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Speaker 1: Coal zone media.

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Speaker 2: Clickbait. If I had to define it, clickbait is a

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Speaker 2: combination of fake news and a freak show. It was

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Speaker 2: a sign of the declining state of reliable journalism in

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Speaker 2: the Western world and an early symptom of where American

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Speaker 2: journalism finds itself. As I record this, major newsrooms are

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Speaker 2: shuddering or vastly reduced during an election year, and most

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Speaker 2: major newspapers are owned by billionaires who resist criticism, resulting

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Speaker 2: in the remaining papers running on people pleasing fumes that

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Speaker 2: encourage their readers to turn their heads from massive human atrocity.

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Speaker 2: Ten years ago, things were bad, but not as bad,

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Speaker 2: which is something people in the present always say. Ten

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Speaker 2: years from now, we'll be pining for the salad days

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Speaker 2: of twenty twenty four as we enlist our third graders

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Speaker 2: in the water Wars. But for now, it's true. The

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Speaker 2: clickbait culture of the mid twenty tens was alarming, it

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Speaker 2: worse annoying at best, but it didn't feel existential in

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Speaker 2: quite the same way. The word clickbait was a runner

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Speaker 2: up for the Oxford Dictionary Word of the Year in

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Speaker 2: twenty fourteen, tied with normcore and mansplain, and if there's

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Speaker 2: a more twenty fourteen sentence than that, I am not

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Speaker 2: aware of it. Here are some major headlines you can

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Speaker 2: find at this time. What were in Hillary's emails? We

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Speaker 2: have some guesses. The gritty Power Rangers reboot with James

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Speaker 2: Vanderbeek is the stuff of nightmares. Thirteen unconventional things to

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Speaker 2: do with your gallantine This year you know garbage, And

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Speaker 2: you can't say I'm being mean because these are headlines

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Speaker 2: I wrote. Come with me if you dare to. Twenty fifteen.

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Speaker 2: The Big Short Steve Jobs, the movie the Boring One,

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Speaker 2: not the hilarious Ashton Kutcher one. Fifty Shades of Gray.

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Speaker 2: We're at peak, justin Bieber peak. Fettiwop. The year of

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Speaker 2: hotline bling and Hamilton. Obama is rounding out his second term.

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Speaker 2: It's the year he allows in air strike on a

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Speaker 2: children's hospital in Afghanistan. Gay marriage will be legal across

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Speaker 2: the US this June, and ten days after that, a

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Speaker 2: dumb bitch named Donald Trump will go down and escalator

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Speaker 2: in New York and announce he's wunning for president. I

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Speaker 2: have managed to graduate from college as semester early, and

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Speaker 2: in fighting for my life at my first proper writing

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Speaker 2: job at the Boston Globe. My job, as will become

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Speaker 2: relevant to this story, is writing clickbait. Or that's not

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Speaker 2: totally right, because yes, I reported to the Boston Globe building.

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Speaker 2: I ate my grilled Jesus among them, but very rarely

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Speaker 2: was I writing actual news. For the most part, I

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Speaker 2: was creating hashtag clickbait content, very little of which required

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Speaker 2: original reporting. While I technically worked for the Globe and

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Speaker 2: was in their newsroom, I was practically two steps removed.

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Speaker 2: Boston dot Com was the Globe's hyperlocal sister site, and

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Speaker 2: a now defunct vertical call, BDC Wire was their clickbait site.

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Speaker 2: I was one of three writers at BDC Wire, populating

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Speaker 2: the site with stories as if there were ten writers,

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Speaker 2: and in twenty fifteen, I worked two full time jobs,

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Speaker 2: first at this now defunct website, as well as co

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Speaker 2: managing a now defunct improv theater and Cards on the Table.

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Speaker 2: I was fired from the Globe not six months later

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Speaker 2: for refusing to take down a tweet about com I

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Speaker 2: don't know you're only twenty two months I'm getting distracted.

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Speaker 2: Early twenty fifteen, I did get to do some original

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Speaker 2: reporting here and there, but my main gig was sourcing

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Speaker 2: viral news and rephrasing my sources with citations that sites

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Speaker 2: like these would adorably and deceptively call hat tips. And

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Speaker 2: I'd love to tell you what more of these stories were,

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Speaker 2: but as any journalist who started working in the age

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Speaker 2: of the Internet knows, almost none of it is available

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Speaker 2: now without the assistance of the Internet archive. BDC wire

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Speaker 2: is gone, along with those articles I wrote, like ten

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Speaker 2: reasons Chris Evans being from Massachusetts is the one reason

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Speaker 2: not to end at all, or whatever I was reading

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Speaker 2: on today's Internet. BBC Wire never existed, so just a

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Speaker 2: reminder you are listening to a future piece of lost media.

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Speaker 2: But in the mid twenty tens, a lot of writers

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Speaker 2: got their start the same way I did. At this time,

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Speaker 2: including probably writers you like now, from young Gen xers

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Speaker 2: to elderly zoomers. We were tasked with regurgitating stories that

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Speaker 2: social media cared about, and regurgitating them to make them

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Speaker 2: seem like actual news. We weren't making shit up like tabloids,

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Speaker 2: but there was a sense that one writer was vomiting

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Speaker 2: into another writer's mouth, and so on and so on

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Speaker 2: to maximize site traffic and profit. We the writers never

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Speaker 2: got paid equitably, but for places like The Boston Globe

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Speaker 2: and other major news organizations who were hemorrhaging money due

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Speaker 2: to the inability to adapt to the Internet. When the

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Speaker 2: moment came, these click sister sites became a temporary attempt

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Speaker 2: at increasing cash flow, or, as I knew it, tricking

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Speaker 2: a group of twenty two year olds into thinking they

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Speaker 2: were real writers in exchange for bad pay and a

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Speaker 2: content mill mentality. So basically, I was working for a

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Speaker 2: regional ripoff of early BuzzFeed. And to be clear, I

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Speaker 2: am not conflating BuzzFeed the website with BuzzFeed News, which

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Speaker 2: is a Pulitzer winning and recently tragically shuttered news division.

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Speaker 2: Nor am I saying that working at a BuzzFeed content

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Speaker 2: mill was bad or even uncreative. I mean I think

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Speaker 2: that my BBC wire magnum opus. I saw Shrek the

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Speaker 2: Musical five times colon. This is my story fucking ripped,

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Speaker 2: not that you can read it anywhere. What's interesting is

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Speaker 2: that this regurgitation media strategy, inspired by BuzzFeed combing the

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Speaker 2: Internet to fill their website full of clickable, eccentric hashtag content,

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Speaker 2: often legitimized Internet characters of the day as legitimate news figures,

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Speaker 2: and this was never more true than in the mid

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Speaker 2: twenty tents. A quick brief on BuzzFeed. At this time,

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Speaker 2: the company had been around since two thousand and six,

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Speaker 2: and its legitimate news arm, BuzzFeed News, wouldn't be launched

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Speaker 2: until a year later in twenty sixteen. Founder Jonah Peretti

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Speaker 2: had made BuzzFeed an algorithmic project from the start. In

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Speaker 2: a twenty thirteen profile of Paretti in New York Magazine,

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Speaker 2: early BuzzFeed is described as Peretti's quote algorithm to cold

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Speaker 2: stories from around the web that were showing stirring the virality.

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Speaker 2: In return for functioning as a sort of early warning system,

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Speaker 2: BuzzFeed persuaded partner sites to install programming code that allowed

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Speaker 2: the company to monitor their traffic. Like a lot of

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Speaker 2: social media we discussed today, Peretti's original project begun while

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Speaker 2: he was working at the similarly click minded Huffington Post

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Speaker 2: was collecting a shitload of data at best. He wanted

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Speaker 2: to know what people were interested in. If I'm being cynical,

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Speaker 2: he wanted to sell it back to us in it

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Speaker 2: easy to consume package, and we were happy to do it.

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Speaker 2: By twenty fifteen, BuzzFeed had grown exponentially and started creating

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Speaker 2: original content in addition to their bread and Butter, which

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Speaker 2: was both curating and rephrasing viral stories from other corners

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Speaker 2: of the Internet to drive traffic. And one thousand different

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Speaker 2: quizzes on which ice cream flavor you were based on

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Speaker 2: your mental illness diagnosis something like that. I took them all,

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Speaker 2: no judgment, min chocolate, chip, OCD, bipolar whatever. Much of

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Speaker 2: early BuzzFeed was extremely goofy and what I think is

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Speaker 2: now sort of associated as being an embarrassingly millennial thing,

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Speaker 2: but there was no shortage of talent there. Quinta Brunson

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Speaker 2: produced a lot of her early viral stuff at BuzzFeed,

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Speaker 2: and many people in their writing and performance stable ended

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Speaker 2: up bailing on BuzzFeed because they were treated like mill

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Speaker 2: workers and not artists. Think comedians and writers like Gabe

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Speaker 2: Dunn and Alison Raskin. Think super successful YouTubers like Sophia

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Speaker 2: and Niguard. Think whether you love them or you hate

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Speaker 2: them for cheating on their wives, the try guys. But

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Speaker 2: regardless of the level of talent, there's no point in

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Speaker 2: denying it. A lot of early BuzzFeed was rehash stuff

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Speaker 2: either sent in by users or gently lifted by a

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Speaker 2: fleet of young, aspiring writers like myself to proliferate a

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Speaker 2: sticky story that someone else had posted online for free already.

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Speaker 2: The goal show it to a wider audience with some

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Speaker 2: light commentary and yield a massive profit for Daddy BuzzFeed.

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Speaker 2: And one of BuzzFeed's greatest main character co signs in

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Speaker 2: its history came on February twenty sixth, twenty fifteen, a

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Speaker 2: day that launched two huge viral stories. One was about

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Speaker 2: a pair of lamas that escaped in an Arizona retirement home.

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Speaker 3: In Sun City. Today this may have been the very

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Speaker 3: definition of the Wild West, a slow, sometimes high speed

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Speaker 3: pursuit of two lamas on the loose, breaking away from

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Speaker 3: their owners, at times from each other, seizing their moment,

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Speaker 3: and they're shot at freedom.

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Speaker 2: And the other one is one whose impact is still

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Speaker 2: almost bafflingly still enduring today. The dress. Your sixteenth minute

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Speaker 2: starts now. The dress. It's a potent and memorable piece

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Speaker 2: of Internet lore. One where your perception mattered was this dress,

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Speaker 2: which was first posted by a Scottish mother of the bride,

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Speaker 2: Cecilia Bleesdale in this weird blown out photo from a

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Speaker 2: designer outlet in England. Was this dress blue and black?

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Speaker 2: Or was it white and gold? It was blue and black,

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Speaker 2: and I knew that right away because I'm perfect and

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Speaker 2: I've never made a mistake. And even though it sounds

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Speaker 2: old timey, I mean, after all, we're talking about an

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Speaker 2: optical illusion that resulted from an objectively shitty phone camera.

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Speaker 2: It's kind of hard to overstate what a sensation the

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Speaker 2: dress became. I remember this day so clearly because this

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Speaker 2: story broke my second week working as a professional writer

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Speaker 2: and as a kid who'd been brought in to write clickbait.

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Speaker 2: It was like a gorgeous gift, had to say and

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Speaker 2: did from space. As I was reflecting, I wanted to

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Speaker 2: make sure I wasn't fluffing the memory in my mind,

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Speaker 2: so I reached out to my buddy, Kevin Slain, who

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Speaker 2: was one of the other three writers working overtime to

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Speaker 2: create hashtag content on BDC wire to be read by

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Speaker 2: hashtag nobody for hashtag seventy hours a week, and he

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Speaker 2: remembered this day really clearly too.

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Speaker 4: My name is Kevin Slain, and I am a staff

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Speaker 4: writer at the Boston Globe, covering entertainment and culture.

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Speaker 2: Hi, Kevin, what's up?

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Speaker 4: Not much, Jamie? How are you?

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Speaker 2: I am good after all these years, I'm good.

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Speaker 4: Real throw it back talking to you today about this day.

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Speaker 2: I know about this historic day that is over nine

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Speaker 2: years ago now, which feels not great. Okay. So I

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Speaker 2: just wanted to go because I feel like I was

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Speaker 2: writing out like how I imagine this day. I think

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Speaker 2: it was like, certainly in my first month working at

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Speaker 2: bad Wire, do you remember the dress day in our

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Speaker 2: little corner?

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Speaker 4: So I remember what I did that day, and at

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Speaker 4: least in my case, we're talking about Thursday, which was

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Speaker 4: kind of like the dress dropped at night, you know.

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Speaker 4: It was the wave the day went for me was

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Speaker 4: I was at work. I wrote a story about the Lamas,

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Speaker 4: which was the big I remember, a predecessor to the dress.

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Speaker 2: I dreamed some Lamas. I can't find it on the

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Speaker 2: way back machine, but remember I was like, okay, okay,

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Speaker 2: Lama's first Lamas are the story.

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Speaker 4: I had to go back through my Twitter archive and

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Speaker 4: I found that I tweeted out the story that I

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Speaker 4: wrote about the Lamas.

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Speaker 5: Thank god.

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Speaker 4: It was just, you know, our house style at the time,

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Speaker 4: lots of gifts, lots of old text, you know, us

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Speaker 4: trying to be BuzzFeed. I guess, yeah. But anyway, so

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Speaker 4: I did that, and then a bunch of us went

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Speaker 4: to have the hour at the Banshee, which was just

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Speaker 4: where a lot of us went after work, and I

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Speaker 4: proceeded to get really drunk and sing a lot of karaoke,

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Speaker 4: and at some point someone pulled out their phone and

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Speaker 4: started showing it to everyone, being like, check this out.

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Speaker 4: This dress thing is crazy. And I reacted to it

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Speaker 4: the way that I think I did to a lot

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Speaker 4: of that stuff for the time, because this was like

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Speaker 4: our first media job, and I just felt simultaneously like

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Speaker 4: enthralled by the conversation but also repulsed, and I felt,

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Speaker 4: you know, probably undeservedly above it all. And so the

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Speaker 4: thing was that back then they really had like a

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Speaker 4: flat hierarchy in terms of like who could say what,

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Speaker 4: and everyone would just send all staff emails all the time.

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Speaker 4: And I remember pulling out my phone and looking and

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Speaker 4: seeing that our old homepage had like three stories on

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Speaker 4: the dress right on the top, and there was like

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Speaker 4: an active email chain going on about this, and me,

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Speaker 4: very drunk.

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Speaker 6: Just was like, this is ridiculous.

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Speaker 4: I can't believe we have three stories on the dress

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Speaker 4: on the homepage. And whoever did that got like, you know,

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Speaker 4: offended that I said that rightfully.

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Speaker 2: So well, I mean that's a lot. I didn't remember

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Speaker 2: there being three stories, but that also makes sense because

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Speaker 2: there were like three different verticals sort of all like

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Speaker 2: going to the same place, and we were all trying

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Speaker 2: to get clicks on the same thing that we were

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Speaker 2: all sort of ripping off for a BuzzFeed. It was

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Speaker 2: just like weird. It was a weird.

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Speaker 4: Time, It really was. And I even went and then

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Speaker 4: looked at the like the next day I looked in

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Speaker 4: the wayback machine and it was even more of that.

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Speaker 4: It was like the night producer who wrote like a

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Speaker 4: short little thing, and then our old boss who I

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Speaker 4: won't name, wrote this like very ridiculous in his own

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Speaker 4: voice style post, and then someone else wrote like their

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Speaker 4: take on the dress, and then another of our old

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Speaker 4: bosses then asked John Henry to wear about the dress.

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Speaker 4: And there's a video I can't find of him talking

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Speaker 4: about the dress, which I wish I could find because

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Speaker 4: I think that's what I.

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Speaker 2: Wish we had. The John Henry funage that's.

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Speaker 4: Unanged, right, like, who needed that?

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Speaker 2: Well, thank you, thank you for reminiscing with me. This

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Speaker 2: was beautiful.

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Speaker 4: Yes, it's weird to think about now, and I know

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Speaker 4: it's objectively not true. It was a totally different time

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Speaker 4: and you can't help but look back at it fondly,

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Speaker 4: despite the fact that I was hating myself through the

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Speaker 4: entire portion of it.

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Speaker 2: Thanks so much to Kevin, who is a real writer

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Speaker 2: for the Boston Globe and has been for years. So

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Speaker 2: this day was genuinely a huge day for news on

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Speaker 2: and about the Internet. The FCC had just voted to

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Speaker 2: classify Internet service providers as public utilities, effectively creating net neutrality,

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Speaker 2: but the dress was indisputably the main character. Here's the

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Speaker 2: cliff notes of what happened. In February twenty fifteen. A

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Speaker 2: Scottish couple was preparing for their wedding and the bride's

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Speaker 2: mother texted her daughter a photo of the dress she

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Speaker 2: was considering wearing. The dress. It's not a great picture,

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Speaker 2: but it is a very mid twenty ten's mother of

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Speaker 2: the bride dress. There is this little cropped jacket, some

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Speaker 2: color blocking unnecessary lace elements, but the color that was

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Speaker 2: the question the bride. Grace posted the photo of the

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Speaker 2: dress to her Facebook and explained the dilemma. Her mom

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Speaker 2: assured her that the dress was blue with black lace,

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Speaker 2: that Grace was seeing it as white with gold lace.

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Speaker 2: So she posted the question to her Facebook so her

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Speaker 2: friends could weigh in, and a week long debate was sparked.

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Speaker 2: So the dress first became a main character in her

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Speaker 2: small community in colin Say, Scotland. Then the wedding happened

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Speaker 2: and Grace's mom wore the dress. It was a topic

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Speaker 2: of discussion at the wedding too, and it's actually members

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Speaker 2: of the wedding band that take the dress from a

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Speaker 2: local phenomenon into a global one. Singer and guitarist Caitlyn McNeil,

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Speaker 2: who was a close friend of the couples, said of

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Speaker 2: the dress, iurl we forgot about it until we saw

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Speaker 2: it at the wedding, which the mother of the bride

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Speaker 2: was wearing, and it was obviously blue and black in

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Speaker 2: any other timeline. This would be a footnote that Grace's

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Speaker 2: mom would half remember when giving the dress to Goodwill

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Speaker 2: a decade later. But this was a time on the

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Speaker 2: Internet where viral stories were social currency. And Katelyn McNeil

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Speaker 2: had taken up the mantle of the dress. It was

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Speaker 2: her who ended up posting what would become the viral

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Speaker 2: seed that would take this story global on her tumbler.

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Speaker 2: The post, attached to a picture of the dress said, guys,

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Speaker 2: please help me. Is this dress white or gold? Or

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Speaker 2: blue and black? Me and my friends can't agree and

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Speaker 2: we are freaking the fuck out. I can't handle this.

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Speaker 2: It got about five thousand notes, and if you aren't

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Speaker 2: Tumblr literate, that was a lot of interaction on the

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Speaker 2: platform for the time. And here's where BuzzFeed comes in,

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Speaker 2: as the legend goes. BuzzFeed writer Kate's Holderness was running

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Speaker 2: the BuzzFeed tumbler at that time and was tasked with

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Speaker 2: what a lot of writers were back then, finding popular

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Speaker 2: garbage to amplify. But this was not petty content.

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Speaker 3: Theft.

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Speaker 2: Caitlyn McNeil was writing so high on the dress fumes

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Speaker 2: that she sent Holderness her post saying, BuzzFeed, please help.

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Speaker 2: I posted a picture of this dress. It's the last

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Speaker 2: post on my tumbler. Okay, and some people see it

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Speaker 2: blue and some people see it white. Can you explain?

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Speaker 2: Because we are going crazy Holderness replied, Holy crap, it's

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Speaker 2: blue and black. I don't understand. I made a pole,

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Speaker 2: and she links to what she posted off of Caitlyn

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Speaker 2: McNeil's tip. A classic BuzzFeed post published in the early evening.

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Speaker 2: As Kevin correctly recalled, the post was simply titled what

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Speaker 2: color is this dress? There was a poll to vote

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Speaker 2: black and blue or white and gold. The post started

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Speaker 2: doing well right away, but pretty much everything on BuzzFeed

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Speaker 2: at that time did. It was after folks left for

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Speaker 2: work that things went nuts. The dress was literally an

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Speaker 2: overnight success. When the BuzzFeed team returned the next morning,

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Speaker 2: Holderness's post had reached eight hundred and forty thousand views

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Speaker 2: per minute. At its peak, it had broken BuzzFeed's traffic record,

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Speaker 2: becoming its most successful post of all time, and once

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Speaker 2: the BuzzFeed post took off, the dress became a popular

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Speaker 2: subject of discussion on virtually every social media platform at

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Speaker 2: the time. But Twitter tends to be the thing that

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Speaker 2: people still talk about these days. Facebook is for moms

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Speaker 2: and creepy cousins, but in twenty fifteen, it was Twitter

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Speaker 2: where famous people ruined their own lives by weighing in

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Speaker 2: like some freaky plebeians just like us. It was right

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Speaker 2: before celebrities all realized in Unison that they needed their

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Speaker 2: own social media managers, and so you could be pretty

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Speaker 2: sure that it was the actual Justin Bieber saying.

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Speaker 7: And for everyone asking, I see blue and.

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Speaker 2: Black, And it was the real Taylor Swift when she said.

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Speaker 1: I don't understand this odd dress debit, and I feel

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Speaker 1: like it's a trick somehow, I'm confused and scared.

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Speaker 2: Ps it's obviously blue and black, or Mindy kaaling with

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00:20:08,200 --> 00:20:10,280
Speaker 2: one of the most twenty fifteen tweets of all time,

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Speaker 2: the dress is worse than the Sony hack to me,

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00:20:13,160 --> 00:20:17,760
Speaker 2: Beyonce said nothing, This is all a little pedestrian for Beyonce,

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00:20:18,320 --> 00:20:20,520
Speaker 2: BuzzFeed was at the top of their game, and so

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00:20:20,640 --> 00:20:24,080
Speaker 2: of course made more content about the dress. Twenty four

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00:20:24,119 --> 00:20:26,680
Speaker 2: hours after the first post about the dress went live

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00:20:26,720 --> 00:20:30,359
Speaker 2: on the site, nine out of ten trending BuzzFeed posts

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Speaker 2: were about the same dress. They were fucking relentless. Why

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Speaker 2: are people seeing different colors in that damn dress? The

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00:20:38,280 --> 00:20:40,320
Speaker 2: dress is blue and black, says the person who saw

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00:20:40,359 --> 00:20:43,359
Speaker 2: it in person. This second photo of the dress definitely

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00:20:43,400 --> 00:20:45,760
Speaker 2: proves that the dress is blue and black. What the

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Speaker 2: dress color you see says about you? And the one

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Speaker 2: post that wasn't about the dress. Here's the first picture

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Speaker 2: of Eddie Redmain as transgender painter Lily elba Oh twenty fifteen.

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Speaker 2: And don't forget at this time, buzzfeeds video vertical was

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00:21:01,080 --> 00:21:04,840
Speaker 2: also really popular on YouTube, and that was also going

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Speaker 2: nuts with traffic from the dress.

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Speaker 3: You are seeing white and gold?

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Speaker 4: Where are you looking at? Oh no, you're kidding.

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Speaker 2: You heard correctly, that's Quinta Brunson. I just think that's

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00:21:16,640 --> 00:21:20,480
Speaker 2: really funny. So yes, the dress is a goofy sensation,

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00:21:20,880 --> 00:21:23,960
Speaker 2: and it even trickles down to lowly worm regional BuzzFeed

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00:21:24,040 --> 00:21:28,159
Speaker 2: ripoff writer Jamie Loftus. But keep in mind BuzzFeed is

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Speaker 2: a business, and people with any monetary stock in BuzzFeed

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00:21:31,760 --> 00:21:35,320
Speaker 2: were doing donuts in their Mercedes about this fucking story.

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00:21:35,520 --> 00:21:38,119
Speaker 2: There was even this bizarre essay by then editor in

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Speaker 2: chief Ben Smith saying that the dress phenomenon was this

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Speaker 2: sign of BuzzFeed's power.

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Speaker 7: He said, the explosion of Kate's holderness is post about

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00:21:48,440 --> 00:21:50,679
Speaker 7: the dress White and Gold, by the way, is a

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Speaker 7: reminder that while we now do so many more things

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Speaker 7: We've never moved away from our roots. Indeed, we launched

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Speaker 7: the Cute or Not app yesterday.

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Speaker 2: Sir, Sure, your underpaid employee reblogged a Tumbler post, but

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00:22:03,720 --> 00:22:06,800
Speaker 2: by all means, keep talking. This post in particular is

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00:22:06,800 --> 00:22:09,919
Speaker 2: so weird to reflect on now. It sounds so certain

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00:22:09,960 --> 00:22:12,800
Speaker 2: that BuzzFeed will be the vanguard of what makes something

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00:22:12,880 --> 00:22:15,119
Speaker 2: special on the Internet, and that a post like the

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00:22:15,240 --> 00:22:17,440
Speaker 2: dress is all a part of the game of five

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00:22:17,520 --> 00:22:20,320
Speaker 2: D chess that BuzzFeed is playing, and that they're the

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00:22:20,400 --> 00:22:24,680
Speaker 2: reason that the dress succeeded. Smith continued, we are interested

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00:22:24,760 --> 00:22:27,439
Speaker 2: most of all in what a story does, not just

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00:22:27,480 --> 00:22:29,679
Speaker 2: in how many people read it, but in what effect

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00:22:29,720 --> 00:22:32,800
Speaker 2: it has on their lives and on the world. Kate's

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00:22:32,800 --> 00:22:35,960
Speaker 2: post delighted people and connected them to steal an idea

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00:22:36,000 --> 00:22:39,160
Speaker 2: from z Frank. Its power was less in the encapsulated

384
00:22:39,200 --> 00:22:42,360
Speaker 2: item itself than in the network around it. That's true

385
00:22:42,400 --> 00:22:44,800
Speaker 2: of a brilliant piece of entertainment. It's true of a

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00:22:44,840 --> 00:22:49,520
Speaker 2: recipe or a DIY suggestion. Meanwhile, BuzzFeed is getting millions

387
00:22:49,520 --> 00:22:52,560
Speaker 2: and millions of impressions from this story, one that Kaitlyn

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00:22:52,640 --> 00:22:56,440
Speaker 2: McNeil literally handed to them. But it's BuzzFeed that keeps

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00:22:56,480 --> 00:22:59,520
Speaker 2: the profits from the engagement. At a time where impressions

390
00:22:59,520 --> 00:23:03,800
Speaker 2: were what find your success and your advertising profits, as

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00:23:03,880 --> 00:23:06,920
Speaker 2: is so common in these stories, it was like everyone

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00:23:07,040 --> 00:23:10,760
Speaker 2: was benefiting from the dress story, except, of course, anyone

393
00:23:10,800 --> 00:23:13,199
Speaker 2: involved in the actual story. It didn't take long for

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00:23:13,280 --> 00:23:16,399
Speaker 2: completely unrelated businesses to start commenting on the dress. To

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00:23:16,520 --> 00:23:19,480
Speaker 2: capitalize on the trend. Pizza Hut posts a picture of

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00:23:19,520 --> 00:23:22,160
Speaker 2: a shitty pizza with the caption It's white and gold.

397
00:23:22,600 --> 00:23:24,920
Speaker 2: Two newscasters wear the dress on air as a bit

398
00:23:25,280 --> 00:23:28,800
Speaker 2: The original dress on a website called Roman Originals sells

399
00:23:28,840 --> 00:23:33,800
Speaker 2: out immediately. Brandon Silverman, the former CEO of social media

400
00:23:33,920 --> 00:23:37,600
Speaker 2: monitoring site crowd Tangled, said this when asked about the

401
00:23:37,760 --> 00:23:41,239
Speaker 2: range of institutions that wanted in on this story. This

402
00:23:41,280 --> 00:23:44,159
Speaker 2: is from a twenty sixteen retrospective on the Day of

403
00:23:44,280 --> 00:23:48,240
Speaker 2: the Lama and the Dress from twenty sixteen, written for

404
00:23:48,640 --> 00:23:50,160
Speaker 2: where Else BuzzFeed.

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00:23:50,720 --> 00:23:54,199
Speaker 6: Silverman says this, We've seen other stories go viral, but

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00:23:54,320 --> 00:23:56,840
Speaker 6: the sheer diversity of outlets that picks it up and

407
00:23:56,880 --> 00:23:59,680
Speaker 6: we're talking about it was unlike anything we had ever seen.

408
00:24:00,200 --> 00:24:03,720
Speaker 6: Everyone from QVC to Warner Bros. To local public libraries

409
00:24:03,760 --> 00:24:06,119
Speaker 6: to Red Cross affiliates were all posting links to it

410
00:24:06,160 --> 00:24:08,880
Speaker 6: on their social accounts. That kind of diversity and who's

411
00:24:08,880 --> 00:24:12,000
Speaker 6: sharing a story pretty much never happens, and certainly never

412
00:24:12,080 --> 00:24:12,879
Speaker 6: to that degree.

413
00:24:13,359 --> 00:24:16,879
Speaker 2: And of course, no Internet main character would be complete

414
00:24:17,160 --> 00:24:20,399
Speaker 2: without the backlash, which in the case of the dress

415
00:24:20,640 --> 00:24:23,360
Speaker 2: came in the form of people bemoaning that this story

416
00:24:23,600 --> 00:24:26,959
Speaker 2: had overshadowed more important news from the same day. And

417
00:24:27,000 --> 00:24:30,160
Speaker 2: even this mentality feels a little bit dated now. Yes,

418
00:24:30,320 --> 00:24:32,720
Speaker 2: it's important to discuss the other news of the day,

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00:24:32,880 --> 00:24:36,760
Speaker 2: but favoring something goofy and meaningless over hard news wasn't

420
00:24:36,760 --> 00:24:40,280
Speaker 2: something that started in twenty fifteen. Although that said, I

421
00:24:40,320 --> 00:24:42,400
Speaker 2: do think there's a case for saying that the success

422
00:24:42,440 --> 00:24:45,360
Speaker 2: of posts like the dress tells people in tech developing

423
00:24:45,400 --> 00:24:48,720
Speaker 2: social media algorithms at this time what kind of stories

424
00:24:48,760 --> 00:24:51,639
Speaker 2: are distracting for people, leading to an era of the

425
00:24:51,720 --> 00:24:57,320
Speaker 2: algorithm favoring engagement driven posts over carefully reported news. To

426
00:24:57,320 --> 00:24:59,560
Speaker 2: talk about what made the dress special in terms of

427
00:24:59,600 --> 00:25:02,159
Speaker 2: what it is said about where social media was picking

428
00:25:02,160 --> 00:25:04,720
Speaker 2: its main characters at this time, I turned to a

429
00:25:04,760 --> 00:25:07,520
Speaker 2: writer who has spent her career writing about the Internet,

430
00:25:07,760 --> 00:25:11,240
Speaker 2: often meaning the Internet turns on her. Taylor Lowrenz released

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00:25:11,280 --> 00:25:15,280
Speaker 2: her book Extremely Online, The Untold Story of Fame, Influence,

432
00:25:15,320 --> 00:25:17,960
Speaker 2: and Power on the Internet last year, taking a look

433
00:25:17,960 --> 00:25:21,960
Speaker 2: at the short and eventful history of online influencers, including

434
00:25:22,040 --> 00:25:24,680
Speaker 2: a fair number of characters of the day, and yes,

435
00:25:25,200 --> 00:25:27,760
Speaker 2: the dress herself. We caught up on zoom about this

436
00:25:28,000 --> 00:25:30,240
Speaker 2: very particular moment in internet history.

437
00:25:31,000 --> 00:25:33,960
Speaker 5: I'm Taylor Lorenz and I'm the author of Extremely Online,

438
00:25:34,000 --> 00:25:37,240
Speaker 5: The Untold Story of Fame, Influence and Power on the Internet.

439
00:25:37,560 --> 00:25:39,840
Speaker 2: This has been your beat for some time. How did

440
00:25:39,880 --> 00:25:43,440
Speaker 2: you become the person to talk to about the Internet.

441
00:25:44,040 --> 00:25:48,000
Speaker 5: Yeah, well, I'm like peak millennial. I graduated directly into

442
00:25:48,080 --> 00:25:51,480
Speaker 5: the recession, the two thousand and eight financial crisis, and

443
00:25:51,960 --> 00:25:55,360
Speaker 5: I had had like random internships in college and summer jobs,

444
00:25:55,400 --> 00:25:58,040
Speaker 5: but I didn't really know what I was doing. And

445
00:25:58,119 --> 00:26:01,000
Speaker 5: so I was working retail foodstarre, I worked for a

446
00:26:01,000 --> 00:26:04,040
Speaker 5: messenger company, I was babysitting. I worked at a call

447
00:26:04,080 --> 00:26:07,359
Speaker 5: center for a while. I was just kind of making

448
00:26:07,400 --> 00:26:10,879
Speaker 5: money and I discovered Tumblr, which back in two thousand

449
00:26:10,880 --> 00:26:14,720
Speaker 5: and nine was ascended as a social platform, and that

450
00:26:14,800 --> 00:26:16,760
Speaker 5: was kind of my gateway into blogging. And I just

451
00:26:16,840 --> 00:26:21,359
Speaker 5: started blogging about stuff. At the time, I was really

452
00:26:21,400 --> 00:26:24,800
Speaker 5: against the mainstream media because they were writing really stupid

453
00:26:24,800 --> 00:26:27,879
Speaker 5: things about tech and millennials and it was making me mad,

454
00:26:27,960 --> 00:26:31,560
Speaker 5: and so I would just go on my blogs and

455
00:26:31,800 --> 00:26:35,000
Speaker 5: rants about whatever I felt like ranting about. I was

456
00:26:35,040 --> 00:26:39,879
Speaker 5: really inspired by this woman Katie Natopoulos, who yes, she

457
00:26:40,040 --> 00:26:42,440
Speaker 5: was blogging back then too, and she had this website.

458
00:26:42,440 --> 00:26:44,760
Speaker 5: She had all these funny like blogs and sites, and

459
00:26:45,520 --> 00:26:48,080
Speaker 5: she seemed to be like the only person that I thought.

460
00:26:48,680 --> 00:26:50,359
Speaker 5: She was like a couple years older than me and

461
00:26:50,440 --> 00:26:52,639
Speaker 5: seemed really cool, And I just thought, I'm going to

462
00:26:52,720 --> 00:26:54,399
Speaker 5: try to write about the Internet, but like from the

463
00:26:54,400 --> 00:26:56,600
Speaker 5: perspective of somebody that actually uses it.

464
00:26:57,400 --> 00:27:01,679
Speaker 2: There are stories that feel or my feeling at first

465
00:27:01,800 --> 00:27:05,919
Speaker 2: was it feels very connected to a certain type of

466
00:27:06,000 --> 00:27:11,040
Speaker 2: platform or like a certain era in social media. When

467
00:27:11,080 --> 00:27:14,040
Speaker 2: I was reading extremely Online, I was like, oh, right,

468
00:27:14,040 --> 00:27:16,959
Speaker 2: the dress was kind of this cross platform success story.

469
00:27:18,040 --> 00:27:20,479
Speaker 2: Could you unpact that a little for me of like

470
00:27:20,960 --> 00:27:25,440
Speaker 2: how the original story and then BuzzFeed kind of had

471
00:27:25,920 --> 00:27:28,840
Speaker 2: a mutual role in this story success. Yeah.

472
00:27:28,920 --> 00:27:31,680
Speaker 5: We was just to kind of like explain the landscape

473
00:27:32,119 --> 00:27:34,800
Speaker 5: throughout the early to mid twenty tens. You had this

474
00:27:35,359 --> 00:27:40,440
Speaker 5: explosion of digital media sites spurred by VC funding, you know, BuzzFeed,

475
00:27:40,640 --> 00:27:42,840
Speaker 5: Mike dot Com, Race to Work, Mashable, like, there were

476
00:27:42,880 --> 00:27:46,000
Speaker 5: all these like digital media sites, and the primary way

477
00:27:46,040 --> 00:27:48,959
Speaker 5: that they were gaining traffic was going into sort of

478
00:27:49,320 --> 00:27:51,800
Speaker 5: what were then the depths of the Internet, mostly Tumblr

479
00:27:51,800 --> 00:27:54,960
Speaker 5: and Reddit, getting the most viral content off those platforms

480
00:27:55,080 --> 00:27:57,400
Speaker 5: and repackaging on the website. Because that was an era

481
00:27:57,480 --> 00:28:00,879
Speaker 5: when not a lot of people were going and spending

482
00:28:00,920 --> 00:28:02,560
Speaker 5: a lot of time and getting their news and information

483
00:28:02,600 --> 00:28:06,679
Speaker 5: and entertainment from social media directly itself. It wasn't as

484
00:28:06,720 --> 00:28:09,400
Speaker 5: widely adopted. People didn't know how to navigate the Internet yet,

485
00:28:10,040 --> 00:28:13,119
Speaker 5: so buzzeed would just sort of like mine this content.

486
00:28:13,640 --> 00:28:16,919
Speaker 5: Keats was working at BuzzFeed at the time, found a

487
00:28:16,920 --> 00:28:19,560
Speaker 5: Tumblr post about the dress and was like, wow, this

488
00:28:19,720 --> 00:28:22,119
Speaker 5: is kind of crazy. It was getting a little bit

489
00:28:22,160 --> 00:28:24,399
Speaker 5: of traction on Tumblr, and she thought, let me post

490
00:28:24,400 --> 00:28:27,880
Speaker 5: it on the BuzzFeed website because that's kind of again

491
00:28:27,960 --> 00:28:29,760
Speaker 5: the business model at the time was finding what did

492
00:28:29,800 --> 00:28:31,800
Speaker 5: while on Tumblr and then bringing it to this wider

493
00:28:31,840 --> 00:28:34,439
Speaker 5: audience a BuzzFeed, And of course she publishes it on

494
00:28:34,520 --> 00:28:38,400
Speaker 5: BuzzFeed and it just goes insane. A bunch of other

495
00:28:38,440 --> 00:28:41,320
Speaker 5: places immediately published their own versions of that story, but

496
00:28:41,800 --> 00:28:45,160
Speaker 5: BuzzFeed got like all the upside kind of from it,

497
00:28:45,200 --> 00:28:46,760
Speaker 5: Like they got all the traffic they were able to

498
00:28:46,800 --> 00:28:52,200
Speaker 5: monetize through ads on that article page. Like right, nobody

499
00:28:52,280 --> 00:28:54,600
Speaker 5: was clicking through as much to the original tumbler.

500
00:28:54,960 --> 00:28:58,080
Speaker 2: That day, I was cruising the way back machine and

501
00:28:58,120 --> 00:29:00,560
Speaker 2: I was like, this is what I think of as

502
00:29:00,640 --> 00:29:06,000
Speaker 2: like peak BuzzFeed. How did they get there? Like what

503
00:29:06,160 --> 00:29:11,719
Speaker 2: made BuzzFeed special or early to this kind of content mining?

504
00:29:12,200 --> 00:29:15,080
Speaker 5: BuzzFeed was one of the first true sort of like

505
00:29:15,240 --> 00:29:18,960
Speaker 5: digital media companies of that wave. There was this idea

506
00:29:19,360 --> 00:29:22,680
Speaker 5: in the two thousands, it's really expurred by the rise

507
00:29:22,720 --> 00:29:28,000
Speaker 5: of blogging, that you could build digital media platforms and

508
00:29:28,160 --> 00:29:31,040
Speaker 5: capture digital media ad dollars. There's all this money moving

509
00:29:31,040 --> 00:29:35,520
Speaker 5: from traditional advertising to digital advertising. The traditional advertising had

510
00:29:35,520 --> 00:29:38,880
Speaker 5: been with newspapers and traditional media, and so the thinking

511
00:29:39,200 --> 00:29:41,480
Speaker 5: was back then was all those traditional ad dollars are

512
00:29:41,520 --> 00:29:46,000
Speaker 5: going to go to the digital version of the news

513
00:29:46,040 --> 00:29:49,560
Speaker 5: media that the advertisers were advertising with. So there was

514
00:29:49,600 --> 00:29:52,600
Speaker 5: this website like vox and you know, other sites were

515
00:29:52,600 --> 00:29:56,240
Speaker 5: sort of created to capture those ad dollars. Obviously that

516
00:29:56,280 --> 00:29:58,720
Speaker 5: didn't happen. The money actually ended up going to Facebook

517
00:29:58,720 --> 00:30:00,960
Speaker 5: and Google directly, which is why that whole crop of

518
00:30:01,000 --> 00:30:03,360
Speaker 5: digital media companies died pretty much or like as a

519
00:30:03,360 --> 00:30:06,320
Speaker 5: shell of themselves. But traffic was the most important thing.

520
00:30:06,360 --> 00:30:09,160
Speaker 5: Traffic was so important because the more views you got,

521
00:30:09,200 --> 00:30:13,640
Speaker 5: the more digital display ads you know we're seeing, the

522
00:30:13,680 --> 00:30:17,080
Speaker 5: more money the platform would make. And this is when

523
00:30:17,120 --> 00:30:20,000
Speaker 5: a lot of platforms, including Facebook, were prioritizing link posts.

524
00:30:20,040 --> 00:30:22,280
Speaker 5: They hadn't pivoted to video yet. There wasn't a lot

525
00:30:22,280 --> 00:30:25,280
Speaker 5: of multimedia because the Internet speeds weren't there yet, so

526
00:30:25,280 --> 00:30:28,280
Speaker 5: it would make the sites really slow and clunky to load.

527
00:30:28,800 --> 00:30:32,560
Speaker 5: Instagram was barely had video at that time, so links

528
00:30:32,560 --> 00:30:35,040
Speaker 5: were really kaying and if you could create a very

529
00:30:35,080 --> 00:30:38,920
Speaker 5: shareable link, you could get tons of traffic. Therefore, the

530
00:30:38,920 --> 00:30:41,000
Speaker 5: company would make tons of money. And BuzzFeed was just

531
00:30:41,080 --> 00:30:45,040
Speaker 5: expert at this because BuzzFeed realized very early that Internet

532
00:30:45,160 --> 00:30:47,520
Speaker 5: users at that time didn't want to go through Reddit.

533
00:30:47,720 --> 00:30:51,240
Speaker 5: You have to remember this was pre algorithmic feeds. Algorithmic

534
00:30:51,240 --> 00:30:53,680
Speaker 5: seeds were not really a thing back then. I think

535
00:30:53,760 --> 00:30:57,760
Speaker 5: Twitter only rolled out their algorithmicfeed in twenty sixteen. Twenty fifteen,

536
00:30:58,080 --> 00:31:01,080
Speaker 5: it was really like the Internet was very manual, and

537
00:31:01,160 --> 00:31:04,440
Speaker 5: so if people went on a platform, they couldn't especially

538
00:31:04,520 --> 00:31:07,959
Speaker 5: Tumblr at that time was completely reverse chronological, right, so

539
00:31:08,360 --> 00:31:10,960
Speaker 5: if you went on the platform, it was hard to

540
00:31:11,040 --> 00:31:15,440
Speaker 5: kind of find the most interesting viral content. So we

541
00:31:15,680 --> 00:31:18,800
Speaker 5: relied on this intermediaries like BuzzFeed to go into these

542
00:31:18,840 --> 00:31:21,800
Speaker 5: platforms scrape out the most engaging content and presented to

543
00:31:21,840 --> 00:31:24,360
Speaker 5: you all in one place. On this case too, I

544
00:31:24,360 --> 00:31:27,160
Speaker 5: think it's really interesting that the dress post it was

545
00:31:27,200 --> 00:31:30,520
Speaker 5: posted on a Tumblr that was like a fan tumbler

546
00:31:30,600 --> 00:31:33,520
Speaker 5: for this girl, Sarah White chol I was mispronounce her

547
00:31:33,560 --> 00:31:37,360
Speaker 5: name even though Yeah lovely who was like a YouTuber manager.

548
00:31:37,560 --> 00:31:40,560
Speaker 5: So it was just like the chances of somebody finding

549
00:31:40,600 --> 00:31:43,760
Speaker 5: that normally, yes, it was getting reblogged on Tumblr, but

550
00:31:43,800 --> 00:31:45,640
Speaker 5: BuzzFeed was really like the amplifier.

551
00:31:45,720 --> 00:31:50,040
Speaker 2: Basically, thanks so much to Taylor and check out Extremely

552
00:31:50,080 --> 00:31:53,280
Speaker 2: Online in stores now, and we'll be right back with

553
00:31:53,320 --> 00:31:56,480
Speaker 2: another interview about why the success of the dress marked

554
00:31:56,520 --> 00:32:07,720
Speaker 2: the beginning of the end for dumb fun on the Internet.

555
00:32:09,800 --> 00:32:12,320
Speaker 2: Welcome back to sixteenth minute. I am The author of

556
00:32:12,360 --> 00:32:16,400
Speaker 2: the twenty fifteen post was SNL's isis sketch too offensive.

557
00:32:17,160 --> 00:32:19,760
Speaker 2: As fascinated as I am about how the dress went

558
00:32:19,840 --> 00:32:23,200
Speaker 2: viral in what now feels like an almost old fashioned way,

559
00:32:23,400 --> 00:32:25,840
Speaker 2: I wanted to take a second to do my due diligence.

560
00:32:26,440 --> 00:32:30,080
Speaker 2: Why were people seeing this dress as two different color schemes.

561
00:32:30,600 --> 00:32:34,080
Speaker 2: This was a major subject of clickbait from Clickbait at

562
00:32:34,080 --> 00:32:37,560
Speaker 2: the time, most notably a Wired piece published the day

563
00:32:37,600 --> 00:32:41,120
Speaker 2: after that ended up racking up over thirty two million views.

564
00:32:41,280 --> 00:32:45,440
Speaker 2: In that piece, writer Adam Rodgers asked Professor Bevill Conway,

565
00:32:45,600 --> 00:32:48,600
Speaker 2: who studied color and vision at Wellesley College, to comment,

566
00:32:48,800 --> 00:32:51,640
Speaker 2: and Professor Conway said that it was some combination of

567
00:32:51,680 --> 00:32:55,160
Speaker 2: the varying ways in which people perceive light, and that

568
00:32:55,200 --> 00:32:58,000
Speaker 2: the picture was just shitty quality and really blown out.

569
00:32:58,240 --> 00:33:02,200
Speaker 2: Here's his fancy science explanation. Your visual system is looking

570
00:33:02,280 --> 00:33:04,920
Speaker 2: at this thing, and you're trying to discount the chromatic

571
00:33:05,040 --> 00:33:09,160
Speaker 2: bias of the daylight axis. People either discount the blue side,

572
00:33:09,280 --> 00:33:11,200
Speaker 2: in which case they end up seeing white and gold,

573
00:33:11,640 --> 00:33:14,320
Speaker 2: or discount the gold side, in which case they end

574
00:33:14,400 --> 00:33:17,280
Speaker 2: up with blue and black. This also connects to the

575
00:33:17,320 --> 00:33:21,040
Speaker 2: maddening feeling of the dress switching colors before your eyes.

576
00:33:21,320 --> 00:33:23,880
Speaker 2: Like Quinta yelled about in the BuzzFeed video. So if

577
00:33:23,920 --> 00:33:26,760
Speaker 2: you were a neuroscientist with a focus on vision, and

578
00:33:26,920 --> 00:33:29,720
Speaker 2: I know you're not, this actually prompted a pretty rich

579
00:33:29,760 --> 00:33:34,080
Speaker 2: scientific discussion. The Journal of Vision dedicated a whole issue

580
00:33:34,120 --> 00:33:37,960
Speaker 2: to the dress with some pretty interesting findings. They spoke

581
00:33:38,000 --> 00:33:41,400
Speaker 2: to fourteen hundred respondents, with fifty seven percent seeing black

582
00:33:41,440 --> 00:33:45,320
Speaker 2: and blue, eleven percent black and brown, thirty percent white

583
00:33:45,320 --> 00:33:49,400
Speaker 2: and gold, and two percent. Other tests included showing the

584
00:33:49,480 --> 00:33:52,080
Speaker 2: dress in artificial yellow and blue light to see if

585
00:33:52,080 --> 00:33:55,640
Speaker 2: that's shifted bias, and a study from Pascal Wallash showed

586
00:33:55,640 --> 00:33:58,720
Speaker 2: the really interesting trend that people who woke up early

587
00:33:58,800 --> 00:34:01,280
Speaker 2: were more likely to see the dress as white and gold,

588
00:34:01,640 --> 00:34:04,200
Speaker 2: and people who went out at night moore were likely

589
00:34:04,240 --> 00:34:07,440
Speaker 2: to see black and blue. And I'll admit reading about

590
00:34:07,440 --> 00:34:09,520
Speaker 2: why your brain makes you see the dress as one

591
00:34:09,520 --> 00:34:12,160
Speaker 2: thing or the other is really really interesting, even when

592
00:34:12,160 --> 00:34:15,719
Speaker 2: what's being explained to you is a pretty straightforward optical illusion.

593
00:34:15,920 --> 00:34:19,160
Speaker 2: And even though as I'm writing this episode this only

594
00:34:19,239 --> 00:34:22,960
Speaker 2: happened about nine years ago, it feels like it's longer.

595
00:34:23,520 --> 00:34:25,400
Speaker 2: It reminds me of a targeted ad I got for

596
00:34:25,480 --> 00:34:28,719
Speaker 2: a T shirt sold by ClickHole which if you're not familiar,

597
00:34:28,960 --> 00:34:32,759
Speaker 2: is a spinoff of the onion Net, specifically satirized clickbait.

598
00:34:33,200 --> 00:34:37,600
Speaker 2: The shirt says the Internet nineteen eighty three to twenty fourteen,

599
00:34:38,120 --> 00:34:40,800
Speaker 2: and it gets at this feeling I'm having that shortly

600
00:34:40,840 --> 00:34:45,520
Speaker 2: after the Dress, the Internet became less fun. Of course,

601
00:34:45,560 --> 00:34:48,480
Speaker 2: this is coming from a very Western, privileged perspective, but

602
00:34:48,560 --> 00:34:51,440
Speaker 2: it's pretty commonly held that in the US, around the

603
00:34:51,440 --> 00:34:54,960
Speaker 2: time that the twenty sixteen election cycle began, social media

604
00:34:55,160 --> 00:34:57,520
Speaker 2: became less fun. Let's not to say it was a

605
00:34:57,520 --> 00:35:01,200
Speaker 2: cakewalk before. There are many stories of discription and harassment

606
00:35:01,239 --> 00:35:03,239
Speaker 2: that took place here, many of which I'll cover on

607
00:35:03,280 --> 00:35:05,760
Speaker 2: this show. But I'm trying to get at the ratio

608
00:35:06,000 --> 00:35:11,560
Speaker 2: of fun stories to existentially terrifying stories, the ratio of hey,

609
00:35:11,640 --> 00:35:15,960
Speaker 2: what color is this? To bigoted conspiracy theories in the mainstream.

610
00:35:16,400 --> 00:35:18,959
Speaker 2: The dress kind of feels like the end of an era.

611
00:35:19,400 --> 00:35:23,680
Speaker 2: Early twenty fifteen wasn't an Internet that wasn't poisoned with hate,

612
00:35:24,080 --> 00:35:28,040
Speaker 2: but it felt like an Internet whose hate didn't poison everything,

613
00:35:28,680 --> 00:35:31,960
Speaker 2: and less than a year later that didn't really feel true.

614
00:35:32,400 --> 00:35:34,719
Speaker 2: I didn't speak to anyone involved in the original dress

615
00:35:34,760 --> 00:35:37,279
Speaker 2: story for reasons I'll get to, but I wanted to

616
00:35:37,280 --> 00:35:40,439
Speaker 2: better understand how the Internet shifted in the year after

617
00:35:40,480 --> 00:35:42,560
Speaker 2: the dress, to bring us to the kind of viral

618
00:35:42,640 --> 00:35:45,840
Speaker 2: stories we were seeing in twenty sixteen. Just a year later.

619
00:35:46,080 --> 00:35:49,480
Speaker 2: For Perspective, the Ringers roundup of twenty sixteen viral stories

620
00:35:49,640 --> 00:35:53,360
Speaker 2: included some classics like chewbacco mom, but also just the

621
00:35:53,400 --> 00:35:57,880
Speaker 2: word Nazism. So, yeah, there was a mainstream shift that

622
00:35:57,920 --> 00:36:00,560
Speaker 2: had taken place. And this was for a reason, not

623
00:36:01,040 --> 00:36:04,760
Speaker 2: just because of the heightened bigoted extremism in the Western world,

624
00:36:05,160 --> 00:36:08,680
Speaker 2: but because of how our social networks were functioning. It's

625
00:36:08,680 --> 00:36:12,000
Speaker 2: something that BuzzFeed founder Jonah Peretti commented on as recently

626
00:36:12,040 --> 00:36:15,480
Speaker 2: as twenty nineteen, less than five years after the dress story.

627
00:36:15,760 --> 00:36:19,400
Speaker 2: In November twenty nineteen, he told Max Read at Intelligencer,

628
00:36:19,960 --> 00:36:20,719
Speaker 2: the dress.

629
00:36:20,520 --> 00:36:22,839
Speaker 7: Was a kind of perfect thing to catch fire. At

630
00:36:22,880 --> 00:36:26,719
Speaker 7: that moment, the Internet was less polarized and politicized, and

631
00:36:26,760 --> 00:36:29,120
Speaker 7: it had shifted to mobile fully, so people were looking

632
00:36:29,200 --> 00:36:32,600
Speaker 7: at mobile devices with the dress. If you saw it

633
00:36:32,719 --> 00:36:35,319
Speaker 7: on your phone and you were with people, you could

634
00:36:35,320 --> 00:36:38,320
Speaker 7: hold the phone up and say what color is this. Plus,

635
00:36:38,440 --> 00:36:40,520
Speaker 7: in the early days of BuzzFeed, our traffic would die

636
00:36:40,520 --> 00:36:42,680
Speaker 7: in the evening because people would watch television or go

637
00:36:42,719 --> 00:36:46,480
Speaker 7: out with their friends. Now, with mobile, we see primetime

638
00:36:46,640 --> 00:36:50,279
Speaker 7: for our content as the same as primetime for television.

639
00:36:50,520 --> 00:36:53,880
Speaker 7: People are sharing content and looking at content later.

640
00:36:54,600 --> 00:36:56,719
Speaker 2: Keep in mind this is a quote from less than

641
00:36:56,800 --> 00:36:59,920
Speaker 2: half a decade later, but the difference between twenty fifteen

642
00:37:00,160 --> 00:37:03,240
Speaker 2: and twenty nineteen on the US Internet made it feel

643
00:37:03,239 --> 00:37:06,160
Speaker 2: like he was talking about twenty years ago. I mean,

644
00:37:06,280 --> 00:37:09,760
Speaker 2: hell on the Internet. Forty years passed between November twenty

645
00:37:09,840 --> 00:37:13,880
Speaker 2: nineteen and November twenty twenty, and since this interview, BuzzFeed

646
00:37:13,960 --> 00:37:16,799
Speaker 2: has changed even more. Paretti pulled the plug on his

647
00:37:16,840 --> 00:37:20,480
Speaker 2: Pulitzer winning news operation only seven years after it launched

648
00:37:20,640 --> 00:37:24,480
Speaker 2: in twenty twenty three, and is currently focused on rerouting

649
00:37:24,520 --> 00:37:31,120
Speaker 2: BuzzFeed to AI. Depressing, yes, but not surprising, because after all,

650
00:37:31,280 --> 00:37:34,520
Speaker 2: Paretti started the company to operate on algorithms with no

651
00:37:34,680 --> 00:37:37,719
Speaker 2: creative at all, and now he has robots instead of

652
00:37:37,719 --> 00:37:41,160
Speaker 2: writers and the company is worth less than ever. I

653
00:37:41,200 --> 00:37:43,560
Speaker 2: talked to a second author who published a great book

654
00:37:43,640 --> 00:37:46,719
Speaker 2: about social media and full disclosure. He's also my best

655
00:37:46,719 --> 00:37:49,520
Speaker 2: friend's boyfriend. He's the best and pertinent to you. He

656
00:37:49,600 --> 00:37:52,799
Speaker 2: has a thorough knowledge on how social media algorithms have

657
00:37:52,880 --> 00:37:56,359
Speaker 2: been forcibly evolved and monetized to serve those that make

658
00:37:56,400 --> 00:37:59,960
Speaker 2: them money, not their users. Max Fisher currently co hosts

659
00:38:00,120 --> 00:38:03,280
Speaker 2: Offline on Crooked Media and wrote about this very topic

660
00:38:03,360 --> 00:38:07,080
Speaker 2: in twenty twenty two's The Chaos Machine, The Inside Story

661
00:38:07,120 --> 00:38:10,440
Speaker 2: of how Social Media Rewired our minds and our world.

662
00:38:11,080 --> 00:38:12,000
Speaker 2: Here are some of our talk.

663
00:38:13,680 --> 00:38:17,040
Speaker 8: My name is Max Fischer and I'm a journalist and podcaster.

664
00:38:17,800 --> 00:38:20,000
Speaker 2: As we're talking about the dress today is what is

665
00:38:20,200 --> 00:38:24,160
Speaker 2: your personal recollection of the dress saga? Were you You

666
00:38:24,200 --> 00:38:26,080
Speaker 2: were working in media at the time.

667
00:38:25,920 --> 00:38:29,360
Speaker 8: Right, I not only was a working in media, but

668
00:38:29,440 --> 00:38:32,080
Speaker 8: I was working not at BuzzFeed, but at Fox dot com,

669
00:38:32,080 --> 00:38:37,399
Speaker 8: which was a fellow kind of web you know, new journalism,

670
00:38:37,760 --> 00:38:41,120
Speaker 8: left leaning startup that was trying to chase audience on

671
00:38:41,160 --> 00:38:44,239
Speaker 8: social media. So it felt very much kind of in

672
00:38:44,440 --> 00:38:47,160
Speaker 8: our wheelhouse. It was something that we were all really

673
00:38:47,200 --> 00:38:49,480
Speaker 8: excited about. Wow, look a post can get this much

674
00:38:49,520 --> 00:38:52,640
Speaker 8: attention by you know, doing something that Facebook really likes

675
00:38:52,680 --> 00:38:55,640
Speaker 8: and that social media algorithms really like. And we were

676
00:38:55,680 --> 00:38:58,040
Speaker 8: trying to do not the dress exactly, but we were

677
00:38:58,080 --> 00:39:01,200
Speaker 8: trying to do like, you know, the policy WONK version

678
00:39:01,320 --> 00:39:03,799
Speaker 8: of the dress every day, So we thought it was

679
00:39:03,880 --> 00:39:06,000
Speaker 8: like this amazing moment that we paid a lot of

680
00:39:06,000 --> 00:39:06,560
Speaker 8: attention to.

681
00:39:07,360 --> 00:39:10,560
Speaker 2: So I know that you've spent a lot of recent

682
00:39:10,640 --> 00:39:15,120
Speaker 2: years thinking about how our algorithms work, and you know

683
00:39:15,200 --> 00:39:18,480
Speaker 2: the implications of that. Were you thinking about this ten

684
00:39:18,560 --> 00:39:22,480
Speaker 2: years ago? What did that algorithm chasing look like?

685
00:39:23,840 --> 00:39:26,200
Speaker 8: I was thinking about it, But like a lot of

686
00:39:26,320 --> 00:39:29,440
Speaker 8: journalists I think of our generation, I thought of it

687
00:39:29,480 --> 00:39:31,680
Speaker 8: as just purely a good thing, as just a thing

688
00:39:31,680 --> 00:39:35,120
Speaker 8: where it's like, oh, if I write my piece that

689
00:39:35,200 --> 00:39:37,759
Speaker 8: I was going to write anyway on you know, the

690
00:39:37,800 --> 00:39:40,319
Speaker 8: Iran nuclear deal, but I phrased the headline in a

691
00:39:40,320 --> 00:39:43,800
Speaker 8: certain way, then Facebook can deliver me, thanks to its algorithm,

692
00:39:43,960 --> 00:39:46,520
Speaker 8: huge amounts of traffic and eyeballs and people who want

693
00:39:46,560 --> 00:39:48,440
Speaker 8: to read the story. And that's great, and that's so

694
00:39:48,600 --> 00:39:51,520
Speaker 8: nice that social media can be there to help us

695
00:39:51,560 --> 00:39:55,120
Speaker 8: reach a larger audience. So I was aware of it,

696
00:39:55,160 --> 00:39:59,839
Speaker 8: but thought of it as kind of a relatively neutral force, like, sure,

697
00:40:00,200 --> 00:40:02,640
Speaker 8: you play up certain emotions in your headlines and certain

698
00:40:02,680 --> 00:40:06,920
Speaker 8: ideas that you know are likelier to go viral. But again,

699
00:40:06,960 --> 00:40:09,680
Speaker 8: maybe like a lot of people, it wasn't really until

700
00:40:09,719 --> 00:40:12,479
Speaker 8: after Trump was elected, like a year and a half

701
00:40:12,520 --> 00:40:14,560
Speaker 8: after that that I started to think of social media

702
00:40:14,600 --> 00:40:17,479
Speaker 8: and social media algorithms as something that could be bad.

703
00:40:17,520 --> 00:40:19,480
Speaker 8: At this point, I really thought that they were just

704
00:40:19,520 --> 00:40:20,719
Speaker 8: a good thing and.

705
00:40:20,640 --> 00:40:24,080
Speaker 2: Also just I think, certainly on my end, there was

706
00:40:24,200 --> 00:40:29,160
Speaker 2: a lack of thinking about how this algorithm chasing translated

707
00:40:29,239 --> 00:40:34,000
Speaker 2: to money, which in retrospect feels very naive of me,

708
00:40:34,400 --> 00:40:37,720
Speaker 2: but there was at the time. It seems like money

709
00:40:37,760 --> 00:40:41,200
Speaker 2: to be had from you know, taking these stories that

710
00:40:41,239 --> 00:40:44,520
Speaker 2: you would find on social media and then you know,

711
00:40:44,600 --> 00:40:49,120
Speaker 2: delivering them and making you know, question mark a shitload of.

712
00:40:49,040 --> 00:40:54,320
Speaker 8: Money, right, I mean, the idea for the web startups

713
00:40:54,320 --> 00:40:57,680
Speaker 8: of that era was that you will get so many

714
00:40:57,680 --> 00:41:00,319
Speaker 8: more eyeballs on social media, and then so many more

715
00:41:00,360 --> 00:41:02,160
Speaker 8: people will click through to your website and they will

716
00:41:02,160 --> 00:41:04,279
Speaker 8: see ads on your website, so then you will make

717
00:41:04,320 --> 00:41:07,760
Speaker 8: more money. And we weren't thinking of it, and maybe

718
00:41:07,800 --> 00:41:11,760
Speaker 8: you guys were breaking it in to the Boston BuzzFeed.

719
00:41:11,320 --> 00:41:15,520
Speaker 2: But almost impossible to can see that we were.

720
00:41:16,719 --> 00:41:19,719
Speaker 8: I mean, we maybe this was idealistic, but we were

721
00:41:19,760 --> 00:41:22,319
Speaker 8: thinking of it as this will be great because it

722
00:41:22,320 --> 00:41:25,200
Speaker 8: will make journalism sustainable, Like we were all veterans of

723
00:41:25,200 --> 00:41:27,000
Speaker 8: the two thousand and eight financial crisis, So we had

724
00:41:27,000 --> 00:41:28,880
Speaker 8: all lived through a time when there were no jobs

725
00:41:28,920 --> 00:41:31,640
Speaker 8: to be had because the industry was collapsing because the

726
00:41:31,640 --> 00:41:35,160
Speaker 8: Internet and social media had taken all of the revenue

727
00:41:35,160 --> 00:41:38,560
Speaker 8: that used to go through classifieds. So the idea that, aha,

728
00:41:38,719 --> 00:41:41,000
Speaker 8: now we're going to leverage that same social media to

729
00:41:41,000 --> 00:41:43,719
Speaker 8: get all these ad impressions. We can have a sustainable

730
00:41:43,760 --> 00:41:46,759
Speaker 8: business again, and won't that be so great for like

731
00:41:46,840 --> 00:41:49,759
Speaker 8: the future of the industry. And like before I worked

732
00:41:49,800 --> 00:41:51,480
Speaker 8: at Fox, I worked at the Washington Post, and they

733
00:41:51,600 --> 00:41:54,279
Speaker 8: loved the idea that you would do the journalism you

734
00:41:54,320 --> 00:41:55,920
Speaker 8: were going to do anyway, but right in a way

735
00:41:55,920 --> 00:41:58,160
Speaker 8: that will please algorithms, or right the headline in a

736
00:41:58,160 --> 00:42:00,879
Speaker 8: way that will please algorithms, so that we can make

737
00:42:00,920 --> 00:42:04,000
Speaker 8: more money and continue to pay for foreign correspondence. So

738
00:42:04,040 --> 00:42:07,520
Speaker 8: it is a very starry eyed era that boy has

739
00:42:07,560 --> 00:42:08,439
Speaker 8: sure not aged well.

740
00:42:08,880 --> 00:42:12,880
Speaker 2: Used a phrase that it just feels very inherent to

741
00:42:12,920 --> 00:42:16,160
Speaker 2: what the dress was. You use a phrase the old algorithm.

742
00:42:16,719 --> 00:42:19,200
Speaker 2: What do you mean when you say the old algorithm?

743
00:42:19,920 --> 00:42:20,160
Speaker 3: Oh?

744
00:42:20,280 --> 00:42:26,000
Speaker 8: Yeah, okay, So I think we all know that algorithms

745
00:42:26,000 --> 00:42:29,360
Speaker 8: are what drive everything and how you experience social media.

746
00:42:29,440 --> 00:42:33,120
Speaker 8: They're the programs on every platform that select what you

747
00:42:33,160 --> 00:42:36,640
Speaker 8: see the order you see it in, that choose what

748
00:42:36,719 --> 00:42:41,640
Speaker 8: kind of emotions to surface, what kind of like political

749
00:42:41,760 --> 00:42:44,719
Speaker 8: valance to surface for you. They're the absolute core of

750
00:42:44,760 --> 00:42:47,839
Speaker 8: the social media business model. The era before algorithms, social

751
00:42:47,920 --> 00:42:50,200
Speaker 8: media was a loser business that didn't make any money,

752
00:42:50,560 --> 00:42:53,000
Speaker 8: and people didn't spend that much time on like rimbor MySpace,

753
00:42:53,040 --> 00:42:54,840
Speaker 8: you didn't spend that much time on the site live journal.

754
00:42:55,320 --> 00:42:59,120
Speaker 8: After they developed algorithms, that is when people's time on

755
00:42:59,200 --> 00:43:05,000
Speaker 8: site explode because they are so effective at making the

756
00:43:05,080 --> 00:43:08,239
Speaker 8: experience of being social media very engaging and addictive, and

757
00:43:08,280 --> 00:43:11,360
Speaker 8: that's what turned social media companies into huge businesses. So

758
00:43:11,480 --> 00:43:15,120
Speaker 8: for a long time, the way that those algorithms worked

759
00:43:15,280 --> 00:43:18,200
Speaker 8: from their first invention and like the end of the

760
00:43:18,239 --> 00:43:20,520
Speaker 8: two thousands, like Facebook kind of starts it in two

761
00:43:20,520 --> 00:43:22,440
Speaker 8: thousand and six with the Facebook news Feed, but it

762
00:43:22,440 --> 00:43:25,920
Speaker 8: takes a while for their platforms to catch up up

763
00:43:26,040 --> 00:43:29,480
Speaker 8: through like the era of the dress, the way that

764
00:43:29,560 --> 00:43:34,040
Speaker 8: it worked, the old quote unquote old algorithm promoted whatever

765
00:43:34,600 --> 00:43:38,520
Speaker 8: content was. They tend to promote outbound links, like it

766
00:43:38,560 --> 00:43:42,040
Speaker 8: would promote a link to some other website or news site,

767
00:43:42,040 --> 00:43:46,920
Speaker 8: even if it's like a lollcat or something off the platform,

768
00:43:47,000 --> 00:43:48,520
Speaker 8: and it would be whatever is the link that people

769
00:43:48,520 --> 00:43:52,319
Speaker 8: would click on the most. And what that tended to

770
00:43:52,400 --> 00:43:57,200
Speaker 8: privilege is if you remember like upworthy, Upworthy was like

771
00:43:57,440 --> 00:44:02,480
Speaker 8: a website that existed to create content specifically decater to

772
00:44:02,640 --> 00:44:06,759
Speaker 8: like the old Facebook algorithm, because it was like curiosity

773
00:44:06,800 --> 00:44:08,879
Speaker 8: gaps or it was like you won't believe what happened next.

774
00:44:08,880 --> 00:44:10,600
Speaker 8: So you're like, oh, I guess I'll click on that link,

775
00:44:10,640 --> 00:44:12,560
Speaker 8: and then you click on that link, and then Facebook

776
00:44:12,640 --> 00:44:15,160
Speaker 8: or Twitter learns, okay, we promote that link to a

777
00:44:15,200 --> 00:44:18,440
Speaker 8: lot of people, they will click on it. And the

778
00:44:18,640 --> 00:44:22,160
Speaker 8: dress is kind of the last gasp of that old

779
00:44:22,200 --> 00:44:25,680
Speaker 8: algorithm because it is again you see the link and

780
00:44:25,719 --> 00:44:28,399
Speaker 8: the way that it displayed on not just Facebook, although

781
00:44:28,400 --> 00:44:29,879
Speaker 8: Facebook was the big one at the time, but all

782
00:44:29,880 --> 00:44:32,160
Speaker 8: of the platforms read it. Twitter, the way that it

783
00:44:32,160 --> 00:44:34,960
Speaker 8: would displays you would see a cutoff image of the

784
00:44:35,040 --> 00:44:37,240
Speaker 8: dress and then you would see a headline about people

785
00:44:37,280 --> 00:44:39,560
Speaker 8: see it in different colors, so it really makes you

786
00:44:39,600 --> 00:44:42,640
Speaker 8: want to click. So everybody who saw this link on

787
00:44:42,680 --> 00:44:44,480
Speaker 8: their news feed, you were like, oh, I have to

788
00:44:44,480 --> 00:44:47,080
Speaker 8: click through and figure out what color the dress is.

789
00:44:47,080 --> 00:44:49,840
Speaker 8: Even if you only spend eight seconds on that post,

790
00:44:50,360 --> 00:44:53,279
Speaker 8: the algorithm learns that serves it to everyone on the

791
00:44:53,280 --> 00:44:56,600
Speaker 8: platforms because it creates these outbound links, and that is

792
00:44:56,640 --> 00:44:59,920
Speaker 8: when BuzzFeed starts to get you know, a million people

793
00:45:00,120 --> 00:45:01,680
Speaker 8: minute or whatever the numbers were.

794
00:45:02,160 --> 00:45:05,400
Speaker 2: Looking at it, a section of your book covers really thoroughly.

795
00:45:06,080 --> 00:45:10,080
Speaker 2: Like twenty fifteen is a really important year for this shift.

796
00:45:10,200 --> 00:45:13,320
Speaker 2: So the way that the algorithm looks in late February

797
00:45:13,320 --> 00:45:15,360
Speaker 2: twenty fifteen and at the end of the year seems

798
00:45:15,560 --> 00:45:20,399
Speaker 2: pretty different. What is changing with how algorithms are used

799
00:45:20,440 --> 00:45:22,840
Speaker 2: on social media throughout this era.

800
00:45:23,520 --> 00:45:26,000
Speaker 8: So there's not like a moment of shift, like they

801
00:45:26,000 --> 00:45:28,200
Speaker 8: don't pull a big lever and go over to new

802
00:45:28,239 --> 00:45:32,760
Speaker 8: set of algorithms. But bummer bummer, I know. But over

803
00:45:33,120 --> 00:45:36,200
Speaker 8: like twenty fourteen and twenty fifteen, the big social media

804
00:45:36,239 --> 00:45:40,319
Speaker 8: platforms make a bunch of incremental changes that shift to

805
00:45:40,800 --> 00:45:44,440
Speaker 8: a set of algorithms that just fundamentally privilege a different

806
00:45:44,560 --> 00:45:47,640
Speaker 8: set of things. The language that Facebook uses for this

807
00:45:48,200 --> 00:45:50,239
Speaker 8: and they are kind of like leading the charge at

808
00:45:50,239 --> 00:45:53,239
Speaker 8: this point because they're still dominant way back in this era,

809
00:45:53,680 --> 00:45:59,120
Speaker 8: is that they want to start privileging quote emotionally engaging interactions.

810
00:45:59,480 --> 00:46:02,440
Speaker 8: And what that means is that instead of pushing up

811
00:46:02,440 --> 00:46:04,839
Speaker 8: to the top of your feed, whatever is the link

812
00:46:04,880 --> 00:46:07,239
Speaker 8: to an outbound website that they think you were likely

813
00:46:07,360 --> 00:46:10,920
Speaker 8: is to click on, they want to promote whatever content

814
00:46:11,000 --> 00:46:14,359
Speaker 8: in your feed, whether that's a link, or it's a discussion,

815
00:46:14,640 --> 00:46:17,360
Speaker 8: or it's a Facebook group or it's a photo that

816
00:46:17,480 --> 00:46:20,480
Speaker 8: is going to generate the most discussion on that post.

817
00:46:20,520 --> 00:46:22,000
Speaker 8: And the reason they do that is they want to

818
00:46:22,040 --> 00:46:23,759
Speaker 8: keep you on the platforms because if you click that

819
00:46:23,800 --> 00:46:26,759
Speaker 8: outbound link, now you're on BuzzFeed, and Facebook doesn't make

820
00:46:26,800 --> 00:46:28,879
Speaker 8: any money from you spending time on BuzzFeed. They want

821
00:46:28,880 --> 00:46:32,319
Speaker 8: you to spend as much time on Facebook in discussions

822
00:46:32,360 --> 00:46:35,239
Speaker 8: whatever as they can. And they frame this is like, oh,

823
00:46:35,280 --> 00:46:37,400
Speaker 8: we're going to start giving you. They call it like

824
00:46:37,560 --> 00:46:41,160
Speaker 8: meaningful connections or like meaningful interactions, as if it's going

825
00:46:41,200 --> 00:46:43,719
Speaker 8: to bring you like all sorts of love and friendship.

826
00:46:44,040 --> 00:46:46,680
Speaker 8: But of course that is not at all what it brings,

827
00:46:46,800 --> 00:46:53,520
Speaker 8: because these algorithms are incredibly ruthless in running billions of

828
00:46:53,560 --> 00:46:57,759
Speaker 8: what are basically tests like little social science experiments every

829
00:46:57,800 --> 00:47:00,760
Speaker 8: single day. And what is the exact kind of content

830
00:47:00,960 --> 00:47:04,200
Speaker 8: that will get people to engage in these quote unquote

831
00:47:04,239 --> 00:47:06,879
Speaker 8: emotionally engaging interactions, not just to spend time on the site,

832
00:47:06,880 --> 00:47:09,280
Speaker 8: but also to post back in the site in ways

833
00:47:09,320 --> 00:47:11,320
Speaker 8: that we'll get other people to spend time on the site.

834
00:47:12,320 --> 00:47:15,239
Speaker 8: And like, what is the content and the set of

835
00:47:15,280 --> 00:47:19,560
Speaker 8: emotions that are going to be most engaging. And like,

836
00:47:20,200 --> 00:47:23,480
Speaker 8: you know, as any student of human nature will know,

837
00:47:23,840 --> 00:47:27,120
Speaker 8: it's not the nice stuff. It's not you know, here's

838
00:47:27,120 --> 00:47:30,040
Speaker 8: your friend's baby, you know, your cousin got a new

839
00:47:30,120 --> 00:47:34,960
Speaker 8: job promotion, your aunt had a great day. It's outrage primarily,

840
00:47:35,040 --> 00:47:39,240
Speaker 8: and especially moral outrage. And it's anything that expressed fear

841
00:47:39,640 --> 00:47:43,160
Speaker 8: or hatred or discussed with some sort of social outgroup.

842
00:47:43,239 --> 00:47:46,600
Speaker 8: And that might be you know, political partisans on the

843
00:47:46,640 --> 00:47:50,280
Speaker 8: other side that for a lot of people, that's racial groups,

844
00:47:50,320 --> 00:47:55,279
Speaker 8: religious groups, immigrants, anything that cultivates a sense of like

845
00:47:55,480 --> 00:47:57,719
Speaker 8: there is a social group out there that I don't

846
00:47:57,800 --> 00:47:59,719
Speaker 8: like and I find them to be scary or I

847
00:47:59,760 --> 00:48:02,320
Speaker 8: find them to be offensive, and I want to rally

848
00:48:02,360 --> 00:48:05,279
Speaker 8: my in group against them. That is the thing that

849
00:48:05,360 --> 00:48:08,840
Speaker 8: the algorithms as they are being introduced to all of

850
00:48:08,880 --> 00:48:12,080
Speaker 8: these platforms over twenty fourteen and twenty fifteen learned to

851
00:48:12,200 --> 00:48:17,520
Speaker 8: surface above all else. And Facebook's own researchers are tracking

852
00:48:17,520 --> 00:48:20,160
Speaker 8: all of this, and they're finding both that this is

853
00:48:20,320 --> 00:48:24,239
Speaker 8: enormously successful at making people spend more time on the website,

854
00:48:24,360 --> 00:48:27,799
Speaker 8: but also that it's really bad for them. There's this

855
00:48:28,000 --> 00:48:31,640
Speaker 8: paper that's kind of the last gasp of when Facebook

856
00:48:31,680 --> 00:48:34,880
Speaker 8: used to publish research into what their algorithm does with users.

857
00:48:34,920 --> 00:48:38,840
Speaker 8: In May twenty fifteen, Facebook's researchers, because they have accessed

858
00:48:38,840 --> 00:48:43,440
Speaker 8: all this data, published this paper warning that this change

859
00:48:43,480 --> 00:48:46,680
Speaker 8: to this new algorithm was sorting people into not just

860
00:48:46,920 --> 00:48:51,680
Speaker 8: like minded discussions, but within those discussions was consistently surfacing

861
00:48:51,719 --> 00:48:55,920
Speaker 8: whatever was the angriest or most emotional or most extreme

862
00:48:56,000 --> 00:48:59,120
Speaker 8: viewpoint that everybody agreed with, and that that created an

863
00:48:59,120 --> 00:49:03,520
Speaker 8: effect that was associated with adopting more extreme attitudes over

864
00:49:03,560 --> 00:49:08,400
Speaker 8: time and misperceiving facts about current events, which is a

865
00:49:08,400 --> 00:49:11,560
Speaker 8: somewhat euphemistic way of saying that people became more extreme

866
00:49:11,560 --> 00:49:15,760
Speaker 8: in whatever beliefs they already held, and that they became

867
00:49:15,960 --> 00:49:19,839
Speaker 8: likelier to believe misinformation or conspiracies, because, of course, if

868
00:49:19,840 --> 00:49:23,680
Speaker 8: you already are angry about something, a conspiracy about it

869
00:49:23,719 --> 00:49:25,520
Speaker 8: will make you feel even angrier.

870
00:49:26,480 --> 00:49:30,880
Speaker 2: Right, I was fascinated to read how that information was

871
00:49:30,920 --> 00:49:35,960
Speaker 2: at one point public, and I think, like how it

872
00:49:35,960 --> 00:49:39,200
Speaker 2: feels completely inconceivable that they would publish anything like that

873
00:49:39,239 --> 00:49:41,839
Speaker 2: to the public just five years later or even two

874
00:49:41,920 --> 00:49:45,440
Speaker 2: years later. And how you know it was? It was

875
00:49:45,719 --> 00:49:48,440
Speaker 2: well known internally and it seems like I mean you

876
00:49:48,880 --> 00:49:52,640
Speaker 2: explained I think inside and also outside of Facebook, the

877
00:49:52,800 --> 00:49:56,680
Speaker 2: sort of attempted, the attempts to blow the whistle on like, hey,

878
00:49:56,760 --> 00:50:00,560
Speaker 2: this is really not good for us. So I mean,

879
00:50:00,600 --> 00:50:04,759
Speaker 2: it's of course like profit is why that happens. But

880
00:50:05,000 --> 00:50:09,520
Speaker 2: I at what point do these platforms sort of switch

881
00:50:10,320 --> 00:50:12,680
Speaker 2: to infinite growth?

882
00:50:13,040 --> 00:50:17,839
Speaker 8: So that had always been the business model since those

883
00:50:17,880 --> 00:50:21,200
Speaker 8: first days of the news feed back on the late

884
00:50:21,800 --> 00:50:25,759
Speaker 8: two thousands. But what happens actually around the same time,

885
00:50:25,840 --> 00:50:29,520
Speaker 8: around twenty fourteen, is they start to realize that the

886
00:50:29,640 --> 00:50:33,480
Speaker 8: pool of human attention is finite, and that's what they

887
00:50:33,520 --> 00:50:36,239
Speaker 8: trade in. That's their asset that they are chasing, is

888
00:50:36,640 --> 00:50:38,719
Speaker 8: seconds of your day that you're spending on the platform

889
00:50:38,760 --> 00:50:41,320
Speaker 8: so they can sell ads against it. And they realize

890
00:50:41,320 --> 00:50:45,560
Speaker 8: around twenty fourteen that they've kind of reached saturation collectively

891
00:50:46,040 --> 00:50:49,160
Speaker 8: on how much of that they hold where at this

892
00:50:49,280 --> 00:50:53,280
Speaker 8: point people spend As of twenty fourteen, people on average

893
00:50:53,320 --> 00:50:55,799
Speaker 8: are spending more time on social media platforms than they

894
00:50:55,800 --> 00:51:00,160
Speaker 8: do interacting with other people socially in real life, which

895
00:51:00,200 --> 00:51:03,320
Speaker 8: is staggering, and that gap, of course has only grown since.

896
00:51:04,040 --> 00:51:07,600
Speaker 8: So what they realize when they get to this point

897
00:51:07,640 --> 00:51:10,120
Speaker 8: where they realize, Okay, we've kind of saturated the market

898
00:51:10,120 --> 00:51:12,919
Speaker 8: for how much human attention we can captures, they get

899
00:51:12,920 --> 00:51:16,120
Speaker 8: into this arms race with each other for who can

900
00:51:16,200 --> 00:51:19,520
Speaker 8: hold more of that attention. And that is when they

901
00:51:19,560 --> 00:51:23,520
Speaker 8: go from just having a existing business model of infinite

902
00:51:23,520 --> 00:51:28,680
Speaker 8: growth to needing to, like you said, take every step

903
00:51:28,719 --> 00:51:31,360
Speaker 8: that they can, no matter how extreme, to try to

904
00:51:31,400 --> 00:51:35,480
Speaker 8: outcompete each other for how much of that attention they hold.

905
00:51:35,520 --> 00:51:37,520
Speaker 8: And this is when all of the social media companies

906
00:51:37,520 --> 00:51:40,799
Speaker 8: again is around like twenty fourteen, twenty fifteen, higher up,

907
00:51:41,239 --> 00:51:44,919
Speaker 8: all of the big names and artificial intelligence, like every

908
00:51:44,920 --> 00:51:48,240
Speaker 8: major European and American researcher at every big research university

909
00:51:48,280 --> 00:51:51,839
Speaker 8: suddenly works at Facebook, which is like that seems unsurprising

910
00:51:51,920 --> 00:51:54,239
Speaker 8: now because these are trillion dollar companies, but at that

911
00:51:54,360 --> 00:51:57,160
Speaker 8: time that was really shocking because this is just like

912
00:51:58,080 --> 00:52:00,960
Speaker 8: this was like fancy or MySpace. It's just like a

913
00:52:01,000 --> 00:52:04,160
Speaker 8: weird little website, and all of a sudden, all of

914
00:52:04,200 --> 00:52:07,680
Speaker 8: these rock stars of artificial intelligence work for them, because

915
00:52:07,680 --> 00:52:10,120
Speaker 8: what they are doing is they are trying to make

916
00:52:10,680 --> 00:52:13,759
Speaker 8: the social media algorithms that govern what you see as

917
00:52:13,800 --> 00:52:17,880
Speaker 8: smart and as ruthless as possible. This is when YouTube

918
00:52:18,440 --> 00:52:22,319
Speaker 8: sets towards this big internal goal they have for one

919
00:52:22,360 --> 00:52:26,080
Speaker 8: billion hours of daily watch time, meaning that everyone who

920
00:52:26,239 --> 00:52:29,200
Speaker 8: uses YouTube will collectively watch one billion hours of video

921
00:52:29,280 --> 00:52:33,080
Speaker 8: every single day, which is ten times what they had

922
00:52:33,280 --> 00:52:35,560
Speaker 8: across the platform at the time they set this goal.

923
00:52:36,239 --> 00:52:40,040
Speaker 8: So they're starting to really design their business models and

924
00:52:40,080 --> 00:52:44,360
Speaker 8: their engineering around the idea that they need to in

925
00:52:44,440 --> 00:52:49,560
Speaker 8: order to survive drastically increase the stickiness and addictiveness of

926
00:52:49,600 --> 00:52:51,480
Speaker 8: being on their platform, which is when they kind of

927
00:52:51,480 --> 00:52:54,680
Speaker 8: start getting into all of these dark arts of emotional

928
00:52:54,719 --> 00:52:56,040
Speaker 8: and psychological manipulation.

929
00:52:57,840 --> 00:52:59,719
Speaker 2: It feels so clear now and at the time you're like,

930
00:53:00,000 --> 00:53:02,080
Speaker 2: this is weird. Why am I so upset?

931
00:53:02,120 --> 00:53:07,560
Speaker 8: On Well, the thing that I always think about that

932
00:53:07,680 --> 00:53:10,160
Speaker 8: I did not appreciate at the time. I didn't get

933
00:53:10,200 --> 00:53:13,400
Speaker 8: its significance. But looking back, I'm like, oh, okay, I

934
00:53:13,400 --> 00:53:16,239
Speaker 8: see how that was like a big watership moment is

935
00:53:16,320 --> 00:53:20,960
Speaker 8: around twenty fifteen, when gamer Gate goes from being this

936
00:53:21,040 --> 00:53:24,800
Speaker 8: thing that is happening on the like fringes of Reddit

937
00:53:24,920 --> 00:53:27,360
Speaker 8: and the fringes of YouTube, where it's like a bunch

938
00:53:27,400 --> 00:53:30,840
Speaker 8: of like young white guys are really mad about video

939
00:53:30,880 --> 00:53:33,200
Speaker 8: games for some reason and I don't understand why, to

940
00:53:33,360 --> 00:53:35,719
Speaker 8: all of a sudden it's completely dominant on all of

941
00:53:35,719 --> 00:53:38,959
Speaker 8: the social platforms, and it's the like number one thing

942
00:53:39,239 --> 00:53:41,399
Speaker 8: day after day on the platforms, and all of these

943
00:53:41,440 --> 00:53:46,680
Speaker 8: like weirdo gamer Gate characters like Miloguianapolis and Mike Cernovich,

944
00:53:46,760 --> 00:53:49,600
Speaker 8: all of a sudden they're like mainstream political figures. And

945
00:53:49,640 --> 00:53:53,239
Speaker 8: that is we know now in retrospect because this is

946
00:53:53,320 --> 00:53:56,480
Speaker 8: the time when social media algorithm started to realize that

947
00:53:56,719 --> 00:54:00,880
Speaker 8: stuff like gamer Gate was going to be so so

948
00:54:01,040 --> 00:54:04,799
Speaker 8: effective at increasing engagement. So that's where when you go

949
00:54:04,880 --> 00:54:07,839
Speaker 8: away from the social media algorithms of the era of

950
00:54:07,960 --> 00:54:10,320
Speaker 8: like the dress, what you get instead of the social

951
00:54:10,360 --> 00:54:13,239
Speaker 8: media algorithms of game or Gate, which I would not

952
00:54:13,320 --> 00:54:14,239
Speaker 8: consider a trade up.

953
00:54:14,800 --> 00:54:18,080
Speaker 2: This is also the time where which I feel like

954
00:54:18,120 --> 00:54:21,320
Speaker 2: ties right into the Milo stuff, where Breitbart really starts

955
00:54:21,400 --> 00:54:24,440
Speaker 2: to pop off, and it feels to me, I mean

956
00:54:25,520 --> 00:54:31,680
Speaker 2: very diabolical match of the BuzzFeed model of like luring

957
00:54:31,719 --> 00:54:35,280
Speaker 2: people in with really splashy headlines. As we're like coming

958
00:54:35,320 --> 00:54:37,440
Speaker 2: out of this era of the dress and things are

959
00:54:37,440 --> 00:54:43,040
Speaker 2: getting more politicized and engagement driven to you know, really

960
00:54:43,120 --> 00:54:47,279
Speaker 2: monetize and push outrage. What are the success stories and

961
00:54:47,320 --> 00:54:50,200
Speaker 2: how do they sort of shape what the new algorithm favors.

962
00:54:51,280 --> 00:54:52,879
Speaker 8: That's a great way to put it. I think you're

963
00:54:52,960 --> 00:54:58,800
Speaker 8: exactly right that if BuzzFeed and like upworthy worthy sites

964
00:54:58,920 --> 00:55:03,360
Speaker 8: that embodied benefited from the kind of pre twenty fifteen

965
00:55:03,760 --> 00:55:08,080
Speaker 8: social media ecosystem and algorithms, then the site that embodies

966
00:55:08,120 --> 00:55:11,120
Speaker 8: and most benefited from the post twenty fifteen social media

967
00:55:11,120 --> 00:55:16,920
Speaker 8: algorithms is definitely Breitbart. There was this huge Harvard study

968
00:55:17,080 --> 00:55:18,440
Speaker 8: or it was like led through Harvard, but it was

969
00:55:18,440 --> 00:55:21,360
Speaker 8: a ton of different scholars that came out after the

970
00:55:21,400 --> 00:55:25,600
Speaker 8: twenty sixteen election and was kind of trying to ask, like, Okay,

971
00:55:26,120 --> 00:55:29,200
Speaker 8: what happened, what was the like something the internet played

972
00:55:29,200 --> 00:55:31,439
Speaker 8: some role in the twenty sixteen election. Social media played

973
00:55:31,480 --> 00:55:32,960
Speaker 8: some role, but we're not quite sure what it is.

974
00:55:32,960 --> 00:55:37,200
Speaker 8: So let's do like a big systemic investigation into what

975
00:55:37,320 --> 00:55:39,560
Speaker 8: the social web looked like in the run up to

976
00:55:39,600 --> 00:55:42,200
Speaker 8: the twenty sixteen election, And one of the big things

977
00:55:42,239 --> 00:55:46,680
Speaker 8: they found is that from May twenty fifteen to November

978
00:55:46,680 --> 00:55:50,120
Speaker 8: twenty sixteen, when the election was held, which is kind

979
00:55:50,160 --> 00:55:52,120
Speaker 8: of the onset of that era of the new social

980
00:55:52,120 --> 00:55:57,320
Speaker 8: media algorithms, Breitbart was the third most shared media outlet

981
00:55:57,600 --> 00:56:01,839
Speaker 8: on all of social media, which is crazy when you

982
00:56:01,960 --> 00:56:06,760
Speaker 8: know that Breitbart has like no staff, they have no budget.

983
00:56:06,880 --> 00:56:09,680
Speaker 8: It's a terrible experience to be on their website. They

984
00:56:09,680 --> 00:56:13,839
Speaker 8: don't produce very many articles, and before this algorithmic shift,

985
00:56:13,840 --> 00:56:16,799
Speaker 8: they had been a teeny tiny little website with no readership,

986
00:56:17,200 --> 00:56:19,520
Speaker 8: and there was like this kind of narrative that like, oh, yes,

987
00:56:19,600 --> 00:56:22,360
Speaker 8: Steve Bannon, who's running Breitbart, is some like genius of

988
00:56:22,440 --> 00:56:24,400
Speaker 8: the dark arts of the social web. And what we

989
00:56:24,480 --> 00:56:26,040
Speaker 8: learned from this Harvard study and from a lot of

990
00:56:26,040 --> 00:56:29,400
Speaker 8: subsequent investigations is that he was not. The people of

991
00:56:29,480 --> 00:56:33,640
Speaker 8: Breitbart were not these geniuses. They were just passive beneficiaries

992
00:56:33,760 --> 00:56:38,000
Speaker 8: of the algorithms of Facebook and Twitter, basically plucking this

993
00:56:38,080 --> 00:56:41,799
Speaker 8: website and similar websites out and pushing them in front

994
00:56:41,840 --> 00:56:46,200
Speaker 8: of huge audiences of people. Like Breitbart got more audience

995
00:56:46,239 --> 00:56:49,480
Speaker 8: and engagement on Facebook than Fox News did in the

996
00:56:49,600 --> 00:56:51,480
Speaker 8: run up to the election. And Fox News is like

997
00:56:51,520 --> 00:56:54,480
Speaker 8: a bajillion dollar company with hundreds of reporters.

998
00:56:55,040 --> 00:56:57,680
Speaker 2: I mean, and certainly in the case of Breitbart, it

999
00:56:57,760 --> 00:57:03,640
Speaker 2: was just saying shit and presenting it as news, and

1000
00:57:04,480 --> 00:57:08,680
Speaker 2: you know, like I wasn't saying fascistic shit, but there

1001
00:57:08,680 --> 00:57:10,399
Speaker 2: were times where I was just kind of saying shit,

1002
00:57:10,560 --> 00:57:12,840
Speaker 2: and they're like, and she's a reporter. I don't know,

1003
00:57:12,920 --> 00:57:16,040
Speaker 2: it's interesting to reflect on.

1004
00:57:17,160 --> 00:57:17,680
Speaker 3: Yeah, that's a.

1005
00:57:17,680 --> 00:57:20,400
Speaker 8: Really interesting point. I hadn't thought about that before. But

1006
00:57:20,480 --> 00:57:24,320
Speaker 8: you're right that this was kind of the first stage

1007
00:57:24,440 --> 00:57:27,400
Speaker 8: and a shift that now feels much more complete, from

1008
00:57:27,720 --> 00:57:31,040
Speaker 8: getting your news from a news source to then you're

1009
00:57:31,080 --> 00:57:34,000
Speaker 8: getting your news from a new source repackaged for social media,

1010
00:57:34,080 --> 00:57:36,480
Speaker 8: to then you're getting your news from a new source

1011
00:57:36,520 --> 00:57:38,919
Speaker 8: that is saying shit. I know, most of us get

1012
00:57:38,920 --> 00:57:41,120
Speaker 8: our news from people who are just saying shit on

1013
00:57:41,160 --> 00:57:44,920
Speaker 8: social media, and we kind of like trust or hope

1014
00:57:45,240 --> 00:57:48,680
Speaker 8: that the like viral posts that we're reading is at

1015
00:57:48,760 --> 00:57:52,120
Speaker 8: some point sourced from something real, and sometimes it is

1016
00:57:52,160 --> 00:57:54,640
Speaker 8: and it's just spun in a way to like get

1017
00:57:54,680 --> 00:57:58,120
Speaker 8: more engagement on social media. But you know, it's just

1018
00:57:58,200 --> 00:58:01,560
Speaker 8: as possible that it's made up, that it's exaggerated, or

1019
00:58:01,560 --> 00:58:04,560
Speaker 8: that it's out of context. So it is, in retrospect

1020
00:58:04,600 --> 00:58:08,720
Speaker 8: like the start of a larger shift to the just

1021
00:58:09,080 --> 00:58:12,880
Speaker 8: saying shit era of media consumption, because on some level,

1022
00:58:13,560 --> 00:58:16,200
Speaker 8: you know, even though we do all care about getting

1023
00:58:16,280 --> 00:58:21,400
Speaker 8: accurate information from credible sources, our brains, unfortunately are really

1024
00:58:21,440 --> 00:58:27,040
Speaker 8: really drawn to things that are emotionally satisfying, or that

1025
00:58:27,240 --> 00:58:30,760
Speaker 8: delivered like outrage, which feels very affirming to indulge in,

1026
00:58:30,920 --> 00:58:34,960
Speaker 8: or that deliver a sense of like moral righteousness, or

1027
00:58:35,000 --> 00:58:39,320
Speaker 8: in group versus outgroup, and that can overpower that desire

1028
00:58:39,360 --> 00:58:42,400
Speaker 8: that we do also feel for real credible information, which

1029
00:58:42,440 --> 00:58:44,400
Speaker 8: is of course only gotten worse since twenty sixteen.

1030
00:58:45,280 --> 00:58:47,760
Speaker 2: Thanks so much to Max for speaking with me in

1031
00:58:47,800 --> 00:58:51,000
Speaker 2: full disclosure. His cat is so large now I have

1032
00:58:51,040 --> 00:58:54,200
Speaker 2: to be honest. In the interest of contextualizing the dress

1033
00:58:54,320 --> 00:58:57,560
Speaker 2: in real time, I did leave something off about its

1034
00:58:57,640 --> 00:59:00,880
Speaker 2: longer future. To this day, there's an an entire page

1035
00:59:00,920 --> 00:59:03,720
Speaker 2: about the dress on the Roman Original's website where it

1036
00:59:03,760 --> 00:59:08,080
Speaker 2: was manufactured, calling it the quote phenomenon that revealed differences

1037
00:59:08,120 --> 00:59:10,920
Speaker 2: in human color perception, which have been the subject of

1038
00:59:10,960 --> 00:59:17,560
Speaker 2: ongoing scientific investigation in neuroscience and vision science. I mean sure.

1039
00:59:18,040 --> 00:59:20,560
Speaker 2: And while some subjects on this show will require a

1040
00:59:20,560 --> 00:59:23,880
Speaker 2: bonk on the head to fully remember who is Chilli neighbor,

1041
00:59:24,000 --> 00:59:27,120
Speaker 2: who is being dad Sidebart, the mid character is so

1042
00:59:27,240 --> 00:59:31,840
Speaker 2: often insert name of food, insert type of person, whatever,

1043
00:59:32,120 --> 00:59:35,400
Speaker 2: but the dress rarely needs introduction if the person was

1044
00:59:35,440 --> 00:59:38,439
Speaker 2: online at the time. There were retrospectives of the dress

1045
00:59:38,480 --> 00:59:42,280
Speaker 2: story being written as recently as last year. When I'm

1046
00:59:42,280 --> 00:59:45,080
Speaker 2: building out episodes for this show, step one is to

1047
00:59:45,160 --> 00:59:47,680
Speaker 2: reach out to the main character themselves to see if

1048
00:59:47,680 --> 00:59:50,320
Speaker 2: they want to talk. After all, the dress was for

1049
00:59:50,360 --> 00:59:53,440
Speaker 2: someone's wedding, and the mom who wore the dress stated

1050
00:59:53,440 --> 00:59:56,280
Speaker 2: at the time of the incessant optical illusion that she

1051
00:59:56,440 --> 00:59:59,760
Speaker 2: was annoyed that she was being excluded from most news coverage.

1052
01:00:00,040 --> 01:00:03,600
Speaker 2: Cecilia and the married couple got the classic fifteen minutes

1053
01:00:03,680 --> 01:00:06,640
Speaker 2: treatment for this era. The married couple, Grace and keirr

1054
01:00:06,960 --> 01:00:09,640
Speaker 2: and Cecilia and her partner Paul, were flown out to

1055
01:00:09,800 --> 01:00:12,240
Speaker 2: la to be on the Ellen Show, and the couple

1056
01:00:12,320 --> 01:00:15,960
Speaker 2: got a briefcase full of ten thousand American dollars. The

1057
01:00:16,000 --> 01:00:19,840
Speaker 2: mother and stepfather of the bride got underwear for some reason,

1058
01:00:20,240 --> 01:00:22,680
Speaker 2: half black and blue and half white and gold, and

1059
01:00:22,720 --> 01:00:26,360
Speaker 2: that was it, and Cecilia was pissed from a Guardian

1060
01:00:26,400 --> 01:00:27,960
Speaker 2: piece from late twenty fifteen.

1061
01:00:28,480 --> 01:00:31,640
Speaker 1: Should it be on display somewhere? Blaisdelle wonders, should it

1062
01:00:31,680 --> 01:00:34,080
Speaker 1: be in a vaulter or whatever. It still got sweaty

1063
01:00:34,080 --> 01:00:37,480
Speaker 1: marks on though needs a clean. So nine years later

1064
01:00:37,720 --> 01:00:40,680
Speaker 1: I wanted to see where everyone was, and that's where

1065
01:00:40,720 --> 01:00:43,880
Speaker 1: this story takes an especially dark turn. When I went

1066
01:00:43,920 --> 01:00:46,560
Speaker 1: to find out where that couple is now, I found

1067
01:00:46,560 --> 01:00:50,400
Speaker 1: this headline from July twenty twenty three. Man behind viral

1068
01:00:50,480 --> 01:00:54,480
Speaker 1: blue black dress illusion charged with trying to kill wife.

1069
01:00:54,760 --> 01:00:56,920
Speaker 1: I don't want to rehash it too heavily here, but

1070
01:00:57,000 --> 01:01:01,560
Speaker 1: the relationship had been tremendously abusive since before the dress

1071
01:01:01,600 --> 01:01:06,880
Speaker 1: and their marriage, culminating in husband Keir making repeated violent

1072
01:01:06,960 --> 01:01:10,840
Speaker 1: attempts on Grace's life, isolating her from friends and family,

1073
01:01:11,120 --> 01:01:14,080
Speaker 1: and attempting to murder her during the spring of twenty

1074
01:01:14,120 --> 01:01:17,240
Speaker 1: twenty two. He then went to court and denied all

1075
01:01:17,360 --> 01:01:21,640
Speaker 1: charges and most clickbait in twenty twenty three because yes,

1076
01:01:22,040 --> 01:01:25,200
Speaker 1: all of the same outlets reported on this story again

1077
01:01:25,640 --> 01:01:29,280
Speaker 1: made the somewhat lazy observation that the dress had previously

1078
01:01:29,320 --> 01:01:32,960
Speaker 1: been used in a domestic violence awareness campaign in South

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01:01:32,960 --> 01:01:36,280
Speaker 1: Africa back in twenty fifteen, featuring a woman wearing the

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01:01:36,400 --> 01:01:39,120
Speaker 1: dress in white and gold while covered in black and

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01:01:39,160 --> 01:01:41,880
Speaker 1: blue bruises. Why is it so hard to see black

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01:01:41,920 --> 01:01:43,120
Speaker 1: and blue? The ad asked.

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01:01:43,720 --> 01:01:46,400
Speaker 2: I think this ad is very weird, even if the

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01:01:46,400 --> 01:01:49,600
Speaker 2: intention was well placed, and the wave of twenty twenty

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01:01:49,600 --> 01:01:52,880
Speaker 2: three press connecting it to the real life terror campaign

1086
01:01:52,960 --> 01:01:56,920
Speaker 2: taken out on Grace felt just as wrong because virtually

1087
01:01:57,120 --> 01:02:00,720
Speaker 2: all of those pieces, while sympathetic to her, ended in

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01:02:00,760 --> 01:02:04,160
Speaker 2: some tired version of by the way, the dress was

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01:02:04,200 --> 01:02:07,840
Speaker 2: black and blue and as of this month, in May

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01:02:07,880 --> 01:02:10,800
Speaker 2: twenty twenty four, I am recording this a month after

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01:02:10,840 --> 01:02:14,600
Speaker 2: we finished this episode. Here, Johnston is being held in

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01:02:14,640 --> 01:02:18,959
Speaker 2: prison after pleading guilty to attacking and attempting to kill

1093
01:02:19,000 --> 01:02:22,400
Speaker 2: Grace back in twenty twenty two. At the time, Grace

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01:02:22,480 --> 01:02:25,960
Speaker 2: called and texted people for help after years of reporting

1095
01:02:25,960 --> 01:02:28,720
Speaker 2: abuse and said that her husband was trying to kill

1096
01:02:28,760 --> 01:02:32,760
Speaker 2: her after she was violently attacked, strangled, and was threatened

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01:02:32,760 --> 01:02:35,720
Speaker 2: with a knife. As of this recording, the case is

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01:02:35,760 --> 01:02:40,000
Speaker 2: ongoing and we wish Grace nothing but peace. And getting

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01:02:40,200 --> 01:02:43,840
Speaker 2: as far away from any of this fucking discourse as

1100
01:02:43,880 --> 01:02:46,560
Speaker 2: she needs to do. And it's further proof that any

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01:02:46,600 --> 01:02:49,920
Speaker 2: Internet story on a long enough timeline begins to reflect

1102
01:02:49,960 --> 01:02:53,160
Speaker 2: the ugliness of the world that the Internet reflects and

1103
01:02:53,240 --> 01:02:57,480
Speaker 2: so often warps. Okay, cutting back into the original episode here,

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01:02:58,240 --> 01:03:00,200
Speaker 2: I'm sort of hesitant to say that there's it's a

1105
01:03:00,200 --> 01:03:03,440
Speaker 2: clear lesson to be learned from the saga of the dress,

1106
01:03:03,960 --> 01:03:07,160
Speaker 2: but it certainly encapsulates a moment in Internet history that

1107
01:03:07,280 --> 01:03:10,520
Speaker 2: feels ungo backable, and there's no better example of that

1108
01:03:10,680 --> 01:03:13,000
Speaker 2: than all the writers who were paid at the time

1109
01:03:13,120 --> 01:03:16,760
Speaker 2: to make it mean something outside of being an increasingly

1110
01:03:16,880 --> 01:03:20,959
Speaker 2: rare monocultural moment that swirled in normal people with tech

1111
01:03:21,040 --> 01:03:25,640
Speaker 2: profits and our attention. So the dress means something, but

1112
01:03:25,760 --> 01:03:29,280
Speaker 2: what exactly that is is a little elusive. Almost ten

1113
01:03:29,360 --> 01:03:31,959
Speaker 2: years on, I feel kind of nostalgic for a time

1114
01:03:32,000 --> 01:03:35,080
Speaker 2: where the Internet could unite over a neutral issue, But

1115
01:03:35,120 --> 01:03:37,560
Speaker 2: as a twenty two year old clickbait writer, I didn't

1116
01:03:37,560 --> 01:03:40,439
Speaker 2: even have the time to appreciate it because I needed

1117
01:03:40,520 --> 01:03:44,920
Speaker 2: toform an edgy, smart adjacent opinion about it as quickly

1118
01:03:44,960 --> 01:03:47,600
Speaker 2: as possible. Here's a passage from a reaction to the

1119
01:03:47,680 --> 01:03:50,640
Speaker 2: dress and the lama's success as stories on the heels

1120
01:03:50,720 --> 01:03:54,200
Speaker 2: of the net neutrality decision. The essay is called the

1121
01:03:54,280 --> 01:03:59,040
Speaker 2: open Internet will keep Us stupid and happy. At this point,

1122
01:03:59,200 --> 01:04:01,840
Speaker 2: it's unfathomable to think of anything short of a Gill

1123
01:04:01,960 --> 01:04:05,120
Speaker 2: force windstorm full of knives that could prevent the Internet

1124
01:04:05,120 --> 01:04:10,360
Speaker 2: from brimming with memes, jifts, and asenine materials from content farms. Hell,

1125
01:04:10,520 --> 01:04:12,640
Speaker 2: I used to write for a content farm in Boston,

1126
01:04:12,880 --> 01:04:16,080
Speaker 2: and yes, I have written an article called how Seth

1127
01:04:16,160 --> 01:04:18,600
Speaker 2: MacFarlane's A Million Ways to Die in the West can

1128
01:04:18,640 --> 01:04:21,160
Speaker 2: teach us about how to run our tech companies? And

1129
01:04:21,240 --> 01:04:25,200
Speaker 2: that is objectively gross. The Internet is stupid, but it

1130
01:04:25,240 --> 01:04:28,280
Speaker 2: was almost stupid and expensive. Thanks to the net neutrality

1131
01:04:28,360 --> 01:04:31,040
Speaker 2: vote passed yesterday, we won't need to worry about our

1132
01:04:31,080 --> 01:04:37,480
Speaker 2: access to lamas and confusing dresses being hindered for now. Anyway. Yeah,

1133
01:04:37,520 --> 01:04:40,960
Speaker 2: I wrote this, and what the fuck was I talking about? Anyways?

1134
01:04:41,000 --> 01:04:43,640
Speaker 2: Good for her, I'm sure she'll never overthink anything again.

1135
01:04:45,600 --> 01:04:49,160
Speaker 2: Sixteenth Minute is a production of Cool Zone Media and iHeartRadio.

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01:04:49,560 --> 01:04:53,120
Speaker 2: It is written, hosted, and executive produced by me Jamie Loftus,

1137
01:04:53,160 --> 01:04:56,760
Speaker 2: our other executive producers are Sophie Lichterman and Robert Evans,

1138
01:04:56,800 --> 01:05:00,400
Speaker 2: and our supervising producer and editor is Ian Johnson. Our

1139
01:05:00,440 --> 01:05:02,720
Speaker 2: theme song is by Sadie Dupui, and I would like

1140
01:05:02,760 --> 01:05:05,800
Speaker 2: to thank all of the pets, Anderson the dog, my

1141
01:05:05,960 --> 01:05:09,240
Speaker 2: cats fleeing Casper, and my pet rock Bird, who'll outlive

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01:05:09,360 --> 01:05:11,360
Speaker 2: us all. Bye,