TEDxMidAtlantic 2013
Jennifer Golbeck: Your social media "likes" expose more than you think
Jennifer Golbeck: Zagonetka kovrčavog krumpirića: Zašto „lajkovi“ društvenih medija govore više no što ste možda mislili
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Volite li kovrčave krumpiriće? Jeste li ih „lajkali“ na Facebooku? Pogledajte ovaj razgovor kako biste otkrili koje iznenađujuće stvari Facebook (i drugi) mogu pogoditi samo na temelju vaših nasumičnih „lajkova“ i podjela. Računalna znanstvenica Jennifer Golbeck objašnjava kako je došlo do toga, kako neke primjene tehnologije nisu toliko ljupke – te zašto misli da bismo trebali vratiti kontrolu nad informacijama njihovim zakonitim vlasnicima.
Jennifer Golbeck - Computer scientist
As the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions. Full bio
As the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions. Full bio
Double-click the English transcript below to play the video.
00:12
If you remember that first decade of the web,
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Ako se sjećate tog prvog
desetljeća na Internetu,
desetljeća na Internetu,
00:14
it was really a static place.
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bilo je to vrlo statično mjesto.
00:16
You could go online, you could look at pages,
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Mogli ste otići online,
mogli ste gledati stranice,
mogli ste gledati stranice,
00:19
and they were put up either by organizations
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a one su bile postavljene
od strane organizacija
od strane organizacija
00:21
who had teams to do it
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koje su imale timove za to
00:23
or by individuals who were really tech-savvy
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ili vrlo tehnološki stručni pojedinci
00:25
for the time.
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za to doba.
00:27
And with the rise of social media
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S porastom društvenih medija
00:28
and social networks in the early 2000s,
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i društvenih mreža ranih 2000 - tih,
00:31
the web was completely changed
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Internet se u potpunosti promijenio
00:33
to a place where now the vast majority of content
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u mjesto gdje velika većina sadržaja
00:36
we interact with is put up by average users,
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s kojim dolazimo u kontakt je
postavljena od strane prosječnih korisnika,
postavljena od strane prosječnih korisnika,
00:40
either in YouTube videos or blog posts
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u YouTube videima ili objavama na blogu
00:42
or product reviews or social media postings.
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ili recenzijama proizvoda
ili objavama društvenih medija.
ili objavama društvenih medija.
00:46
And it's also become a much more interactive place,
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Također je postao puno
više interaktivno mjesto,
više interaktivno mjesto,
00:48
where people are interacting with others,
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gdje ljudi uzajamno djeluju,
00:51
they're commenting, they're sharing,
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komentiraju, dijele,
00:52
they're not just reading.
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nije da samo čitaju.
00:54
So Facebook is not the only place you can do this,
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Facebook nije jedino mjesto
gdje to možete činiti,
gdje to možete činiti,
00:56
but it's the biggest,
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ali je najveće,
00:57
and it serves to illustrate the numbers.
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i služi za ilustriranje podataka.
00:59
Facebook has 1.2 billion users per month.
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Facebook ima 1.2 milijardu
korisnika na mjesec.
korisnika na mjesec.
01:02
So half the Earth's Internet population
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Stoga pola Zemljine internetske populacije
01:04
is using Facebook.
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koristi Facebook.
01:06
They are a site, along with others,
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Oni su stranica, zajedno s drugima,
01:08
that has allowed people to create an online persona
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koja je dozvolila ljudima
da stvore online personu
da stvore online personu
01:11
with very little technical skill,
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s vrlo malo tehničkih vještina,
01:13
and people responded by putting huge amounts
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a ljudi su na to odgovorili postavljanjem
01:15
of personal data online.
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gomile osobnih podataka na mrežu.
01:17
So the result is that we have behavioral,
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Rezultat je da imamo podatke o ponašanju,
01:20
preference, demographic data
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podatke o sklonostima, demografske podatke,
01:22
for hundreds of millions of people,
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za stotine milijuna ljudi,
01:24
which is unprecedented in history.
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što je jedinstven slučaj u povijesti.
01:26
And as a computer scientist,
what this means is that
what this means is that
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Kao računalnome znanstveniku, to znači da
01:29
I've been able to build models
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sam mogla napraviti modele
01:30
that can predict all sorts of hidden attributes
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koji mogu predvidjeti
razne skrivene osobine
razne skrivene osobine
01:32
for all of you that you don't even know
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za sve vas, a da ni ne znate
01:35
you're sharing information about.
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da dijelite informacije o tome.
01:37
As scientists, we use that to help
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Kao znanstvenici, koristimo to kao pomoć
01:39
the way people interact online,
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u načinu kako ljudi komuniciraju online,
01:41
but there's less altruistic applications,
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ali ima i manje altruističnih primjena,
01:44
and there's a problem in that users don't really
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a problem je u tome što korisnici
01:46
understand these techniques and how they work,
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ne razumiju baš te tehnike i kako one rade,
01:49
and even if they did, they don't
have a lot of control over it.
have a lot of control over it.
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a čak i kada bi razumjeli,
nemaju puno kontrole nad time.
nemaju puno kontrole nad time.
01:52
So what I want to talk to you about today
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Ono o čemu želim pričati danas
01:53
is some of these things that we're able to do,
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su neke stvari koje možemo napraviti,
01:56
and then give us some ideas
of how we might go forward
of how we might go forward
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i onda dati neke ideje
kako bismo mogli napredovati
kako bismo mogli napredovati
01:59
to move some control back into the hands of users.
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da vratimo dio kontrole natrag korisnicima.
02:02
So this is Target, the company.
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Ovo je tvrtka Target.
02:03
I didn't just put that logo
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Nisam ja stavila taj logo
02:05
on this poor, pregnant woman's belly.
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na trbuh ove jadne, trudne žene.
02:07
You may have seen this anecdote that was printed
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Možda ste čuli ovu anegdotu
02:09
in Forbes magazine where Target
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objavljenu u časopisu Forbes;
02:11
sent a flyer to this 15-year-old girl
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Target je poslao letak 15 godišnjoj djevojci
02:13
with advertisements and coupons
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s reklamama i kuponima
02:15
for baby bottles and diapers and cribs
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za dječje bočice, pelene i kolijevke
02:17
two weeks before she told her parents
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dva tjedna prije no što je rekla
02:19
that she was pregnant.
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roditeljima da je trudna.
02:21
Yeah, the dad was really upset.
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Da, otac je bio zbilja uzrujan.
02:24
He said, "How did Target figure out
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Rekao je, “Kako je Target saznao
02:25
that this high school girl was pregnant
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da je ova srednjoškolka trudna
02:27
before she told her parents?"
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prije nego što je ona rekla roditeljima?”
02:29
It turns out that they have the purchase history
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Ispostavilo se da imaju prošlost kupovine
02:32
for hundreds of thousands of customers
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za stotine tisuća kupaca
02:34
and they compute what they
call a pregnancy score,
call a pregnancy score,
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te izračunavaju ono što oni
nazivaju domet trudnoće,
nazivaju domet trudnoće,
02:37
which is not just whether or
not a woman's pregnant,
not a woman's pregnant,
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koji govori ne samo je li žena trudna,
02:39
but what her due date is.
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već i koji joj je termin porođaja.
02:41
And they compute that
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Oni to računaju
02:42
not by looking at the obvious things,
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ne gledajući samo očite stvari
02:44
like, she's buying a crib or baby clothes,
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poput: kupuje li kolijevku ili odjeću za bebe,
02:46
but things like, she bought more vitamins
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nego i stvari kao što su: kupila je
02:49
than she normally had,
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više vitamina no obično,
02:51
or she bought a handbag
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ili kupila je ručnu torbu
02:52
that's big enough to hold diapers.
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u koju stanu pelene.
02:54
And by themselves, those purchases don't seem
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Same po sebi, te se kupovine
02:56
like they might reveal a lot,
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ne čine kao da otkrivaju mnogo,
02:59
but it's a pattern of behavior that,
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ali uzorak takvog ponašanja,
03:01
when you take it in the context
of thousands of other people,
of thousands of other people,
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kada ga se stavi u kontekst tisuća drugih ljudi,
03:04
starts to actually reveal some insights.
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zapravo otkriva neka shvaćanja.
03:06
So that's the kind of thing that we do
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To je način na koji predviđamo
03:08
when we're predicting stuff
about you on social media.
about you on social media.
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stvari o vama u društvenim medijima.
03:11
We're looking for little
patterns of behavior that,
patterns of behavior that,
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Tražimo male obrasce ponašanja koji,
03:14
when you detect them among millions of people,
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kada ih otkrijete među milijunima ljudi,
03:16
lets us find out all kinds of things.
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otkrivaju nam razne stvari.
03:19
So in my lab and with colleagues,
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U laboratoriju s mojim kolegama,
03:21
we've developed mechanisms where we can
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razvili smo mehanizme pomoću kojih možemo
03:22
quite accurately predict things
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prilično precizno predvidjeti stvari
03:24
like your political preference,
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poput vaše političke preferencije,
03:26
your personality score, gender, sexual orientation,
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tipa osobnosti, spol, seksualnu orijentaciju,
03:29
religion, age, intelligence,
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vjeru, dob, inteligenciju,
03:32
along with things like
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zajedno sa stvarima poput
03:34
how much you trust the people you know
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koliko vjerujete ljudima koje poznajete
03:36
and how strong those relationships are.
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i koliko su jake te veze.
03:38
We can do all of this really well.
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Sve to jako dobro radimo.
03:39
And again, it doesn't come from what you might
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I opet, to ne dolazi od onoga
03:41
think of as obvious information.
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što vam se čini kao očiti izvor informacija.
03:44
So my favorite example is from this study
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Moj najdraži primjer iz ovog je istraživanja
03:46
that was published this year
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koje je objavljeno ove godine
03:47
in the Proceedings of the National Academies.
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u broju časopisa Postupci Nacionalne
Akademije S.A.D.
Akademije S.A.D.
03:49
If you Google this, you'll find it.
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Ako ga guglate, pronaći ćete ga.
03:50
It's four pages, easy to read.
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Ima četiri stranice, lak je za čitanje.
03:52
And they looked at just people's Facebook likes,
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Gledali su samo “lajkove” na Facebooku,
03:55
so just the things you like on Facebook,
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samo one stvari koje vam se sviđaju,
03:57
and used that to predict all these attributes,
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te su ih koristili za
predviđanje ovih obilježja,
predviđanje ovih obilježja,
03:59
along with some other ones.
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zajedno s još nekim drugima.
04:01
And in their paper they listed the five likes
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U svojem radu nabrojali su pet “lajkova“
04:04
that were most indicative of high intelligence.
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koji su najviše ukazivali
na visok stupanj inteligencije.
na visok stupanj inteligencije.
04:07
And among those was liking a page
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A među njima bila je stranica
04:09
for curly fries. (Laughter)
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za kovrčave krumpiriće. (Smijeh)
04:11
Curly fries are delicious,
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Kovrčavi krumpirići zbilja su ukusni,
04:13
but liking them does not necessarily mean
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no ako ste ih označili da vam se sviđaju,
04:15
that you're smarter than the average person.
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ne znači da ste pametniji od prosječne osobe.
04:17
So how is it that one of the strongest indicators
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Stoga, kako da je jedan
od snažnijih indikatora
od snažnijih indikatora
04:21
of your intelligence
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vaše inteligencije
04:22
is liking this page
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“lajkanje” stranice
04:24
when the content is totally irrelevant
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čiji je sadržaj u potpunosti ireleventan
04:26
to the attribute that's being predicted?
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za atribut koji predviđa?
04:28
And it turns out that we have to look at
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Ispostavlja se da moramo sagledati
04:30
a whole bunch of underlying theories
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golemi broj temeljnih teorija
04:32
to see why we're able to do this.
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da bismo mogli vidjeti zašto
možemo to predvidjeti.
možemo to predvidjeti.
04:34
One of them is a sociological
theory called homophily,
theory called homophily,
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Jedna od njih je sociološka
teorija imena homofilija,
teorija imena homofilija,
04:37
which basically says people are
friends with people like them.
friends with people like them.
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koja u biti govori da su ljudi
prijatelji s onima koji su im slični.
prijatelji s onima koji su im slični.
04:40
So if you're smart, you tend to
be friends with smart people,
be friends with smart people,
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Stoga, ako ste pametni,
težite biti prijatelj s takvima
težite biti prijatelj s takvima
04:42
and if you're young, you tend
to be friends with young people,
to be friends with young people,
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ako ste mladi, težite biti
prijatelj s mladim ljudima,
prijatelj s mladim ljudima,
04:45
and this is well established
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i to je uzorak koji je utvrđen
04:46
for hundreds of years.
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već stotinama godina.
04:48
We also know a lot
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Također znamo mnogo o tome
04:49
about how information spreads through networks.
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kako se informacije šire mrežama.
04:52
It turns out things like viral videos
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Ispostavlja se da se stvari poput viralnih videa,
04:54
or Facebook likes or other information
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“lajkova” na Facebooku ili drugih informacija
04:56
spreads in exactly the same way
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šire na potpuno jednak način
04:58
that diseases spread through social networks.
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kako se zarazne bolesti šire,
ali kroz društvene mreže.
ali kroz društvene mreže.
05:01
So this is something we've studied for a long time.
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To je nešto što smo proučavali dugo vremena.
05:02
We have good models of it.
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Imamo dobre modele tog obrasca.
05:04
And so you can put those things together
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Možete posložiti pojave poput te
05:06
and start seeing why things like this happen.
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i uvidjeti zašto se takve stvari događaju.
05:09
So if I were to give you a hypothesis,
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Pa, ako bi vam dala hipotezu,
05:11
it would be that a smart guy started this page,
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ona bi glasila da je pametan
tip pokrenuo tu stranicu,
tip pokrenuo tu stranicu,
05:14
or maybe one of the first people who liked it
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ili nekoliko prvih ljudi koji su ju “lajkali”
05:16
would have scored high on that test.
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su postigli visoke rezultate
na ispitu inteligencije.
na ispitu inteligencije.
05:18
And they liked it, and their friends saw it,
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Svidjela im se, njihovi su je prijatelji vidjeli,
05:20
and by homophily, we know that
he probably had smart friends,
he probably had smart friends,
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i prema načelu homofilije znamo
da su vjerojatno imali pametne prijatelje
da su vjerojatno imali pametne prijatelje
05:23
and so it spread to them,
and some of them liked it,
and some of them liked it,
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pa se to proširilo do njih,
neki od njih su ju “lajkali”,
neki od njih su ju “lajkali”,
05:26
and they had smart friends,
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a i oni su imali pametne prijatelje,
05:28
and so it spread to them,
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pa se proširilo do njih,
05:28
and so it propagated through the network
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tako se propagira kroz mrežu
05:30
to a host of smart people,
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koristeći pametne ljude kao domaćine,
05:33
so that by the end, the action
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stoga je na kraju čin sviđanja
05:35
of liking the curly fries page
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stranice kovrčavih krumpirića
05:37
is indicative of high intelligence,
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postao indikacija visoke inteligencije,
05:39
not because of the content,
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ne zbog njezina sadržaja,
05:41
but because the actual action of liking
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već zato što sam pritisak na gumb sviđanja
05:43
reflects back the common attributes
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zrcali učestale atribute
05:45
of other people who have done it.
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ljudi koji su ga pritisnuli.
05:48
So this is pretty complicated stuff, right?
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Ovo su poprilično komplicirane stvari, jelda?
05:51
It's a hard thing to sit down and explain
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Teško je sjesti i objasniti
to
to
05:53
to an average user, and even if you do,
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prosječnom korisniku,
no čak i ako to učinite,
no čak i ako to učinite,
05:56
what can the average user do about it?
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što može prosječan korisnik
u vezi toga napraviti?
u vezi toga napraviti?
05:58
How do you know that
you've liked something
you've liked something
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Kako znate da nešto što ste “lajkali”
06:00
that indicates a trait for you
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indicira vašu osobinu
06:01
that's totally irrelevant to the
content of what you've liked?
content of what you've liked?
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koja je u potpunosti nepovezana
sa sadržajem onoga što ste “lajkali”?
sa sadržajem onoga što ste “lajkali”?
06:05
There's a lot of power that users don't have
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Postoji puno snage koju korisnici nemaju
06:08
to control how this data is used.
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za kontrolu kako se koriste ti podatci.
06:10
And I see that as a real
problem going forward.
problem going forward.
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i vidim to kao velik problem gledajući unaprijed.
06:13
So I think there's a couple paths
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Mislim da postoji nekoliko načina
06:15
that we want to look at
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koje želimo sagledati
06:16
if we want to give users some control
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ako bismo korisnicima donekle dali kontrolu
06:18
over how this data is used,
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nad time kako se koriste ti podatci,
06:20
because it's not always going to be used
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jer neće uvijek biti iskorišteni
06:21
for their benefit.
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u njihovu korist.
06:23
An example I often give is that,
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Primjer koji često dajem jest,
06:24
if I ever get bored being a professor,
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ako mi ikad dosadi biti profesor,
06:26
I'm going to go start a company
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pokrenut ću kompaniju
06:28
that predicts all of these attributes
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koja predviđa sve te atribute
06:29
and things like how well you work in teams
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i stvari kao što su, radite li dobro u timu,
06:31
and if you're a drug user, if you're an alcoholic.
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koristite li droge, jeste li alkoholičar.
06:33
We know how to predict all that.
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Sve to znamo predvidjeti.
06:35
And I'm going to sell reports
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I prodavat ću izvješća
06:36
to H.R. companies and big businesses
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kompanijama ljudskih resursa i velikim tvrtkama
06:39
that want to hire you.
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koje vas žele zaposliti.
06:41
We totally can do that now.
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Mi to možemo napraviti.
06:42
I could start that business tomorrow,
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Mogla bih pokrenuti taj posao sutra,
06:44
and you would have
absolutely no control
absolutely no control
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a vi ne biste imali apsolutno nikakvu kontrolu
06:46
over me using your data like that.
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nada mnom koja koristim
vaše podatke na taj način.
vaše podatke na taj način.
06:48
That seems to me to be a problem.
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To mi se čini kao problem.
06:50
So one of the paths we can go down
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Jedan put kojim možemo poći
06:52
is the policy and law path.
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je put politike i prava.
06:54
And in some respects, I think
that that would be most effective,
that that would be most effective,
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I u nekim pogledima, mislim
da bi to bilo najučinkovitije,
da bi to bilo najučinkovitije,
06:57
but the problem is we'd
actually have to do it.
actually have to do it.
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no problem je da bismo
to zbilja morali napraviti.
to zbilja morali napraviti.
07:00
Observing our political process in action
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Promatrajući naše političke procese u akciji
07:03
makes me think it's highly unlikely
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čini mi se da je malo vjerojatno
07:05
that we're going to get a bunch of representatives
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da ćemo dobiti gomilu predstavnika
07:07
to sit down, learn about this,
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koji će sjesti, naučiti o tome
07:09
and then enact sweeping changes
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i onda donijeti dalekosežne promjene
07:11
to intellectual property law in the U.S.
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u američke zakone o intelektualnom vlasništvu
07:13
so users control their data.
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da bi korisnici mogli kontrolirati svoje podatke.
07:16
We could go the policy route,
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Možemo krenuti putem politike,
07:17
where social media companies say,
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gdje kompanije društvenih medija kažu:
07:18
you know what? You own your data.
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znate što? Vi posjedujete podatke.
07:20
You have total control over how it's used.
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Imate potpunu kontrolu nad time kako se koriste.
07:22
The problem is that the revenue models
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Problem je u tome što se modeli prihoda
07:24
for most social media companies
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za većinu kompanija društvenih medija
07:26
rely on sharing or exploiting
users' data in some way.
users' data in some way.
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oslanjaju na dijeljenje ili, na neki način,
iskorištavanje podataka korisnika.
iskorištavanje podataka korisnika.
07:30
It's sometimes said of Facebook that the users
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Ponekad se kaže za Facebook da korisnici
07:32
aren't the customer, they're the product.
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nisu mušterije, već proizvod.
07:34
And so how do you get a company
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Stoga kako natjerati kompaniju
07:37
to cede control of their main asset
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da korisnicima vrati kontrolu
07:39
back to the users?
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nad njihovom glavnom imovinom?
07:41
It's possible, but I don't think it's something
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Moguće je, ali ne mislim da je to promjena
07:42
that we're going to see change quickly.
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koju ćemo ubrzo vidjeti.
07:45
So I think the other path
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Stoga mislim da će drugi put
07:46
that we can go down that's
going to be more effective
going to be more effective
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kojim možemo poći biti djelotvorniji
07:48
is one of more science.
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i to je onaj sa više znanosti.
07:50
It's doing science that allowed us to develop
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Prije svega, znanost nam dopušta razvijanje
07:52
all these mechanisms for computing
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svih ovih mehanizama za računanje
07:54
this personal data in the first place.
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osobnih podataka.
07:56
And it's actually very similar research
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I zapravo bismo morali provesti
07:58
that we'd have to do
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vrlo slično istraživanje
08:00
if we want to develop mechanisms
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ako bismo htjeli razviti mehanizme
08:02
that can say to a user,
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koji bi korisniku rekli:
08:04
"Here's the risk of that action you just took."
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“Ovo je rizik radnje koju ste upravo napravili.”
08:06
By liking that Facebook page,
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Lajkanjem te Facebook stranice
08:08
or by sharing this piece of personal information,
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ili dijeljenjem ove osobne informacije,
08:10
you've now improved my ability
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upravo ste poboljšali moju sposobnost
08:12
to predict whether or not you're using drugs
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predviđanja koristite li droge,
08:14
or whether or not you get
along well in the workplace.
along well in the workplace.
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ili slažete li se na radnom mjestu.
08:17
And that, I think, can affect whether or not
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A to mislim da bi moglo
08:19
people want to share something,
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utjecati na želju ljudi da nešto podijele,
08:20
keep it private, or just keep it offline altogether.
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zadrže to privatnim, ili uopće ne stave online.
08:24
We can also look at things like
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Možemo se također osvrnuti na stvari
08:25
allowing people to encrypt data that they upload,
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poput dopuštanja ljudima da kodiraju
podatke koje su objavili,
podatke koje su objavili,
08:28
so it's kind of invisible and worthless
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pa oni postaju nevidljivi i beskorisni
08:30
to sites like Facebook
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stranicama poput Facebooka
08:31
or third party services that access it,
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ili uslugama trećih stranaka
koje im imaju pristup,
koje im imaju pristup,
08:34
but that select users who the person who posted it
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ali da odabrani korisnici
08:37
want to see it have access to see it.
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za koje vlasnici žele da vide
informacije ih mogu i vidjeti.
informacije ih mogu i vidjeti.
08:40
This is all super exciting research
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Ovo je vrlo uzbudljivo istraživanje
08:42
from an intellectual perspective,
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iz intelektualne perspektive,
08:43
and so scientists are going to be willing to do it.
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stoga će ga znanstvenici biti voljni napraviti.
08:45
So that gives us an advantage over the law side.
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To nam daje prednost pred zakonskom stranom.
08:49
One of the problems that people bring up
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Jedan od problema koji ljudi spomenu
08:51
when I talk about this is, they say,
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kada pričam o ovome jest, kažu,
08:52
you know, if people start
keeping all this data private,
keeping all this data private,
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znate, ako ljudi počnu zadržavati
sve svoje podatke privatnima,
sve svoje podatke privatnima,
08:55
all those methods that you've been developing
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sve ove metode koje ste razvijali
08:57
to predict their traits are going to fail.
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za predviđanje njihovih osobina će propasti.
09:00
And I say, absolutely, and for me, that's success,
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A ja kažem, apsolutno, i za mene je to uspjeh,
09:03
because as a scientist,
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jer kao znanstveniku,
09:05
my goal is not to infer information about users,
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moj cilj nije dolaziti do
informacija o korisnicima,
informacija o korisnicima,
09:09
it's to improve the way people interact online.
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već poboljšati način kako ljudi
međusobno komuniciraju online.
međusobno komuniciraju online.
09:11
And sometimes that involves
inferring things about them,
inferring things about them,
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Ponekad to uključuje
saznavanje njihovih informacija,
saznavanje njihovih informacija,
09:15
but if users don't want me to use that data,
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ali ako korisnici ne žele da koristim te podatke,
09:18
I think they should have the right to do that.
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mislim da bi trebali imati to pravo i učiniti.
09:20
I want users to be informed and consenting
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Želim da korisnici budu informirani
09:22
users of the tools that we develop.
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i da prihvaćaju alate koje razvijamo.
09:24
And so I think encouraging this kind of science
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Mislim da ohrabrivanje ove vrste znanosti
09:27
and supporting researchers
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i podupiranje istraživača
09:29
who want to cede some of that control back to users
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koji žele povratiti dio kontrole korisnicima
09:32
and away from the social media companies
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od kompanija društvenih medija
09:34
means that going forward, as these tools evolve
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znači da idući naprijed,
kako se ova oruđa razvijaju
kako se ova oruđa razvijaju
09:37
and advance,
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i napreduju,
09:38
means that we're going to have an educated
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imat ćemo obrazovanu
09:40
and empowered user base,
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i osposobljenu bazu korisnika,
09:41
and I think all of us can agree
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te mislim da se svi možemo složiti
09:42
that that's a pretty ideal way to go forward.
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da je to prilično idealan način za napredovanje.
09:45
Thank you.
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Hvala.
09:47
(Applause)
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(Pljesak)
ABOUT THE SPEAKER
Jennifer Golbeck - Computer scientistAs the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions.
Why you should listen
Jennifer Golbeck is an associate professor in the College of Information Studies at the University of Maryland, where she also moonlights in the department of computer science. Her work invariably focuses on how to enhance and improve the way that people interact with their own information online. "I approach this from a computer science perspective and my general research hits social networks, trust, web science, artificial intelligence, and human-computer interaction," she writes.
Author of the 2013 book, Analyzing the Social Web, Golbeck likes nothing more than to immerse herself in the inner workings of the Internet tools so many millions of people use daily, to understand the implications of our choices and actions. Recently, she has also been working to bring human-computer interaction ideas to the world of security and privacy systems.
Jennifer Golbeck | Speaker | TED.com