The curly fry conundrum: Why social media “likes” say more than you might think
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0:01 - 0:03If you remember that first decade of the web,
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0:03 - 0:05it was really a static place.
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0:05 - 0:07You could go online, you could look at pages,
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0:07 - 0:10and they were put up either by organizations
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0:10 - 0:11who had teams to do it
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0:11 - 0:13or by individuals who were really tech-savvy
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0:13 - 0:15for the time.
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0:15 - 0:17And with the rise of social media
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0:17 - 0:19and social networks in the early 2000s,
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0:19 - 0:21the web was completely changed
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0:21 - 0:25to a place where now the vast majority of content
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0:25 - 0:28we interact with is put up by average users,
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0:28 - 0:31either in YouTube videos or blog posts
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0:31 - 0:34or product reviews or social media postings.
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0:34 - 0:37And it's also become a much more interactive place,
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0:37 - 0:39where people are interacting with others,
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0:39 - 0:41they're commenting, they're sharing,
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0:41 - 0:43they're not just reading.
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0:43 - 0:44So Facebook is not the only place you can do this,
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0:44 - 0:46but it's the biggest,
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0:46 - 0:47and it serves to illustrate the numbers.
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0:47 - 0:51Facebook has 1.2 billion users per month.
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0:51 - 0:53So half the Earth's Internet population
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0:53 - 0:54is using Facebook.
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0:54 - 0:56They are a site, along with others,
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0:56 - 1:00that has allowed people to create an online persona
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1:00 - 1:01with very little technical skill,
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1:01 - 1:04and people responded by putting huge amounts
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1:04 - 1:06of personal data online.
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1:06 - 1:08So the result is that we have behavioral,
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1:08 - 1:10preference, demographic data
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1:10 - 1:12for hundreds of millions of people,
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1:12 - 1:14which is unprecedented in history.
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1:14 - 1:17And as a computer scientist,
what this means is that -
1:17 - 1:19I've been able to build models
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1:19 - 1:21that can predict all sorts of hidden attributes
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1:21 - 1:23for all of you that you don't even know
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1:23 - 1:25you're sharing information about.
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1:25 - 1:28As scientists, we use that to help
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1:28 - 1:30the way people interact online,
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1:30 - 1:32but there's less altruistic applications,
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1:32 - 1:35and there's a problem in that users don't really
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1:35 - 1:37understand these techniques and how they work,
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1:37 - 1:40and even if they did, they don't
have a lot of control over it. -
1:40 - 1:42So what I want to talk to you about today
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1:42 - 1:45is some of these things that we're able to do,
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1:45 - 1:47and then give us some ideas
of how we might go forward -
1:47 - 1:50to move some control back into the hands of users.
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1:50 - 1:52So this is Target, the company.
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1:52 - 1:53I didn't just put that logo
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1:53 - 1:55on this poor, pregnant woman's belly.
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1:55 - 1:57You may have seen this anecdote that was printed
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1:57 - 1:59in Forbes magazine where Target
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1:59 - 2:02sent a flyer to this 15-year-old girl
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2:02 - 2:03with advertisements and coupons
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2:03 - 2:06for baby bottles and diapers and cribs
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2:06 - 2:07two weeks before she told her parents
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2:07 - 2:09that she was pregnant.
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2:09 - 2:12Yeah, the dad was really upset.
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2:12 - 2:14He said, "How did Target figure out
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2:14 - 2:16that this high school girl was pregnant
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2:16 - 2:18before she told her parents?"
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2:18 - 2:20It turns out that they have the purchase history
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2:20 - 2:22for hundreds of thousands of customers
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2:22 - 2:25and they compute what they
call a pregnancy score, -
2:25 - 2:28which is not just whether or
not a woman's pregnant, -
2:28 - 2:29but what her due date is.
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2:29 - 2:31And they compute that
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2:31 - 2:32not by looking at the obvious things,
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2:32 - 2:35like, she's buying a crib or baby clothes,
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2:35 - 2:38but things like, she bought more vitamins
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2:38 - 2:39than she normally had,
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2:39 - 2:41or she bought a handbag
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2:41 - 2:43that's big enough to hold diapers.
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2:43 - 2:45And by themselves, those purchases don't seem
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2:45 - 2:47like they might reveal a lot,
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2:47 - 2:49but it's a pattern of behavior that,
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2:49 - 2:52when you take it in the context
of thousands of other people, -
2:52 - 2:55starts to actually reveal some insights.
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2:55 - 2:57So that's the kind of thing that we do
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2:57 - 2:59when we're predicting stuff
about you on social media. -
2:59 - 3:02We're looking for little
patterns of behavior that, -
3:02 - 3:05when you detect them among millions of people,
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3:05 - 3:07lets us find out all kinds of things.
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3:07 - 3:09So in my lab and with colleagues,
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3:09 - 3:11we've developed mechanisms where we can
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3:11 - 3:13quite accurately predict things
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3:13 - 3:14like your political preference,
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3:14 - 3:18your personality score, gender, sexual orientation,
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3:18 - 3:21religion, age, intelligence,
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3:21 - 3:22along with things like
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3:22 - 3:24how much you trust the people you know
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3:24 - 3:26and how strong those relationships are.
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3:26 - 3:28We can do all of this really well.
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3:28 - 3:30And again, it doesn't come from what you might
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3:30 - 3:32think of as obvious information.
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3:32 - 3:34So my favorite example is from this study
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3:34 - 3:36that was published this year
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3:36 - 3:37in the Proceedings of the National Academies.
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3:37 - 3:39If you Google this, you'll find it.
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3:39 - 3:41It's four pages, easy to read.
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3:41 - 3:44And they looked at just people's Facebook likes,
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3:44 - 3:45so just the things you like on Facebook,
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3:45 - 3:48and used that to predict all these attributes,
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3:48 - 3:49along with some other ones.
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3:49 - 3:52And in their paper they listed the five likes
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3:52 - 3:55that were most indicative of high intelligence.
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3:55 - 3:57And among those was liking a page
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3:57 - 3:59for curly fries. (Laughter)
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3:59 - 4:01Curly fries are delicious,
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4:01 - 4:04but liking them does not necessarily mean
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4:04 - 4:06that you're smarter than the average person.
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4:06 - 4:09So how is it that one of the strongest indicators
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4:09 - 4:11of your intelligence
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4:11 - 4:12is liking this page
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4:12 - 4:14when the content is totally irrelevant
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4:14 - 4:17to the attribute that's being predicted?
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4:17 - 4:19And it turns out that we have to look at
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4:19 - 4:20a whole bunch of underlying theories
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4:20 - 4:23to see why we're able to do this.
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4:23 - 4:26One of them is a sociological
theory called homophily, -
4:26 - 4:29which basically says people are
friends with people like them. -
4:29 - 4:31So if you're smart, you tend to
be friends with smart people, -
4:31 - 4:33and if you're young, you tend
to be friends with young people, -
4:33 - 4:35and this is well established
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4:35 - 4:37for hundreds of years.
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4:37 - 4:38We also know a lot
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4:38 - 4:41about how information spreads through networks.
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4:41 - 4:42It turns out things like viral videos
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4:42 - 4:45or Facebook likes or other information
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4:45 - 4:47spreads in exactly the same way
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4:47 - 4:49that diseases spread through social networks.
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4:49 - 4:51So this is something we've studied for a long time.
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4:51 - 4:52We have good models of it.
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4:52 - 4:55And so you can put those things together
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4:55 - 4:58and start seeing why things like this happen.
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4:58 - 4:59So if I were to give you a hypothesis,
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4:59 - 5:03it would be that a smart guy started this page,
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5:03 - 5:05or maybe one of the first people who liked it
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5:05 - 5:06would have scored high on that test.
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5:06 - 5:09And they liked it, and their friends saw it,
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5:09 - 5:12and by homophily, we know that
he probably had smart friends, -
5:12 - 5:15and so it spread to them,
and some of them liked it, -
5:15 - 5:16and they had smart friends,
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5:16 - 5:17and so it spread to them,
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5:17 - 5:19and so it propagated through the network
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5:19 - 5:21to a host of smart people,
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5:21 - 5:23so that by the end, the action
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5:23 - 5:26of liking the curly fries page
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5:26 - 5:28is indicative of high intelligence,
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5:28 - 5:29not because of the content,
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5:29 - 5:32but because the actual action of liking
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5:32 - 5:34reflects back the common attributes
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5:34 - 5:36of other people who have done it.
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5:36 - 5:39So this is pretty complicated stuff, right?
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5:39 - 5:41It's a hard thing to sit down and explain
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5:41 - 5:44to an average user, and even if you do,
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5:44 - 5:46what can the average user do about it?
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5:46 - 5:48How do you know that
you've liked something -
5:48 - 5:50that indicates a trait for you
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5:50 - 5:53that's totally irrelevant to the
content of what you've liked? -
5:53 - 5:56There's a lot of power that users don't have
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5:56 - 5:58to control how this data is used.
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5:58 - 6:01And I see that as a real
problem going forward. -
6:01 - 6:03So I think there's a couple paths
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6:03 - 6:04that we want to look at
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6:04 - 6:06if we want to give users some control
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6:06 - 6:08over how this data is used,
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6:08 - 6:10because it's not always going to be used
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6:10 - 6:11for their benefit.
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6:11 - 6:13An example I often give is that,
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6:13 - 6:14if I ever get bored being a professor,
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6:14 - 6:16I'm going to go start a company
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6:16 - 6:17that predicts all of these attributes
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6:17 - 6:19and things like how well you work in teams
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6:19 - 6:22and if you're a drug user, if you're an alcoholic.
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6:22 - 6:23We know how to predict all that.
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6:23 - 6:25And I'm going to sell reports
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6:25 - 6:27to H.R. companies and big businesses
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6:27 - 6:29that want to hire you.
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6:29 - 6:31We totally can do that now.
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6:31 - 6:32I could start that business tomorrow,
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6:32 - 6:34and you would have
absolutely no control -
6:34 - 6:36over me using your data like that.
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6:36 - 6:39That seems to me to be a problem.
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6:39 - 6:41So one of the paths we can go down
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6:41 - 6:43is the policy and law path.
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6:43 - 6:46And in some respects, I think
that that would be most effective, -
6:46 - 6:49but the problem is we'd
actually have to do it. -
6:49 - 6:51Observing our political process in action
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6:51 - 6:54makes me think it's highly unlikely
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6:54 - 6:55that we're going to get a bunch of representatives
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6:55 - 6:57to sit down, learn about this,
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6:57 - 6:59and then enact sweeping changes
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6:59 - 7:02to intellectual property law in the U.S.
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7:02 - 7:04so users control their data.
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7:04 - 7:05We could go the policy route,
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7:05 - 7:07where social media companies say,
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7:07 - 7:08you know what? You own your data.
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7:08 - 7:11You have total control over how it's used.
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7:11 - 7:13The problem is that the revenue models
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7:13 - 7:14for most social media companies
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7:14 - 7:18rely on sharing or exploiting
users' data in some way. -
7:18 - 7:20It's sometimes said of Facebook that the users
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7:20 - 7:23aren't the customer, they're the product.
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7:23 - 7:25And so how do you get a company
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7:25 - 7:28to cede control of their main asset
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7:28 - 7:29back to the users?
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7:29 - 7:31It's possible, but I don't think it's something
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7:31 - 7:33that we're going to see change quickly.
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7:33 - 7:35So I think the other path
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7:35 - 7:37that we can go down that's
going to be more effective -
7:37 - 7:38is one of more science.
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7:38 - 7:41It's doing science that allowed us to develop
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7:41 - 7:43all these mechanisms for computing
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7:43 - 7:45this personal data in the first place.
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7:45 - 7:47And it's actually very similar research
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7:47 - 7:48that we'd have to do
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7:48 - 7:51if we want to develop mechanisms
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7:51 - 7:52that can say to a user,
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7:52 - 7:54"Here's the risk of that action you just took."
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7:54 - 7:56By liking that Facebook page,
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7:56 - 7:59or by sharing this piece of personal information,
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7:59 - 8:00you've now improved my ability
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8:00 - 8:03to predict whether or not you're using drugs
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8:03 - 8:05or whether or not you get
along well in the workplace. -
8:05 - 8:07And that, I think, can affect whether or not
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8:07 - 8:09people want to share something,
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8:09 - 8:12keep it private, or just keep it offline altogether.
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8:12 - 8:14We can also look at things like
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8:14 - 8:16allowing people to encrypt data that they upload,
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8:16 - 8:18so it's kind of invisible and worthless
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8:18 - 8:20to sites like Facebook
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8:20 - 8:22or third party services that access it,
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8:22 - 8:25but that select users who the person who posted it
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8:25 - 8:28want to see it have access to see it.
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8:28 - 8:30This is all super exciting research
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8:30 - 8:32from an intellectual perspective,
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8:32 - 8:34and so scientists are going to be willing to do it.
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8:34 - 8:37So that gives us an advantage over the law side.
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8:37 - 8:39One of the problems that people bring up
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8:39 - 8:41when I talk about this is, they say,
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8:41 - 8:43you know, if people start
keeping all this data private, -
8:43 - 8:45all those methods that you've been developing
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8:45 - 8:48to predict their traits are going to fail.
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8:48 - 8:52And I say, absolutely, and for me, that's success,
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8:52 - 8:53because as a scientist,
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8:53 - 8:57my goal is not to infer information about users,
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8:57 - 9:00it's to improve the way people interact online.
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9:00 - 9:03And sometimes that involves
inferring things about them, -
9:03 - 9:06but if users don't want me to use that data,
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9:06 - 9:08I think they should have the right to do that.
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9:08 - 9:11I want users to be informed and consenting
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9:11 - 9:13users of the tools that we develop.
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9:13 - 9:16And so I think encouraging this kind of science
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9:16 - 9:17and supporting researchers
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9:17 - 9:20who want to cede some of that control back to users
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9:20 - 9:23and away from the social media companies
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9:23 - 9:25means that going forward, as these tools evolve
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9:25 - 9:27and advance,
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9:27 - 9:28means that we're going to have an educated
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9:28 - 9:30and empowered user base,
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9:30 - 9:31and I think all of us can agree
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9:31 - 9:33that that's a pretty ideal way to go forward.
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9:33 - 9:36Thank you.
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9:36 - 9:39(Applause)
- Title:
- The curly fry conundrum: Why social media “likes” say more than you might think
- Speaker:
- Jennifer Golbeck
- Description:
-
Do you like curly fries? Have you Liked them on Facebook? Watch this talk to find out the surprising things Facebook (and others) can guess about you from your random Likes and Shares. Computer scientist Jennifer Golbeck explains how this came about, how some applications of the technology are not so cute -- and why she thinks we should return the control of information to its rightful owners.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 10:01
Morton Bast edited English subtitles for Your social media "likes" expose more than you think | ||
Yasushi Aoki commented on English subtitles for Your social media "likes" expose more than you think | ||
Yasushi Aoki commented on English subtitles for Your social media "likes" expose more than you think | ||
Yasushi Aoki commented on English subtitles for Your social media "likes" expose more than you think | ||
Morton Bast approved English subtitles for Your social media "likes" expose more than you think | ||
Morton Bast edited English subtitles for Your social media "likes" expose more than you think | ||
Morton Bast edited English subtitles for Your social media "likes" expose more than you think | ||
Morton Bast edited English subtitles for Your social media "likes" expose more than you think |
Yasushi Aoki
do down -> go down
Yasushi Aoki
The description is different from the one on the TED page.
http://www.ted.com/talks/jennifer_golbeck_the_curly_fry_conundrum_why_social_media_likes_say_more_than_you_might_think
Yasushi Aoki
He said, "How did Target figure out...
# This is not words of the father.
http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/