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Speaker 1: From UFOs to psychic powers and government conspiracies. History is

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Speaker 1: riddled with unexplained events. You can turn back now or

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Speaker 1: learn the stuff they don't want you to know. M Hello,

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Speaker 1: welcome back to the show. My name is Matt. My

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Speaker 1: name is no. They called me Ben. We are joined

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Speaker 1: with our super producer Paul Mission Controlled decond You are you,

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Speaker 1: You are here that makes this stuff they don't want

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Speaker 1: you to know. As you're listening to today's show, feel

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Speaker 1: free to drop in your own emoji reaction, whether it's

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Speaker 1: a wow, it's sad, I'm mad. I don't know the

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Speaker 1: names of those reactions available on Facebook. One of them

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Speaker 1: is the oh face. It's just the open mouth. I

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Speaker 1: guess that's wow. Yeah, I call it wow. It could

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Speaker 1: be wow or it could be Are there official names?

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Speaker 1: Is there a company line on what these reactions should

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Speaker 1: be known as? I hope they all have specific, proper

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Speaker 1: nown names like Derek or Calliope. I'm sure there actually

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Speaker 1: is a title to each of those images. Right. It

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Speaker 1: seems like a pretty um everything in its right place

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Speaker 1: kind of company. Facebook. They don't really let much fall

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Speaker 1: through the cracks. Yeah, I'm speaking of Facebook. While you're listening,

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Speaker 1: to this. Feel free to peruse our Facebook page stuff

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Speaker 1: they don't want you to know at conspiracy stuff or

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Speaker 1: our new uh here's where it gets crazy group on Facebook.

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Speaker 1: Just you know, hang out on there the whole time

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Speaker 1: you're listening. It's fine. Nothing bad will happen to you,

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Speaker 1: nothing bad that hasn't already happened. We'd like to introduce

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Speaker 1: you to Facebook if for some reason you managed to

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Speaker 1: listen to podcast and uh, never heard of this before,

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Speaker 1: and you're wondering what the heck is Facebook? I mean

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Speaker 1: the Facebook, right, not a Facebook, And we'll get to

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Speaker 1: that because there was an earlier physical duration of Facebook.

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Speaker 1: A little known fact, if you were like over two

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Speaker 1: billion people across this fair planet of ours, you use

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Speaker 1: Facebook on a daily basis. Mobile traffic is a huge

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Speaker 1: part of Facebook's operation nowadays because originally was desktop and

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Speaker 1: it was only for members of certain universities or colleges.

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Speaker 1: But now anyone can get on it were provided they

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Speaker 1: have the right equipment and internet connection. Isn't it crazy

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Speaker 1: to how mobile has advanced so exponentially. Remember a time

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Speaker 1: where you still had a phone that barely had stuff

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Speaker 1: you could do on it had snake and like a calculator. Yeah,

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Speaker 1: it's what has it been twelve sixteen years, as it's

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Speaker 1: been really really developing, but it's really ramped up in

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Speaker 1: the last more like ten years, you know, with like

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Speaker 1: the I mean the iPhones only you know, a little

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Speaker 1: more than a decade old. Right, Yeah, it's true, and

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Speaker 1: it's an exponential growth that has prompted some questions in

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Speaker 1: the industry and outside of the industry, and in the

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Speaker 1: minds of fringe researchers as well. Did this stuff grow organically?

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Speaker 1: Was state supported on some level? The answer to that

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Speaker 1: question both of those questions is actually yes, but maybe

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Speaker 1: not in a super nefarious way. Regardless of the origin

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Speaker 1: of this exponential growth, we know that mobile is, as

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Speaker 1: Knell said, huge, and many people would not have predicted it.

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Speaker 1: It's safe to say, but as of December, there were

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Speaker 1: one point one five billion mobile daily active users on

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Speaker 1: Facebook's platforms, whether mobile through an OS, which is the

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Speaker 1: safer way, or mobile through Facebook App. Does that include

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Speaker 1: Instagram as well? Since Facebook owns Instagram. No, that does

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Speaker 1: not include Instagram, And that's a great question. That number

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Speaker 1: shoots even further through the roof if you include Instagram,

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Speaker 1: which is also hugely popular right, Instagram has been one

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Speaker 1: of the fastest growing, especially since Facebook has been taking

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Speaker 1: an increasing amount of flak over the years. That one

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Speaker 1: point one five billion number, that was an increase of

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Speaker 1: twenty from December. And this means, you know, if we

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Speaker 1: read between the lines that there are a lot of

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Speaker 1: people who do not have a desktop computer or a laptop,

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Speaker 1: have have no intention of buying one, and say I

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Speaker 1: can just do everything in my digital life on my phone.

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Speaker 1: It's becoming increasingly more true. I mean you, there's a

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Speaker 1: many things that we would rely on a laptop or

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Speaker 1: computer to be able to do that you can totally

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Speaker 1: do on your mobile device. And it's so much more

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Speaker 1: convenient and easy and you always have it right in

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Speaker 1: front of your eyeballs. Yeah, and you can put your

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Speaker 1: friends on Pokemon Go so that their faces will be

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Speaker 1: introduced even if they introduced the system, even if they

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Speaker 1: don't have an online profile or a desire to create one.

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Speaker 1: You can poke your friends. If you're not into Pokemon,

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Speaker 1: you can just pope them. So, so, okay, here's here's

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Speaker 1: my a big question. And most of you already know this,

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Speaker 1: but you think to yourself, well, Facebook is this massive

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Speaker 1: thing with all of these people walking around on their

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Speaker 1: phones looking at their profiles and stuff. It's all free though,

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Speaker 1: I don't have to pay any money to download Facebook. Yes,

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Speaker 1: if you have a service for free that uses ad revenue,

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Speaker 1: you are not the customer. You are the what the product?

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Speaker 1: M Yep, that's true, folks. You are not the customer

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Speaker 1: of social media. You are the product of social media

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Speaker 1: unless you are paying a monthly fee for it. And

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Speaker 1: even then, if you are paying a monthly fee for something,

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Speaker 1: uh if it's not social media. If you're paying a

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Speaker 1: monthly fee for your cable company or your internet connection,

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Speaker 1: they're also taking your data. You are more than one

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Speaker 1: revenue stream for them, and for Facebook. This is usually important.

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Speaker 1: We looked into the advertising revenue right where Facebook publicly

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Speaker 1: makes a lot of its money, and mobile advertisement alone

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Speaker 1: represents eight per cent of all Facebook's ad money from

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Speaker 1: Q three. Quick explanation if we in case, we're getting

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Speaker 1: a little too inside baseball. In businesses, as you may

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Speaker 1: or may not know, profits and losses and revenue are

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Speaker 1: divided up into four segments per year that reached three

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Speaker 1: months long. They're referred to quarters and in business speak

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Speaker 1: in the lands where people say things like Wheelhouse and

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Speaker 1: Granular and Cadence. These quarters are simply referred to as

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Speaker 1: Q one, Q two, Q three, Q four. So this

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Speaker 1: mobile advertising is huge for Facebook. Facebook needs this stuff

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Speaker 1: to happen, and for that to work, they need to

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Speaker 1: have someone looking at ads. They need to have users.

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Speaker 1: That's where you come in. Uh. You might be saying,

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Speaker 1: not me, fellas, not me, you jib brownies because I

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Speaker 1: don't have a Facebook profile. Well just hold on, hold on,

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Speaker 1: just just for now, pretend you do. Uh, let's let's

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Speaker 1: talk a little bit about you. Let's talk about you,

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Speaker 1: the Facebook users. What do we know about you? So? Um,

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Speaker 1: twenty nine point seven percent of users range in age

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Speaker 1: between twenty five and thirty four years old. Uh, and

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Speaker 1: that is the most common age demographic of Facebook use.

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Speaker 1: And that's also a pretty sweet marketing spot too, isn't it.

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Speaker 1: Oh yeah, all those young professionals, all that new income

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Speaker 1: fresh for the harvesting, or you know, the folks who

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Speaker 1: are staying at home with their parents because they can't

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Speaker 1: afford to buy, you know, pay rent anywhere. Because that's

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Speaker 1: a whole other issue we're having to deal with. Well

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Speaker 1: that's a lot less fun, Matt, But Anyway, Yeah, we

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Speaker 1: we do know that we should do an episode on

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Speaker 1: the looming financial crisis of the millennial generation and the

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Speaker 1: post millennial generation. Tion stay tuned. As a matter of fact,

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Speaker 1: if you're listening and you're in that age group, right

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Speaker 1: to us and let you let us know what you think.

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Speaker 1: The biggest misconceptions are about your generation, and everybody does

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Speaker 1: want to have some sort of attention or connection on

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Speaker 1: social media, right. That's why it works. You know, you

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Speaker 1: you say something that is a funny one liner or whatever,

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Speaker 1: and then you check back a few hours later you're like, wow,

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Speaker 1: twenty four people know I exist. You know what I mean?

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Speaker 1: Sweet addiction, isn't it? It is? And we do see

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Speaker 1: dopamine spikes both when you check out mobile phone just

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Speaker 1: in general for text messages and when you look at

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Speaker 1: Instagram or Facebook or Twitter or what have you. So

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Speaker 1: we are going to help you a little bit with this.

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Speaker 1: It turns out that Facebook users in general are most

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Speaker 1: often on this platform in the middle of the week

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Speaker 1: between one to three pm, and they're eighteen percent more

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Speaker 1: likely to engage with whatever is posted. That means throw

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Speaker 1: alike or comment or reaction sub sort. If it's Thursday

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Speaker 1: and Friday. So if you want a lot of people

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Speaker 1: to see your stuff and talk to you, do it

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Speaker 1: middle of the day on Thursday or Friday. So what

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Speaker 1: part of the bell curve am I if I'm on

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Speaker 1: it Monday through Friday. Sev We're a little bit different

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Speaker 1: in that regard because in interest of full transparency, we

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Speaker 1: use Facebook for work. That's that's what I tell myself anyway.

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Speaker 1: And and there are a lot of people I think

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Speaker 1: who who would say who would be in the same

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Speaker 1: boat and say, well, I may only check it every

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Speaker 1: so often, but I get notifications automatically pushed, or I

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Speaker 1: am always logged in because Facebook wants you to feel

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Speaker 1: like it's more convenient to always be logged in. Not

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Speaker 1: all of these users, though, are real, right. CNN estimates

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Speaker 1: their eighty three million fake profiles floating around, and that's

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Speaker 1: probably a lower number than it is in actuality. Wow,

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Speaker 1: I mean, that's a sizeable chunk of one point something billion,

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Speaker 1: you know, users, But that still leaves you with over

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Speaker 1: a billion users, even if it's a hundred million, even

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Speaker 1: if it's two million. There's still so many human beings

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Speaker 1: using this thing. And there are also people who have

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Speaker 1: multiple accounts of their own, so there's still a meat

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Speaker 1: sack at the other side of the keyboard or the phone.

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Speaker 1: But does that mean that only one of those profiles

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Speaker 1: accounts is real? Well, it's true. It's like you know, um,

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Speaker 1: some folks that we work with will make themselves a

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Speaker 1: work related Facebook what they call a public facing Facebook profile,

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Speaker 1: and then they'll keep their personal one, so they just

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Speaker 1: you know, a friend there actual friends, not that we're

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Speaker 1: not all friends with, you know, are people that like

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Speaker 1: the show and then we interact with We're all internet friends.

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Speaker 1: But you know, definitely you have to separate it. I,

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Speaker 1: on the other hand, I only use my one because

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Speaker 1: I don't care and I'm an attention hord. Same. Actually,

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Speaker 1: on average, regardless of whether the user is real or

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Speaker 1: a bot, uh, the user will tend to spend twenty

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Speaker 1: minutes on Facebook per visit. Advertisers love this number because

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Speaker 1: that is an amazing amount of time to spend on

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Speaker 1: one website, especially one that's continually refreshing ads for you.

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Speaker 1: Can I ask you guys a quick question if either

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Speaker 1: of you ever been taken in by an online ad

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Speaker 1: as an online ad ever done anything more to you

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Speaker 1: than just register annoyance? Have you have you? Have you

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Speaker 1: add a successful interaction with an online ad where it

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Speaker 1: knew something about you and served you just the right

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Speaker 1: product or service that you were immediately like, I must

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Speaker 1: have this. Do you mean like a civilian thing? Yeah,

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Speaker 1: instead of going to complain about the ad never, I

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Speaker 1: always wonder it just it just seems very ham fisted

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Speaker 1: most of the time the way you get served ads online.

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Speaker 1: I'm just wondering, like, how successful is it really? I

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Speaker 1: don't I don't know, I can't think of one. It's

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Speaker 1: interesting because you're right, it reminds me of the early

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Speaker 1: days in the Terminator universe when they say the first

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Speaker 1: androids were easy to spot, right, they didn't sweat or

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Speaker 1: bleed or whatever, and they got increasingly sophisticated. That's what's

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Speaker 1: happening now. So it maybe ideal, well in the ideal

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Speaker 1: world for some of these ad providers and data aggregation centers,

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Speaker 1: it may be that they want you to not know

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Speaker 1: that you are being taken in by an AD. They

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Speaker 1: want you to feel like you just had an epiphany

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Speaker 1: and realized you needed a whopper. Yeah, you gotta wonder, like,

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Speaker 1: maybe even like the ads that I see as being unsuccessful.

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Speaker 1: Maybe there's somehow implanting that whopper idea in my head

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Speaker 1: through subliminal means. I don't know, I don't know, or

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Speaker 1: they're just doing it outright. If it's a beauty product

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Speaker 1: it's you know, someone that she likes in, is interested in,

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Speaker 1: it all of a sudden. Well, and that's you know,

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Speaker 1: that's a super effective thing that's happened ever since we

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Speaker 1: were children with toys. Right, and then there's right now

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Speaker 1: for a lot of Facebook ads. If you have a

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Speaker 1: You have, let's say, made a big purchase. You have

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Speaker 1: Toyota Corolla, and that information bounces around backstage where you

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Speaker 1: can't see, and then boom, you all of a sudden

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Speaker 1: are inundated with ads for Toyota Corolla. And it makes

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Speaker 1: no sense, but that's what happens. You know, they're like,

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Speaker 1: you know what, this person who just bought a boat

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Speaker 1: not like a boat shammy though, or like a boat

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Speaker 1: tray tailor you know, why not like, you know, get

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Speaker 1: a little smarter and granular with it, because you're right

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Speaker 1: every time, like or even if like I've looked at

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Speaker 1: Maybe this is smart. It inundates me with ads for

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Speaker 1: that same product that I almost bought that I could

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Speaker 1: see being like, well, I guess I I could buy it.

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Speaker 1: I keep getting reminded maybe I do need it after all.

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Speaker 1: But the whole boat with a boat thing just seems

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Speaker 1: like a waste of of algorithm. But if twenty people

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Speaker 1: ignore it and one person says, you know what, Yes,

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Speaker 1: is that the ratio youthing two boats? No, that's way

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Speaker 1: too high. It's way less. But we know that regardless

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Speaker 1: of the reaction, people are seeing this, and you don't

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Speaker 1: always have to be on Facebook to see it. On average,

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Speaker 1: those like and share buttons that go across all these websites,

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Speaker 1: they're viewed across ten million websites daily, again estimated probably

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Speaker 1: a lower end estimation to every sixty on Facebook, five

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Speaker 1: hundred and ten thousand comments are posted, two nine three

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Speaker 1: thousand status is updated, and one hundred and thirty six

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Speaker 1: photos are uploaded. And one in five page views in

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Speaker 1: the US occurs on Facebook, one in five twenty percent

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Speaker 1: of every single page view. That is crazy. But how

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Speaker 1: did Facebook get there? How did it start? What what

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Speaker 1: led us to this strange thing? Well, you have to

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Speaker 1: go back to two thousand and four year of our Lord,

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Speaker 1: February five. In fact, it was the brainchild of a

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Speaker 1: man named Mark Zuckerberg, Marcus Zuckerberg. He and his roommate.

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Speaker 1: You've all seen the movie. We've all seen the movie,

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Speaker 1: Fantastic Movie. It's pretty good. Remember when you first heard

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Speaker 1: there was gonna be a Facebook movie and you're like,

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Speaker 1: how's that gonna work? Yeah, that's gonna be dull. I

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Speaker 1: had no idea there was actually compelling stuff behind the

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Speaker 1: creation of this. But yes, Zuckerberg and his heart roommate

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Speaker 1: Eduardo Severin, they created this website and it was built

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Speaker 1: after or off of another program he'd already created called

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Speaker 1: face mash all one word yep and um that one.

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Speaker 1: That one was created in two thousand three year prior

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Speaker 1: to it. So facemash was developed by Zuckerberg. He wrote

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Speaker 1: all the software for it um. He made a website

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Speaker 1: when he was in his second year of college, and

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Speaker 1: the website was set up as this, it's like a

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Speaker 1: hot or not game. You've seen it before online. At

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Speaker 1: least around that time, there was even hot or Not

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Speaker 1: dot com. That was a thing you just say, yes,

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Speaker 1: this person is attractive or no, this person is not attractive.

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Speaker 1: And it was entirely female students to female students, side

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Speaker 1: by side, and then you would devote on who was

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Speaker 1: hot and who was not. So this sounds yeah, who

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Speaker 1: like tender? Basically, um, you know, I don't. I don't

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Speaker 1: participate in online dating apps. And but that was his

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Speaker 1: information stored. Was it was just some kind of leaderboard.

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Speaker 1: I thought. I remember hearing something about that, like, was

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Speaker 1: it literally does you do it and it goes away? Hey? Man,

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Speaker 1: I don't know the ins and outs of facemasha. They did,

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Speaker 1: they did store, they did store the data, and they

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Speaker 1: also at Harvard at the time, they had a physical

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Speaker 1: thing called a Facebook. There was more than one, and

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Speaker 1: these just had a picture of someone's staff, alumni or

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Speaker 1: current student and a little blurb of information about them,

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Speaker 1: sort of who's who kind of thing. And so Facebook,

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Speaker 1: the online platform, was named after this because Zuckerberg I

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Speaker 1: thought they would try to digitize and he's like, look,

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Speaker 1: I could do it better, and I could do it

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Speaker 1: in a week. And it turns out he was right,

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Speaker 1: because today Facebook is one of the largest repositories of

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Speaker 1: this sort of information on the planet. And you know,

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Speaker 1: to your question, I wonder, I wonder if somewhere deep

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Speaker 1: in his own secret files completely air gapped, if Mark

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Speaker 1: Zuckerberg is still going through his hot or not and

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Speaker 1: thinking for the world, for the world. Well, and you know,

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Speaker 1: it's one of those things where when you're creating a

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Speaker 1: Facebook like that, you're gathering information and one person or

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Speaker 1: a team of people are creating that physical book with

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Speaker 1: that information. Right, what Suckerberg realized is that people would

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Speaker 1: voluntarily insert their own blurbs and then add pictures and

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Speaker 1: then videos, and then check into places and say where

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Speaker 1: I've been. He was onto something pretty crazy. That's kind

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Speaker 1: of deep in the heart of all of us. It's

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Speaker 1: very true. It's very true, and you know that's why

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Speaker 1: we're gonna get into this later. But it's hard to

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Speaker 1: accuse people of stealing your data when you give it

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Speaker 1: so willingly. Yeah, it's a it's that. I think. The

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Speaker 1: common human trait we're talking about is narcissism is look

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Speaker 1: at me itis, right, which we we suffer from as

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Speaker 1: a species, but also compels our species to do some

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Speaker 1: amazing things. And now what happens. What happens when one

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Speaker 1: company has all of this information at its fingertips? Fast

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Speaker 1: forward way past two thousand four, but not as far

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Speaker 1: as you would think, and enter Cambridge Analytica. That's right. Um,

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Speaker 1: this company, Cambridge Analytica, which I knew precious little about

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Speaker 1: until you know the kerfuffle of late, was started in um.

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Speaker 1: The company markets and Marketed, and markets continues to market

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Speaker 1: itself as a source of consumer research, targeted advertising, and

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Speaker 1: other data related services to both political and corporate clients.

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Speaker 1: So you know, in short, they're they're kind of mining

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Speaker 1: data and crunching the numbers, running it right. According to

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Speaker 1: The New York Times, it was launched with fifteen million

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Speaker 1: dollars of seed money, backing money by billionaire Republican donor

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Speaker 1: Robert Mercer and Steve Bannon Steven Stevie b Yes, the

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Speaker 1: advisor for the Trump campaign and later for a time

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Speaker 1: an adviser to the Trump administration. Now bitter enemy also

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Speaker 1: was the kind of power behind the throne of bright Bart,

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Speaker 1: this very divisive right wing um website. And everything was

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Speaker 1: going along swimmingly until that is in March of when

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Speaker 1: the house of cards began to tumble. And we will

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Speaker 1: sort through the debris when we return from a quick

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Speaker 1: sponsor break. Here are the facts. On March seventeen, reports

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Speaker 1: from the Guardian and The New York Times revealed that

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Speaker 1: Cambridge Analytica, again a data analysis firm that had worked

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Speaker 1: with President Trump's campaign, had harvested the personal information of quote,

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Speaker 1: around fifty million Facebook users without permission. Whether that's permission

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Speaker 1: from Facebook, whether that's permission from the users like they

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Speaker 1: would ask. This revelation came from a whistleblower named Christopher

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Speaker 1: Wiley w y l i E, who states that he

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Speaker 1: helped build the firm and worked there until twenty fourteen. Uh.

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Speaker 1: The way this worked is that Cambridge Analytica retrieved the

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Speaker 1: data from an outfit called Global Science Research or gs R,

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Speaker 1: a company owned by University of Cambridge professor Alexander Kogan. Yeah,

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Speaker 1: and he collected all of this data in He used

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Speaker 1: a psycho graphic personality test app UM and it was

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Speaker 1: called this is Your Digital Life all one word, just

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Speaker 1: this is your Digital Life. There were there were around

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Speaker 1: three thousand people who and who actually like got this

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Speaker 1: app and installed it, and then they gave it permission

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Speaker 1: to collect their personal information from Facebook, including the city

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Speaker 1: where they set up the profile, the content they liked,

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Speaker 1: and the information about their friends. Now this is the

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Speaker 1: most important part. It you gave it permission to collect

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Speaker 1: information about your friends, so that three hundred thousand people

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Speaker 1: jumped up exponentially. It's like even when you hear about

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Speaker 1: like recon data recon that UM law enforcement agencies or

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Speaker 1: the FBI do where you can know something about who

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Speaker 1: people interact with that actually knowing who they are kind

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Speaker 1: of like you can see the web of communications and

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Speaker 1: it starts to give you a really clear picture even

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Speaker 1: without knowing the specifics about who's in the chain. Dude,

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Speaker 1: you can do that with the white pages, that's right,

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Speaker 1: saying like with this, it's like they literally have like

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Speaker 1: all the information. So it's like it's there's no no

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Speaker 1: end to the kind of stuff you could learn about

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Speaker 1: people with this kind of access, right, So, so this

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Speaker 1: is what happens. You get three thousand thousand people to

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Speaker 1: bite on the hook, then all of a sudden you've

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Speaker 1: got the information on over fifty million people at the time.

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Speaker 1: That's what we estimate exactly. And then the the other

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Speaker 1: the other sticky problem with this. Without going too far

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Speaker 1: into big data, we can recommend that you check out

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Speaker 1: our earlier episode on it. If you would like to

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Speaker 1: learn some frightening things about target, you can find that

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Speaker 1: on our website. Uh One one of the sticky and

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Speaker 1: citious things about this is what Facebook would refer to

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Speaker 1: as frictionless sharing. Right. It's a very it's very fluffy

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Speaker 1: euphemistic phrase for the process that Noel and Matt are

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Speaker 1: describing here, and it does expand It's like that uh

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Speaker 1: six degrees of Kevin Bacon game, although using their using

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Speaker 1: their information that they had aggregated, Facebook later found people

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Speaker 1: are more like three and a half degrees separated from

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Speaker 1: each other, including maybe Kevin Bacon. I don't know, it's

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Speaker 1: an average. I mean, how often if you haven't met

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Speaker 1: a new random person on Facebook or and then you've

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Speaker 1: ended up befriend of them on Facebook and realize that

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Speaker 1: you have several mutual friends, even if there's somebody from

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Speaker 1: like another state, you know what I mean, Like, it's

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Speaker 1: not that far fetched. So the web starts to shrink,

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Speaker 1: or at least the access to it, you know more

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Speaker 1: you think about you know, these connections. Yeah, absolutely, And

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Speaker 1: on the so March seventeenth, this all goes public. Right

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Speaker 1: the next day, Facebook bands Cambridge Analytica and they ban UH,

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Speaker 1: its parent company, and then they banned Cogan himself. They

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Speaker 1: also on the same day band Christopher Wiley, the whistleblower.

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Speaker 1: March nineteen, Facebook stock plummets takes a nosedive. On the

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Speaker 1: same day, Congress starts to stiff around. Senator Edward Markey

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Speaker 1: from Massachusetts says, hey, we gotta hold some hearings against

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Speaker 1: these Facebook and Cambridge analytic a jokers and paraphrasing, and

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Speaker 1: specifically he wrote to the ranking members of the Senate

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Speaker 1: Committee on Commerce at Senator John Thune and Senator Bill

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Speaker 1: Nelson of South Dakota and Florida, respectively, and Markey says

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Speaker 1: we need to move quickly to hold hearings. Since Facebook

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Speaker 1: is required to quote obtain explicit permission before sharing data

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Speaker 1: about its users. Facebook holds an emerging see meeting. Uh.

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Speaker 1: They also and this is enjoyable. Uh. They also for

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Speaker 1: everyone at the meeting, their version of an all hands,

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Speaker 1: they have a poll for the employees. That's what you know,

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Speaker 1: what you think? Uh. Zuckerberg doesn't say anything about this

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Speaker 1: for three days and it's not until March twenty one

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Speaker 1: that he addresses it with a post on his Facebook accounts.

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Speaker 1: Got to respect the consistency, and he says what happens

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Speaker 1: is his responsibility, but there are change is made to

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Speaker 1: the platform that will prevent it from happening again. He

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Speaker 1: proposes a three step plan going forward to investigate third

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Speaker 1: party apps, restrict developers access to user data on the whole,

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Speaker 1: and give users tools to see what information of theirs

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Speaker 1: is being shared. So, man, if I didn't install my

428
00:25:43,080 --> 00:25:47,960
Speaker 1: Digital Life, what was my data breached? Yeah? Quite possible.

429
00:25:48,560 --> 00:25:51,200
Speaker 1: So can you explain? So there's this there's this thing

430
00:25:51,240 --> 00:25:54,840
Speaker 1: where in the rules of Facebook, if you're an advertiser

431
00:25:54,880 --> 00:25:57,720
Speaker 1: and you are collecting data in this way, it says

432
00:25:57,760 --> 00:26:00,800
Speaker 1: that you can, as the developer of an app, take

433
00:26:01,200 --> 00:26:04,680
Speaker 1: like glean the information of friends of the people who

434
00:26:04,680 --> 00:26:07,520
Speaker 1: have actually installed your app or whatever. However, you have

435
00:26:07,640 --> 00:26:11,040
Speaker 1: to use that data in a very limited way, and

436
00:26:11,200 --> 00:26:14,720
Speaker 1: only to improve your app that you're using. You can't

437
00:26:14,720 --> 00:26:17,879
Speaker 1: actually sell that data to a third party or use

438
00:26:17,960 --> 00:26:19,760
Speaker 1: that data for any other reason. That that was the

439
00:26:19,800 --> 00:26:22,280
Speaker 1: original like rules that were set up. And then just

440
00:26:22,400 --> 00:26:25,920
Speaker 1: to clarify, that's exactly why they made that change where

441
00:26:26,320 --> 00:26:29,679
Speaker 1: the third parties don't have quite carte blanche access to

442
00:26:30,280 --> 00:26:32,960
Speaker 1: um your friends data, only to yours directly because you

443
00:26:33,000 --> 00:26:37,000
Speaker 1: have opted in to that third part Again, it's restricted.

444
00:26:37,440 --> 00:26:40,199
Speaker 1: That's a lot like not. That's a lot like flame

445
00:26:40,400 --> 00:26:44,520
Speaker 1: retardant or water resistant. It's not proof, not water or

446
00:26:44,560 --> 00:26:47,880
Speaker 1: flame proof. It's not it's still not banned. Yeah, there's

447
00:26:47,880 --> 00:26:50,520
Speaker 1: still a fire. And this is where some dicey things

448
00:26:50,520 --> 00:26:53,800
Speaker 1: start happening. March twenty two, this all happened so quickly. March,

449
00:26:55,119 --> 00:26:58,920
Speaker 1: President Donald Trump goes on Twitter, which is his social

450
00:26:58,920 --> 00:27:02,560
Speaker 1: media of choice, and he appears to boast about a

451
00:27:02,680 --> 00:27:06,280
Speaker 1: partnership with Cambridge Analytica. He doesn't name them, but he

452
00:27:06,359 --> 00:27:09,320
Speaker 1: says people are no longer claiming his use of social

453
00:27:09,359 --> 00:27:12,399
Speaker 1: media was less robust than Clinton, that being Hillary Clinton

454
00:27:12,880 --> 00:27:16,440
Speaker 1: during the sixteen campaign in election, and while he doesn't

455
00:27:16,480 --> 00:27:21,399
Speaker 1: outright name them, the timing suggest that he was talking

456
00:27:21,440 --> 00:27:24,200
Speaker 1: about Cambridge Analytica. It's sort of like that Boasteammates where

457
00:27:24,240 --> 00:27:26,600
Speaker 1: he says I don't pay my taxes, so I'm smart.

458
00:27:27,119 --> 00:27:29,239
Speaker 1: It's kind of like him saying I figured out how

459
00:27:29,240 --> 00:27:34,040
Speaker 1: to exploit social media, so I'm smart. Yeah, I guess

460
00:27:34,200 --> 00:27:36,960
Speaker 1: you know. I don't. I don't follow his tweets very closely.

461
00:27:37,119 --> 00:27:38,679
Speaker 1: It is it, don't you think it's sort of an

462
00:27:38,720 --> 00:27:42,320
Speaker 1: odd thing to say, given how controversial this stuff has

463
00:27:42,320 --> 00:27:44,280
Speaker 1: been for him to come out and say that, if

464
00:27:44,280 --> 00:27:47,479
Speaker 1: he wasn't kind of trying to vindicate himself in some

465
00:27:47,520 --> 00:27:49,240
Speaker 1: way or say that, like, I don't know, it just

466
00:27:49,240 --> 00:27:52,000
Speaker 1: seems a little boastful to me. It's it's puzzling. Indeed,

467
00:27:52,600 --> 00:27:55,600
Speaker 1: Yeah he did. I mean, it's clearly a boast On

468
00:27:55,760 --> 00:28:00,919
Speaker 1: March twe ellen musk our modern tone, me stark are

469
00:28:01,040 --> 00:28:04,120
Speaker 1: modern Tesla. I guess he would want us to say,

470
00:28:04,760 --> 00:28:08,480
Speaker 1: gets spooked and deletes his space x account. He deletes

471
00:28:08,520 --> 00:28:12,560
Speaker 1: a Tesla account. Zuckerberg is invited on the same day

472
00:28:12,560 --> 00:28:15,520
Speaker 1: to testify in front of the House of Reps. House representatives,

473
00:28:16,040 --> 00:28:19,520
Speaker 1: many of who will go on to thoroughly embarrass themselves

474
00:28:20,160 --> 00:28:23,800
Speaker 1: with their lack of knowledge regarding social media platforms and

475
00:28:23,840 --> 00:28:26,000
Speaker 1: technology in general. I saw a really good meme where

476
00:28:26,000 --> 00:28:27,880
Speaker 1: it was like a screenshot of of all the people

477
00:28:27,880 --> 00:28:30,680
Speaker 1: that were quizzing him, and it just had like fifty

478
00:28:30,720 --> 00:28:34,000
Speaker 1: spyware search bars like stacked up on top of themselves,

479
00:28:34,000 --> 00:28:35,679
Speaker 1: like in their browser, you know, like those ones that

480
00:28:35,720 --> 00:28:37,800
Speaker 1: like automatically installed, and you can't you rid of them. Well,

481
00:28:37,840 --> 00:28:40,600
Speaker 1: they can't. There's to fill their whole screen, gating his photoshop,

482
00:28:41,480 --> 00:28:46,360
Speaker 1: his visit. It's worth it. Yeah. Well, and here's here's

483
00:28:46,400 --> 00:28:48,360
Speaker 1: the whole other thing that we're not even gonna get

484
00:28:48,400 --> 00:28:53,280
Speaker 1: into too deeply in this episode. But yeah, some of

485
00:28:53,360 --> 00:28:56,480
Speaker 1: the some of the ways that Cambridge Analytica was using

486
00:28:56,480 --> 00:29:01,880
Speaker 1: this data included influencing if just you know, the American

487
00:29:01,960 --> 00:29:08,280
Speaker 1: president presidential election, the entire Brexit situation. Yes, that is true.

488
00:29:08,400 --> 00:29:10,760
Speaker 1: That is true. And this is one of and their

489
00:29:10,920 --> 00:29:15,720
Speaker 1: UK based company to Cambridge. So this is the primary

490
00:29:15,760 --> 00:29:20,680
Speaker 1: impetus for officers from Britain's Information Commissioner's Office getting involved.

491
00:29:20,880 --> 00:29:24,680
Speaker 1: There a data watchdog group, so they raid the offices

492
00:29:24,720 --> 00:29:27,520
Speaker 1: at Cambridge Analytica in London. They take their time and

493
00:29:27,600 --> 00:29:30,440
Speaker 1: there for seven hours. I heard an NPR piece where

494
00:29:30,520 --> 00:29:34,280
Speaker 1: someone like was able to get their kind of dossier there,

495
00:29:34,320 --> 00:29:39,000
Speaker 1: their digital you know, dossier that Cambridge Analytica assembled about

496
00:29:39,040 --> 00:29:43,160
Speaker 1: them particularly, and it's not the most alarming kind of

497
00:29:43,200 --> 00:29:46,600
Speaker 1: sounding stuff that you might imagine. It's like you're who

498
00:29:46,640 --> 00:29:50,000
Speaker 1: were you? What party are you more likely to identify with?

499
00:29:50,120 --> 00:29:53,400
Speaker 1: You know, it represented that kind of stuff, and in Zuckerberg,

500
00:29:53,440 --> 00:29:56,040
Speaker 1: even in his testimony said this is all stuff people

501
00:29:56,040 --> 00:29:59,360
Speaker 1: would share openly themselves. So you know, he that him

502
00:29:59,360 --> 00:30:01,560
Speaker 1: in an attempt to absolve himself. I was wondering what

503
00:30:01,640 --> 00:30:05,320
Speaker 1: you guys think, like how is this different? Like how

504
00:30:05,520 --> 00:30:08,160
Speaker 1: what what makes this so different? I want to walk

505
00:30:08,200 --> 00:30:10,160
Speaker 1: through the rest of the timeline and get to the

506
00:30:10,200 --> 00:30:14,560
Speaker 1: testimony here, because that is a good question. So the organization,

507
00:30:14,720 --> 00:30:17,959
Speaker 1: the Information Commissioner's Office, was given the search warrant by

508
00:30:18,040 --> 00:30:22,040
Speaker 1: the High Court judge to determine whether the group tampered

509
00:30:22,080 --> 00:30:24,720
Speaker 1: with Brexit, as Matt as you just brought up, and

510
00:30:24,760 --> 00:30:29,120
Speaker 1: they are currently still analyzing and considering the evidence. Right

511
00:30:29,520 --> 00:30:33,600
Speaker 1: on March, Zuckerberg, who has become at this point known

512
00:30:33,640 --> 00:30:37,240
Speaker 1: as the Zuck in a lot of online forums, takes

513
00:30:37,240 --> 00:30:40,280
Speaker 1: out a full page ad in multiple UK US papers.

514
00:30:40,640 --> 00:30:44,360
Speaker 1: It's a it's a profound apology, repeats his three step plan.

515
00:30:44,880 --> 00:30:48,360
Speaker 1: The FTC the next day confirms that they're investigating Facebook

516
00:30:48,400 --> 00:30:53,280
Speaker 1: for these privacy practices. On the Zuckerberg refuses to meet

517
00:30:53,280 --> 00:30:56,320
Speaker 1: with British lawmakers. He sends one of his employees, but

518
00:30:56,400 --> 00:31:00,320
Speaker 1: he does agree to meet with US Congress. And while

519
00:31:01,160 --> 00:31:04,960
Speaker 1: Britain is investigating this, Wiley, that whistle blewer from earlier

520
00:31:05,080 --> 00:31:08,760
Speaker 1: tells Parliament that Facebook uses microphones to improve ad targeting,

521
00:31:08,800 --> 00:31:11,360
Speaker 1: which we've talked about before on this show. What do

522
00:31:11,360 --> 00:31:12,719
Speaker 1: you guys think? Do you think that's a real thing?

523
00:31:12,760 --> 00:31:14,800
Speaker 1: Do you mean like on your phone? Yeah? Yeah, yeah,

524
00:31:14,880 --> 00:31:16,920
Speaker 1: I've certainly and we all have I think had a

525
00:31:16,960 --> 00:31:19,840
Speaker 1: situation when we were chatting about something and then got

526
00:31:19,840 --> 00:31:22,000
Speaker 1: served with an AD about it the next day or later.

527
00:31:22,040 --> 00:31:25,920
Speaker 1: I mean, I yeah, I think it's highly plausible. I'm

528
00:31:26,000 --> 00:31:30,320
Speaker 1: utterly convinced, even though I have not seen absolute, um

529
00:31:30,440 --> 00:31:34,160
Speaker 1: like damning evidence, I'm convinced. I you know, we we

530
00:31:34,240 --> 00:31:38,000
Speaker 1: have to ask ourselves, at what threshold can the sheer

531
00:31:38,720 --> 00:31:44,480
Speaker 1: number of anecdotes and firsthand experiences be accepted as as

532
00:31:44,520 --> 00:31:47,360
Speaker 1: you said, Matt, damning evidence. I think we're close to

533
00:31:47,400 --> 00:31:50,320
Speaker 1: that threshold. There's there's something we should fast forward to

534
00:31:50,360 --> 00:31:53,840
Speaker 1: in April four, buried in a blog post about changes

535
00:31:53,880 --> 00:31:57,720
Speaker 1: Facebook is making to quote clarify in terms of service,

536
00:31:58,120 --> 00:32:01,960
Speaker 1: like that clarifying not correct. The company casually increases the

537
00:32:02,040 --> 00:32:05,000
Speaker 1: number of people affected by Cambridge analytic as efforts from

538
00:32:05,040 --> 00:32:10,480
Speaker 1: fifty million to eighties seven million. Okay, just a couple more,

539
00:32:11,520 --> 00:32:14,680
Speaker 1: just a few, just a few dollars almost half again,

540
00:32:16,160 --> 00:32:20,240
Speaker 1: these are these are individuals that were compromised, right, yeah, yeah, yeah.

541
00:32:20,320 --> 00:32:22,120
Speaker 1: And they do go out of their way to say

542
00:32:22,200 --> 00:32:25,080
Speaker 1: people not profiles because they think their PR team is

543
00:32:25,120 --> 00:32:29,440
Speaker 1: working around the clot to humanize. Do you think they've

544
00:32:29,480 --> 00:32:31,560
Speaker 1: called out like a lot of it. We talked about

545
00:32:31,600 --> 00:32:34,480
Speaker 1: fake profiles and bots and stuff. I mean, how do

546
00:32:34,560 --> 00:32:36,960
Speaker 1: we know that number? Like, how many in that number

547
00:32:36,960 --> 00:32:39,720
Speaker 1: do you think could be fake profiles? And like, I

548
00:32:40,200 --> 00:32:43,000
Speaker 1: don't know, do you remember the number of fake profiles

549
00:32:43,040 --> 00:32:45,240
Speaker 1: from the top? No, it was how was high? Eight

550
00:32:45,440 --> 00:32:48,440
Speaker 1: three million? So what if these are all fake? No,

551
00:32:48,560 --> 00:32:50,800
Speaker 1: they're not all fake that it's just it's a proportion.

552
00:32:51,120 --> 00:32:53,040
Speaker 1: I'm sorry, I just I think it'd be hilarious if

553
00:32:53,040 --> 00:32:55,520
Speaker 1: they only got fake profiles. That would be you know what,

554
00:32:55,560 --> 00:32:59,479
Speaker 1: that would be a relief for everyone involved. Except for

555
00:32:59,560 --> 00:33:04,600
Speaker 1: the bot masters, but they continue to their credit. Facebook

556
00:33:04,600 --> 00:33:08,440
Speaker 1: suspends two more companies doing similar stuff, one called Cube

557
00:33:08,520 --> 00:33:12,000
Speaker 1: You and one called Aggregate i Q, and then they

558
00:33:12,040 --> 00:33:15,440
Speaker 1: start informing users who are affected by the breach, and folks,

559
00:33:15,680 --> 00:33:18,320
Speaker 1: you're listening to this today, the odds are you probably

560
00:33:18,360 --> 00:33:21,080
Speaker 1: have some sort of Facebook profile, And if you have

561
00:33:21,160 --> 00:33:27,920
Speaker 1: some sort of Facebook profile, the odds are not insanely low.

562
00:33:29,040 --> 00:33:32,600
Speaker 1: It's it's not super likely, but it's it's quite possible

563
00:33:32,880 --> 00:33:36,080
Speaker 1: that you opened your Facebook one day and you saw

564
00:33:36,440 --> 00:33:38,959
Speaker 1: a notice that says that you may have been affected

565
00:33:38,960 --> 00:33:43,400
Speaker 1: by this because either you or a friend used the app.

566
00:33:44,520 --> 00:33:49,440
Speaker 1: This is your digital life, Oh, this is your digital life.

567
00:33:49,520 --> 00:33:51,840
Speaker 1: That sounds like a soap opera or something for like

568
00:33:51,960 --> 00:33:57,560
Speaker 1: the future. Yeah, oh man, or a Black Mirror episode, right,

569
00:33:57,680 --> 00:33:59,560
Speaker 1: that'd be a good one. I think, yeah, we should

570
00:33:59,600 --> 00:34:04,120
Speaker 1: pocket like at that. So they appear to have also

571
00:34:04,360 --> 00:34:08,280
Speaker 1: harvested not just the what we will call like the metadata,

572
00:34:08,560 --> 00:34:11,720
Speaker 1: the connections, the time of log in, the time spent somewhere,

573
00:34:11,880 --> 00:34:15,799
Speaker 1: but they've also harvested content, private messages. Because that we're

574
00:34:15,920 --> 00:34:19,520
Speaker 1: harvested again, that's isn't that that's dark to me? That's

575
00:34:19,600 --> 00:34:24,040
Speaker 1: really creepy. The other synonym is scraped. Yeah, I've a lot. Yeah,

576
00:34:24,160 --> 00:34:26,960
Speaker 1: uh so, they said a small number of people who

577
00:34:27,080 --> 00:34:30,440
Speaker 1: logged into This is Your Digital Life also shared their

578
00:34:30,600 --> 00:34:33,960
Speaker 1: news feed, timeline, post and messages, which may have included

579
00:34:33,960 --> 00:34:38,880
Speaker 1: posting messages from you. That's according to Cambridge Analytic and Facebook.

580
00:34:39,120 --> 00:34:41,920
Speaker 1: They also gained access to information from the friends of

581
00:34:41,960 --> 00:34:46,800
Speaker 1: the apps users and it's still, honestly, is not clear

582
00:34:47,200 --> 00:34:51,680
Speaker 1: how many users were affected. Look's like a disease. It's

583
00:34:51,680 --> 00:34:55,239
Speaker 1: like it's it's can contract it from others. It's a

584
00:34:55,320 --> 00:35:01,319
Speaker 1: vector from contact and astute listeners. You noticed earlier that

585
00:35:01,640 --> 00:35:06,520
Speaker 1: Mark Zuckerberg himself refused to go to British lawmakers, but

586
00:35:06,680 --> 00:35:10,440
Speaker 1: did consent to go to US lawmakers. And we were

587
00:35:10,520 --> 00:35:12,839
Speaker 1: kicking this around a little bit off air. Why why

588
00:35:12,880 --> 00:35:15,400
Speaker 1: do you think that is? Well, I mean, he spent

589
00:35:15,560 --> 00:35:21,360
Speaker 1: two days giving testimony to to the United States lawmakers.

590
00:35:21,160 --> 00:35:24,000
Speaker 1: It sounds so much better when you when you call

591
00:35:24,080 --> 00:35:27,640
Speaker 1: them lawmakers instead of just you know, the House or

592
00:35:27,840 --> 00:35:31,120
Speaker 1: congress Um. But anyway, yeah, he spent two days there

593
00:35:31,200 --> 00:35:33,719
Speaker 1: and he had not much of a problem at all

594
00:35:33,840 --> 00:35:37,440
Speaker 1: fielding questions, like you said, Ben from people who had

595
00:35:37,600 --> 00:35:42,480
Speaker 1: barely a grasp on what an online advertising is or

596
00:35:42,480 --> 00:35:47,200
Speaker 1: what an app actually is, or how data transfers occur,

597
00:35:47,480 --> 00:35:50,000
Speaker 1: and correct me if I'm wrong. But it's my understanding

598
00:35:50,080 --> 00:35:56,160
Speaker 1: that British lawmakers do more so than than American lawmakers,

599
00:35:56,440 --> 00:35:59,440
Speaker 1: or at least they know how to dig deep and

600
00:35:59,440 --> 00:36:01,960
Speaker 1: and at people's feet to the fire, maybe a little

601
00:36:01,960 --> 00:36:03,360
Speaker 1: bit more than we do. It felt like this is

602
00:36:03,360 --> 00:36:07,000
Speaker 1: a bit of a softball kind of situation. Oh agreed. Yeah,

603
00:36:07,040 --> 00:36:11,240
Speaker 1: And this goes into maybe journalistic culture too, because BBC

604
00:36:11,440 --> 00:36:17,000
Speaker 1: reporters are generally thought to be harder hitting as journalists.

605
00:36:17,239 --> 00:36:20,720
Speaker 1: I've got a short just just a couple of choice

606
00:36:20,800 --> 00:36:25,040
Speaker 1: questions you want? Lawmakers asked? Uh, Mark Zuckerberg, would you

607
00:36:25,040 --> 00:36:30,960
Speaker 1: like to hear something? Please? Okay? Senator Lindsay Graham from

608
00:36:31,040 --> 00:36:34,520
Speaker 1: South Carolina asked, Mark Zuckerberg? Is Twitter the same as

609
00:36:34,560 --> 00:36:39,799
Speaker 1: what you do? Lindsey Graham asking, like, Lindsay, Grandpa? Am

610
00:36:39,840 --> 00:36:45,799
Speaker 1: I right? Oh boy? Uh? Florida Senator Bill Nelson is

611
00:36:46,680 --> 00:36:49,799
Speaker 1: fond of a particular type of chocolate and he had

612
00:36:49,840 --> 00:36:52,239
Speaker 1: mentioned that fact to some Facebook friends and now he's

613
00:36:52,239 --> 00:36:54,719
Speaker 1: seen ads for that chocolate. So he said, what if

614
00:36:54,760 --> 00:37:01,239
Speaker 1: I don't want to receive ads? For chocolate. Yeah uh.

615
00:37:01,280 --> 00:37:04,879
Speaker 1: And then Senator Roy Blunt from Missouri says, my son

616
00:37:05,040 --> 00:37:07,520
Speaker 1: is dedicated to Instagram, so he'd want to be sure

617
00:37:07,560 --> 00:37:10,560
Speaker 1: I mentioned him while I was here with you. It's

618
00:37:10,560 --> 00:37:13,000
Speaker 1: not even a question, can I can? I can? Can

619
00:37:13,000 --> 00:37:18,279
Speaker 1: you sign this for me, Mark Zuckerberg for my grandson? Yeah? Man?

620
00:37:18,640 --> 00:37:21,560
Speaker 1: Good lord? How much more? This is comically a softball

621
00:37:22,160 --> 00:37:25,040
Speaker 1: And again, like I mean, you know, there's probably a

622
00:37:25,080 --> 00:37:28,319
Speaker 1: committee that like knows how the internet works or why

623
00:37:28,360 --> 00:37:30,520
Speaker 1: why why aren't the FCC involved? I don't know. I

624
00:37:30,560 --> 00:37:33,319
Speaker 1: just it just seems very like what's the word, Just

625
00:37:33,360 --> 00:37:37,000
Speaker 1: a lot of pageantry and no real like like action,

626
00:37:37,320 --> 00:37:39,279
Speaker 1: like a lot like a lot of theater. And you

627
00:37:39,320 --> 00:37:44,120
Speaker 1: can you can read, uh in depth some of these

628
00:37:45,440 --> 00:37:49,520
Speaker 1: some of these questions. Matt, you really took the really

629
00:37:49,520 --> 00:37:53,280
Speaker 1: took the took one for the team because you watched

630
00:37:53,320 --> 00:37:55,880
Speaker 1: a lot of this I did. I started watching the

631
00:37:55,960 --> 00:37:59,480
Speaker 1: first day and I got lost. After about forty minutes,

632
00:37:59,520 --> 00:38:03,080
Speaker 1: I just could it became noise and I just shut

633
00:38:03,120 --> 00:38:05,880
Speaker 1: it down. Can you do an impression of Zuckerberg? I

634
00:38:05,880 --> 00:38:08,040
Speaker 1: don't know that I can. I was listening to NPR

635
00:38:08,600 --> 00:38:10,239
Speaker 1: and they were kind of summing up some of the

636
00:38:10,280 --> 00:38:13,720
Speaker 1: major parts. And I heard him say this is a quote,

637
00:38:13,760 --> 00:38:16,080
Speaker 1: so I guess I'll try it here. We didn't take

638
00:38:16,120 --> 00:38:19,160
Speaker 1: a broad enough view of our responsibility and that was

639
00:38:19,200 --> 00:38:22,880
Speaker 1: a big mistake and it was my mistake, and I'm sorry.

640
00:38:23,200 --> 00:38:26,359
Speaker 1: I started Facebook, I I run it, and I'm responsible

641
00:38:26,360 --> 00:38:29,880
Speaker 1: for what happens here. And I swear I am human. Yes,

642
00:38:30,280 --> 00:38:36,799
Speaker 1: I swear. Hello, friend, it's just the responses that were

643
00:38:36,840 --> 00:38:39,080
Speaker 1: given to a lot of the major things. As you

644
00:38:39,080 --> 00:38:42,880
Speaker 1: can expect, since he's under oath, he is, he's on

645
00:38:43,239 --> 00:38:46,600
Speaker 1: the surface. Everything is on the surface, and he just

646
00:38:46,600 --> 00:38:50,480
Speaker 1: seems pretty surfacing in general. And I don't know, I

647
00:38:50,520 --> 00:38:52,600
Speaker 1: don't know much about the zuck Man. I feel like

648
00:38:52,600 --> 00:38:56,399
Speaker 1: when I talk about the zuck I have this bit

649
00:38:56,400 --> 00:39:00,520
Speaker 1: of envy just about how young and successful really you

650
00:39:00,520 --> 00:39:02,839
Speaker 1: think he's happy that I think he'd feel really isolated

651
00:39:02,880 --> 00:39:06,200
Speaker 1: and and I don't know, you know, more money, more

652
00:39:06,200 --> 00:39:09,400
Speaker 1: problems than me. He's just trying to connect to the world. Well, Mark,

653
00:39:09,440 --> 00:39:12,239
Speaker 1: if you would like to hop on our show in

654
00:39:12,280 --> 00:39:16,120
Speaker 1: the future for an interview, or if you just need

655
00:39:16,239 --> 00:39:19,720
Speaker 1: some actual friends to maybe go bowling with you or something,

656
00:39:19,800 --> 00:39:21,600
Speaker 1: let us know, hit us up on Facebook where we

657
00:39:21,640 --> 00:39:26,799
Speaker 1: are conspiracy. Yes, just so I will say one last thing.

658
00:39:27,040 --> 00:39:30,480
Speaker 1: There was one one, even though it's kind of a

659
00:39:30,520 --> 00:39:34,239
Speaker 1: low blow shot. Senator Dick Durban at one point of

660
00:39:34,440 --> 00:39:37,319
Speaker 1: all of a sudden, asked Mark Zuckerberg what hotel he

661
00:39:37,360 --> 00:39:39,480
Speaker 1: was staying at. He's like, well, so, what what hotel

662
00:39:39,520 --> 00:39:41,680
Speaker 1: are you staying at while you're here? Would you be

663
00:39:41,719 --> 00:39:45,279
Speaker 1: comfortable revealing that information exactly? And you know, Mark of

664
00:39:45,280 --> 00:39:49,319
Speaker 1: course is like, no, absolutely not, because again, all of

665
00:39:49,360 --> 00:39:54,280
Speaker 1: the information sharing here that we're talking about is voluntary. Yep.

666
00:39:55,120 --> 00:39:57,960
Speaker 1: And as a point, he has a point. It's an

667
00:39:58,000 --> 00:40:00,960
Speaker 1: interesting argument because a lot of this is based on

668
00:40:01,360 --> 00:40:04,719
Speaker 1: the inherent problems of social media. But it's a contract

669
00:40:04,760 --> 00:40:08,360
Speaker 1: that we all sign on for and we accept the

670
00:40:08,400 --> 00:40:12,200
Speaker 1: trade off, you know, because surely we all took the

671
00:40:12,280 --> 00:40:15,640
Speaker 1: time to read the terms of agreement in full. Now, Ben,

672
00:40:15,680 --> 00:40:18,920
Speaker 1: I would think that's possible, mind you possible that you

673
00:40:19,080 --> 00:40:23,560
Speaker 1: have it is quite possible. But one thing that is

674
00:40:23,640 --> 00:40:26,719
Speaker 1: impossible for us to do now is to pretend that

675
00:40:26,760 --> 00:40:29,439
Speaker 1: this is not an ongoing thing, or pretend that there's

676
00:40:29,640 --> 00:40:33,040
Speaker 1: a full number of full solid number of people that

677
00:40:33,080 --> 00:40:37,160
Speaker 1: we know have been impacted. We can't predict the future,

678
00:40:37,200 --> 00:40:42,280
Speaker 1: but what we can do is explore the troubling implications.

679
00:40:43,360 --> 00:40:52,359
Speaker 1: After that is a word from our sponsor. Here's where

680
00:40:52,360 --> 00:40:57,719
Speaker 1: it gets crazy. This granted, even though the three of

681
00:40:57,800 --> 00:41:02,200
Speaker 1: us are hilarious, sounds sounds kind of dry, you know

682
00:41:02,200 --> 00:41:06,759
Speaker 1: what I mean. But that's because we're at the beginning

683
00:41:07,000 --> 00:41:09,960
Speaker 1: of a much longer story, in a much larger revelation.

684
00:41:10,000 --> 00:41:14,000
Speaker 1: It's a story that's been going on untold silently for

685
00:41:14,360 --> 00:41:19,160
Speaker 1: you know, since shortly after Facebook was opened to anyone

686
00:41:19,160 --> 00:41:22,520
Speaker 1: who wanted to log in. And while those investigations are ongoing,

687
00:41:22,600 --> 00:41:26,040
Speaker 1: it's safe to say more and more unpleasant discoveries linger

688
00:41:26,239 --> 00:41:29,080
Speaker 1: on the horizon waiting to be discovered. One of the

689
00:41:29,120 --> 00:41:34,520
Speaker 1: first questions is what other operations are occurring at this point.

690
00:41:35,320 --> 00:41:39,400
Speaker 1: We don't know. A very few people actually know. Like

691
00:41:39,480 --> 00:41:42,239
Speaker 1: just just before we hopped in the studio today, a

692
00:41:42,239 --> 00:41:45,719
Speaker 1: few hours ago, it was revealed that Cambridge Analytica was

693
00:41:45,760 --> 00:41:50,600
Speaker 1: planning to launch its own cryptocurrency. They want to raise money,

694
00:41:50,680 --> 00:41:52,479
Speaker 1: don't they. They want to They want to raise money

695
00:41:52,480 --> 00:41:55,600
Speaker 1: to pay for the creation of a system to help

696
00:41:55,680 --> 00:42:00,200
Speaker 1: people store and sell their online personal data to advertisers.

697
00:42:00,680 --> 00:42:04,760
Speaker 1: Come on. True story called dragon Coin after a famous

698
00:42:04,760 --> 00:42:07,480
Speaker 1: gambler in Macau. But I thought we agreed that like,

699
00:42:07,560 --> 00:42:11,239
Speaker 1: while an aggregate on the whole, our data is worth

700
00:42:11,280 --> 00:42:13,919
Speaker 1: a lot, my data is not worth a lot. Your

701
00:42:14,040 --> 00:42:16,799
Speaker 1: data is not worth a lot. I couldn't sell my

702
00:42:17,040 --> 00:42:19,480
Speaker 1: data and have it. Maybe, I mean, maybe I'm correct

703
00:42:19,480 --> 00:42:21,799
Speaker 1: me if I'm wrong. Oh no, they're using The thing

704
00:42:21,880 --> 00:42:26,120
Speaker 1: is they were hoping to build a system that would

705
00:42:26,200 --> 00:42:30,520
Speaker 1: combat the same kind of activities they did, the idea

706
00:42:30,560 --> 00:42:32,440
Speaker 1: of being that who knows better how to build a

707
00:42:32,480 --> 00:42:35,320
Speaker 1: hin house than the fox who got fat? All, Okay,

708
00:42:35,600 --> 00:42:38,120
Speaker 1: that's interesting. I understand what you're saying. Almost like a

709
00:42:38,160 --> 00:42:40,640
Speaker 1: password wallet or something like that, or like a vault

710
00:42:40,960 --> 00:42:44,120
Speaker 1: that you can keep your stuff under wraps, which would

711
00:42:44,160 --> 00:42:48,160
Speaker 1: also be supervised by Cambridge Analytica and their parent company.

712
00:42:48,440 --> 00:42:52,880
Speaker 1: There's there's this other question, which is, uh, is it

713
00:42:52,960 --> 00:42:56,960
Speaker 1: that bad? What gives you know? The The effort for

714
00:42:57,000 --> 00:43:02,480
Speaker 1: this was overseen by their four mr CEO, the disgraced

715
00:43:02,680 --> 00:43:06,000
Speaker 1: chief executive, a guy named Alexander Nicks. He was for

716
00:43:06,160 --> 00:43:08,480
Speaker 1: style the company in March of this year because he

717
00:43:08,560 --> 00:43:11,440
Speaker 1: was caught on tape bragging about his company's approach to

718
00:43:11,520 --> 00:43:15,640
Speaker 1: political work in other countries across the world. Including Eugene

719
00:43:15,719 --> 00:43:21,920
Speaker 1: shell companies and strategies to entrap opponents. Yeah. They they

720
00:43:22,120 --> 00:43:25,760
Speaker 1: famously state, and this is Cambridge themselves, that their database

721
00:43:25,840 --> 00:43:30,360
Speaker 1: contains over five thousand data points on nearly every American consumer.

722
00:43:30,440 --> 00:43:33,399
Speaker 1: And if you do some due diligence, like NOL did,

723
00:43:33,640 --> 00:43:35,560
Speaker 1: then you will see that a lot of it seems

724
00:43:35,600 --> 00:43:37,479
Speaker 1: kind of innocuous. That's what I was talking about earlier.

725
00:43:37,480 --> 00:43:39,279
Speaker 1: I think I jumped the gun a little bit and

726
00:43:39,320 --> 00:43:41,920
Speaker 1: talking about that NPRPS I heard where a gentleman um

727
00:43:42,080 --> 00:43:45,439
Speaker 1: was able to obtain his Cambridge analytic a profile, let's

728
00:43:45,440 --> 00:43:48,080
Speaker 1: call it, I think, I said, darcier Um. And he

729
00:43:49,320 --> 00:43:52,000
Speaker 1: was pretty sure that it wasn't all of it, because

730
00:43:52,000 --> 00:43:54,920
Speaker 1: it certainly wasn't that five thousand points, but it was,

731
00:43:55,160 --> 00:43:59,200
Speaker 1: you know, a pretty basic little collection of data points that, yes,

732
00:43:59,280 --> 00:44:03,160
Speaker 1: you could absolutely glean from just kind of trolling someone's feed,

733
00:44:03,280 --> 00:44:05,960
Speaker 1: their their their page, you know, their wall, all the

734
00:44:06,040 --> 00:44:08,280
Speaker 1: kind of like is this person and Republican or a Democrat?

735
00:44:08,360 --> 00:44:11,239
Speaker 1: You know most likely, um are they? What kind of

736
00:44:11,239 --> 00:44:13,040
Speaker 1: income bracket are they? And this is all stuff you

737
00:44:13,040 --> 00:44:15,239
Speaker 1: could kind of figure out by looking at pictures and

738
00:44:15,280 --> 00:44:18,400
Speaker 1: seeing the kinds of stuff people actually post about themselves already,

739
00:44:18,560 --> 00:44:21,200
Speaker 1: but then much fewer than five thousand, and he was

740
00:44:21,280 --> 00:44:24,120
Speaker 1: pretty sure that he didn't he that they weren't going

741
00:44:24,160 --> 00:44:27,480
Speaker 1: to give him all five thousand. It would be stuff

742
00:44:27,480 --> 00:44:30,799
Speaker 1: that I love that point you make about a lot

743
00:44:30,840 --> 00:44:34,160
Speaker 1: of this stuff being things that you or I could

744
00:44:34,320 --> 00:44:37,680
Speaker 1: find just by looking at someone's page without even being

745
00:44:37,680 --> 00:44:40,359
Speaker 1: their Facebook friends. Well, it's not my point. At Zuckerberg's point,

746
00:44:40,360 --> 00:44:44,080
Speaker 1: he said that to US lawmakers. That was sort of

747
00:44:44,080 --> 00:44:46,439
Speaker 1: his defense, right, I mean, and that's why I think

748
00:44:46,440 --> 00:44:48,520
Speaker 1: this is a really interesting question that you you raise, Ben,

749
00:44:48,840 --> 00:44:50,680
Speaker 1: isn't that bad? And I want I want to know

750
00:44:51,040 --> 00:44:53,359
Speaker 1: what's the difference between the stuff we share and what

751
00:44:53,520 --> 00:44:59,040
Speaker 1: Cambridge Analytica is doing with this stuff they're harvesting from us. Yes, okay,

752
00:44:59,480 --> 00:45:03,640
Speaker 1: it because problems murky and dangerous when it goes into

753
00:45:03,760 --> 00:45:08,600
Speaker 1: political influence. So Cambridge Analytica did use this information, this

754
00:45:08,760 --> 00:45:15,200
Speaker 1: data to influence elections, per Matthew Oskowski. At Cambridge Analytica,

755
00:45:15,320 --> 00:45:18,880
Speaker 1: we break up our data into three buckets, Political, commercial,

756
00:45:18,880 --> 00:45:21,359
Speaker 1: and first Party. We work with several of the main

757
00:45:21,480 --> 00:45:24,720
Speaker 1: voter file providers depending on the preference of our clients

758
00:45:24,960 --> 00:45:29,320
Speaker 1: to access vote history and voter profiles, and then they

759
00:45:29,320 --> 00:45:33,040
Speaker 1: couple that with top commercial data providers for on a

760
00:45:33,120 --> 00:45:39,080
Speaker 1: licensing basis that stuff like general demographics, geographics, purchase history,

761
00:45:39,320 --> 00:45:41,840
Speaker 1: interest and so on. Then they get R and D

762
00:45:42,000 --> 00:45:48,720
Speaker 1: project data, internal surveys research what they call exclusive data relationships, which,

763
00:45:49,000 --> 00:45:52,200
Speaker 1: as you know, in this kind of conversation, the most

764
00:45:53,719 --> 00:45:57,840
Speaker 1: blase phrase is usually hiding the strangest things. Uh. And

765
00:45:58,040 --> 00:46:01,360
Speaker 1: data collection through direct response objects, which is actually asking

766
00:46:01,400 --> 00:46:05,640
Speaker 1: people what they think. Whistleblowers not just wildly alleged that

767
00:46:05,920 --> 00:46:09,480
Speaker 1: Cambridge worked with Steve Bannon to sway the vote starting

768
00:46:09,640 --> 00:46:14,360
Speaker 1: as far back as fourteen. And this is not really

769
00:46:15,320 --> 00:46:19,120
Speaker 1: a big deal, does it seem? It's not unique there.

770
00:46:19,160 --> 00:46:23,960
Speaker 1: Everybody we ever meet is going to try to persuade

771
00:46:24,040 --> 00:46:27,879
Speaker 1: us to do something without falling into relativism, right, Like,

772
00:46:28,160 --> 00:46:31,399
Speaker 1: the three of us are pretty opinionated people, and we're

773
00:46:31,400 --> 00:46:34,480
Speaker 1: trying to persuade each other's stuff all the time. Usually

774
00:46:34,520 --> 00:46:38,840
Speaker 1: we agree, but if it's a larger company, that doesn't

775
00:46:39,560 --> 00:46:45,440
Speaker 1: make that impulse itself inherently nefarious. Until we get to

776
00:46:45,840 --> 00:46:50,919
Speaker 1: what Nick's actually said, Yeah, he was secretly recorded being

777
00:46:50,920 --> 00:46:56,600
Speaker 1: a bit of a braggadocia, a bragga douche trying to

778
00:46:56,719 --> 00:46:58,880
Speaker 1: squeeze a line in there somehow. But yeah, he was

779
00:46:58,920 --> 00:47:04,160
Speaker 1: bragging about how his firm, Cambrage Analytica, was largely responsible

780
00:47:04,520 --> 00:47:08,480
Speaker 1: for the the election of Donald Trump as presidents United States.

781
00:47:08,480 --> 00:47:12,759
Speaker 1: He described all kinds of questionable practices, um that they

782
00:47:13,120 --> 00:47:15,960
Speaker 1: used to influence foreign elections. Like because remember this is

783
00:47:15,960 --> 00:47:19,520
Speaker 1: a British firm and they're influencing the American election. But

784
00:47:19,640 --> 00:47:23,360
Speaker 1: what I thought Russia was influencing the election. You might say, oh, well,

785
00:47:23,600 --> 00:47:27,719
Speaker 1: maybe it was also our friends over in Britain. I mean, look,

786
00:47:27,840 --> 00:47:29,800
Speaker 1: I just want to say I pulled up this press

787
00:47:29,840 --> 00:47:33,279
Speaker 1: release from Cambridge Analytica's website and they they lay out

788
00:47:33,280 --> 00:47:36,720
Speaker 1: a few points here. One, no laws were broken. Cambridge

789
00:47:36,760 --> 00:47:39,680
Speaker 1: Analytica did not hack Facebook to Cambridge Analytica did not

790
00:47:39,880 --> 00:47:42,360
Speaker 1: use the data or any derivatives of this data in

791
00:47:42,400 --> 00:47:45,799
Speaker 1: the U S presidential election. Uh Three, Cambridge Analytica did

792
00:47:45,880 --> 00:47:49,279
Speaker 1: not work at all on the Brexit referendum. For Mr

793
00:47:49,360 --> 00:47:53,080
Speaker 1: Wiley is not a whistleblower. It goes on well well,

794
00:47:53,680 --> 00:47:57,600
Speaker 1: in this secret recording of Alexander Nicks, the former chief

795
00:47:57,600 --> 00:48:01,319
Speaker 1: executive of Cambridge Analytica, he that his firm did all

796
00:48:01,360 --> 00:48:04,880
Speaker 1: the research, analytics and targeting of voters for Trump's digital

797
00:48:04,960 --> 00:48:07,880
Speaker 1: and TV campaigns. He was recorded saying that. He also

798
00:48:08,000 --> 00:48:11,200
Speaker 1: said that he had met personally met Trump when he

799
00:48:11,239 --> 00:48:14,360
Speaker 1: was the candidate many times, and that they deployed dirty

800
00:48:14,400 --> 00:48:18,200
Speaker 1: tricks including old school espionage stuff like honey traps, setting

801
00:48:18,280 --> 00:48:21,839
Speaker 1: someone up with a sex worker, prostitute, fake news campaigns,

802
00:48:21,880 --> 00:48:26,480
Speaker 1: fake bribery scandals, operations with X spies and espionage agents

803
00:48:26,520 --> 00:48:30,280
Speaker 1: to swing election campaigns. And this was this was recorded

804
00:48:30,280 --> 00:48:33,880
Speaker 1: by an underground reporter working for The Observer. What they

805
00:48:33,880 --> 00:48:36,000
Speaker 1: did is they posed as a member of a wealthy

806
00:48:36,080 --> 00:48:40,480
Speaker 1: family from Sri Lanka seeking political influence. And then when

807
00:48:40,480 --> 00:48:46,200
Speaker 1: the reporter as this wealthy Sri Lankan person, when they

808
00:48:46,280 --> 00:48:50,560
Speaker 1: asked if Cambridge Analytica could offer investigations into damaging secrets

809
00:48:50,560 --> 00:48:54,759
Speaker 1: of rivals, digging up dirt. Essentially, Nick said that Cambridge

810
00:48:54,760 --> 00:48:57,759
Speaker 1: worked with former spies from Britain and Israel to look

811
00:48:57,800 --> 00:49:00,080
Speaker 1: for dirt and said they were doing it as they

812
00:49:00,120 --> 00:49:02,960
Speaker 1: were speaking. He also volunteered that his team was ready

813
00:49:03,040 --> 00:49:07,799
Speaker 1: to go further than uh, just a keyboard investigation. He's like,

814
00:49:07,840 --> 00:49:11,040
Speaker 1: I've got one guy, He's a master disguise we can

815
00:49:11,120 --> 00:49:13,960
Speaker 1: send him. He can be anybody. It's like, Also, we

816
00:49:14,040 --> 00:49:17,200
Speaker 1: can entrap people with some sex workers, bring a few

817
00:49:17,400 --> 00:49:19,640
Speaker 1: Ukrainians with us on the trip, if you know what

818
00:49:19,680 --> 00:49:22,239
Speaker 1: I mean, have a p P party. That was the

819
00:49:22,280 --> 00:49:28,760
Speaker 1: implication something something untoward, right and uh, then he said

820
00:49:28,840 --> 00:49:31,600
Speaker 1: that we do. Uh he said, deep digging is interesting,

821
00:49:31,719 --> 00:49:33,560
Speaker 1: but you know, it can be equally effective to just

822
00:49:33,640 --> 00:49:35,920
Speaker 1: go in and speak to the incumbents and offer them

823
00:49:35,920 --> 00:49:37,879
Speaker 1: a deal that's too good to be true and make

824
00:49:37,920 --> 00:49:42,080
Speaker 1: sure the video of it is recorded. Hence the dirty tricks.

825
00:49:42,760 --> 00:49:45,560
Speaker 1: And also Okay, so according to Wiley the whistle blower,

826
00:49:46,120 --> 00:49:49,440
Speaker 1: he says, quote, we exploited Facebook to harvest millions of

827
00:49:49,480 --> 00:49:52,680
Speaker 1: people's profiles and built models to exploit what we knew

828
00:49:52,719 --> 00:49:55,880
Speaker 1: about them and target their inner demons. That was the

829
00:49:55,920 --> 00:49:59,799
Speaker 1: basis the entire company was built on, alleged whistle blower. Bro,

830
00:50:01,000 --> 00:50:02,839
Speaker 1: that's what we have to Oh yeah, we're gonna get

831
00:50:02,880 --> 00:50:06,759
Speaker 1: alleged heavy here. Allegedly heavy. That's not a bad name

832
00:50:06,760 --> 00:50:08,360
Speaker 1: for an album of some sort. We should put that

833
00:50:08,360 --> 00:50:13,279
Speaker 1: in the book, Allegedly heavy. If it's heavy, allegedly, I

834
00:50:13,320 --> 00:50:16,680
Speaker 1: feel like there's a good uh got a concept album

835
00:50:16,719 --> 00:50:22,279
Speaker 1: here now, to disc uh. But the the question remains, like,

836
00:50:22,440 --> 00:50:26,960
Speaker 1: what is what is wrong with this? In a legal sense?

837
00:50:27,200 --> 00:50:30,600
Speaker 1: If we have signed a contract, essentially, if we have

838
00:50:30,640 --> 00:50:35,920
Speaker 1: assented to having this data traded in exchange for using

839
00:50:35,920 --> 00:50:40,520
Speaker 1: this free service, then what's what's the big hubbub? The

840
00:50:40,560 --> 00:50:45,200
Speaker 1: problem here is that it was used without notifying Facebook users,

841
00:50:45,600 --> 00:50:49,480
Speaker 1: and Facebook really does not want this to be characterized

842
00:50:49,520 --> 00:50:52,839
Speaker 1: as a data breach for a number of reasons, one

843
00:50:52,880 --> 00:50:56,360
Speaker 1: of which is a majority of American states have laws

844
00:50:56,400 --> 00:51:01,319
Speaker 1: requiring notification in different cases of data breaches, including California,

845
00:51:01,400 --> 00:51:03,200
Speaker 1: where Facebook is based. How you ever got one of

846
00:51:03,239 --> 00:51:07,000
Speaker 1: the emails from your bank or target or something saying, hey,

847
00:51:07,160 --> 00:51:10,040
Speaker 1: you were part of a potential bundle of a trillion

848
00:51:10,680 --> 00:51:14,680
Speaker 1: users who may or may not have had your information compromise.

849
00:51:14,680 --> 00:51:17,080
Speaker 1: You should probably change your password. They didn't just send

850
00:51:17,120 --> 00:51:18,440
Speaker 1: that to you out of the goodness of their heart,

851
00:51:18,520 --> 00:51:20,880
Speaker 1: did they. No? No, there's a lot of c y

852
00:51:20,960 --> 00:51:24,880
Speaker 1: a there. Uh And as we know, being a family

853
00:51:24,920 --> 00:51:27,520
Speaker 1: show that stands for cover your actions or ard vark

854
00:51:27,880 --> 00:51:30,560
Speaker 1: or cover your ardvark, which I like better actually, and

855
00:51:31,360 --> 00:51:35,680
Speaker 1: the ultimate goal of Cogan, who worked at the University

856
00:51:35,680 --> 00:51:38,600
Speaker 1: of St. Petersburg by the way and received Russian grant money.

857
00:51:39,080 --> 00:51:42,160
Speaker 1: Was not just to collect this and build an end

858
00:51:42,480 --> 00:51:46,320
Speaker 1: anonymous database. He was working with Cambridge Analytica to build

859
00:51:46,320 --> 00:51:50,000
Speaker 1: a database of identified profiles, or what they would call

860
00:51:50,080 --> 00:51:53,400
Speaker 1: matched profiles. They started with the aim of making two

861
00:51:53,520 --> 00:52:00,320
Speaker 1: million match profiles, meaning tied to electoral registers in eleven eights,

862
00:52:00,560 --> 00:52:03,520
Speaker 1: with room to expand much further, ultimately aiming to get

863
00:52:03,560 --> 00:52:06,040
Speaker 1: all fifty states to what end to to send them

864
00:52:06,040 --> 00:52:11,839
Speaker 1: targeted messages about candidates, very specific personalized targeted messages. So

865
00:52:11,880 --> 00:52:16,120
Speaker 1: like if you're working for uh, if you're working for

866
00:52:16,239 --> 00:52:20,000
Speaker 1: the Republican Party or something that's your client, right, and

867
00:52:20,280 --> 00:52:25,720
Speaker 1: you want to split a vote, you could say, well,

868
00:52:25,920 --> 00:52:31,920
Speaker 1: here's some fake news about Bernie Sanders or Hillary Clinton

869
00:52:32,040 --> 00:52:35,160
Speaker 1: or something. Or you could also put out some fake

870
00:52:35,280 --> 00:52:41,480
Speaker 1: or embarrassing news about a Republican rival and say, oh,

871
00:52:41,680 --> 00:52:46,279
Speaker 1: Senator Ted Cruz is I don't know, stealing peeps from

872
00:52:46,360 --> 00:52:49,000
Speaker 1: kids on Easter Sunday. Yeah, And you can do it

873
00:52:49,040 --> 00:52:52,160
Speaker 1: with a Facebook ad, Like you could buy a Facebook

874
00:52:52,200 --> 00:52:55,759
Speaker 1: ad spot that only targets one human person. And I

875
00:52:55,760 --> 00:52:57,640
Speaker 1: think another thing that came from this, in terms of

876
00:52:57,640 --> 00:53:00,680
Speaker 1: Facebook changing their guidelines, there are going to be much

877
00:53:00,680 --> 00:53:02,600
Speaker 1: to they say they're going to be much more strict

878
00:53:02,640 --> 00:53:06,520
Speaker 1: about selling political advertisements, that they're going to be much

879
00:53:06,520 --> 00:53:09,759
Speaker 1: more like knowing where the business is coming from or

880
00:53:09,840 --> 00:53:12,920
Speaker 1: something right this. Yeah, and and that's a that's a

881
00:53:12,960 --> 00:53:15,880
Speaker 1: tough question too, because you know, it's very easy to

882
00:53:15,960 --> 00:53:19,680
Speaker 1: vilify Facebook. Note but there's it's similar to the problem

883
00:53:19,840 --> 00:53:24,680
Speaker 1: with YouTube, like how can YouTube catch every falling sparrow

884
00:53:25,080 --> 00:53:28,960
Speaker 1: of inappropriate stuff people post? The Wild West man? You know,

885
00:53:29,160 --> 00:53:30,840
Speaker 1: I bet you it's gonna come out in this that

886
00:53:30,920 --> 00:53:34,840
Speaker 1: Facebook didn't really break the law. That not not necessarily

887
00:53:34,920 --> 00:53:38,120
Speaker 1: true of Cambridge Analytica, but it doesn't seem like it's

888
00:53:38,120 --> 00:53:39,880
Speaker 1: like the thing. It's like, why would you do something

889
00:53:39,920 --> 00:53:44,080
Speaker 1: if you're not feel if there's no monetary consequence in

890
00:53:44,080 --> 00:53:45,480
Speaker 1: place for doing it, you know what I mean? Like

891
00:53:45,480 --> 00:53:47,960
Speaker 1: that's the regulations are all about, right, So, but you

892
00:53:48,000 --> 00:53:50,680
Speaker 1: also don't know how to make a regulation until the

893
00:53:50,920 --> 00:53:53,120
Speaker 1: case comes up where it's needed. And I think that

894
00:53:53,160 --> 00:53:56,239
Speaker 1: we're going to see that now. But I do think

895
00:53:56,239 --> 00:54:00,760
Speaker 1: that Facebook probably didn't break the law. It's interesting because

896
00:54:00,800 --> 00:54:05,880
Speaker 1: you know, legislation lags so far behind technological innovation, you know,

897
00:54:06,040 --> 00:54:08,319
Speaker 1: which we've talked about in the show for for long

898
00:54:08,360 --> 00:54:12,200
Speaker 1: time listeners. But there's something else, a big implication that

899
00:54:12,239 --> 00:54:16,160
Speaker 1: a lot of people are just now becoming aware of,

900
00:54:16,200 --> 00:54:21,000
Speaker 1: in a real spooky one that that might surprise some folks.

901
00:54:21,080 --> 00:54:24,399
Speaker 1: And that is the question. Okay, let's say I don't

902
00:54:24,440 --> 00:54:28,279
Speaker 1: have a Facebook profile. Let's say I've never logged onto

903
00:54:28,360 --> 00:54:31,680
Speaker 1: Facebook or Instagram I know their fingers on the same hand,

904
00:54:31,760 --> 00:54:35,000
Speaker 1: and Twitter or whatever, and I didn't even I didn't

905
00:54:35,000 --> 00:54:37,680
Speaker 1: even mess with my Space or friends stars. How much

906
00:54:37,680 --> 00:54:40,839
Speaker 1: could Facebook know about me? While they know a whole

907
00:54:40,880 --> 00:54:44,320
Speaker 1: heck of a lot because of things that are called

908
00:54:44,600 --> 00:54:50,080
Speaker 1: shadow profiles, called shadow profiles by literally everyone but Facebook. Yeah,

909
00:54:50,120 --> 00:54:54,080
Speaker 1: so like like we're going to call them shadow profiles

910
00:54:54,080 --> 00:54:57,120
Speaker 1: because it's an awesome sounding, ominous thing. Here we go.

911
00:54:57,719 --> 00:55:00,440
Speaker 1: You may have never started Facebook, right, you never even

912
00:55:00,480 --> 00:55:03,200
Speaker 1: opened it up, not once, not a single time. But

913
00:55:03,239 --> 00:55:06,399
Speaker 1: guess what, I hate books and faces. Yeah right, I'm

914
00:55:06,400 --> 00:55:08,600
Speaker 1: with you, brother, but I'm off the grid. Matt, you

915
00:55:08,600 --> 00:55:10,520
Speaker 1: are off the grid. But you've got this friend that

916
00:55:10,560 --> 00:55:13,640
Speaker 1: works over at the Piggly Wiggly. Here's the problem. She

917
00:55:13,760 --> 00:55:17,040
Speaker 1: loves Facebook. She's on it all the time, right, and

918
00:55:17,120 --> 00:55:19,919
Speaker 1: you know, you guys, you're just Palso, you're texting every

919
00:55:19,920 --> 00:55:22,239
Speaker 1: once in a while and on my flip phone. Well, yeah,

920
00:55:22,320 --> 00:55:24,720
Speaker 1: you're on your flip phone, but she's got that sweet

921
00:55:24,800 --> 00:55:28,960
Speaker 1: iPhone five and uh, you know she's on Facebook with

922
00:55:28,960 --> 00:55:31,759
Speaker 1: her app. That could be a problem for you. And

923
00:55:31,800 --> 00:55:35,080
Speaker 1: then she maybe saves your number and labels you as

924
00:55:35,680 --> 00:55:39,560
Speaker 1: hot Maddie. Yeah, hot Maddie in her contacts. That's that's

925
00:55:39,800 --> 00:55:43,160
Speaker 1: the off the grid guy that I am. And my

926
00:55:43,239 --> 00:55:47,879
Speaker 1: piggly wiggly wiggly wiggly paramore. Yeah yeah, and so yeah,

927
00:55:48,040 --> 00:55:52,360
Speaker 1: so then uh the then Facebook has that piece of information.

928
00:55:52,680 --> 00:55:55,799
Speaker 1: And then let's say, because you allow Facebook to see

929
00:55:55,840 --> 00:56:00,319
Speaker 1: your context, yes, and depending uponing your interaction or your

930
00:56:01,000 --> 00:56:03,520
Speaker 1: the amount of trouble you're willing to go to. It's

931
00:56:03,640 --> 00:56:05,840
Speaker 1: very easy for Facebook to do that. In some cases

932
00:56:05,880 --> 00:56:07,800
Speaker 1: you cannot avoid it, and you do have to opt

933
00:56:07,840 --> 00:56:09,880
Speaker 1: in for that though. You notice any apps like that,

934
00:56:10,000 --> 00:56:11,719
Speaker 1: as soon as you do an action on that app

935
00:56:11,760 --> 00:56:14,360
Speaker 1: that requires it to have access to your camera or

936
00:56:14,360 --> 00:56:17,200
Speaker 1: have access to your microphone or have access to your contacts,

937
00:56:17,320 --> 00:56:18,960
Speaker 1: a little pop up comes up and you have to

938
00:56:19,000 --> 00:56:22,759
Speaker 1: say yes, and you know it's a permission thing. But

939
00:56:22,800 --> 00:56:25,319
Speaker 1: I guess once you've done that. That's just for you,

940
00:56:25,400 --> 00:56:28,040
Speaker 1: it's not for those around you. Well, it's also if

941
00:56:28,040 --> 00:56:30,879
Speaker 1: you use an iPhone, it's going to be a tad

942
00:56:30,960 --> 00:56:34,680
Speaker 1: bit better about that. And the bluewear can be put

943
00:56:34,719 --> 00:56:37,960
Speaker 1: in by a provider. I see that in that bluewear,

944
00:56:38,040 --> 00:56:40,680
Speaker 1: maybe such that you would have to perform a process

945
00:56:40,719 --> 00:56:43,400
Speaker 1: called rooting your phone to actually get rid of it.

946
00:56:43,440 --> 00:56:45,319
Speaker 1: You mean on a on an Android, like on an

947
00:56:45,600 --> 00:56:48,280
Speaker 1: because if you've always you I've always heard that PCs

948
00:56:48,400 --> 00:56:51,759
Speaker 1: are more susceptible to spywear and things. I guess that's

949
00:56:51,760 --> 00:56:54,239
Speaker 1: true of the phones as well. Yeah, I think yeah,

950
00:56:54,320 --> 00:56:56,759
Speaker 1: I could easily see that. So let's just let's just

951
00:56:56,880 --> 00:57:00,440
Speaker 1: quickly jump into why Facebook hates that term shadow p files.

952
00:57:00,440 --> 00:57:03,520
Speaker 1: Oh why do they Well, it's because it sounds like

953
00:57:03,680 --> 00:57:07,120
Speaker 1: they're straight up making these hidden profiles somewhere on your

954
00:57:07,200 --> 00:57:10,440
Speaker 1: phone or your app, or maybe just in Facebook at large,

955
00:57:10,760 --> 00:57:14,279
Speaker 1: profiles of people's people in your contacts list that don't

956
00:57:14,320 --> 00:57:17,080
Speaker 1: have a Facebook profile. And they say, that's not what

957
00:57:17,120 --> 00:57:23,520
Speaker 1: they're doing. It doesn't do that. But in twenty thirteen,

958
00:57:24,680 --> 00:57:29,800
Speaker 1: Facebook said it discovered and fixed a quote unquote bug.

959
00:57:32,840 --> 00:57:36,200
Speaker 1: The bug was that when a user downloaded their Facebook file,

960
00:57:36,280 --> 00:57:38,560
Speaker 1: which you can still do today, and will give you

961
00:57:38,880 --> 00:57:41,400
Speaker 1: all the information they say they have on you. It

962
00:57:41,440 --> 00:57:44,080
Speaker 1: will give you all the at least the information you're

963
00:57:44,080 --> 00:57:46,640
Speaker 1: allowed to see they have on you, all your likes, comments,

964
00:57:46,760 --> 00:57:52,360
Speaker 1: et cetera, your messages. The thing was that in this

965
00:57:52,520 --> 00:57:59,120
Speaker 1: quote unquote bug included not just people's visible contact information

966
00:57:59,200 --> 00:58:04,080
Speaker 1: for their friends, but also their friends shadow contact information.

967
00:58:04,400 --> 00:58:06,640
Speaker 1: So they were seeing stuff that they weren't supposed to see.

968
00:58:06,640 --> 00:58:09,080
Speaker 1: And the problem with the bug for Facebook was not

969
00:58:09,200 --> 00:58:12,000
Speaker 1: that all of this stuff was lumped together. It was

970
00:58:12,080 --> 00:58:16,320
Speaker 1: that it had shown people that it existed, so the

971
00:58:16,400 --> 00:58:19,560
Speaker 1: extent of the connections that it built around every user

972
00:58:19,880 --> 00:58:24,160
Speaker 1: was supposed to only be visible to Facebook. And they

973
00:58:24,640 --> 00:58:29,120
Speaker 1: admit that this is in their phrase, it's getting information

974
00:58:29,200 --> 00:58:32,080
Speaker 1: from a friend or someone you know or might know.

975
00:58:32,480 --> 00:58:35,640
Speaker 1: But what does that mean? That means anyone at any

976
00:58:35,680 --> 00:58:39,560
Speaker 1: point who might have somehow labeled your phone number, your email,

977
00:58:39,680 --> 00:58:42,880
Speaker 1: or even your physical trying not to curse here, even

978
00:58:42,920 --> 00:58:49,560
Speaker 1: your physical address will be added to that that uh

979
00:58:49,960 --> 00:58:53,400
Speaker 1: agglomeration of information that is you. So whether it's the

980
00:58:53,440 --> 00:58:58,200
Speaker 1: piggly wiggly Hot Maddie, whether it's an old email address

981
00:58:58,320 --> 00:59:00,960
Speaker 1: from college that a friend of wars has, you know,

982
00:59:01,040 --> 00:59:05,760
Speaker 1: and it's like, um, it's like hot dot Maddie at

983
00:59:06,040 --> 00:59:09,000
Speaker 1: u g A dot e du it was Obadiah fourteen

984
00:59:09,240 --> 00:59:12,560
Speaker 1: at a O L. I knew it was him. Dang,

985
00:59:12,840 --> 00:59:19,960
Speaker 1: it was the whole time. Well Obadiah its thought he

986
00:59:20,000 --> 00:59:23,600
Speaker 1: looked like an ob Yeah, I've always thought that. Yeah.

987
00:59:24,080 --> 00:59:27,520
Speaker 1: So this means that the average user is much more

988
00:59:27,560 --> 00:59:32,680
Speaker 1: exposed than they were led to believe. And Facebook's position

989
00:59:32,960 --> 00:59:39,080
Speaker 1: is that mistakes happen and will be improved. And I think, no,

990
00:59:39,400 --> 00:59:43,200
Speaker 1: you make a really interesting point and prediction here. Will

991
00:59:43,240 --> 00:59:46,440
Speaker 1: it turn out to be something illegal? Do laws exist

992
00:59:46,560 --> 00:59:48,720
Speaker 1: for this sort of situation? It seems like right now

993
00:59:49,160 --> 00:59:51,800
Speaker 1: not many do. It seems like this is a case

994
00:59:51,880 --> 00:59:55,200
Speaker 1: that will lead to change. We're already seeing it, but

995
00:59:55,560 --> 00:59:59,040
Speaker 1: you know, big big changes then tech companies will have

996
00:59:59,080 --> 01:00:01,600
Speaker 1: to abide by. And also makes you wonder is this

997
01:00:01,680 --> 01:00:06,640
Speaker 1: going to affect user experience in a way that is unpleasant?

998
01:00:06,720 --> 01:00:08,920
Speaker 1: Because I think it might. I think some of the

999
01:00:08,960 --> 01:00:12,160
Speaker 1: things that we take for granted on an open internet

1000
01:00:12,280 --> 01:00:14,800
Speaker 1: or on like an app like Facebook are largely because

1001
01:00:14,840 --> 01:00:17,720
Speaker 1: of the the lack of barriers in some of these

1002
01:00:17,800 --> 01:00:21,360
Speaker 1: kinds of things, and we may find ourselves being inconvenience

1003
01:00:21,440 --> 01:00:26,000
Speaker 1: because of fallout from the story, right, I mean, that's

1004
01:00:26,040 --> 01:00:28,240
Speaker 1: a that's a good that's a good point, and it's

1005
01:00:28,240 --> 01:00:32,080
Speaker 1: a great perspective to raise because as we know, history

1006
01:00:32,200 --> 01:00:39,400
Speaker 1: proves that privacy as a concept is a relatively recent phenomenon,

1007
01:00:39,800 --> 01:00:43,800
Speaker 1: you know, the the idea of privacies. We understand it

1008
01:00:43,840 --> 01:00:47,439
Speaker 1: did not exist in centuries past, and it may well

1009
01:00:47,440 --> 01:00:51,280
Speaker 1: not exist by the end of this century, by the

1010
01:00:51,400 --> 01:00:55,840
Speaker 1: end of by the end of this next decade started.

1011
01:00:57,720 --> 01:01:02,120
Speaker 1: But the other thing is this is the current lay

1012
01:01:02,120 --> 01:01:04,400
Speaker 1: of the land, folks. The other thing is that, as

1013
01:01:04,400 --> 01:01:07,600
Speaker 1: far as Facebook is concerned, none of that information that

1014
01:01:07,640 --> 01:01:11,959
Speaker 1: other people have about you counts as your information inmation. Yeah,

1015
01:01:12,000 --> 01:01:15,000
Speaker 1: it belongs to the people who uploaded it. They're the

1016
01:01:15,040 --> 01:01:18,560
Speaker 1: only ones with any control over it. That's why. For instance,

1017
01:01:18,600 --> 01:01:21,840
Speaker 1: you can remove a tag of yourself on a photograph, right,

1018
01:01:22,000 --> 01:01:25,240
Speaker 1: and this is not shadow stuff. This is public profile stuff.

1019
01:01:25,240 --> 01:01:27,280
Speaker 1: You can remove a tag of yourself in a photograph,

1020
01:01:27,480 --> 01:01:29,720
Speaker 1: but if you want the photograph to be taken down,

1021
01:01:30,160 --> 01:01:31,880
Speaker 1: you have to get the person who put it up

1022
01:01:32,120 --> 01:01:36,320
Speaker 1: to take it down. This is dangerous stuff. And this

1023
01:01:36,440 --> 01:01:40,280
Speaker 1: is why even though it's not illegal. This is why

1024
01:01:40,360 --> 01:01:43,560
Speaker 1: it could put people in real danger. Someone who cannot

1025
01:01:43,600 --> 01:01:47,600
Speaker 1: have their identity known. Yeah, like an undercover cop for instance,

1026
01:01:48,440 --> 01:01:53,480
Speaker 1: or let's say a prosecutor for criminal cases, anybody in

1027
01:01:53,560 --> 01:01:56,280
Speaker 1: law enforcement, anybody in law enforcement. Let's say people who

1028
01:01:56,280 --> 01:02:01,240
Speaker 1: have lives working in uh, well it's not legal in

1029
01:02:01,320 --> 01:02:03,080
Speaker 1: a lot of the US here, but somebody who's working

1030
01:02:03,080 --> 01:02:06,040
Speaker 1: in the sex trade or something. And you can find

1031
01:02:06,080 --> 01:02:11,240
Speaker 1: these various stories of people who deleted their Facebook because

1032
01:02:11,240 --> 01:02:14,479
Speaker 1: they were in a profession like this and they got

1033
01:02:14,520 --> 01:02:19,400
Speaker 1: the scariest thing that can happen, which is someone they knew,

1034
01:02:19,560 --> 01:02:21,680
Speaker 1: maybe even someone they like, put away in jail, or

1035
01:02:21,720 --> 01:02:24,560
Speaker 1: something pops up on the people you may know list,

1036
01:02:25,680 --> 01:02:29,400
Speaker 1: or for instance, there was there little things, they're just

1037
01:02:29,440 --> 01:02:33,600
Speaker 1: twilight zone moments, like you'll hear stories about someone who

1038
01:02:33,600 --> 01:02:36,720
Speaker 1: works as a receptionist at in office meets somebody for

1039
01:02:36,760 --> 01:02:39,160
Speaker 1: the second time and they call them by their nickname

1040
01:02:40,360 --> 01:02:43,120
Speaker 1: in their second conversation because they popped up on their

1041
01:02:43,200 --> 01:02:46,960
Speaker 1: Facebook list. Or what if you are a therapist and

1042
01:02:47,040 --> 01:02:51,080
Speaker 1: your clients start popping up on each person's Facebook list.

1043
01:02:51,560 --> 01:02:54,160
Speaker 1: This is I mean, this is dangerous stuff, especially when

1044
01:02:54,200 --> 01:02:58,400
Speaker 1: we're what we're getting to is um. Regardless of the intent,

1045
01:02:58,480 --> 01:03:04,320
Speaker 1: what we're getting to in action is a violation of confidentiality.

1046
01:03:03,960 --> 01:03:06,160
Speaker 1: It's it's a dangerous thing. But then we have to ask,

1047
01:03:06,520 --> 01:03:08,400
Speaker 1: and I keep going back to the question US, No,

1048
01:03:08,840 --> 01:03:13,920
Speaker 1: how can government's police international entities like this? Should they

1049
01:03:13,960 --> 01:03:17,240
Speaker 1: even be trying? You know, it seems like the US

1050
01:03:17,280 --> 01:03:22,880
Speaker 1: taking this more seriously than the US, But there's pretty

1051
01:03:22,920 --> 01:03:26,919
Speaker 1: compelling evidence that this is not in any way new

1052
01:03:27,000 --> 01:03:30,400
Speaker 1: We've talked about alphabet and Google, talked about the CIA

1053
01:03:30,520 --> 01:03:35,160
Speaker 1: and Facebook, Cambridge Analytica. It's just it's a gimme. It's

1054
01:03:35,160 --> 01:03:38,600
Speaker 1: a home run. If if we were intelligence agencies, or

1055
01:03:38,640 --> 01:03:42,920
Speaker 1: if we were these businesses, we would feel like it

1056
01:03:43,040 --> 01:03:47,120
Speaker 1: was foolish not to be doing this. It feels like

1057
01:03:47,200 --> 01:03:51,240
Speaker 1: there needs to be some kind of United Nations style

1058
01:03:51,400 --> 01:03:59,200
Speaker 1: thing just for digital matters, for for international digital matters.

1059
01:03:59,200 --> 01:04:02,440
Speaker 1: Something really good points something where it's like an international

1060
01:04:02,520 --> 01:04:05,720
Speaker 1: body that can make decisions and say anything that occurs

1061
01:04:05,840 --> 01:04:12,480
Speaker 1: on networks that go across what lines of of states

1062
01:04:13,120 --> 01:04:17,040
Speaker 1: and countries they you have, you are beholden to our group.

1063
01:04:17,720 --> 01:04:19,960
Speaker 1: I say, we set it up and then immediately newter

1064
01:04:20,040 --> 01:04:22,520
Speaker 1: the heck out of it. Yeah, yeah, yeah, let's give

1065
01:04:22,520 --> 01:04:24,680
Speaker 1: it the legal nations treatment. Uh, And I think it

1066
01:04:24,720 --> 01:04:26,520
Speaker 1: needs to be run by a group of AI S

1067
01:04:27,200 --> 01:04:37,440
Speaker 1: Paul's nodding robots. Mark Zuckerberg will be the head. Oh boy,

1068
01:04:38,120 --> 01:04:39,760
Speaker 1: have you seen those memes where they like paint him

1069
01:04:39,840 --> 01:04:42,600
Speaker 1: up like a data from Star Trek. Yes, it's really

1070
01:04:42,640 --> 01:04:49,200
Speaker 1: good and it's eerily eerily works. But I am kind

1071
01:04:49,200 --> 01:04:52,360
Speaker 1: of serious with people that people that actually know what

1072
01:04:52,360 --> 01:04:56,720
Speaker 1: they're talking about and understand. I mean, I think there

1073
01:04:56,720 --> 01:05:00,200
Speaker 1: should be a body that at least can enforce regulation. Well,

1074
01:05:00,240 --> 01:05:03,840
Speaker 1: there's several there's a wealth of bodies in the US

1075
01:05:03,920 --> 01:05:07,960
Speaker 1: and abroad that we're built with that purpose. But of course,

1076
01:05:08,520 --> 01:05:12,080
Speaker 1: as you mentioned before, are a little close to the

1077
01:05:12,360 --> 01:05:16,840
Speaker 1: entities and institutions and industries that they are supposed to oversee. Well,

1078
01:05:16,880 --> 01:05:19,680
Speaker 1: what's the question, what what happens next? Is this gonna

1079
01:05:19,720 --> 01:05:22,800
Speaker 1: be a a flash in the pan news wise? Will

1080
01:05:22,840 --> 01:05:25,760
Speaker 1: the news cycle move on to a different thing as

1081
01:05:25,800 --> 01:05:28,720
Speaker 1: the US and Russia continue to wage war for the

1082
01:05:28,760 --> 01:05:33,200
Speaker 1: pipeline in Syria? I'm sorry the the wait, what did

1083
01:05:33,240 --> 01:05:36,600
Speaker 1: you say? Proxy war? Freedom? And whatever? Was it? But

1084
01:05:36,800 --> 01:05:40,800
Speaker 1: the but the you know, is is this going to

1085
01:05:41,000 --> 01:05:43,960
Speaker 1: stay in the headlines? Will we ever know the full story,

1086
01:05:44,000 --> 01:05:46,560
Speaker 1: the full cast of players, the extent of their involvement.

1087
01:05:47,320 --> 01:05:52,440
Speaker 1: Will will anybody at Facebook be charged with a crime?

1088
01:05:52,840 --> 01:05:56,720
Speaker 1: Will anybody at Cambridge Analytica? Did Facebook even commit a crime?

1089
01:05:57,160 --> 01:06:00,600
Speaker 1: You know that That's what I keep going back to.

1090
01:06:01,520 --> 01:06:04,040
Speaker 1: Cynics in the audience would probably say, no, guys, if

1091
01:06:04,080 --> 01:06:06,720
Speaker 1: you token skpegoats will be profit up for some public

1092
01:06:06,760 --> 01:06:13,360
Speaker 1: pitchforks and political theater and then it'll be business as usual. Yeah, man,

1093
01:06:13,920 --> 01:06:19,919
Speaker 1: but uh that's our show. But for today. By the way,

1094
01:06:20,000 --> 01:06:22,520
Speaker 1: we're on Facebook. Yeah, yeah, if you have any thoughts

1095
01:06:22,760 --> 01:06:27,440
Speaker 1: on Cambridge Analytica and Facebook's use of of data and

1096
01:06:27,560 --> 01:06:30,280
Speaker 1: data and however you say that, uh you know, just

1097
01:06:30,400 --> 01:06:32,400
Speaker 1: right to us. You can find us on Facebook. We

1098
01:06:32,520 --> 01:06:36,680
Speaker 1: are conspiracy stuff. Like really, just find us there. Make

1099
01:06:36,720 --> 01:06:40,000
Speaker 1: sure you put any and all identifying information just directly

1100
01:06:40,000 --> 01:06:43,240
Speaker 1: into the common box. Okay, um, but just you know,

1101
01:06:43,280 --> 01:06:45,840
Speaker 1: put more just as much as you possibly list to

1102
01:06:45,920 --> 01:06:50,120
Speaker 1: your fears, your blood type things that you worry about

1103
01:06:50,160 --> 01:06:52,840
Speaker 1: when you walk into an elevator full of strangers. You know.

1104
01:06:52,880 --> 01:06:54,920
Speaker 1: We had a funny realization the other day. We were

1105
01:06:54,920 --> 01:06:57,720
Speaker 1: looking at some of our breakdowns of like listenership and

1106
01:06:57,760 --> 01:07:00,960
Speaker 1: there's a really huge percentage of our ostnership that is

1107
01:07:01,320 --> 01:07:05,200
Speaker 1: comes up in the data that we get as unknown

1108
01:07:05,480 --> 01:07:09,360
Speaker 1: or yeah, and Ben immediately it was like, well, that's

1109
01:07:09,400 --> 01:07:12,480
Speaker 1: probably because our listeners might be more likely than some

1110
01:07:12,520 --> 01:07:14,680
Speaker 1: other shows to use a VPN. Yeah, we know what

1111
01:07:14,720 --> 01:07:17,160
Speaker 1: you're doing right now with all your encryption, and we

1112
01:07:17,200 --> 01:07:23,120
Speaker 1: applaud it. We think it's awesome VP of virtual private network. Right, yes,

1113
01:07:23,640 --> 01:07:26,120
Speaker 1: let us know what you think about this. What do

1114
01:07:26,160 --> 01:07:28,840
Speaker 1: you think about the scandal? Would you, for example, would

1115
01:07:28,880 --> 01:07:32,560
Speaker 1: you be okay with it if you received some sort

1116
01:07:32,560 --> 01:07:35,280
Speaker 1: of compensation other than the use of that platform, like

1117
01:07:35,320 --> 01:07:39,440
Speaker 1: if you got to check every three months or something

1118
01:07:39,880 --> 01:07:43,560
Speaker 1: where they said, hey, great, great job, here's for being you.

1119
01:07:43,760 --> 01:07:48,080
Speaker 1: Here's perfect. Yes, here's for being you. Here's two dragon coins.

1120
01:07:48,160 --> 01:07:52,640
Speaker 1: Here's two dragon coins. Uh, and a couple of embarrassing

1121
01:07:52,640 --> 01:07:55,720
Speaker 1: pictures of you from middle school. Don't don't thank us.

1122
01:07:56,400 --> 01:07:58,120
Speaker 1: We like to thank you. And if you just you

1123
01:07:58,160 --> 01:08:01,720
Speaker 1: just know which side your bet is. May yeah, I

1124
01:08:01,880 --> 01:08:04,480
Speaker 1: just remember if you know you stop using our services.

1125
01:08:04,560 --> 01:08:06,760
Speaker 1: We do have these pictures and they may or may

1126
01:08:06,760 --> 01:08:08,800
Speaker 1: not end up in the hands of your enemies, to

1127
01:08:08,920 --> 01:08:12,080
Speaker 1: be a shame. If your enemies found out about your

1128
01:08:12,120 --> 01:08:16,320
Speaker 1: your head gear, your your brace face. So do you

1129
01:08:16,360 --> 01:08:20,439
Speaker 1: click like angry or wow? Why was it? Oh face? Yeah,

1130
01:08:20,520 --> 01:08:22,960
Speaker 1: oh face? I don't know. I do the heart. Yeah,

1131
01:08:23,000 --> 01:08:26,280
Speaker 1: that's my favorite, like the heart. So let us know.

1132
01:08:26,520 --> 01:08:29,240
Speaker 1: Thank you so much as always for tuning in. Stay

1133
01:08:29,240 --> 01:08:34,160
Speaker 1: tuned as we return with more strange, unusual, fascinating, and

1134
01:08:34,600 --> 01:08:38,439
Speaker 1: at times terrifying stuff they don't want you to know.

1135
01:08:38,600 --> 01:08:41,879
Speaker 1: In the meantime, you can, as we have said multiple

1136
01:08:41,960 --> 01:08:44,839
Speaker 1: times of this podcast, you can find us on Facebook,

1137
01:08:44,880 --> 01:08:50,680
Speaker 1: but hey, we're also on Instagram, which is uh uh.

1138
01:08:50,720 --> 01:08:53,960
Speaker 1: We're also on Twitter. You can find every episode we've

1139
01:08:54,000 --> 01:08:56,320
Speaker 1: ever done on our website stuff they don't want you

1140
01:08:56,400 --> 01:09:00,559
Speaker 1: to know? Dot com. You can also call us directly,

1141
01:09:00,600 --> 01:09:04,240
Speaker 1: should the spirit so move you. We are one eight

1142
01:09:04,479 --> 01:09:10,040
Speaker 1: three three st d w y t K. You got it.

1143
01:09:10,200 --> 01:09:12,000
Speaker 1: That's correct. We're not going to give you the numbers.

1144
01:09:12,000 --> 01:09:13,839
Speaker 1: You can figure it out. It's a fun little puzzle.

1145
01:09:14,240 --> 01:09:16,000
Speaker 1: Just do it if you want. You can write U

1146
01:09:16,080 --> 01:09:19,599
Speaker 1: some snail mail too. That's on our website somewhere right. Sure,

1147
01:09:20,479 --> 01:09:22,639
Speaker 1: we probably shouldn't read that over the air. It's fine.

1148
01:09:22,680 --> 01:09:24,839
Speaker 1: You can look it up and how stuff works. Offices

1149
01:09:24,880 --> 01:09:28,320
Speaker 1: at Pont City Market in Atlanta, Georgia, where we live.

1150
01:09:28,400 --> 01:09:31,639
Speaker 1: Come find us. Well, we'll just be hanging out. Paul

1151
01:09:31,680 --> 01:09:34,760
Speaker 1: will greet you at the door. UM, he'll probably help

1152
01:09:34,800 --> 01:09:39,080
Speaker 1: you get a beverage. UM. I will assist you. Know.

1153
01:09:39,840 --> 01:09:41,599
Speaker 1: I can do some laundry for you while you're here.

1154
01:09:41,600 --> 01:09:43,719
Speaker 1: Whatever you need. I'll give you a nice shoulder rub.

1155
01:09:44,960 --> 01:09:48,880
Speaker 1: I will do a slow clap. Okay, or you know,

1156
01:09:49,880 --> 01:09:51,400
Speaker 1: don't come to wrong. And if you don't want any

1157
01:09:51,439 --> 01:09:55,439
Speaker 1: of that which God knows, you can write to us.

1158
01:09:55,520 --> 01:10:00,920
Speaker 1: We are conspiracy at how stuff works dot com. I