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It was the biggest political earthquake of the century.
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We will make America great again!
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But just how did Donald Trump defy the predictions
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of political pundits and pollsters?
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The secret lies here, in San Antonio, Texas.
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This was our Project Alamo.
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This is where the digital arm of the Trump campaign operation was held.
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This is the extraordinary story of two men
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with two very different views of the world.
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We will build the wall...
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The path forward is to connect more, not less.
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And how Facebook's Mark Zuckerberg inadvertently helped Donald Trump
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become the most powerful man on the planet.
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Without Facebook, we wouldn't have won.
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I mean, Facebook really and truly put us over the edge.
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With their secret algorithms and online tracking,
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social media companies know more about us than anyone.
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So you can predict mine and everybody else's personality
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based on the things that they've liked?
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That's correct.
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A very accurate prediction of your intimate traits such as
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religiosity, political views, intelligence, sexual orientation.
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Now, this power is transforming politics.
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Can you understand though why maybe
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some people find it a little bit creepy?
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No, I can't - quite the opposite. That is the way the world is moving.
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Whether you like it or not, it's an inevitable fact.
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Social media can bring politics closer to the people,
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but its destructive power
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is creating a new and unpredictable world.
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This is the story of how Silicon Valley's mission to connect
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all of us is disrupting politics,
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plunging us into a world of political turbulence
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that no-one can control.
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It might not look like it, but anger is building in Silicon Valley.
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It's usually pretty quiet around here.
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But not today.
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Every day you wake up and you wonder what's going to be today's grief,
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brought by certain politicians and leaders in the world.
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This is a very unusual demonstration.
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I've been to loads of demonstrations,
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but these aren't the people
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that usually go to demonstrations.
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In some ways, these are the winners of society.
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This is the tech community in Silicon Valley.
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Some of the wealthiest people in the world.
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And they're here protesting and demonstrating...
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against what they see as the kind of changing world
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that they don't like.
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The problem, of course, is the election of Donald Trump.
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Every day, I'm sure, you think,
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"I could be a part of resisting those efforts
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"to mess things up for the rest of us."
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Trump came to power promising to control immigration...
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We will build the wall, 100%.
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..and to disengage from the world.
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From this day forward, it's going to be
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only America first.
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America first.
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Now, Silicon Valley is mobilising against him.
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We are seeing this explosion of political activism, you know,
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all through the US and in Europe.
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Before he became an activist,
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Dex Torricke-Barton was speech writer to the chairman of Google
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and the founder of Facebook.
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It's a moment when people who believe in this global vision,
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as opposed to the nationalist vision of the world,
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who believe in a world that isn't about protectionism,
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whether it's data or whether it's about trade,
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they're coming to stand up and to mobilise in response to that.
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Because it feels like the whole of Silicon Valley
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- has been slightly taken by surprise by what's happening.
- Absolutely.
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But these are the smartest minds in the world.
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- Yeah.
- With the most amazing data models and polling.
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The smartest minds in the world often can be very,
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very ignorant of the things that are going on in the world.
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The tech god with the most ambitious global vision is Dex's old boss -
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Facebook founder Mark Zuckerberg.
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One word captures the world Zuck, as he's known here,
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is trying to build.
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Connectivity and access. If we connected them...
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You give people connectivity... That's the mission.
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Connecting with their friends... You connect people over time...
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Connect everyone in the world... I'm really optimistic about that.
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Make the world more open and connected, that's what I care about.
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This is part of the critical enabling infrastructure for the world.
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Thank you, guys.
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What's Mark Zuckerberg worried about most?
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Well, you know, Mark has dedicated his life to connecting, you know,
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the world. You know, this is something that he really, you know,
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cares passionately about.
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And, you know, as I said, you know,
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the same worldview and set of policies that, you know,
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we'll build walls here,
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we'll build walls against the sharing of information
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and building those kind of, you know, networks.
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It's bigger than just the tech.
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- It is.
- It's about the society that Silicon Valley also wants to create?
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Absolutely.
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The tech gods believe the election
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of Donald Trump threatens their vision
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of a globalised world.
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But in a cruel twist,
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is it possible their mission to connect the world
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actually helped bring him to power?
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The question I have is whether the revolution brought about
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by social media companies like Facebook
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has actually led to the political changes in the world
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that these guys are so worried about.
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To answer that question,
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you have to understand how the tech titans of Silicon Valley
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rose to power.
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For that, you have to go back 20 years
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to a time when the online world was still in its infancy.
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MUSIC: Rock N Roll Star by Oasis
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There were fears the new internet was like the Wild West,
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anarchic and potentially harmful.
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Today our world is being remade, yet again, by an information revolution.
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Changing the way we work, the way we live,
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the way we relate to each other.
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The Telecommunications Act of 1996
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was designed to civilise the internet,
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including protect children from pornography.
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Today with the stroke of a pen, our laws will catch up with our future.
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But buried deep within the act was a secret whose impact no-one foresaw.
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Jeremy?
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Jamie. How are you doing?
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Nice to meet you.
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VOICEOVER: Jeremy Malcolm is an analyst
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at the Electronic Frontier Foundation,
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a civil liberties group for the digital age.
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Much of Silicon Valley's accelerated growth in the last two decades
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has been enabled by one clause in the legislation.
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Hidden away in the middle of that is this Section 230.
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- What's the key line?
- It literally just says,
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no provider or user of an interactive computer service
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shall be treated as the publisher or speaker
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of any information provided
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by another information content provider. That's it.
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So what that basically means is, if you're an internet platform,
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you don't get treated as the publisher or speaker
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of something that your users say using your platform.
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If the user says something online that is, say, defamatory,
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the platform that they communicate on
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isn't going to be held responsible for it.
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And the user, of course, can be held directly responsible.
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How important is this line for social media companies today?
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I think if we didn't have this, we probably wouldn't have
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the same kind of social media companies that we have today.
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They wouldn't be willing to take on the risk of having so much
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- unfettered discussion.
- It's key to the internet's freedom, really?
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We wouldn't have the internet of today without this.
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And so, if we are going to make any changes to it,
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we have to be really, really careful.
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These 26 words changed the world.
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They allowed a new kind of business to spring up -
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online platforms that became the internet giants of today.
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Facebook, Google, YouTube - they encouraged users to upload content,
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often things about their lives or moments that mattered to them,
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onto their sites for free.
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And in exchange, they got to hoard all of that data
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but without any real responsibility
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for the effects of the content that people were posting.
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Hundreds of millions of us flocked to these new sites,
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putting more of our lives online.
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At first, the tech firms couldn't figure out
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how to turn that data into big money.
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But that changed when a secret within that data was unlocked.
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Antonio, Jamie.
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'A secret Antonio Garcia Martinez helped reveal at Facebook.'
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Tell me a bit about your time at Facebook.
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Well, that was interesting.
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I was what's called a product manager for ads targeting.
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That means basically taking your data and using it to basically
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make money on Facebook, to monetise Facebook's data.
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If you go browse the internet or buy stuff in stores or whatever,
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and then you see ads related to all that stuff
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inside Facebook - I created that.
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Facebook offers advertisers ways
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to target individual users of the site with adverts.
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It can be driven by data about how we use the platform.
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Here's some examples of what's data for Facebook that makes money.
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What you've liked on Facebook, links that you shared,
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who you happen to know on Facebook, for example.
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Where you've used Facebook, what devices, your iPad, your work computer,
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your home computer. In the case of Amazon,
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it's obviously what you've purchased.
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In the case of Google, it's what you searched for.
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How do they turn me... I like something on Facebook,
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and I share a link on Facebook, how could they turn that
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into something that another company would care about?
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There is what's called a targeting system,
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and so the advertiser can actually go in and specify,
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I want people who are within this city and who have liked BMW or Burberry, for example.
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So an advertiser pays Facebook and says,
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- I want these sorts of people?
- That's effectively it, that's right.
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The innovation that opened up bigger profits was to allow Facebook users
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to be targeted using data about what they do on the rest of the internet.
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The real key thing that marketers want
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is the unique, immutable, flawless, high fidelity ID
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for one person on the internet, and Facebook provides that.
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It is your identity online.
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Facebook can tell an advertiser, this is the real Jamie Barlow,
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- this is what he's like?
- Yeah.
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A company like Walmart can literally take your data, your e-mail,
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phone number, whatever you use for their frequent shopper programme, etc,
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and join that to Facebook and literally target those people based on that data.
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That's part of what I built.
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The tech gods suck in all this data about how we use their technologies
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to build their vast fortunes.
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I mean, it sounds like data is like oil, it's keeping the economy going?
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Right. I mean, the difference is these companies,
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instead of drilling for this oil, they generate this oil via,
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by getting users to actually use their apps and then they actually
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monetise it. Usually via advertising or other mechanisms.
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But, yeah, it is the new oil.
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Data about billions of us is propelling Silicon Valley
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to the pinnacle of the global economy.
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The world's largest hotel company, Airbnb,
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doesn't own a single piece of real estate.
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The world's largest taxi company, Uber, doesn't own any cars.
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The world's largest media company, Facebook, doesn't produce any media, right?
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So what do they have?
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Well, they have the data around how you use those resources and how you
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use those assets. And that's really what they are.
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The secret of targeting us with adverts is keeping us online
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for as long as possible.
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I thought I'd just see how much time I spend on here.
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So I've got an app that counts how often I pick this thing up.
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Our time is the Holy Grail of Silicon Valley.
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Here's what my life looks like on a typical day.
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- Yeah, could I have a flat white, please?
- Flat white?
- Yeah.
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Like more and more of us, my phone is my gateway to the online world.
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It's how I check my social media accounts.
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On average, Facebook users spend 50 minutes every day on the site.
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The longer we spend connected,
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the more Silicon Valley can learn about us,
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and the more targeted and effective their advertising can be.
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So apparently, today, I...
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I've checked my phone 117 times...
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..and I've been on this phone for nearly five and a half hours.
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Well, I mean, that's a lot, that's a lot of hours.
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I mean, it's kind of nearly half the day,
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spent on this phone.
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But it's weird, because it doesn't feel like I spend that long on it.
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The strange thing about it is
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that I don't even really know what I'm doing
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for these five hours that I'm spending on this phone.
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What is it that is keeping us hooked to Silicon Valley's global network?
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I'm in Seattle to meet someone who saw how the tech gods embraced
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new psychological insights into how we all make decisions.
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I was a post-doc with Stephen Hawking.
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Once Chief Technology Officer at Microsoft,
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Nathan Myhrvold is the most passionate technologist
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I have ever met.
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Some complicated-looking equations in the background.
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Well, it turns out if you work with Stephen Hawking,
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you do work with complicated equations.
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It's kind of the nature of the beast!
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Amazing picture.
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A decade ago, Nathan brought together Daniel Kahneman,
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pioneer of the new science of behavioural economics,
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and Silicon Valley's leaders, for a series of meetings.
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I came and Jeff Bezos came.
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That's Jeff Bezos, the founder of Amazon, worth 76 billion.
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Sean Parker. Sean was there.
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That's Sean Parker, the first president of Facebook.
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And the Google founders were there.
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The proposition was, come to this very nice resort in Napa,
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and for several days, just have Kahneman
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and then also a couple of
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other behavioural economists explain things.
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And ask questions and see what happens.
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Kahneman had a simple but brilliant theory on how we make decisions.
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He had found we use one of two different systems of thinking.
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In this dichotomy, you have...
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over here is a hunch...
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..a guess,
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a gut feeling...
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..and "I just know".
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So this is sort of emotional and more like, just instant stuff?
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That's the idea. This set of things
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is not particularly good at a different set of stuff
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that involves...
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..analysis...
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..numbers...
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..probability.
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The meetings in Napa didn't deal with the basics
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of behavioural economics, but how might the insights of the new
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science have helped the tech gods?
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A lot of advertising is about trying to hook people
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in these type-one things to get interested one way or the other.
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Technology companies undoubtedly use that to one degree or another.
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You know, the term "clickbait",
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for things that look exciting to click on.
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There's billions of dollars change hands
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because we all get enticed into clicking something.
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And there's a lot of things that I click on and then you get there,
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you're like, OK, fine, you were just messing with me.
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You're playing to the type-one things,
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you're putting a set of triggers out there
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that make me want to click on it,
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and even though, like, I'm aware of that, I still sometimes click!
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Tech companies both try to understand
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our behaviour by having smart humans think about it and increasingly
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by having machines think about it.
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By having machines track us
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to see, what is the clickbait Nathan falls for?
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What are the things he really likes to spend time on?
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Let's show him more of that stuff!
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Trying to grab the attention of the consumer is nothing new.
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That's what advertising is all about.
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But insights into how we make decisions helped Silicon Valley
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to shape the online world.
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And little wonder, their success depends on keeping us engaged.
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From 1-Click buying on Amazon to the Facebook like,
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the more they've hooked us, the more the money has rolled in.
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As Silicon Valley became more influential,
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it started attracting powerful friends...in politics.
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In 2008, Barack Obama had pioneered political campaigning on Facebook.
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As President, he was drawn to Facebook's founder, Zuck.
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Sorry, I'm kind of nervous.
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We have the President of the United States here!
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My name is Barack Obama and I'm the guy who got Mark
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to wear a jacket and tie!
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How you doing?
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- Great.
- I'll have huevos rancheros, please.
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And if I could have an egg and cheese sandwich on English muffin?
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Aneesh Chopra was Obama's first Chief Technology Officer,
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and saw how close the relationship
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between the White House and Silicon Valley became.
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The President's philosophy and his approach to governing
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garnered a great deal of personal interest
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among many executives in Silicon Valley.
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They were donors to his campaign, volunteers,
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active recruiters of engineering talent
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to support the campaign apparatus.
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He had struck a chord.
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- Why?
- Because frankly,
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it was an inspiring voice that really tapped into
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the hopefulness of the country.
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And a lot of Silicon Valley shares this sense of hopefulness,
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this optimistic view that we can solve problems
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if we would work together
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and take advantage of these new capabilities that are coming online.
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So people in Silicon Valley saw President Obama
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- as a bit of a kindred spirit?
- Oh, yeah. Oh, yeah.
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If you'd like, Mark, we can take our jackets off.
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That's good!
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Facebook's mission to connect the world
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went hand-in-hand with Obama's policies promoting globalisation
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and free markets.
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And Facebook was seen to be improving
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the political process itself.
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Part of what makes for a healthy democracy
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is when you've got citizens who are informed, who are engaged,
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and what Facebook allows us to do
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is make sure this isn't just a one-way conversation.
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You have the tech mind-set and governments increasingly share
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the same view of the world. That's not a natural...
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It's a spirit of liberty.
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It's a spirit of freedom.
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That is manifest today in these new technologies.
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It happens to be that freedom means I can tweet something offensive.
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But it also means that I have a voice.
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Please raise your right hand and repeat after me...
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By the time Obama won his second term,
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he was feted for his mastery of social media's persuasive power.
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But across the political spectrum,
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the race was on to find new ways to gain a digital edge.
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The world was about to change for Facebook.
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Stanford University, in the heart of Silicon Valley.
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Home to a psychologist investigating just how revealing
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Facebook's hoard of information about each of us could really be.
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How are you doing? Nice to meet you.
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Dr Michal Kosinski specialises in psychometrics -
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the science of predicting psychological traits
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like personality.
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So in the past, when you wanted to measure someone's personality
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or intelligence, you needed to give them a question or a test,
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and they would have to answer a bunch of questions.
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Now, many of those questions would basically ask you
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about whether you like poetry,
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or you like hanging out with other people,
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or you like the theatre, and so on.
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But these days, you don't need to ask these questions any more.
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Why? Because while going through our lives,
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we are leaving behind a lot of digital footprints
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that basically contain the same information.
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So instead of asking you whether you like poetry,
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I can just look at your reading history on Amazon
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or your Facebook likes,
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and I would just get exactly the same information.
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In 2011, Dr Kosinski and his team at the University of Cambridge
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developed an online survey
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to measure volunteers' personality traits.
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With their permission, he matched their results
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with their Facebook data.
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More than 6 million people took part.
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We have people's Facebook likes,
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people's status updates and profile data,
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and this allows us to build those... to gain better understanding
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of how psychological traits are being expressed
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in the digital environment.
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How you can measure psychological traits using digital footprints.
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An algorithm that can look at millions of people
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and it can look at hundreds of thousands
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or tens of thousands of your likes
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can extract and utilise even those little pieces of information
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and combine it into a very accurate profile.
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You can quite accurately predict mine, and in fact, everybody else's
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personality, based on the things that they've liked?
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That's correct.
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It can also be used to turn your digital footprint
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into a very accurate prediction
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of your intimate traits, such as religiosity,
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political views, personality, intelligence,
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sexual orientation and a bunch of other psychological traits.
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If I'm logged in, we can maybe see how accurate this actually is.
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So I hit "make prediction" and it's going to try and predict
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my personality from my Facebook page.
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From your Facebook likes.
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According to your likes,
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you're open-minded, liberal and artistic.
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Judges your intelligence to be extremely high.
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- Well done.
- Yes. I'm extremely intelligent!
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You're not religious, but if you are religious,
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- most likely you'd be a Catholic.
- I was raised a Catholic!
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I can't believe it knows that.
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Because I... I don't say anything about being a Catholic anywhere,
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but I was raised as a Catholic, but I'm not a practising Catholic.
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So it's like...
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It's absolutely spot on.
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Oh, my God! Journalism, but also what did I study?
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Studied history, and I didn't put anything about history in there.
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I think this is one of the things
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that people don't really get about those predictions, that they think,
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look, if I like Lady Gaga on Facebook,
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obviously people will know that I like Lady Gaga,
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or the Government will know that I like Lady Gaga.
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Look, you don't need a rocket scientist
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to look at your Spotify playlist or your Facebook likes
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to figure out that you like Lady Gaga.
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00:25:42,160 --> 00:25:47,880
What's really world-changing about those algorithms is that they can
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take your music preferences or your book preferences
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00:25:51,920 --> 00:25:55,560
and extract from this seemingly innocent information
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very accurate predictions about your religiosity, leadership potential,
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political views, personality and so on.
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00:26:02,760 --> 00:26:05,560
Can you predict people's political persuasions with this?
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00:26:05,560 --> 00:26:08,400
In fact, the first dimension here, openness to experience,
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is a very good predictor of political views.
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People scoring high on openness, they tend to be liberal,
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people who score low on openness, we even call it conservative and
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traditional, they tend to vote for conservative candidates.
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00:26:22,640 --> 00:26:25,520
What about the potential to manipulate people?
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00:26:25,520 --> 00:26:29,280
So, obviously, if you now can use an algorithm to get to know millions
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of people very intimately and then use another algorithm
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to adjust the message that you are sending to them,
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to make it most persuasive,
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obviously gives you a lot of power.
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00:26:52,040 --> 00:26:55,560
I'm quite surprised at just how accurate this model could be.
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00:26:55,560 --> 00:27:00,880
I mean, I cannot believe that on the basis of a few things
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I just, you know, carelessly clicked that I liked,
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the model was able to work out that I could have been Catholic,
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or had a Catholic upbringing.
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00:27:12,920 --> 00:27:18,400
And clearly, this is a very powerful way of understanding people.
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00:27:19,400 --> 00:27:22,800
Very exciting possibilities, but I can't help
468
00:27:22,800 --> 00:27:28,720
fearing that there is that potential, whoever has that power,
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00:27:28,720 --> 00:27:31,480
whoever can control that model...
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00:27:33,160 --> 00:27:37,920
..will have sort of unprecedented possibilities of manipulating
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00:27:37,920 --> 00:27:41,880
what people think, how they behave, what they see,
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whether that's selling things to people or how people vote,
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and that's pretty scary too.
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00:27:52,000 --> 00:27:55,520
Our era is defined by political shocks.
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None bigger than the rise of Donald Trump,
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00:28:02,520 --> 00:28:05,040
who defied pollsters and the mainstream media
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to win the American presidency.
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00:28:08,560 --> 00:28:12,240
Now questions swirl around his use of the American affiliate
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of a British insights company,
480
00:28:14,440 --> 00:28:17,960
Cambridge Analytica, who use psychographics.
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I'm in Texas to uncover how far
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00:28:27,600 --> 00:28:31,920
Cambridge Analytica's expertise in personality prediction
483
00:28:31,920 --> 00:28:36,320
played a part in Trump's political triumph,
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00:28:36,320 --> 00:28:38,760
and how his revolutionary campaign
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00:28:38,760 --> 00:28:42,240
exploited Silicon Valley's social networks.
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00:28:44,080 --> 00:28:45,960
Everyone seems to agree
487
00:28:45,960 --> 00:28:49,480
that Trump ran an exceptional election campaign
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using digital technologies.
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But no-one really knows what they did,
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00:28:56,880 --> 00:28:59,840
who they were working with, who was helping them,
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00:28:59,840 --> 00:29:03,360
what the techniques they used were.
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00:29:03,360 --> 00:29:07,920
So I've come here to try to unravel the mystery.
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00:29:09,280 --> 00:29:13,120
The operation inside this unassuming building in San Antonio
494
00:29:13,120 --> 00:29:18,080
was largely hidden from view, but crucial to Trump's success.
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00:29:18,080 --> 00:29:22,600
Since then, I'm the only person to get in here
496
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to find out what really happened.
497
00:29:25,000 --> 00:29:27,040
This was our Project Alamo,
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00:29:27,040 --> 00:29:30,480
so this was where the digital arm
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00:29:30,480 --> 00:29:33,680
of the Trump campaign operation was held.
500
00:29:33,680 --> 00:29:36,200
Theresa Hong is speaking publicly
501
00:29:36,200 --> 00:29:40,440
for the first time about her role as digital content director
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00:29:40,440 --> 00:29:42,320
for the Trump campaign.
503
00:29:43,480 --> 00:29:47,240
So, why is it called Project Alamo?
504
00:29:47,240 --> 00:29:51,560
It was called Project Alamo based on the data, actually,
505
00:29:51,560 --> 00:29:53,960
that was Cambridge Analytica -
506
00:29:53,960 --> 00:29:56,640
they came up with the Alamo data set, right?
507
00:29:56,640 --> 00:30:00,200
So, we just kind of adopted the name Project Alamo.
508
00:30:00,200 --> 00:30:04,920
It does conjure up sort of images of a battle of some sort.
509
00:30:04,920 --> 00:30:06,720
Yeah. It kind of was!
510
00:30:06,720 --> 00:30:09,760
In a sense, you know. Yeah, yeah.
511
00:30:13,640 --> 00:30:16,320
Project Alamo was so important,
512
00:30:16,320 --> 00:30:20,040
Donald Trump visited the hundred or so workers based here
513
00:30:20,040 --> 00:30:22,520
during the campaign.
514
00:30:22,520 --> 00:30:26,000
Ever since he started tweeting in 2009,
515
00:30:26,000 --> 00:30:29,640
Trump had grasped the power of social media.
516
00:30:29,640 --> 00:30:32,640
Now, in the fight of his life,
517
00:30:32,640 --> 00:30:36,200
the campaign manipulated his Facebook presence.
518
00:30:36,200 --> 00:30:40,800
Trump's Twitter account, that's all his, he's the one that ran that,
519
00:30:40,800 --> 00:30:43,280
and I did a lot of his Facebook,
520
00:30:43,280 --> 00:30:48,880
so I wrote a lot for him, you know, I kind of channelled Mr Trump.
521
00:30:48,880 --> 00:30:52,320
How do you possibly write a post on Facebook like Donald Trump?
522
00:30:52,320 --> 00:30:54,000
"Believe me."
523
00:30:54,000 --> 00:30:58,040
A lot of believe me's, a lot of alsos, a lot of...verys.
524
00:30:58,040 --> 00:31:01,640
Actually, he was really wonderful to write for, just because
525
00:31:01,640 --> 00:31:06,240
it was so refreshing, it was so authentic.
526
00:31:09,760 --> 00:31:11,800
We headed to the heart of the operation.
527
00:31:14,520 --> 00:31:16,320
Cambridge Analytica was here.
528
00:31:16,320 --> 00:31:19,320
It was just a line of computers, right?
529
00:31:19,320 --> 00:31:22,520
This is where their operation was and this was kind of
530
00:31:22,520 --> 00:31:26,360
the brain of the data, this was the data centre.
531
00:31:26,360 --> 00:31:30,000
This was the data centre, this was the centre of the data centre.
532
00:31:30,000 --> 00:31:33,040
Exactly right, yes, it was. Yes, it was.
533
00:31:33,040 --> 00:31:35,040
Cambridge Analytica were using data
534
00:31:35,040 --> 00:31:40,600
on around 220 million Americans to target potential donors and voters.
535
00:31:40,600 --> 00:31:43,120
It was, you know,
536
00:31:43,120 --> 00:31:48,200
a bunch of card tables that sat here and a bunch of computers and people
537
00:31:48,200 --> 00:31:51,680
that were behind the computers, monitoring the data.
538
00:31:51,680 --> 00:31:55,400
We've got to target this state, that state or this universe or whatever.
539
00:31:55,400 --> 00:31:58,280
So that's what they were doing, they were gathering all the data.
540
00:31:58,280 --> 00:32:02,160
A "universe" was the name given to a group of voters
541
00:32:02,160 --> 00:32:05,560
defined by Cambridge Analytica.
542
00:32:05,560 --> 00:32:07,440
What sort of attributes did these people have
543
00:32:07,440 --> 00:32:09,480
that they had been able to work out?
544
00:32:09,480 --> 00:32:12,880
Some of the attributes would be, when was the last time they voted?
545
00:32:12,880 --> 00:32:14,560
Who did they vote for?
546
00:32:14,560 --> 00:32:17,080
You know, what kind of car do they drive?
547
00:32:17,080 --> 00:32:20,760
What kind of things do they look at on the internet?
548
00:32:20,760 --> 00:32:22,200
What do they stand for?
549
00:32:22,200 --> 00:32:25,200
I mean, one person might really be about job creation
550
00:32:25,200 --> 00:32:27,080
and keeping jobs in America, you know.
551
00:32:27,080 --> 00:32:30,200
Another person, they might resonate with, you know,
552
00:32:30,200 --> 00:32:33,160
Second Amendment and gun rights, so...
553
00:32:33,160 --> 00:32:34,560
How would they know that?
554
00:32:34,560 --> 00:32:38,280
- ..within that...
- How would they know that?
- That is their secret sauce.
555
00:32:40,360 --> 00:32:42,040
Did the "secret sauce"
556
00:32:42,040 --> 00:32:46,880
contain predictions of personality and political leanings?
557
00:32:46,880 --> 00:32:50,480
Were they able to kind of understand people's personalities?
558
00:32:50,480 --> 00:32:52,200
Yes, I mean, you know,
559
00:32:52,200 --> 00:32:56,200
they do specialise in psychographics, right?
560
00:32:56,200 --> 00:33:01,400
But based on personal interests and based on what a person cares for
561
00:33:01,400 --> 00:33:03,440
and what means something to them,
562
00:33:03,440 --> 00:33:06,760
they were able to extract and then we were able to target.
563
00:33:06,760 --> 00:33:10,680
So, the psychographic stuff, were they using that here?
564
00:33:10,680 --> 00:33:13,480
Was that part of the model you were working on?
565
00:33:13,480 --> 00:33:17,160
Well, I mean, towards the end with the persuasion, absolutely.
566
00:33:17,160 --> 00:33:19,120
I mean, we really were targeting
567
00:33:19,120 --> 00:33:22,120
on these universes that they had collected.
568
00:33:22,120 --> 00:33:25,440
I mean, did some of the attributes that you were able to use
569
00:33:25,440 --> 00:33:29,240
from Cambridge Analytica have a sort of emotional effect, you know,
570
00:33:29,240 --> 00:33:32,800
happy, sad, anxious, worried - moods, that kind of thing?
571
00:33:32,800 --> 00:33:34,080
Right, yeah.
572
00:33:34,080 --> 00:33:38,960
I do know Cambridge Analytica follows something called Ocean
573
00:33:38,960 --> 00:33:41,880
and it's based on, you know, whether or not, like,
574
00:33:41,880 --> 00:33:43,880
are you an extrovert?
575
00:33:43,880 --> 00:33:45,800
Are you more of an introvert?
576
00:33:45,800 --> 00:33:50,720
Are you fearful? Are you positive?
577
00:33:50,720 --> 00:33:52,840
So, they did use that.
578
00:33:54,880 --> 00:33:58,280
Armed with Cambridge Analytica's revolutionary insights,
579
00:33:58,280 --> 00:34:02,160
the next step in the battle to win over millions of Americans
580
00:34:02,160 --> 00:34:06,040
was to shape the online messages they would see.
581
00:34:06,040 --> 00:34:10,760
Now we're going to go into the big kind of bull pen where a lot of
582
00:34:10,760 --> 00:34:14,920
- the creatives were, and this is where I was as well.
- Right.
583
00:34:14,920 --> 00:34:17,440
For today's modern working-class families,
584
00:34:17,440 --> 00:34:19,680
the challenge is very, very real.
585
00:34:19,680 --> 00:34:21,160
With childcare costs...
586
00:34:21,160 --> 00:34:25,120
Adverts were tailored to particular audiences, defined by data.
587
00:34:25,120 --> 00:34:28,360
Donald Trump wants to give families the break they deserve.
588
00:34:28,360 --> 00:34:31,160
This universe right here that Cambridge Analytica,
589
00:34:31,160 --> 00:34:34,920
they've collected data and they have identified as working mothers
590
00:34:34,920 --> 00:34:37,200
that are concerned about childcare,
591
00:34:37,200 --> 00:34:40,560
and childcare, obviously, that's not going to be, like,
592
00:34:40,560 --> 00:34:43,560
a war-ridden, you know, destructive ad, right?
593
00:34:43,560 --> 00:34:44,880
That's more warm and fuzzy,
594
00:34:44,880 --> 00:34:46,800
and this is what Trump is going to do for you.
595
00:34:46,800 --> 00:34:49,720
But if you notice, Trump's not speaking.
596
00:34:49,720 --> 00:34:51,760
He wasn't speaking, he wasn't in it at all.
597
00:34:51,760 --> 00:34:53,280
Right, Trump wasn't speaking in it.
598
00:34:53,280 --> 00:34:56,800
That audience there, we wanted a softer approach,
599
00:34:56,800 --> 00:34:59,520
so this is the type of approach that we would take.
600
00:34:59,520 --> 00:35:03,760
The campaign made thousands of different versions
601
00:35:03,760 --> 00:35:05,920
of the same fundraising adverts.
602
00:35:05,920 --> 00:35:11,120
The design was constantly tweaked to see which version performed best.
603
00:35:11,120 --> 00:35:15,680
It wasn't uncommon to have about 35-45,000 iterations
604
00:35:15,680 --> 00:35:18,320
of these types of ads every day, right?
605
00:35:18,320 --> 00:35:22,800
So, you know, it could be
606
00:35:22,800 --> 00:35:28,080
as subtle and just, you know, people wouldn't even notice,
607
00:35:28,080 --> 00:35:30,440
where you have the green button,
608
00:35:30,440 --> 00:35:32,800
sometimes a red button would work better
609
00:35:32,800 --> 00:35:34,400
or a blue button would work better.
610
00:35:34,400 --> 00:35:38,200
Now, the voters Cambridge Analytica had targeted
611
00:35:38,200 --> 00:35:40,360
were bombarded with adverts.
612
00:35:46,320 --> 00:35:47,680
I may have short-circuited...
613
00:35:49,120 --> 00:35:51,560
I'm Donald Trump and I approve this message.
614
00:35:52,760 --> 00:35:57,280
On a typical day, the campaign would run more than 100 different adverts.
615
00:35:57,280 --> 00:36:02,120
The delivery system was Silicon Valley's vast social networks.
616
00:36:02,120 --> 00:36:05,600
We had the Facebook and YouTube and Google people,
617
00:36:05,600 --> 00:36:07,600
they would congregate here.
618
00:36:07,600 --> 00:36:12,800
Almost all of America's voters could now be reached online in an instant.
619
00:36:12,800 --> 00:36:16,600
I mean, what were Facebook and Google and YouTube people actually
620
00:36:16,600 --> 00:36:19,840
- doing here, why were they here?
- They were helping us, you know.
621
00:36:19,840 --> 00:36:24,760
They were basically our kind of hands-on partners
622
00:36:24,760 --> 00:36:27,440
as far as being able to utilise the platform
623
00:36:27,440 --> 00:36:29,600
as effectively as possible.
624
00:36:29,600 --> 00:36:34,160
The Trump campaign spent the lion's share of its advertising budget,
625
00:36:34,160 --> 00:36:38,480
around 85 million, on Facebook.
626
00:36:38,480 --> 00:36:42,440
When you're pumping in millions and millions of dollars
627
00:36:42,440 --> 00:36:44,240
to these social platforms,
628
00:36:44,240 --> 00:36:46,320
you're going to get white-glove treatment,
629
00:36:46,320 --> 00:36:48,560
so, they would send people, you know,
630
00:36:48,560 --> 00:36:51,200
representatives to, you know,
631
00:36:51,200 --> 00:36:56,200
Project Alamo to ensure that all of our needs were being met.
632
00:36:56,200 --> 00:36:59,440
The success of Trump's digital strategy was built on
633
00:36:59,440 --> 00:37:03,000
the effectiveness of Facebook as an advertising medium.
634
00:37:03,000 --> 00:37:06,240
It's become a very powerful political tool
635
00:37:06,240 --> 00:37:08,800
that's largely unregulated.
636
00:37:08,800 --> 00:37:12,040
Without Facebook, we wouldn't have won.
637
00:37:12,040 --> 00:37:16,000
I mean, Facebook really and truly put us over the edge,
638
00:37:16,000 --> 00:37:18,560
I mean, Facebook was the medium
639
00:37:18,560 --> 00:37:21,960
that proved most successful for this campaign.
640
00:37:25,680 --> 00:37:28,680
Facebook didn't want to meet me, but made it clear that like all
641
00:37:28,680 --> 00:37:32,560
advertisers on Facebook, political campaigns must ensure
642
00:37:32,560 --> 00:37:36,480
their ads comply with all applicable laws and regulations.
643
00:37:36,480 --> 00:37:40,000
The company also said no personally identifiable
644
00:37:40,000 --> 00:37:42,760
information can be shared with advertising,
645
00:37:42,760 --> 00:37:46,520
measurement or analytics partners unless people give permission.
646
00:37:51,400 --> 00:37:52,960
In London, I'm on the trail
647
00:37:52,960 --> 00:37:56,320
of the data company Cambridge Analytica.
648
00:37:59,760 --> 00:38:03,280
Ever since the American election, the firm has been under pressure
649
00:38:03,280 --> 00:38:08,320
to come clean over its use of personality prediction.
650
00:38:08,320 --> 00:38:12,080
I want to know exactly how the firm used psychographics
651
00:38:12,080 --> 00:38:16,400
to target voters for the Trump campaign.
652
00:38:16,400 --> 00:38:18,000
- Alexander.
- Hi.
653
00:38:18,000 --> 00:38:19,960
How do you do? Pleasure.
654
00:38:19,960 --> 00:38:23,720
VOICEOVER: Alexander Nix's firm first used data to target voters
655
00:38:23,720 --> 00:38:27,360
in the American presidential elections while working on
656
00:38:27,360 --> 00:38:31,640
the Ted Cruz campaign for the Republican nomination.
657
00:38:31,640 --> 00:38:35,200
When Cruz lost, they went to work for Trump.
658
00:38:35,200 --> 00:38:37,320
I want to start with the Trump campaign.
659
00:38:37,320 --> 00:38:40,000
Did Cambridge Analytica ever use
660
00:38:40,000 --> 00:38:44,000
psychometric or psychographic methods in this campaign?
661
00:38:44,000 --> 00:38:47,840
We left the Cruz campaign in April after the nomination was over.
662
00:38:47,840 --> 00:38:50,160
We pivoted right across onto the Trump campaign.
663
00:38:50,160 --> 00:38:53,560
It was about five and a half months before polling.
664
00:38:53,560 --> 00:38:58,560
And whilst on the Cruz campaign, we were able to invest
665
00:38:58,560 --> 00:39:01,280
a lot more time into building psychographic models,
666
00:39:01,280 --> 00:39:04,600
into profiling, using behavioural profiling to understand different
667
00:39:04,600 --> 00:39:07,560
personality groups and different personality drivers,
668
00:39:07,560 --> 00:39:10,760
in order to inform our messaging and our creative.
669
00:39:10,760 --> 00:39:14,520
We simply didn't have the time to employ this level of
670
00:39:14,520 --> 00:39:17,000
rigorous methodology for Trump.
671
00:39:17,000 --> 00:39:21,560
For Cruz, Cambridge Analytica built computer models which could crunch
672
00:39:21,560 --> 00:39:26,600
huge amounts of data on each voter, including psychographic data
673
00:39:26,600 --> 00:39:30,440
predicting voters' personality types.
674
00:39:30,440 --> 00:39:32,600
When the firm transferred to the Trump campaign,
675
00:39:32,600 --> 00:39:34,840
they took data with them.
676
00:39:34,840 --> 00:39:40,480
Now, there is clearly some legacy psychographics in the data,
677
00:39:40,480 --> 00:39:44,080
because the data is, um, model data,
678
00:39:44,080 --> 00:39:46,800
or a lot of it is model data that we had used across
679
00:39:46,800 --> 00:39:50,400
the last 14, 15 months of campaigning through the midterms,
680
00:39:50,400 --> 00:39:52,480
and then through the primaries.
681
00:39:52,480 --> 00:39:57,720
But specifically, did we build specific psychographic models
682
00:39:57,720 --> 00:39:59,800
for the Trump campaign? No, we didn't.
683
00:39:59,800 --> 00:40:03,200
So you didn't build specific models for this campaign,
684
00:40:03,200 --> 00:40:07,720
but it sounds like you did use some element of psychographic modelling,
685
00:40:07,720 --> 00:40:10,960
as an approach in the Trump campaign?
686
00:40:10,960 --> 00:40:13,960
Only as a result of legacy data models.
687
00:40:13,960 --> 00:40:17,200
So, the answer... The answer you are looking for is no.
688
00:40:17,200 --> 00:40:20,360
The answer I am looking for is the extent to which it was used.
689
00:40:20,360 --> 00:40:23,520
I mean, legacy... I don't know what that means, legacy data modelling.
690
00:40:23,520 --> 00:40:26,000
What does that mean for the Trump campaign?
691
00:40:26,000 --> 00:40:28,560
Well, so we were able to take models we had made previously
692
00:40:28,560 --> 00:40:30,040
over the last two or three years,
693
00:40:30,040 --> 00:40:33,160
and integrate those into some of the work we were doing.
694
00:40:33,160 --> 00:40:34,920
Where did all the information
695
00:40:34,920 --> 00:40:38,880
to predict voters' personalities come from?
696
00:40:38,880 --> 00:40:42,920
Very originally, we used a combination of telephone surveys
697
00:40:42,920 --> 00:40:45,960
and then we used a number of...
698
00:40:46,960 --> 00:40:50,080
..online platforms...
699
00:40:50,080 --> 00:40:52,600
for gathering questions.
700
00:40:52,600 --> 00:40:55,480
As we started to gather more data,
701
00:40:55,480 --> 00:40:57,920
we started to look at other platforms.
702
00:40:57,920 --> 00:41:00,440
Such as Facebook, for instance.
703
00:41:02,320 --> 00:41:05,480
Predicting personality is just one element in the big data
704
00:41:05,480 --> 00:41:09,600
companies like Cambridge Analytica are using to revolutionise
705
00:41:09,600 --> 00:41:11,680
the way democracy works.
706
00:41:11,680 --> 00:41:13,960
Can you understand, though, why maybe
707
00:41:13,960 --> 00:41:16,080
some people find it a little bit creepy?
708
00:41:16,080 --> 00:41:17,640
No, I can't. Quite the opposite.
709
00:41:17,640 --> 00:41:21,000
I think that the move away from blanket advertising,
710
00:41:21,000 --> 00:41:24,560
the move towards ever more personalised communication,
711
00:41:24,560 --> 00:41:25,880
is a very natural progression.
712
00:41:25,880 --> 00:41:27,760
I think it is only going to increase.
713
00:41:27,760 --> 00:41:30,960
I find it a little bit weird, if I had a very sort of detailed
714
00:41:30,960 --> 00:41:35,080
personality assessment of me, based on all sorts of different data
715
00:41:35,080 --> 00:41:37,520
that I had put all over the internet,
716
00:41:37,520 --> 00:41:40,840
and that as a result of that, some profile had been made of me
717
00:41:40,840 --> 00:41:43,320
that was then used to target me for adverts,
718
00:41:43,320 --> 00:41:45,920
and I didn't really know that any of that had happened.
719
00:41:45,920 --> 00:41:48,800
That's why some people might find it a little bit sinister.
720
00:41:48,800 --> 00:41:50,240
Well, you have just said yourself,
721
00:41:50,240 --> 00:41:52,680
you are putting this data out into the public domain.
722
00:41:52,680 --> 00:41:56,680
I'm sure that you have a supermarket loyalty card.
723
00:41:56,680 --> 00:42:01,400
I'm sure you understand the reciprocity that is going on there -
724
00:42:01,400 --> 00:42:05,080
you get points, and in return, they gather your data
725
00:42:05,080 --> 00:42:07,280
on your consumer behaviour.
726
00:42:07,280 --> 00:42:08,920
I mean, we are talking about politics
727
00:42:08,920 --> 00:42:12,120
and we're talking about shopping. Are they really the same thing?
728
00:42:12,120 --> 00:42:14,240
The technology is the same.
729
00:42:14,240 --> 00:42:15,640
In the next ten years,
730
00:42:15,640 --> 00:42:18,560
the sheer volumes of data that are going to be available,
731
00:42:18,560 --> 00:42:20,840
that are going to be driving all sorts of things
732
00:42:20,840 --> 00:42:23,200
including marketing and communications,
733
00:42:23,200 --> 00:42:25,960
is going to be a paradigm shift from where we are now
734
00:42:25,960 --> 00:42:27,520
and it's going to be a revolution,
735
00:42:27,520 --> 00:42:29,520
and that is the way the world is moving.
736
00:42:29,520 --> 00:42:31,400
And, you know, I think,
737
00:42:31,400 --> 00:42:35,000
whether you like it or not, it, it...
738
00:42:35,000 --> 00:42:37,800
it is an inevitable fact.
739
00:42:39,120 --> 00:42:42,840
To the new data barons, this is all just business.
740
00:42:42,840 --> 00:42:46,080
But to the rest of us, it's more than that.
741
00:42:49,160 --> 00:42:52,000
By the time of Donald Trump's inauguration,
742
00:42:52,000 --> 00:42:55,760
it was accepted that his mastery of data and social media
743
00:42:55,760 --> 00:42:59,840
had made him the most powerful man in the world.
744
00:42:59,840 --> 00:43:04,320
We will make America great again.
745
00:43:04,320 --> 00:43:06,400
The election of Donald Trump
746
00:43:06,400 --> 00:43:10,240
was greeted with barely concealed fury in Silicon Valley.
747
00:43:10,240 --> 00:43:13,280
But Facebook and other tech companies had made
748
00:43:13,280 --> 00:43:16,600
millions of dollars by helping to make it happen.
749
00:43:16,600 --> 00:43:19,040
Their power as advertising platforms
750
00:43:19,040 --> 00:43:21,840
had been exploited by a politician
751
00:43:21,840 --> 00:43:24,880
with a very different view of the world.
752
00:43:27,600 --> 00:43:30,360
But Facebook's problems were only just beginning.
753
00:43:32,480 --> 00:43:34,120
Another phenomenon of the election
754
00:43:34,120 --> 00:43:37,600
was plunging the tech titan into crisis.
755
00:43:43,440 --> 00:43:46,560
Fake news, often targeting Hillary Clinton,
756
00:43:46,560 --> 00:43:49,760
had dominated the election campaign.
757
00:43:52,080 --> 00:43:55,560
Now, the departing President turned on the social media giant
758
00:43:55,560 --> 00:43:58,040
he had once embraced.
759
00:43:59,240 --> 00:44:01,800
In an age where, uh...
760
00:44:01,800 --> 00:44:06,720
there is so much active misinformation,
761
00:44:06,720 --> 00:44:08,480
and it is packaged very well,
762
00:44:08,480 --> 00:44:11,840
and it looks the same when you see it on a Facebook page,
763
00:44:11,840 --> 00:44:13,960
or you turn on your television,
764
00:44:13,960 --> 00:44:17,240
if everything, uh...
765
00:44:17,240 --> 00:44:20,040
seems to be the same,
766
00:44:20,040 --> 00:44:22,280
and no distinctions are made,
767
00:44:22,280 --> 00:44:25,400
then we won't know what to protect.
768
00:44:25,400 --> 00:44:27,840
We won't know what to fight for.
769
00:44:33,440 --> 00:44:36,480
Fake news had provoked a storm of criticism
770
00:44:36,480 --> 00:44:39,280
over Facebook's impact on democracy.
771
00:44:39,280 --> 00:44:42,800
Its founder, Zuck, claimed it was extremely unlikely
772
00:44:42,800 --> 00:44:46,200
fake news had changed the election's outcome.
773
00:44:46,200 --> 00:44:49,120
But he didn't address why it had spread like wildfire
774
00:44:49,120 --> 00:44:51,400
across the platform.
775
00:44:51,400 --> 00:44:53,280
- Jamie.
- Hi, Jamie.
776
00:44:53,280 --> 00:44:55,080
VOICEOVER: Meet Jeff Hancock,
777
00:44:55,080 --> 00:44:58,640
a psychologist who has investigated a hidden aspect of Facebook
778
00:44:58,640 --> 00:45:03,440
that helps explain how the platform became weaponised in this way.
779
00:45:06,360 --> 00:45:11,400
It turns out the power of Facebook to affect our emotions is key,
780
00:45:11,400 --> 00:45:13,600
something that had been uncovered
781
00:45:13,600 --> 00:45:18,040
in an experiment the company itself had run in 2012.
782
00:45:18,040 --> 00:45:19,760
It was one of the earlier, you know,
783
00:45:19,760 --> 00:45:22,880
what we would call big data, social science-type studies.
784
00:45:26,200 --> 00:45:30,920
The newsfeeds of nearly 700,000 users were secretly manipulated
785
00:45:30,920 --> 00:45:35,880
so they would see fewer positive or negative posts.
786
00:45:35,880 --> 00:45:38,400
Jeff helped interpret the results.
787
00:45:38,400 --> 00:45:40,320
So, what did you actually find?
788
00:45:40,320 --> 00:45:43,080
We found that if you were one of those people
789
00:45:43,080 --> 00:45:46,280
that were seeing less negative emotion words in their posts,
790
00:45:46,280 --> 00:45:49,880
then you would write with less negative emotion in your own posts,
791
00:45:49,880 --> 00:45:51,840
and more positive emotion.
792
00:45:51,840 --> 00:45:55,600
- This is emotional contagion.
- And what about positive posts?
793
00:45:55,600 --> 00:45:57,640
Did that have the same kind of effect?
794
00:45:57,640 --> 00:45:59,840
Yeah, we saw the same effect
795
00:45:59,840 --> 00:46:03,520
when positive emotion worded posts were decreased.
796
00:46:03,520 --> 00:46:06,320
We saw the same thing. So I would produce fewer positive
797
00:46:06,320 --> 00:46:08,880
emotion words, and more negative emotion words.
798
00:46:08,880 --> 00:46:11,560
And that is consistent with emotional contagion theory.
799
00:46:11,560 --> 00:46:15,520
Basically, we were showing that people were writing in a way
800
00:46:15,520 --> 00:46:18,480
that was matching the emotion that they were seeing
801
00:46:18,480 --> 00:46:21,160
in the Facebook news feed.
802
00:46:21,160 --> 00:46:27,520
Emotion draws people to fake news, and then supercharges its spread.
803
00:46:27,520 --> 00:46:29,200
So the more emotional the content,
804
00:46:29,200 --> 00:46:31,760
the more likely it is to spread online.
805
00:46:31,760 --> 00:46:34,600
- Is that true?
- Yeah, the more intense the emotion in content,
806
00:46:34,600 --> 00:46:38,280
the more likely it is to spread, to go viral.
807
00:46:38,280 --> 00:46:42,040
It doesn't matter whether it is sad or happy, like negative or positive,
808
00:46:42,040 --> 00:46:44,880
the more important thing is how intense the emotion is.
809
00:46:44,880 --> 00:46:47,000
The process of emotional contagion
810
00:46:47,000 --> 00:46:50,040
helps explain why fake news has spread
811
00:46:50,040 --> 00:46:52,960
so far across social media.
812
00:46:55,080 --> 00:46:58,040
Advertisers have been well aware of how emotion can be used
813
00:46:58,040 --> 00:46:59,920
to manipulate people's attention.
814
00:46:59,920 --> 00:47:02,480
But now we are seeing this with a whole host of other actors,
815
00:47:02,480 --> 00:47:04,000
some of them nefarious.
816
00:47:04,000 --> 00:47:06,440
So, other state actors trying to influence your election,
817
00:47:06,440 --> 00:47:08,440
people trying to manipulate the media
818
00:47:08,440 --> 00:47:10,720
are using emotional contagion,
819
00:47:10,720 --> 00:47:14,240
and also using those original platforms like Facebook
820
00:47:14,240 --> 00:47:19,120
to accomplish other objectives, like sowing distrust,
821
00:47:19,120 --> 00:47:21,040
or creating false beliefs.
822
00:47:21,040 --> 00:47:23,640
You see it sort of, maybe weaponised
823
00:47:23,640 --> 00:47:26,320
and being used at scale.
824
00:47:35,680 --> 00:47:37,080
I'm in Germany,
825
00:47:37,080 --> 00:47:39,680
where the presence of more than a million new refugees
826
00:47:39,680 --> 00:47:43,000
has caused tension across the political spectrum.
827
00:47:43,000 --> 00:47:44,880
Earlier this year,
828
00:47:44,880 --> 00:47:49,280
a story appeared on the American alt-right news site Breitbart
829
00:47:49,280 --> 00:47:54,080
about the torching of a church in Dortmund by a North African mob.
830
00:47:54,080 --> 00:47:57,520
It was widely shared on social media -
831
00:47:57,520 --> 00:47:59,440
but it wasn't true.
832
00:48:04,800 --> 00:48:06,440
The problem with social networks
833
00:48:06,440 --> 00:48:08,600
is that all information is treated equally.
834
00:48:08,600 --> 00:48:12,480
So you have good, honest, accurate information
835
00:48:12,480 --> 00:48:15,840
sitting alongside and treated equally to
836
00:48:15,840 --> 00:48:18,360
lies and propaganda.
837
00:48:18,360 --> 00:48:21,480
And the difficulty for citizens is that it can be very hard
838
00:48:21,480 --> 00:48:24,400
to tell the difference between the two.
839
00:48:24,400 --> 00:48:30,040
But one data scientist here has discovered an even darker side
840
00:48:30,040 --> 00:48:32,600
to the way Facebook is being manipulated.
841
00:48:33,920 --> 00:48:37,080
We created a network of all the likes
842
00:48:37,080 --> 00:48:40,280
in the refugee debate on Facebook.
843
00:48:42,360 --> 00:48:46,120
Professor Simon Hegelich has found evidence the debate
844
00:48:46,120 --> 00:48:47,880
about refugees on Facebook
845
00:48:47,880 --> 00:48:52,720
is being skewed by anonymous political forces.
846
00:48:52,720 --> 00:48:56,800
So you are trying to understand who is liking pages?
847
00:48:56,800 --> 00:48:58,080
Exactly.
848
00:48:58,080 --> 00:49:01,080
One statistic among many used by Facebook to rank stories in
849
00:49:01,080 --> 00:49:04,160
your news feed is the number of likes they get.
850
00:49:04,160 --> 00:49:08,960
The red area of this chart shows a network of people liking
851
00:49:08,960 --> 00:49:12,200
anti-refugee posts on Facebook.
852
00:49:12,200 --> 00:49:18,560
Most of the likes are sent by just a handful of people - 25 people are...
853
00:49:20,000 --> 00:49:24,040
..responsible for more than 90% of all these likes.
854
00:49:24,040 --> 00:49:26,560
25 Facebook accounts each liked
855
00:49:26,560 --> 00:49:30,240
more than 30,000 comments over six months.
856
00:49:30,240 --> 00:49:36,480
These hyperactive accounts could be run by real people, or software.
857
00:49:36,480 --> 00:49:38,400
I think the rationale behind this
858
00:49:38,400 --> 00:49:42,400
is that they tried to make their content viral.
859
00:49:42,400 --> 00:49:46,000
They think if we like all this stuff, then Facebook,
860
00:49:46,000 --> 00:49:47,920
the algorithm of Facebook,
861
00:49:47,920 --> 00:49:51,560
will pick up our content and show it to other users,
862
00:49:51,560 --> 00:49:54,920
and then the whole world sees that refugees are bad
863
00:49:54,920 --> 00:49:57,520
and that they shouldn't come to Germany.
864
00:49:57,520 --> 00:50:02,160
This is evidence the number of likes on Facebook can be easily gamed
865
00:50:02,160 --> 00:50:05,240
as part of an effort to try to influence the prominence
866
00:50:05,240 --> 00:50:08,520
of anti-refugee content on the site.
867
00:50:08,520 --> 00:50:10,560
Does this worry you, though?
868
00:50:10,560 --> 00:50:14,680
It's definitely changing structure of public opinion.
869
00:50:14,680 --> 00:50:18,560
Democracy is built on public opinion,
870
00:50:18,560 --> 00:50:23,640
so such a change definitely has to change the way democracy works.
871
00:50:26,520 --> 00:50:30,080
Facebook told us they are working to disrupt the economic incentives
872
00:50:30,080 --> 00:50:34,480
behind false news, removing tens of thousands of fake accounts,
873
00:50:34,480 --> 00:50:36,240
and building new products
874
00:50:36,240 --> 00:50:39,720
to identify and limit the spread of false news.
875
00:50:39,720 --> 00:50:44,160
Zuck is trying to hold the line that his company is not a publisher,
876
00:50:44,160 --> 00:50:49,560
based on that obscure legal clause from the 1990s.
877
00:50:49,560 --> 00:50:51,680
You know, Facebook is a new kind of platform.
878
00:50:51,680 --> 00:50:54,840
You know, it's not a traditional technology company,
879
00:50:54,840 --> 00:50:57,760
it's not a traditional media company.
880
00:50:57,760 --> 00:50:59,840
Um, we don't write the news that people...
881
00:50:59,840 --> 00:51:01,880
that people read on the platform.
882
00:51:03,920 --> 00:51:08,440
In Germany, one man is taking on the tech gods over their responsibility
883
00:51:08,440 --> 00:51:10,960
for what appears on their sites.
884
00:51:12,720 --> 00:51:17,080
Ulrich Kelber is a minister in the Justice Department.
885
00:51:17,080 --> 00:51:20,480
He was once called a "Jewish pig" in a post on Facebook.
886
00:51:20,480 --> 00:51:24,600
He reported it to the company, who refused to delete it.
887
00:51:24,600 --> 00:51:26,560
IN GERMAN:
888
00:51:43,760 --> 00:51:45,280
Under a new law,
889
00:51:45,280 --> 00:51:47,520
Facebook and other social networking sites
890
00:51:47,520 --> 00:51:50,440
could be fined up to 50 million euros
891
00:51:50,440 --> 00:51:55,360
if they fail to take down hate speech posts that are illegal.
892
00:51:55,360 --> 00:51:56,760
Is this too much?
893
00:51:56,760 --> 00:51:59,360
Is this a little bit Draconian?
894
00:52:20,080 --> 00:52:23,640
This is the first time a government has challenged
895
00:52:23,640 --> 00:52:27,000
the principles underlying a Silicon Valley platform.
896
00:52:27,000 --> 00:52:29,640
Once this thread has been pulled,
897
00:52:29,640 --> 00:52:32,760
their whole world could start to unravel.
898
00:52:32,760 --> 00:52:35,080
They must find you a pain.
899
00:52:35,080 --> 00:52:37,160
I mean, this must be annoying for them.
900
00:52:37,160 --> 00:52:38,760
It must be.
901
00:52:47,400 --> 00:52:51,360
Facebook told us they share the goal of fighting hate speech,
902
00:52:51,360 --> 00:52:54,720
and they have made substantial progress
903
00:52:54,720 --> 00:52:56,520
in removing illegal content,
904
00:52:56,520 --> 00:53:00,560
adding 3,000 people to their community operations team.
905
00:53:04,280 --> 00:53:08,440
Facebook now connects more than two billion people around the world,
906
00:53:08,440 --> 00:53:12,320
including more and more voters in the West.
907
00:53:12,320 --> 00:53:14,440
When you think of what Facebook has become,
908
00:53:14,440 --> 00:53:18,440
in such a short space of time, it's actually pretty bizarre.
909
00:53:18,440 --> 00:53:20,080
I mean, this was just a platform
910
00:53:20,080 --> 00:53:23,200
for sharing photos or chatting to friends,
911
00:53:23,200 --> 00:53:27,040
but in less than a decade, it has become a platform that has
912
00:53:27,040 --> 00:53:30,720
dramatic implications for how our democracy works.
913
00:53:32,480 --> 00:53:35,400
Old structures of power are falling away.
914
00:53:35,400 --> 00:53:40,080
Social media is giving ordinary people access to huge audiences.
915
00:53:40,080 --> 00:53:43,200
And politics is changing as a result.
916
00:53:46,120 --> 00:53:47,920
Significant numbers were motivated
917
00:53:47,920 --> 00:53:50,480
through social media to vote for Brexit.
918
00:53:52,320 --> 00:53:54,800
Jeremy Corbyn lost the general election,
919
00:53:54,800 --> 00:53:58,000
but enjoyed unexpected gains,
920
00:53:58,000 --> 00:54:03,200
an achievement he put down in part to the power of social media.
921
00:54:06,640 --> 00:54:12,160
One influential force in this new world is found here in Bristol.
922
00:54:12,160 --> 00:54:15,840
The Canary is an online political news outlet.
923
00:54:15,840 --> 00:54:18,240
During the election campaign,
924
00:54:18,240 --> 00:54:22,800
their stories got more than 25 million hits on a tiny budget.
925
00:54:24,160 --> 00:54:27,800
So, how much of your readership comes through Facebook?
926
00:54:27,800 --> 00:54:30,160
It's really high, it's about 80%.
927
00:54:30,160 --> 00:54:33,560
So it is an enormously important distribution mechanism.
928
00:54:33,560 --> 00:54:35,240
And free.
929
00:54:35,240 --> 00:54:39,720
The Canary's presentation of its pro-Corbyn news
930
00:54:39,720 --> 00:54:43,360
is tailored to social media.
931
00:54:43,360 --> 00:54:45,920
I have noticed that a lot of your headlines, or your pictures,
932
00:54:45,920 --> 00:54:47,680
they are quite emotional.
933
00:54:47,680 --> 00:54:50,360
They are sort of pictures of sad Theresa May,
934
00:54:50,360 --> 00:54:52,040
or delighted Jeremy Corbyn.
935
00:54:52,040 --> 00:54:54,240
Is that part of the purpose of it all?
936
00:54:54,240 --> 00:54:57,640
Yeah, it has to be. We are out there trying to have a conversation
937
00:54:57,640 --> 00:55:00,960
with a lot of people, so it is on us to be compelling.
938
00:55:00,960 --> 00:55:04,240
Human beings work on facts, but they also work on gut instinct,
939
00:55:04,240 --> 00:55:09,080
they work on emotions, feelings, and fidelity and community.
940
00:55:09,080 --> 00:55:10,760
All of these issues.
941
00:55:10,760 --> 00:55:15,560
Social media enables those with few resources to compete
942
00:55:15,560 --> 00:55:20,800
with the mainstream media for the attention of millions of us.
943
00:55:20,800 --> 00:55:23,800
You put up quite sort of clickbait-y stories, you know,
944
00:55:23,800 --> 00:55:26,000
the headlines are there to get clicks.
945
00:55:26,000 --> 00:55:28,480
- Yeah.
- Um... Is that a fair criticism?
946
00:55:28,480 --> 00:55:30,080
Of course they're there to get clicks.
947
00:55:30,080 --> 00:55:32,240
We don't want to have a conversation with ten people.
948
00:55:32,240 --> 00:55:34,280
You can't change the world talking to ten people.
949
00:55:42,880 --> 00:55:48,000
The tech gods are giving all of us the power to influence the world.
950
00:55:48,000 --> 00:55:50,400
Connectivity and access... Connect everyone in the world...
951
00:55:50,400 --> 00:55:51,920
Make the world more open and connected...
952
00:55:51,920 --> 00:55:54,040
You connect people over time... You get people connectivity...
953
00:55:54,040 --> 00:55:57,280
That's the mission. That's what I care about.
954
00:55:57,280 --> 00:56:01,040
Social media's unparalleled power to persuade,
955
00:56:01,040 --> 00:56:03,280
first developed for advertisers,
956
00:56:03,280 --> 00:56:06,920
is now being exploited by political forces of all kinds.
957
00:56:06,920 --> 00:56:11,120
Grassroots movements are regaining their power,
958
00:56:11,120 --> 00:56:13,480
challenging political elites.
959
00:56:13,480 --> 00:56:19,040
Extremists are discovering new ways to stoke hatred and spread lies.
960
00:56:19,040 --> 00:56:21,480
And wealthy political parties are developing the ability
961
00:56:21,480 --> 00:56:24,360
to manipulate our thoughts and feelings
962
00:56:24,360 --> 00:56:27,120
using powerful psychological tools,
963
00:56:27,120 --> 00:56:32,040
which is leading to a world of unexpected political opportunity,
964
00:56:32,040 --> 00:56:33,880
and turbulence.
965
00:56:33,880 --> 00:56:37,720
I think the people that connected the world really believed that
966
00:56:37,720 --> 00:56:42,760
somehow, just by us being connected, our politics would be better.
967
00:56:42,760 --> 00:56:48,880
But the world is changing in ways that they never imagined,
968
00:56:48,880 --> 00:56:51,480
and they are probably not happy about any more.
969
00:56:51,480 --> 00:56:53,080
But in truth,
970
00:56:53,080 --> 00:56:56,840
they are no more in charge of this technology than any of us are now.
971
00:56:56,840 --> 00:57:00,760
Silicon Valley's philosophy is called disruption.
972
00:57:00,760 --> 00:57:02,760
Breaking down the way we do things
973
00:57:02,760 --> 00:57:06,520
and using technology to improve the world.
974
00:57:06,520 --> 00:57:08,240
In this series, I have seen how
975
00:57:08,240 --> 00:57:11,680
sharing platforms like Uber and Airbnb
976
00:57:11,680 --> 00:57:14,360
are transforming our cities.
977
00:57:18,200 --> 00:57:21,320
And how automation and artificial intelligence
978
00:57:21,320 --> 00:57:24,240
threaten to destroy millions of jobs.
979
00:57:24,240 --> 00:57:27,560
Within 30 years, half of humanity won't have a job.
980
00:57:27,560 --> 00:57:29,960
It could get ugly, there could be revolution.
981
00:57:29,960 --> 00:57:34,800
Now, the technology to connect the world unleashed by a few billionaire
982
00:57:34,800 --> 00:57:39,440
entrepreneurs is having a dramatic influence on our politics.
983
00:57:39,440 --> 00:57:42,960
The people who are responsible for building this technology,
984
00:57:42,960 --> 00:57:47,880
for unleashing this disruption onto all of us,
985
00:57:47,880 --> 00:57:51,520
don't ever feel like they are responsible for the consequences
986
00:57:51,520 --> 00:57:53,400
of any of that.
987
00:57:53,400 --> 00:57:58,680
They retain this absolute religious faith that technology and
988
00:57:58,680 --> 00:58:03,320
connectivity is always going to make things turn out for the best.
989
00:58:03,320 --> 00:58:05,000
And it doesn't matter what happens,
990
00:58:05,000 --> 00:58:08,040
it doesn't matter how much that's proven not to be the case,
991
00:58:08,040 --> 00:58:10,400
they still believe.
992
00:58:22,520 --> 00:58:25,200
How did Silicon Valley become so influential?
993
00:58:25,200 --> 00:58:28,120
The Open University has produced an interactive timeline
994
00:58:28,120 --> 00:58:30,680
exploring the history of this place.
995
00:58:30,680 --> 00:58:32,720
To find out more, visit...
996
00:58:36,880 --> 00:58:40,080
..and follow the links to the Open University.
81104
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