and

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Speaker 1: more prime for bad actors to disinform and misinform them.

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Speaker 1: And I think the way that you put it it

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Speaker 1: is completely correct. And I think you know I I

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Speaker 1: say this all the time on my own podcast. I

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Speaker 1: want to one of the reasons why I'm very interested

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Speaker 1: in disinformation is because I think it's a good example

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Speaker 1: of the way that algorithmic thinking has really failed us.

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Speaker 1: You know, I want to build an internet that prioritizes

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Speaker 1: and privileges things like thoughtfulness or you know, discourse, or

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Speaker 1: really engaging with opinions that are different from your own.

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Speaker 1: And I feel like algorithms and platforms have given us

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Speaker 1: the opposite. It's a media landscape that rewards quick decision

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Speaker 1: making and being less engaged and less thoughtful and engaging

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Speaker 1: less and thus leaving people really able to be exploited

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Speaker 1: by people who do want to push misinformation and disinformation.

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Speaker 1: And I think when platforms like Facebook get rich off

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Speaker 1: of it, we really have to ask some questions. You know,

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Speaker 1: Facebook's own internal report says that six or four percent

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Speaker 1: at a time, when somebody joins an extremist Facebook group,

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Speaker 1: it's because Facebook itself recommended it, right, They should not

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Speaker 1: then be getting more money based on something that they

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Speaker 1: admit has such a corrosive impact on our society. Hey, guys,

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Speaker 1: Jonathan from the future here is coming in to say,

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Speaker 1: we're going to take a quick break with our conversation

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Speaker 1: with bridget Todd of there are no girls on the Internet,

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Speaker 1: and we will be back right after this break. You

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Speaker 1: can't play both sides of the issue saying, uh, we

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Speaker 1: don't have any responsibility, We're we're neutral, We're just a

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Speaker 1: platform that people post to, and then also reaping the

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Speaker 1: billions of dollars generated through the process of radicalization. If

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Speaker 1: you are directly profiting from that process. You are at

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Speaker 1: least in some way culpable for it. You can't like

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Speaker 1: the idea of being able to wash your hands and

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Speaker 1: walk away is uh alien to me. I can't imagine

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Speaker 1: being able to shed that kind of responsibility. And you know,

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Speaker 1: we talk about Facebook revenue in the billions of dollars

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Speaker 1: every single quarter. It's not not billions of dollars per year.

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Speaker 1: Every quarter of that company is making billions and a

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Speaker 1: lot of this is because of these algorithms that you know,

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Speaker 1: Facebook's their value proposition is that they can put very

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Speaker 1: specific groups of people in front of very specific content.

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Speaker 1: And it's it's because the the the Facebook engine learns

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Speaker 1: so much about us and what how we interact that

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Speaker 1: it can start predicting the things that we're going to

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Speaker 1: react to next and thus serve up that stuff in

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Speaker 1: front of us, and sure enough, we're bound to act

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Speaker 1: on it. And in if this were a world where

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Speaker 1: radicalization wasn't a thing, that would mostly mean we would

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Speaker 1: all be buying more crap and that would be the

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Speaker 1: worst of it. But we're seeing in the same thing

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Speaker 1: is true of YouTube, right that YouTube Google when you

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Speaker 1: log into YouTube, not if you're just browsing it, like,

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Speaker 1: without being logged in. But if you're logged into YouTube,

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Speaker 1: it is constantly tweaking your profile of the sort of

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Speaker 1: things that you interact with, both on and off the site,

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Speaker 1: and building on that and determining what you want to

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Speaker 1: see next. In fact, there were there was a study

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Speaker 1: self published, not even pure reviewed. I hesitate to even

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Speaker 1: bring it up, but there was one report that was

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Speaker 1: published a couple of years ago where a pair of

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Speaker 1: researchers and I used the term lightly, argued that YouTube

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Speaker 1: wasn't contributing to radicalization. And they had this whole thing

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Speaker 1: where they talked about watching different styles of videos and

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Speaker 1: seeing what got recommended next. Except for one tiny little thing.

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Speaker 1: They didn't log into YouTube. They were they were watching

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Speaker 1: it without logging in, And it's the log in process

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Speaker 1: where Google starts to build that profile, figuring out, oh, well,

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Speaker 1: they're inter acting with a lot of content that's in

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Speaker 1: say the video game space. That's another one that I

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Speaker 1: watch a lot of. So now I get puppies and

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Speaker 1: kittens and video games, not all in the same video

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Speaker 1: there separate, but I get a good mix. Um. But

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Speaker 1: if I were if I were to start watching videos

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Speaker 1: that were let's say a little to the far right

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Speaker 1: or the far left of the spectrum. Uh, then the

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Speaker 1: those algorithms start working and start determining what other sort

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Speaker 1: of videos might I respond well to, and based upon

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Speaker 1: my activities on the site, it tweaks those waitings, right,

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Speaker 1: It says, it's like machine learning. It says, all right, well,

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Speaker 1: we saw that they that this person spent an hour

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Speaker 1: when we shared this kind of video, so let's push

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Speaker 1: even harder on that and see if we can get

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Speaker 1: that engagement up even more. And from the platform side,

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Speaker 1: they're just thinking, how can we maximize this person's time

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Speaker 1: on our platform and make the most money. But it's

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Speaker 1: it's the same general approach as trying to radicalize someone,

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Speaker 1: where you're trying to continually serve them up information from

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Speaker 1: a very specific ideology and to reinforce that over and over.

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Speaker 1: It just so happens that these two things are in

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Speaker 1: aligned with one another. And so from that algorithmic standpoint,

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Speaker 1: we see the process of radicalization somewhat automated, and that

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Speaker 1: is where the real concerns are, and we know that

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Speaker 1: this is still a thing. There was a report just

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Speaker 1: earlier this month about how researchers were still finding that

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Speaker 1: extremist videos would get pop up in the recommendation uh

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Speaker 1: sidebar if you were to to watch them, whereas YouTube

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Speaker 1: has been working pretty hard to to to call that,

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Speaker 1: but it's still happening. So this is I can't see

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Speaker 1: how if you run so shual media platform that has

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Speaker 1: an algorithm to determine what order you see stuff in,

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Speaker 1: how you can step back and say you're not responsible

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Speaker 1: for at least playing a role in leading people toward extremism.

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Speaker 1: I mean, I wish I had that ability to wash

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Speaker 1: my hands of responsibility of things that I don't want

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Speaker 1: you be responsible for. But it's difficult to see how people,

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Speaker 1: how tech leaders cannot see their culpability in this. Just

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Speaker 1: as you described, it's it's very difficult to make the

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Speaker 1: argument that they don't have a responsibility when these kinds

432
00:26:36,720 --> 00:26:40,000
Speaker 1: of things, by their own metrics, are happening on their platform.

433
00:26:40,040 --> 00:26:43,280
Speaker 1: It's very clear study after study after study, particularly on YouTube,

434
00:26:43,440 --> 00:26:46,720
Speaker 1: which frankly, you know, in all of the conversations we've

435
00:26:46,720 --> 00:26:49,520
Speaker 1: been happening about having about you know, platform accountability, I've

436
00:26:49,560 --> 00:26:53,280
Speaker 1: been surprised the way that YouTube has really been able

437
00:26:53,320 --> 00:26:55,240
Speaker 1: to kind of skirt a lot of that heat. Like

438
00:26:55,240 --> 00:26:57,280
Speaker 1: we talk a lot about Facebook, we talk about Twitter,

439
00:26:57,600 --> 00:26:59,520
Speaker 1: but YouTube. I don't know how they've done it, but

440
00:26:59,520 --> 00:27:02,720
Speaker 1: they've been a this sort of sidestep that conversation, which

441
00:27:02,760 --> 00:27:05,880
Speaker 1: I think is really not good considering the role they

442
00:27:05,960 --> 00:27:08,199
Speaker 1: do play in radicalization. You know, I think that we

443
00:27:08,320 --> 00:27:11,360
Speaker 1: really I'm very curious how they've been able to avoid

444
00:27:11,440 --> 00:27:15,480
Speaker 1: that kind of responsibility for so long. Your point about platforms,

445
00:27:15,520 --> 00:27:16,720
Speaker 1: I think it is such a good one, and that

446
00:27:17,359 --> 00:27:18,800
Speaker 1: if they're if if we didn't, if you lived in

447
00:27:18,840 --> 00:27:22,160
Speaker 1: a wrote that did not have radical views or extremist views,

448
00:27:22,600 --> 00:27:25,320
Speaker 1: even then I would I would want to ask questions

449
00:27:25,359 --> 00:27:29,120
Speaker 1: about whether or not these platforms are actually doing good

450
00:27:29,240 --> 00:27:31,679
Speaker 1: or doing harm. I remember this time. This is kind

451
00:27:31,720 --> 00:27:33,080
Speaker 1: of a weird story, but I was going through a

452
00:27:33,080 --> 00:27:36,160
Speaker 1: breakup and I was still following my ex on Facebook.

453
00:27:36,200 --> 00:27:37,879
Speaker 1: And it was one of those breakups where I was like,

454
00:27:38,000 --> 00:27:40,919
Speaker 1: you know, like it was really getting me down, and

455
00:27:40,960 --> 00:27:45,760
Speaker 1: I realized Facebook must have sensed that my relationship with

456
00:27:45,800 --> 00:27:48,560
Speaker 1: this other account was something was going on there. So

457
00:27:48,880 --> 00:27:51,600
Speaker 1: every little update that I got about my ex, Facebook

458
00:27:51,680 --> 00:27:53,680
Speaker 1: was like shoving it in my face. And I realized

459
00:27:54,000 --> 00:27:56,359
Speaker 1: a platform can make you feel bad about yourself. A

460
00:27:56,359 --> 00:27:58,440
Speaker 1: platform can make you if it has the power to

461
00:27:58,480 --> 00:28:01,240
Speaker 1: shape you to extremist ideality, to your extremist thinking or

462
00:28:01,280 --> 00:28:04,800
Speaker 1: political content. It has that power to really shape how

463
00:28:04,880 --> 00:28:07,000
Speaker 1: we feel in our day to day. I wish it

464
00:28:07,080 --> 00:28:10,120
Speaker 1: wasn't true, but it is. And so I think even

465
00:28:10,359 --> 00:28:13,200
Speaker 1: from that perspective, it is completely fair to step back

466
00:28:13,240 --> 00:28:16,960
Speaker 1: and ask, well, how are these platforms contributing to harm?

467
00:28:17,000 --> 00:28:19,720
Speaker 1: Whether it's extremist content or just how someone feels on

468
00:28:19,760 --> 00:28:21,480
Speaker 1: their day to day, like if they feel like they're

469
00:28:21,520 --> 00:28:25,040
Speaker 1: able to step away from platforms, if platforms have prioritized

470
00:28:25,359 --> 00:28:28,439
Speaker 1: amount of time on screen or amount of time on

471
00:28:28,480 --> 00:28:31,080
Speaker 1: a page, or what have you. Is that kind of

472
00:28:31,080 --> 00:28:34,080
Speaker 1: thinking doing harm in society? I think it's completely fair

473
00:28:34,119 --> 00:28:36,199
Speaker 1: to ask these questions. And I think, you know, I

474
00:28:36,240 --> 00:28:39,880
Speaker 1: would like platforms. I would like tech leaders and the

475
00:28:39,960 --> 00:28:42,840
Speaker 1: people that you know use these these technologies to have

476
00:28:43,000 --> 00:28:45,160
Speaker 1: that conversation. But I feel like getting to a point

477
00:28:45,160 --> 00:28:46,560
Speaker 1: where we're all on the same page it has been

478
00:28:46,600 --> 00:28:50,479
Speaker 1: so tough. Yeah, And I would say that the the

479
00:28:50,600 --> 00:28:55,520
Speaker 1: efforts we have seen kind of illustrate the point you're making,

480
00:28:55,520 --> 00:29:00,520
Speaker 1: Bridget in that we see these tech companies occasionally spawned

481
00:29:01,400 --> 00:29:04,280
Speaker 1: when things come to a point where there's no other option,

482
00:29:04,360 --> 00:29:08,240
Speaker 1: they have to respond right there, called before Congress. Perhaps

483
00:29:08,440 --> 00:29:12,960
Speaker 1: that's happened multiple times now, or they're under pressure because

484
00:29:13,240 --> 00:29:17,600
Speaker 1: of advertisers that don't want to be associated with things

485
00:29:17,640 --> 00:29:21,400
Speaker 1: that are spiraling out of control. Uh, we see that

486
00:29:21,680 --> 00:29:24,600
Speaker 1: those cases when I have things get to the extreme,

487
00:29:24,680 --> 00:29:30,760
Speaker 1: But often the responses are very uh, surface level. So

488
00:29:31,000 --> 00:29:35,760
Speaker 1: things like we now have a part of our app

489
00:29:35,840 --> 00:29:39,000
Speaker 1: where it will alert you if you've spent X amount

490
00:29:39,000 --> 00:29:41,200
Speaker 1: of time on the app, So it's a little screen

491
00:29:41,200 --> 00:29:46,080
Speaker 1: alert time. And you're thinking, well, I see how you're

492
00:29:46,080 --> 00:29:50,280
Speaker 1: trying to address the issue kind of, but you're not

493
00:29:50,400 --> 00:29:54,120
Speaker 1: getting at the underlying problem. You're just looking at a symptom.

494
00:29:54,160 --> 00:29:58,440
Speaker 1: It's like treating someone who is very, very sick, but

495
00:29:58,480 --> 00:30:00,640
Speaker 1: all you can do is alleviate the terms but not

496
00:30:00,840 --> 00:30:05,000
Speaker 1: cure the sickness. The same sort of issue. And uh,

497
00:30:05,400 --> 00:30:09,840
Speaker 1: when we're talking about platforms that have the capacity to

498
00:30:10,080 --> 00:30:16,600
Speaker 1: make massive changes in people's behavior over time, that's not

499
00:30:16,680 --> 00:30:22,280
Speaker 1: really good enough, right, That's it. We are We are

500
00:30:22,360 --> 00:30:27,480
Speaker 1: having countless people go down dark pathways where it's very

501
00:30:27,480 --> 00:30:31,400
Speaker 1: hard to turn back, and there's they're going into communities

502
00:30:31,480 --> 00:30:37,120
Speaker 1: where there's all this reinforcement that's again supplemented by the

503
00:30:37,640 --> 00:30:40,880
Speaker 1: wave platforms work in the first place. Now, one of

504
00:30:40,880 --> 00:30:43,000
Speaker 1: the other things I wanted to mention is that and

505
00:30:43,040 --> 00:30:45,120
Speaker 1: I we talked about at the very beginning about how

506
00:30:46,200 --> 00:30:50,440
Speaker 1: we don't know really the full scope of the effect

507
00:30:50,480 --> 00:30:52,880
Speaker 1: of this. We know what's happening, we know that it's

508
00:30:52,880 --> 00:30:58,560
Speaker 1: a problem. The interesting thing is seeing some disagreements among

509
00:30:59,000 --> 00:31:02,480
Speaker 1: researchers who have really looked into this as to the extent.

510
00:31:02,880 --> 00:31:06,600
Speaker 1: So one of the articles I referenced with you, Bridget

511
00:31:06,720 --> 00:31:10,320
Speaker 1: was one that an opinion piece and wired by Emma Bryant.

512
00:31:11,200 --> 00:31:15,080
Speaker 1: She was writing about the Oxford Institute. They had released

513
00:31:15,080 --> 00:31:19,840
Speaker 1: a study that was showing an increase in UH companies

514
00:31:19,880 --> 00:31:24,920
Speaker 1: participating in influenced. Influenced campaigns is the way they word it.

515
00:31:25,120 --> 00:31:28,480
Speaker 1: I hate these I hate these words that we use

516
00:31:29,080 --> 00:31:32,040
Speaker 1: where we take the staying out of what's happening. It's

517
00:31:32,040 --> 00:31:35,000
Speaker 1: an influence campaing. Although it does also bring give you

518
00:31:35,040 --> 00:31:37,440
Speaker 1: a new appreciation of what it means to be an influencer.

519
00:31:37,480 --> 00:31:43,560
Speaker 1: It makes it much more sinister, realistic, but sinister. Yeah,

520
00:31:44,280 --> 00:31:47,680
Speaker 1: campaign sounds like what a Instagram influencer does to get

521
00:31:47,720 --> 00:31:54,040
Speaker 1: you to buy coffee or something. Yeah. And now, granted

522
00:31:54,120 --> 00:31:58,080
Speaker 1: you could argue that the same same ideas that are

523
00:31:58,200 --> 00:32:00,800
Speaker 1: used by influencers to try and get their followers to

524
00:32:00,840 --> 00:32:04,720
Speaker 1: go and buy whatever brand is supporting them are it's

525
00:32:04,760 --> 00:32:07,240
Speaker 1: somewhat aligned with some of these other ideas. But I

526
00:32:07,280 --> 00:32:09,680
Speaker 1: think we could agree that there's a spectrum of harm

527
00:32:09,760 --> 00:32:14,240
Speaker 1: here from from oh man, this drink that I bought

528
00:32:14,360 --> 00:32:16,240
Speaker 1: is real nasty. I wish I hadn't bought it too.

529
00:32:16,720 --> 00:32:19,200
Speaker 1: Oh man, I found myself on the steps of the

530
00:32:19,240 --> 00:32:21,800
Speaker 1: Capitol on January six that boy, well, am I sorry

531
00:32:21,800 --> 00:32:25,240
Speaker 1: that I've done that? Um, that's a that's a spectrum

532
00:32:25,680 --> 00:32:28,720
Speaker 1: right there, That's what we call that. But what am

533
00:32:28,720 --> 00:32:31,040
Speaker 1: I Brant was writing about was that so Oxford Institut

534
00:32:31,080 --> 00:32:33,680
Speaker 1: who comes out and says that that there's been an

535
00:32:33,720 --> 00:32:39,000
Speaker 1: increase year over year of this form of of misinformation

536
00:32:39,120 --> 00:32:43,720
Speaker 1: campaigns and the role of the Internet, and uh, Bryant's

537
00:32:43,800 --> 00:32:48,920
Speaker 1: point was that we don't even understand the full scope

538
00:32:49,000 --> 00:32:51,920
Speaker 1: of this, and it's more like we're paying more attention,

539
00:32:52,080 --> 00:32:54,960
Speaker 1: so we're seeing more of this, but it doesn't tell

540
00:32:55,040 --> 00:32:57,480
Speaker 1: us that there's actually been an increase year over year.

541
00:32:57,560 --> 00:33:00,480
Speaker 1: What it tells us is that we're finally paying attention

542
00:33:00,520 --> 00:33:03,040
Speaker 1: to a very real problem that's been around for a while.

543
00:33:03,520 --> 00:33:05,800
Speaker 1: If I can make an analogy, it's kind of like

544
00:33:06,400 --> 00:33:09,680
Speaker 1: when people started to see, uh, what appeared to be

545
00:33:09,720 --> 00:33:13,320
Speaker 1: autism rates rise, but in reality it looked like it

546
00:33:13,400 --> 00:33:16,800
Speaker 1: was more that the definition and the way of diagnosing

547
00:33:16,840 --> 00:33:20,600
Speaker 1: autism had expanded to a point where we were just

548
00:33:20,760 --> 00:33:24,520
Speaker 1: realizing the number of cases that actually exist, as opposed

549
00:33:24,560 --> 00:33:28,200
Speaker 1: to it's increasing. It was more that, oh, no, it

550
00:33:28,320 --> 00:33:31,240
Speaker 1: was here, we just didn't recognize a lot of this

551
00:33:31,520 --> 00:33:35,080
Speaker 1: as autism. It was sort of the same argument that

552
00:33:35,360 --> 00:33:38,560
Speaker 1: Emma Brian is making in that if you if you

553
00:33:38,800 --> 00:33:41,200
Speaker 1: come back and you say, well, I looked at the ocean,

554
00:33:41,480 --> 00:33:44,320
Speaker 1: and we took a bucket out and we filled it

555
00:33:44,400 --> 00:33:46,360
Speaker 1: up five times, so we know there's at least five

556
00:33:46,400 --> 00:33:49,360
Speaker 1: buckets of water in the ocean. And you say, well, yes,

557
00:33:49,960 --> 00:33:51,840
Speaker 1: but there's a lot else that's there that we don't

558
00:33:51,880 --> 00:33:54,280
Speaker 1: know about yet, and we we haven't measured and we

559
00:33:54,320 --> 00:33:59,280
Speaker 1: haven't quantified. And to me, that is it's interesting. It

560
00:33:59,520 --> 00:34:04,080
Speaker 1: shows an opportunity for actual, real research and analysis and

561
00:34:04,120 --> 00:34:08,320
Speaker 1: study into what's actually there as opposed to what's being reported,

562
00:34:08,560 --> 00:34:13,320
Speaker 1: and it's frightening. It's so scary because we don't even

563
00:34:13,840 --> 00:34:16,239
Speaker 1: like like she says, you know, they looked at a

564
00:34:16,320 --> 00:34:20,320
Speaker 1: list of a couple of dozen different um UH platforms

565
00:34:20,360 --> 00:34:24,440
Speaker 1: that were actively pushing out misinformation back in twenty nineteen,

566
00:34:24,480 --> 00:34:28,000
Speaker 1: I think it was, And she said, listen in Cambridge,

567
00:34:28,000 --> 00:34:31,799
Speaker 1: Analytica was active in six different countries by itself, that

568
00:34:31,880 --> 00:34:34,600
Speaker 1: was just one and says I've I've just started and

569
00:34:34,640 --> 00:34:37,440
Speaker 1: I've made a list of more than six hundred companies

570
00:34:37,480 --> 00:34:44,960
Speaker 1: that are actively push pushing out influencer operations. So that's

571
00:34:45,120 --> 00:34:47,680
Speaker 1: why I wanted to set that ground early on. Is

572
00:34:47,840 --> 00:34:51,719
Speaker 1: it's not to say that what we're talking about UH

573
00:34:51,920 --> 00:34:56,520
Speaker 1: isn't relevant, but rather we can't speak in any any

574
00:34:56,640 --> 00:34:59,960
Speaker 1: definitive scope because we just don't know what that scope is.

575
00:35:00,719 --> 00:35:04,520
Speaker 1: And to that means that we've gotta be on the lookout. Yeah,

576
00:35:04,640 --> 00:35:07,480
Speaker 1: and and I'm sorry to say this, but that Oxford

577
00:35:07,520 --> 00:35:11,799
Speaker 1: study that that she references in that piece is incredibly influential, right,

578
00:35:11,840 --> 00:35:16,920
Speaker 1: and so it is it's upsetting to see, I mean,

579
00:35:17,000 --> 00:35:20,880
Speaker 1: the name like Oxford, you expect a level of rigor,

580
00:35:21,239 --> 00:35:23,040
Speaker 1: you know, in in the kind of research they're putting

581
00:35:23,040 --> 00:35:24,879
Speaker 1: out right, I'm not a researcher, so I I can't

582
00:35:24,880 --> 00:35:27,799
Speaker 1: really say. But the name Oxford, You're like, Okay, that's

583
00:35:27,800 --> 00:35:29,360
Speaker 1: going to be a very reputable you know, it's a

584
00:35:29,400 --> 00:35:34,160
Speaker 1: reputable place. But it's got some it's got some research

585
00:35:34,160 --> 00:35:38,799
Speaker 1: street cred. Yeah. Absolutely. Um. And it's upsetting that that

586
00:35:38,920 --> 00:35:43,120
Speaker 1: study was so influential in the disinformation space when a

587
00:35:43,200 --> 00:35:46,160
Speaker 1: lot of the points that it makes are really flawed.

588
00:35:46,239 --> 00:35:47,879
Speaker 1: And she also, I have to say, she points out

589
00:35:47,920 --> 00:35:50,480
Speaker 1: that there the study itself had a lot of typos

590
00:35:50,520 --> 00:35:53,239
Speaker 1: and like spelling errors, which I would be so embarrassed

591
00:35:53,440 --> 00:35:55,560
Speaker 1: if I put out an influential study and someone was like,

592
00:35:55,560 --> 00:35:59,279
Speaker 1: actually it was really sloppy, I would be like mortified.

593
00:35:59,480 --> 00:36:02,440
Speaker 1: Her point is that the work is largely without meaning

594
00:36:02,600 --> 00:36:07,600
Speaker 1: because it's creating a sense that we have metrics for

595
00:36:07,800 --> 00:36:12,879
Speaker 1: something where uh, you know, really it's it's it's such

596
00:36:12,920 --> 00:36:16,440
Speaker 1: a small subsection of the overall problem that her point

597
00:36:16,480 --> 00:36:18,600
Speaker 1: is that it doesn't really tell us anything, right, It

598
00:36:18,719 --> 00:36:23,399
Speaker 1: just it tells us of the reported incidents, But that,

599
00:36:23,840 --> 00:36:27,640
Speaker 1: unfortunately is not really meaningful if you're looking at a

600
00:36:27,680 --> 00:36:33,640
Speaker 1: holistic approach to misinformation, and in fact, you know, you know,

601
00:36:33,719 --> 00:36:36,520
Speaker 1: and in fact, she was also pointing out that the

602
00:36:36,520 --> 00:36:41,440
Speaker 1: study looked at countries where the reporting mechanism might not

603
00:36:41,520 --> 00:36:44,640
Speaker 1: be as vigorous as in others, like countries that don't

604
00:36:45,560 --> 00:36:48,000
Speaker 1: have the same sort of perspective on things like the

605
00:36:48,040 --> 00:36:51,640
Speaker 1: freedom of the press, maybe state controlled press. You have

606
00:36:51,680 --> 00:36:54,920
Speaker 1: places like China and Russia where the state has a

607
00:36:55,040 --> 00:36:57,680
Speaker 1: large amount of influence on the media that goes out

608
00:36:57,719 --> 00:37:02,359
Speaker 1: in those countries. Uh, It's it does show that that

609
00:37:03,000 --> 00:37:08,400
Speaker 1: it's dangerous to take any stance where you're making absolute

610
00:37:08,480 --> 00:37:12,520
Speaker 1: claims because we just don't have the study to really

611
00:37:13,239 --> 00:37:16,319
Speaker 1: do that justifiably. We we don't have the evidence to

612
00:37:16,360 --> 00:37:19,120
Speaker 1: back that up. You're exactly right, We don't know the

613
00:37:19,160 --> 00:37:21,319
Speaker 1: real scope of the problem and the sort of lay

614
00:37:21,320 --> 00:37:23,640
Speaker 1: of the land. I also think something that she points

615
00:37:23,640 --> 00:37:25,480
Speaker 1: out that's really important to keep in mind is that

616
00:37:25,920 --> 00:37:29,600
Speaker 1: a lot of folks from a media perspective just weren't

617
00:37:29,640 --> 00:37:33,319
Speaker 1: really talking about disinformation in a serious way prior to

618
00:37:33,360 --> 00:37:37,080
Speaker 1: like right, and so I think that an issue that

619
00:37:37,120 --> 00:37:39,719
Speaker 1: we do have is people kind of getting up to

620
00:37:39,760 --> 00:37:42,279
Speaker 1: speed with how we think about this issue in a

621
00:37:42,320 --> 00:37:46,840
Speaker 1: holistic way, because I think, you know, after and after

622
00:37:47,080 --> 00:37:50,560
Speaker 1: you know, the insurrection, I feel almost overnight, this was

623
00:37:50,600 --> 00:37:53,040
Speaker 1: an issue that was getting more buzz and more pressed

624
00:37:53,040 --> 00:37:55,640
Speaker 1: and people were talking about it more. But with that

625
00:37:55,760 --> 00:37:58,719
Speaker 1: really does come and need to take a take a

626
00:37:58,760 --> 00:38:01,840
Speaker 1: beat and would have analyzed how we've got to the

627
00:38:01,920 --> 00:38:04,759
Speaker 1: space and and what, you know, try to put some

628
00:38:04,800 --> 00:38:08,920
Speaker 1: more research around, you know, understanding what is actually going on.

629
00:38:08,960 --> 00:38:11,600
Speaker 1: And I agree that we haven't really gotten there yet. Um,

630
00:38:11,640 --> 00:38:14,880
Speaker 1: and I am happy that there are more people who

631
00:38:14,920 --> 00:38:16,800
Speaker 1: are interested in this issue, but I don't want it

632
00:38:16,840 --> 00:38:20,040
Speaker 1: to become an issue where people are claiming to have

633
00:38:20,120 --> 00:38:22,719
Speaker 1: more knowledge than they do, or people are claiming that

634
00:38:22,760 --> 00:38:25,120
Speaker 1: we know more than we do. Because this this is

635
00:38:25,400 --> 00:38:28,359
Speaker 1: very much still developing problem that we're still sort of

636
00:38:28,520 --> 00:38:31,600
Speaker 1: trying to get a hold on. I would say, yeah,

637
00:38:31,840 --> 00:38:34,279
Speaker 1: we don't want there to be misinformation about how much

638
00:38:34,360 --> 00:38:41,839
Speaker 1: misinformation there is. Yeah, hey, guys, Jonathan from the future. Again,

639
00:38:41,880 --> 00:38:43,880
Speaker 1: we're going to take a quick break, but we'll be

640
00:38:44,000 --> 00:38:50,320
Speaker 1: right back with more about social media platforms, algorithms and radicalization.

641
00:38:58,120 --> 00:39:03,480
Speaker 1: So this issue is getting to a point where the

642
00:39:03,520 --> 00:39:06,640
Speaker 1: concern is great, and yet you also look at the

643
00:39:06,760 --> 00:39:09,600
Speaker 1: tactics that are being used. You realize that the tactics

644
00:39:09,600 --> 00:39:12,920
Speaker 1: that are used are very old. I mean, you know,

645
00:39:13,000 --> 00:39:19,120
Speaker 1: the the approaches to extremism, to propaganda, to misinformation, that stuff.

646
00:39:19,160 --> 00:39:21,839
Speaker 1: We we've got a handle on that. I mean, you know,

647
00:39:21,920 --> 00:39:25,759
Speaker 1: you can see numerous documentaries from everything from people who

648
00:39:25,760 --> 00:39:28,839
Speaker 1: were experts in creating the propaganda during World War Two,

649
00:39:29,320 --> 00:39:32,960
Speaker 1: both on both sides of the conflict, up to you know,

650
00:39:33,040 --> 00:39:36,480
Speaker 1: the ad executives. I mean, that's what Madman was all about,

651
00:39:36,680 --> 00:39:40,040
Speaker 1: was the idea of framing. You know, how do you

652
00:39:40,040 --> 00:39:42,200
Speaker 1: frame information in a way to get people to do

653
00:39:42,239 --> 00:39:45,399
Speaker 1: what you want them to do. Um. You know, there's

654
00:39:45,560 --> 00:39:49,680
Speaker 1: the whole discussion about the advertising about cigarettes, was all

655
00:39:49,719 --> 00:39:53,080
Speaker 1: about that. So, like this is all old stuff. What's

656
00:39:53,120 --> 00:40:01,800
Speaker 1: new is this this method of compartmentalizing communities and reinforcing

657
00:40:01,880 --> 00:40:06,799
Speaker 1: the delivery system of that material. So it's the identifying

658
00:40:06,840 --> 00:40:13,080
Speaker 1: of a potential candidate, the introduction of material to that

659
00:40:13,200 --> 00:40:17,600
Speaker 1: candidate that will set them potentially down this pathway, and

660
00:40:17,640 --> 00:40:21,680
Speaker 1: then the methods of reinforcing that and indoctrinating that person

661
00:40:22,239 --> 00:40:27,840
Speaker 1: into more radical views. Interestingly, probably not surprisingly, some of

662
00:40:27,880 --> 00:40:30,960
Speaker 1: the studies I was looking at suggested that this is

663
00:40:31,040 --> 00:40:34,720
Speaker 1: most effective for people who kind of have the lone

664
00:40:34,760 --> 00:40:38,919
Speaker 1: wolf kind of approach to radicalism, that in cases where

665
00:40:38,920 --> 00:40:44,440
Speaker 1: you're looking at groups of extremists, typically the meat space

666
00:40:44,680 --> 00:40:48,160
Speaker 1: is where that kind of radicalization still happens primarily. So,

667
00:40:49,120 --> 00:40:52,920
Speaker 1: but I would also argue that the insurrection on January six,

668
00:40:53,440 --> 00:40:56,640
Speaker 1: that that was in large part a lot of different

669
00:40:56,680 --> 00:41:00,120
Speaker 1: individuals who all just sort of kind of converged on

670
00:41:00,160 --> 00:41:03,319
Speaker 1: the same point. That it wasn't as much let's all

671
00:41:03,400 --> 00:41:08,759
Speaker 1: join these online groups and then we start to plan

672
00:41:08,880 --> 00:41:11,319
Speaker 1: from there. It was that it was a bunch of

673
00:41:11,360 --> 00:41:18,640
Speaker 1: individuals who started slowly gravitating toward one another through methods

674
00:41:18,680 --> 00:41:21,920
Speaker 1: like this. And of course there are other communities I

675
00:41:22,000 --> 00:41:28,239
Speaker 1: mentioned before, communities like parlor or parley if you prefer, uh, which, hey,

676
00:41:28,320 --> 00:41:33,520
Speaker 1: they're back, that's great, um that where you know, there's

677
00:41:33,520 --> 00:41:35,839
Speaker 1: not so much the algorithm there. It's it's that it's

678
00:41:35,840 --> 00:41:40,560
Speaker 1: a community that is actively reinforcing beliefs. So in that case,

679
00:41:41,040 --> 00:41:45,440
Speaker 1: it's it's almost more like the traditional method of radicalization

680
00:41:46,040 --> 00:41:50,440
Speaker 1: in the sense that you have this this self um

681
00:41:50,800 --> 00:41:57,920
Speaker 1: selected community that is following this process. So it we

682
00:41:58,000 --> 00:42:00,160
Speaker 1: have we have the whole spectrum here. We still have

683
00:42:00,239 --> 00:42:03,680
Speaker 1: the meat space stuff. We have the online communities that

684
00:42:03,719 --> 00:42:08,239
Speaker 1: are specifically geared uh if not specifically geared overtly at

685
00:42:08,280 --> 00:42:13,120
Speaker 1: least effectively, they're geared towards radicalization. And then you've got

686
00:42:13,480 --> 00:42:18,000
Speaker 1: the stuff that everybody's using that can lead you to

687
00:42:18,080 --> 00:42:24,279
Speaker 1: that pathway. So, um, how you feeling, bridget I mean,

688
00:42:24,360 --> 00:42:27,520
Speaker 1: as you were describing the the issue, I almost felt

689
00:42:27,520 --> 00:42:30,200
Speaker 1: this this paying in the pit of my stomach because

690
00:42:30,680 --> 00:42:33,319
Speaker 1: we're up like, we are up against so much. There

691
00:42:33,360 --> 00:42:36,360
Speaker 1: are so many you know, we have we have the

692
00:42:36,480 --> 00:42:40,480
Speaker 1: in real life meat space organizing radicalizing people. We have

693
00:42:40,560 --> 00:42:44,840
Speaker 1: these platforms that are engineered to radicalized folks. That scope

694
00:42:44,840 --> 00:42:48,920
Speaker 1: of the problem is quite large. And I often wonder like,

695
00:42:49,120 --> 00:42:51,120
Speaker 1: and I'm actually will be curious to know your thoughts.

696
00:42:51,520 --> 00:42:54,080
Speaker 1: Do you think will ever tackle this? I do you

697
00:42:54,120 --> 00:42:55,880
Speaker 1: think that we will ever get to a place where

698
00:42:56,080 --> 00:42:58,000
Speaker 1: it is not just the norm for folks to be

699
00:42:58,080 --> 00:43:02,000
Speaker 1: having these kinds of experiences being rad clies online. I

700
00:43:02,080 --> 00:43:13,560
Speaker 1: think without without forcing the platforms through legislation, regulations, whatever

701
00:43:13,600 --> 00:43:18,600
Speaker 1: it may be, to take a truly active role and

702
00:43:18,680 --> 00:43:23,399
Speaker 1: to and also to be incredibly transparent with how their

703
00:43:23,440 --> 00:43:28,319
Speaker 1: algorithms work, we won't get there. And companies are going

704
00:43:28,360 --> 00:43:32,520
Speaker 1: to be extremely resistant to that, obviously, because the algorithm

705
00:43:32,880 --> 00:43:35,640
Speaker 1: is that's the secret sauce for making the money, so

706
00:43:35,719 --> 00:43:38,600
Speaker 1: you don't. The companies are very resistant to make that

707
00:43:38,640 --> 00:43:42,319
Speaker 1: a transparent process because, for one thing, it could give

708
00:43:42,520 --> 00:43:46,120
Speaker 1: a competitor the opportunity to beat them at their own game,

709
00:43:46,360 --> 00:43:48,960
Speaker 1: make a better algorithm that does essentially the same thing

710
00:43:49,040 --> 00:43:51,880
Speaker 1: but in a slightly different way, and then they're no

711
00:43:51,920 --> 00:43:55,279
Speaker 1: longer king of the hill. Uh. I think it's gonna

712
00:43:55,320 --> 00:43:58,440
Speaker 1: be really tricky. I think the moves we're seeing in

713
00:43:58,480 --> 00:44:01,920
Speaker 1: the US government where there's the potential of breaking up

714
00:44:01,960 --> 00:44:05,640
Speaker 1: some of these companies, I don't think that that's necessarily

715
00:44:05,680 --> 00:44:08,560
Speaker 1: going to solve this problem. There's going to be there.

716
00:44:08,640 --> 00:44:12,280
Speaker 1: There will need to be additional measures put in place

717
00:44:12,320 --> 00:44:17,080
Speaker 1: for that too to be effective. Otherwise, all you're doing

718
00:44:17,160 --> 00:44:19,239
Speaker 1: is taking one big piece and you're making a bunch

719
00:44:19,239 --> 00:44:21,439
Speaker 1: of smaller pieces. But if they're all working the same

720
00:44:21,480 --> 00:44:24,480
Speaker 1: way as the big piece was, we haven't really solved

721
00:44:24,480 --> 00:44:28,120
Speaker 1: any issues. Um. I think everyone recognizes the amount of

722
00:44:28,120 --> 00:44:32,160
Speaker 1: power these companies have there that's undeniable. The question is

723
00:44:32,160 --> 00:44:35,000
Speaker 1: how do we deal with that. My answer from my

724
00:44:35,000 --> 00:44:38,799
Speaker 1: my from a personal standpoint is not satisfying, because I

725
00:44:38,920 --> 00:44:44,280
Speaker 1: just disengaged. I quit Facebook, But that that's one person,

726
00:44:45,239 --> 00:44:48,000
Speaker 1: and I would never tell anyone else like, you've got

727
00:44:48,000 --> 00:44:51,719
Speaker 1: to quit Facebook. I might believe it really hard, but

728
00:44:51,800 --> 00:44:53,759
Speaker 1: I can't tell them that because that's the way a

729
00:44:53,760 --> 00:44:55,640
Speaker 1: lot of people stay in touch with their friends and family.

730
00:44:55,680 --> 00:44:57,720
Speaker 1: It's a way a lot of people rely on Facebook

731
00:44:57,760 --> 00:45:01,440
Speaker 1: for their own businesses. I am in a luxurious position

732
00:45:01,480 --> 00:45:06,520
Speaker 1: where I can disengage and uh, I got a dog.

733
00:45:06,600 --> 00:45:10,279
Speaker 1: I'm okay. I might not talked about friends anymore, but

734
00:45:10,320 --> 00:45:12,880
Speaker 1: I got a dog. Um, and I still got all

735
00:45:12,880 --> 00:45:15,200
Speaker 1: those dog videos on YouTube too, So really I'm living

736
00:45:15,239 --> 00:45:17,840
Speaker 1: it up. But but yeah, I mean, like that's the

737
00:45:17,880 --> 00:45:20,160
Speaker 1: that's the thing is that this is a huge problem,

738
00:45:20,239 --> 00:45:23,080
Speaker 1: and like a lot of huge problems, there may not

739
00:45:23,280 --> 00:45:27,200
Speaker 1: be a simple solution. There may not be one that

740
00:45:27,400 --> 00:45:32,080
Speaker 1: is uh completely satisfying, and it may be really messy

741
00:45:32,160 --> 00:45:36,279
Speaker 1: to implement solutions that themselves could have unintended consequences that

742
00:45:36,320 --> 00:45:40,960
Speaker 1: will have to deal with later. Um. The important thing

743
00:45:41,040 --> 00:45:46,200
Speaker 1: is really acknowledging that problem, putting more effort into understanding

744
00:45:47,200 --> 00:45:50,680
Speaker 1: the scope and impact of that problem, and making sure

745
00:45:50,719 --> 00:45:54,319
Speaker 1: that our our energy for solutions is directed in the

746
00:45:54,400 --> 00:45:57,560
Speaker 1: right place. Because without really understanding the scope and the

747
00:45:58,160 --> 00:46:01,480
Speaker 1: nature of the issue, the best we can do is

748
00:46:01,560 --> 00:46:04,719
Speaker 1: try random solutions and hope they work. But with a

749
00:46:04,840 --> 00:46:08,719
Speaker 1: deeper understanding, you can craft a pathway that is at

750
00:46:08,800 --> 00:46:11,920
Speaker 1: least has a better chance of making a positive impact.

751
00:46:12,480 --> 00:46:14,520
Speaker 1: That's kind of it's the lame way of saying it,

752
00:46:14,600 --> 00:46:17,560
Speaker 1: But the more I looked into this, the more I thought,

753
00:46:18,280 --> 00:46:21,839
Speaker 1: we just don't have a deep enough understanding, and it's

754
00:46:21,880 --> 00:46:25,319
Speaker 1: largely because we didn't take it seriously, like you were saying,

755
00:46:25,320 --> 00:46:30,160
Speaker 1: bridget I mean, before Brexit, people were aware that there

756
00:46:30,160 --> 00:46:33,960
Speaker 1: are lies posted on the internet because people lie. You know,

757
00:46:34,000 --> 00:46:38,280
Speaker 1: wherever people are, we're gonna find falsehoods. But when Brexit happened,

758
00:46:39,160 --> 00:46:44,400
Speaker 1: and after all the fallout about accusations that the support

759
00:46:44,400 --> 00:46:49,799
Speaker 1: for Brexit was largely based off of unsupportable claims, that's

760
00:46:49,840 --> 00:46:53,759
Speaker 1: where it kind of started, the snowball effect of Wow,

761
00:46:54,280 --> 00:46:57,400
Speaker 1: we've really let this get to a place where we

762
00:46:57,440 --> 00:47:00,360
Speaker 1: don't have a handle on it um and we honestly

763
00:47:00,400 --> 00:47:03,040
Speaker 1: don't even know how bad it is, and we still

764
00:47:03,080 --> 00:47:06,640
Speaker 1: don't five years later. So yeah, I mean I feel

765
00:47:06,719 --> 00:47:10,440
Speaker 1: very similarly, but I am hopeful that we are finally

766
00:47:10,520 --> 00:47:13,680
Speaker 1: taking it seriously. I wish we had gotten here three

767
00:47:13,760 --> 00:47:16,560
Speaker 1: years ago, five years ago, ten years ago about understanding

768
00:47:16,600 --> 00:47:18,839
Speaker 1: the impact that this has. But I'm glad that we're

769
00:47:18,840 --> 00:47:20,920
Speaker 1: here now, and I think what we need to do

770
00:47:20,960 --> 00:47:24,360
Speaker 1: now is have these honest conversations, put money behind the

771
00:47:24,480 --> 00:47:27,799
Speaker 1: research to actually understand what's going on, because I think

772
00:47:27,800 --> 00:47:29,799
Speaker 1: we're tackling it late. You know, I wish we had

773
00:47:29,800 --> 00:47:32,120
Speaker 1: gotten there earlier. But I'm glad we're here now. And

774
00:47:32,600 --> 00:47:35,520
Speaker 1: you know, you talked about how you deleted Facebook. I

775
00:47:35,560 --> 00:47:38,000
Speaker 1: still have Facebook. I need it for work. Um, But

776
00:47:38,040 --> 00:47:40,600
Speaker 1: I always say, you know, a lot of these issues

777
00:47:40,680 --> 00:47:44,680
Speaker 1: are big systemic. You know, we're talking about your Mark Zuckerberg,

778
00:47:44,800 --> 00:47:46,839
Speaker 1: so we're talking about policy level things that it's very

779
00:47:46,880 --> 00:47:49,359
Speaker 1: difficult to feel as an individual like you have any

780
00:47:49,400 --> 00:47:53,279
Speaker 1: impact over that. But and that's true, but I think

781
00:47:53,320 --> 00:47:56,600
Speaker 1: we can all take small steps in our individual lives

782
00:47:56,640 --> 00:47:59,279
Speaker 1: to assess and be critical of the role that platforms

783
00:47:59,280 --> 00:48:00,840
Speaker 1: play in our own to get diets and how we

784
00:48:00,920 --> 00:48:04,000
Speaker 1: use those platforms. And so you know, I would also

785
00:48:04,080 --> 00:48:06,760
Speaker 1: never tell anybody to delete Facebook, especially in a pandemic

786
00:48:06,760 --> 00:48:08,920
Speaker 1: where you might not have the ability to see your

787
00:48:08,960 --> 00:48:10,839
Speaker 1: friends every day but you want to feel connected. But

788
00:48:11,200 --> 00:48:13,480
Speaker 1: maybe you can delete Facebook from your phone so you're

789
00:48:13,520 --> 00:48:17,120
Speaker 1: not carrying around a physical reminder of its presence on

790
00:48:17,200 --> 00:48:19,640
Speaker 1: your person at all times. Right, maybe you only go

791
00:48:19,680 --> 00:48:22,600
Speaker 1: on the browser, and you and and on your laptop

792
00:48:22,680 --> 00:48:26,040
Speaker 1: or whatever, and you're deciding when you're gonna engage with Facebook. Right,

793
00:48:26,040 --> 00:48:28,680
Speaker 1: maybe you're like, I have a new rule in my house,

794
00:48:28,760 --> 00:48:31,400
Speaker 1: no phones and no phones in the bedroom. Right. You

795
00:48:31,440 --> 00:48:34,680
Speaker 1: can we can all find ways of having an impact

796
00:48:34,760 --> 00:48:37,720
Speaker 1: on the way that Facebook plays out in our personal

797
00:48:37,760 --> 00:48:40,160
Speaker 1: media diets and how we use it in our personal lives.

798
00:48:40,200 --> 00:48:42,560
Speaker 1: And so while we may not be able to have

799
00:48:42,719 --> 00:48:46,160
Speaker 1: a huge impact on the grip that platforms have on

800
00:48:46,200 --> 00:48:48,640
Speaker 1: our democracy and our discourse, those are small things that

801
00:48:48,680 --> 00:48:50,719
Speaker 1: we can all do. You know, if you're gonna share

802
00:48:50,760 --> 00:48:52,960
Speaker 1: something on Facebook, read it first. You know, before you

803
00:48:53,000 --> 00:48:54,719
Speaker 1: comment on something, make sure that you read it. You know,

804
00:48:54,760 --> 00:48:59,560
Speaker 1: just little things, little steps. Agreed, Agreed. I think also

805
00:49:00,120 --> 00:49:02,799
Speaker 1: we should point out the fact that you and I

806
00:49:02,840 --> 00:49:06,000
Speaker 1: also host shows where we get the opportunity to reach

807
00:49:06,000 --> 00:49:09,239
Speaker 1: out to our listeners and to suggest to them that

808
00:49:09,600 --> 00:49:13,880
Speaker 1: they incorporate qualities like critical thinking. I always partner critical

809
00:49:13,920 --> 00:49:16,560
Speaker 1: thinking with compassion. I think if you have one without

810
00:49:16,600 --> 00:49:19,080
Speaker 1: the other, things don't go so well. But when you

811
00:49:19,160 --> 00:49:22,600
Speaker 1: got both you don't go too wrong. You can't you

812
00:49:22,600 --> 00:49:25,040
Speaker 1: can't go wrong. Yeah, you really just got to employ

813
00:49:25,120 --> 00:49:28,879
Speaker 1: both of those things. And I am so thankful that

814
00:49:28,920 --> 00:49:31,799
Speaker 1: you joined me for this discussion, and I hope that

815
00:49:31,840 --> 00:49:35,759
Speaker 1: our listeners really get an understanding. Like, the reason we

816
00:49:35,840 --> 00:49:40,880
Speaker 1: talk about this is because the stakes are very high,

817
00:49:40,920 --> 00:49:45,480
Speaker 1: and clearly, if you recognize there are elements of friends

818
00:49:45,600 --> 00:49:49,680
Speaker 1: or family of yours who appear to be maybe going

819
00:49:49,840 --> 00:49:54,120
Speaker 1: down a path, you should seek out resources to help

820
00:49:54,200 --> 00:49:57,680
Speaker 1: you and perhaps help them. It's a very delicate thing.

821
00:49:58,040 --> 00:50:00,560
Speaker 1: I certainly am not an expert to all you how

822
00:50:00,640 --> 00:50:03,440
Speaker 1: to handle a situation in every situation is different, but

823
00:50:04,640 --> 00:50:08,399
Speaker 1: that I'm seeing this real impact on people. I know.

824
00:50:08,520 --> 00:50:11,239
Speaker 1: There was a person that I worked with years ago

825
00:50:11,800 --> 00:50:15,520
Speaker 1: in a theatrical production, and several years ago I saw

826
00:50:15,600 --> 00:50:18,560
Speaker 1: him making some statements that kind of had him going

827
00:50:18,560 --> 00:50:23,719
Speaker 1: down the men's rights pathway, and uh it, that was

828
00:50:24,000 --> 00:50:26,359
Speaker 1: a big warning sign at the beginning, but it has

829
00:50:26,760 --> 00:50:29,359
Speaker 1: since gotten worse, and I just wish that I had

830
00:50:29,640 --> 00:50:36,000
Speaker 1: recognized things earlier and perhaps been a a positive influence

831
00:50:36,040 --> 00:50:39,839
Speaker 1: on his life too, and not have him go down

832
00:50:39,840 --> 00:50:44,279
Speaker 1: that pathway quite so wholeheartedly. Um, people are their own people.

833
00:50:44,320 --> 00:50:46,680
Speaker 1: They'll make their own decisions, but we can always be

834
00:50:46,760 --> 00:50:50,600
Speaker 1: supportive and helpful in certain situations. And one way you

835
00:50:50,880 --> 00:50:54,040
Speaker 1: can be supportive and helpful for yourself and for others

836
00:50:54,520 --> 00:50:57,360
Speaker 1: is to subscribe to There Are No Girls on the Internet.

837
00:50:57,400 --> 00:51:00,319
Speaker 1: Because it's an incredible show. I don't just say that

838
00:51:00,360 --> 00:51:03,320
Speaker 1: because I am the executive producer of that show. I

839
00:51:03,400 --> 00:51:05,640
Speaker 1: say it because it's I say it because if I

840
00:51:05,719 --> 00:51:10,799
Speaker 1: had no involvement whatsoever, it's still an amazing show. In fact,

841
00:51:10,840 --> 00:51:13,480
Speaker 1: I should I should point out I have a very

842
00:51:13,560 --> 00:51:16,080
Speaker 1: light touch on that show. That show is amazing because

843
00:51:16,120 --> 00:51:20,560
Speaker 1: of Bridget, because of Tari, and because of the incredible

844
00:51:20,560 --> 00:51:23,319
Speaker 1: amount of work you you put into it. And uh,

845
00:51:23,520 --> 00:51:25,960
Speaker 1: definitely go and check out Bridget show. There Are No

846
00:51:26,080 --> 00:51:29,359
Speaker 1: Girls on the Internet. Um, see what all the all

847
00:51:29,400 --> 00:51:31,520
Speaker 1: the all the accolades are about. Because this is a

848
00:51:31,560 --> 00:51:34,080
Speaker 1: show that's received quite a few of them. Oh, I mean,

849
00:51:34,120 --> 00:51:35,880
Speaker 1: I couldn't. I couldn't do it without you, John at

850
00:51:35,880 --> 00:51:38,920
Speaker 1: that Entari. This means so much coming from you, you know,

851
00:51:39,120 --> 00:51:44,200
Speaker 1: the tech podcast Guru. I got a poster of myself

852
00:51:44,200 --> 00:51:47,880
Speaker 1: in the background. It's that's how important I am. I

853
00:51:47,920 --> 00:51:50,399
Speaker 1: don't know if you notice that Bridget but that's that's

854
00:51:50,440 --> 00:51:53,200
Speaker 1: me back there. That's if you ever wondered who's the

855
00:51:53,239 --> 00:51:55,399
Speaker 1: kind of guy who hangs a poster of himself up,

856
00:51:55,440 --> 00:51:58,280
Speaker 1: well it's it's me and apparently Donald Trump. I guess

857
00:51:58,360 --> 00:52:01,240
Speaker 1: that's I'm not in good I'm not in good company,

858
00:52:01,360 --> 00:52:04,920
Speaker 1: but I can't deny it. It's on the door. Uh,

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00:52:05,120 --> 00:52:09,000
Speaker 1: thank you guys for listening. Make sure you again subscribe

860
00:52:09,000 --> 00:52:10,680
Speaker 1: to There Are No Girls on the Internet. Go check

861
00:52:10,719 --> 00:52:13,160
Speaker 1: that out. Look at the list of shows, because there

862
00:52:13,160 --> 00:52:17,160
Speaker 1: have been some really incredible episodes. You've had some amazing

863
00:52:17,239 --> 00:52:20,239
Speaker 1: guests on that show, and uh, it just makes me

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00:52:20,280 --> 00:52:23,120
Speaker 1: want to become a better interviewer. So I thank you

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00:52:23,200 --> 00:52:26,160
Speaker 1: for that too, because that's been very inspirational to me.

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00:52:26,480 --> 00:52:29,280
Speaker 1: And if you guys have any suggestions for future topics

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00:52:29,360 --> 00:52:32,040
Speaker 1: of tech stuff, you can reach out to me on Twitter.

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00:52:32,360 --> 00:52:35,120
Speaker 1: Yes I'm I'm still active there and the handle to

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00:52:35,239 --> 00:52:38,319
Speaker 1: use this text stuff h s W and I'll talk

870
00:52:38,360 --> 00:52:45,960
Speaker 1: to you again really soon. Text Stuff is an I

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00:52:46,080 --> 00:52:49,560
Speaker 1: Heart Radio production. For more podcasts from My Heart Radio,

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00:52:49,880 --> 00:52:53,080
Speaker 1: visit the I Heart Radio app, Apple Podcasts, or wherever

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00:52:53,160 --> 00:52:54,680
Speaker 1: you listen to your favorite shows.