*36C3 preroll music*
Herald: OK. So inside the fake like factories. I'm going to date myself. I
remember it was the Congress around 1990,1991 or so, where I was sitting
together with some people who came over to the states to visit the CCC Congress. And
we were kind of riffing on how great the internet is gonna make the world, you
know, how how it's gonna bring world peace and truth will rule and everything like
that. Boy, were we naive, boy, where we totally wrong. And today I'm going to be
schooled in how wrong I actually was because we have Svea, Dennis and Philip to
tell us all about the fake like factories around the world. And with that, could you
please help me in welcoming them onto the stage? Svea, Dennis and Philip.
Philip: Thank you very much. Welcome to our talk "Inside the Fake Like Factories
". My name is Philip. I'm an Internet activist against disinformation and I'm
also a student of the University of Bamberg.
Svea: Hi. Thank you that you listen to us tonight. My name is Svea. I'm an
investigative journalist, freelance mostly for the NDR and ARD. It's a public
broadcaster in Germany. And I focus on tech issues. And I had the pleasure to
work with these two guys on, for me, a journalistic project and for them on a
scientific project. Dennis: Yeah. Hi, everyone. My name is
Dennis. I'm a PhD student from Ruhr University Bochum. I'm working as a
research assistant for the chair for System Security. My research focuses on
network security topics and Internet measurements. And as Svea said, Philip and
myself, we are here for the scientific part and Svea is for the journalistic part
here. Philip: So here's our outline for today.
So first, I'm going to briefly talk about our motivation for our descent into the
fake like factories and then we are going to show you how we got our hands on ninety
thousand fake like campaigns of a major crowd working platform. And we are also
going to show you why we think that there are 10 billion registered Facebook users
today. So first, I'm going to talk about the like button. The like button is the
ultimate indicator for popularity on social media. It shows you how trustworthy
someone is. It shows how how popular someone is. It shows, it is an indicator
for economic success of brands and it also influences the Facebook algorithm. And as
we are going to show now, these kind of likes can be easily forged and
manipulated. But the problem is that many users will still prefer this bad info on
Facebook about the popularity of a product to no info at all. And so this is a real
problem. And there is no real solution to this. So first, we are going to talk about
the factories and the workers in the fake like factories.
Svea: That there are fake likes and that you can buy likes everywhere, it's well
known. So if you Google "buying fake likes" or even "fake comments" for
Instagram or for Facebook, then you will get like a hundreds of results and you can
buy them very cheap and very expensive. It doesn't matter, you can buy them from
every country. But when you think of these bought likes, then you may think of this.
So you may think of somebody sitting in China, Pakistan or India, and you think of
computers and machines doing all this and that they are, yeah, that they are fake
and also that they can easily be detected and that maybe they are not a big problem.
But it's not always like this. It also can be like this. So, I want you to meet
Maria, I met her in Berlin. And Harald, he lives near Mönchen-Gladbach. So Maria, she
is a a retiree. She was a former police officer. And as money is always short, she
is clicking Facebook likes for money. She earns between 2 cent and 6 cent per like.
And Harald, he was a baker once, is now getting social aid and he is also clicking
and liking and commenting the whole day. We met them during our research project
and did some interviews about their likes. And one platform they are clicking and
working for is PaidLikes. It's only one platform out of a universe, out of a
cosmos. PaidLikes, they are sitting just a couple of minutes from here in Magdeburg
and they are offering that you can earn money with liking on different platforms.
And it looks like this when you log into the platform with your Facebook account
then you get in the morning, in the afternoon, in the evening, you get, we
call it campaigns. But these are pages, Facebook fan pages or Instagram pages, or
posts, or comments. You can, you know, you can work your way through them and click
them. And I blurred you see here the blue bar; I blurred them because we don't want
to get sued from all these companies, which you can see there. To take you a
little bit with me on the journey. Harald, he was okay with us coming by for
television and he was okay that we did a long interview with him, and I want to
show you a very small piece out of his daily life sitting there doing the
household, the washing and the cleaning, and clicking.
Come on. It could be like that. You click and you earn some money. How did we meet
him and all the others? Of course, because Philip and Dennis, they have a more
scientific approach. So it was also important not only to talk to one or two,
but to talk to many. So we created a Facebook fan page, which we call "Eine
Linie unterm Strich" (a line under a line) because I thought, okay, nobody will like
this freely. And then we did a post. This post, and we bought likes, and you won't
believe it, it worked so well; 222 people, all the people I paid for liked this. And
then we wrote all of them and we talked to many of them. Some of them only in
writing, some of them only we just called or had a phone chat. But they gave us a
lot of information about their life as a click worker, which I will sum up. So what
PaidLikes by itself says, they say that they have 30000 registered users, and it's
really interesting because you might think that they are all registered with 10 or 15
accounts, but most of them, they are not. They are clicking with their real account,
which makes it really hard to detect them. So they even scan their I.D. so that the
company knows that they are real. Then they earn their money. And we met men,
women, stay-at-home moms, low-income earners, retirees, people who are getting
social care. So, basically, anybody. There was no kind of bias. And many of them are
clicking for two and more platforms. That was, I didn't meet anybody who's only
clicking for one platform. They all have a variety of platforms where they are
writing comments or clicking likes. And you can make - this is what they told us -
between 15 euro and 450 euro monthly, if you are a so-called power clicker and you
do this some kind of professional. But this are only the workers, and maybe you
are more interested in who are the buyers? Who benefits?
Dennis: Yeah. Let's come to step two. Who benefits from the campaigns? So I think
you all remember this page. This is the screen if you log into PaidLikes and,
you'll see the campaigns with, you have to click in order to get a little bit of
money. And by luck we've noticed that if you go over a URL, we see in the left
bottom side of the browser, a URL redirecting to the campaign. You have to
click and you see that every campaign is using a unique ID. It is just a simple
integer, and the good thing is, it is just incremented. So now maybe some of you guys
notice what we can do with that. And yeah, it is really easy with these constructed
URLs to implement a crawler for data gathering, and our crawler simply
requested all campaign IDs between 0 and 90000. Maybe some of you ask why 90000? As
I already said, we were also registered as click workers and we see, we saw that the
highest ID campaign used is about 88000. So we thought OK, 90000 is a good value
and we check for every request between these 90000 requests if it got resolved or
not, and if it got resolved, we redirected the URL we present this source. That
should be liked or followed. And we did not save the page sources from the
resolved URLs, we only save the resolved URLs in the list of campaigns, and this
list was then the basis for further analysis. And here you see our list.
Svea: Yes. This was the point when Dennis and Philip, when they came to us and said,
hey, we have a list. So what can you find? And of course we searched AfD, was one of
the first search queries. And yeah, of course, AfD is also in that list. Maybe
not so surprisingly for some. And when you look, it is AFD Gelsenkirchen. And the fan
page. And we asked AfD Gelsenkirchen, did you buy likes? And they said, we don't
know how we got on that list. But however, we do not rule out an anonymous donation.
But now you would think, Ok, they found AfD; this is very expectable. But no, all
political parties – mostly local and regional entities - showed up on that
list. So we have CDU/CSU. We have had FDP, SPD, AfD, Die Grünen and Die Linke. But
not that you think Angela Merkel or some very big Facebook fan pages just showed
up. No, no. Very small entities with a couple of hundreds or maybe 10000 or 15000
followers. And I think this makes perfectly sense, because somebody who has
already very, very much many fans probably would not buy them there at
PaidLikes. And we asked many of them, and mostly they could not explain it. They
would never do something like that. Yeah, they were completely over asked. But you
have to think that we only saw the campaign. The campaigns, their Facebook
fan pages, we could not see who bought the likes. And as you can imagine, everybody
could have done it like the mother, the brother, the fan, you know, the dog. So
this was a case we would have needed a lot of luck to call anybody out of the blue
and then he would say, oh, yes, I did this. And there was one, or there were
some politicians who admitted it. And one of them, she did it also publicly and gave
us an interview. It's Tanja Kühne. She is a regional politician from Walsrode,
Niedersachsen. And she was in the..., it was the case that it was after an election
and she was not very happy with her fan page. That is what she told us. She was
very unlucky and she wanted, you know, to push herself and to boost it a little bit,
and get more friends and followers and reach. And then she bought 500 followers.
And then we had a nice interview with her about that. Show you a small piece.
Okay, so you see – answers are pretty interesting. And she.. I think she was
that courageous to speak out to us. Many of others did too, but only on the phone.
And they didn't want to go on the record. But she's not the only one who answered
like this. Because, of course, if you call through a list of potential fake like
buyers, of course they answer like, no, it's not a scam. And I also think from a
jurisdictional way, it's it's also very hard to show that this is fraud and a
scam. And it's more an ethical problem that you can that you can see here, that
it's manipulative if you buy likes. We also found a guy from FSP from the
Bundestag. But yeah, he ran away and didn't want to get interviewed, so I
couldn't show you. So bought, or no probably... He was like 40 times in our
list for various Facebook posts and videos and also for his Instagram account. But we
could not get him on, we could not get him on record. So what did others say? We, of
course, confronted Facebook, Instagram and YouTube with this small research. And they
said, no, we don't want fake likes on our platform. PaidLikes is active since 2012,
you know. So they waited seven years. But after our report, at least, Facebook
temporarily blocked PaidLikes. And of course, we asked them too, and spoke to
them and wrote with PaidLikes in Magdeburg. And they said, of course, it's
not a scam because the click workers they are freely clicking on pages. So, yeah,
kind of nobody cares. But PaidLikes, this is only the tip of the iceberg.
Philip: So we also wanted to dive a little bit into this fake like universe outside
of PaidLikes and to see what else is out there. And so we did an analysis of
account creation on Facebook. So what Facebook is saying about account creation
is that they are very effective against fake accounts. So they say they remove
billions of accounts each year, and that most of these accounts never reach any
real users and they remove them before they get reported. So what Facebook
basically wants to tell you is that they have it under control. However, there are
a number of reports that suggest otherwise. For example, recently at NATO-
Stratcom Taskforce released a report where they actually bought 54000 likes, 54000
social media interactions for just 300 Euros. So this is a very low price. And I
think you wouldn't expect such a low price if it would be hard to get that many
interactions. They bought 3500 comments, 25000 likes, 20000 views and 5100
followers. Everything for just 300 Euros. So, you know, the thing they have in
common, they are cheap, the fake likes and the fake interactions. So we also have,
there was also another report from Vice Germany recently. And they reported on
some interesting facts about automated fake accounts. They reported on findings
that suggest that actually people use internet or hacked internet of things
devices and to use them to create these fake accounts and to manage them. And so
it's actually kind of interesting to think about this this wa. To say, OK, maybe next
election your fridge is actually going to support the other candidate on Facebook.
And so we also wanted to look into this and we wanted to go a step further and to
look at who these people are. Who are they, and what what are they doing on
Facebook? And so we actually examined the profiles of purchased likes. For this we
created four comments under arbitrary posts, and then we bought likes for these
comments, and then we examined the resulting profiles of the fake likes. So
it was pretty cheap to buy these likes. Comment likes are always a little bit more
expensive than other likes. And we found all these offerings on Google and we paid
with PayPal. So we actually used a pretty neat trick to estimate the age of these
fake accounts. So as you can see here, the Facebook user ID is incremented. So
Facebook started in 2009 to use incremented Facebook ID, and they use this
pattern of 1 0 0 0 and then the incremented number. And as you can see, in
2009 this incremented number was very close to zero. And then today it is close
to 40 billion. And in this time period, you can see that you can kind of get a
rather fitting line through all these points. And you can see that the likes are
in fact incremented, ... the account IDs are in fact incremented over time. So we
can use this fact in reverse to estimate the creation date of an account where we
know the Facebook ID. And that's exactly what we did with these fake likes. So we
estimated the account creation dates. And as you can see, we get kind of different
results from different services. For example, PaidLikes, they had rather old
accounts. So this means they use very authentic accounts. And we already know
that because we talked to them. So these are very authentic accounts. Also like
Service A over here also uses very, very authentic accounts. But on the other hand,
like service B uses very new accounts, they were all created in the last three
years. So if you look at the accounts and also from these numbers, we think that
these accounts were bots and on service C it's kind of not clear, are these are
these accounts bots or are these clickworkers? Maybe it's a mixture of
both, we don't know exactly for sure. But this is an interesting metric to measure
the age of the accounts to determine if some of them might be bots. And that's
exactly what we did on this page. So this is actually a page for garden furniture
and we found it in our list that we got from paid likes. So they bought, obviously
they were on this list for bought likes on Facebook, on PaidLikes. And they caught
our eye because they had one million likes. And that's rather unusual for a
shop for garden furniture in Germany. And so we looked at this page further and we
noticed other interesting things. For example, there are posts, all the time,
they got like thousands of likes. And that's also kind of unusual for a garden
furniture shop. And so we looked into the likes and as you can see, they all look
like they come from Southeast Asia and they don't look very authentic. And we
were actually able to estimate the creation dates of these accounts. And we
found that most of these accounts that were used for liking these posts on this
page were actually created in the last three years. So this is a page where
everything, from the number of people who like to page to the number of people who
like to posts is complete fraud. So nothing about this is real. And it's
obvious that this can happen on Facebook and that this is a really, really big
problem. I mean, this is a, this is a shop for garden furniture. Obviously, they
probably don't have such huge sums of money. So it was probably very cheap to
buy this amount of fake accounts. And it is really shocking to see how, how big,
how big the scale is of this kind of operations. And so what we have to say is,
OK, when Facebook says they have it under control, we have to doubt that. So now we
can look at the bigger picture. And what we are going to do here is we are going to
use this same graph that we used before to estimate the creation dates, but in a
different way. So we can actually see that the lowest and the highest points of
Facebook IDs in this graph. So we know the newest Facebook ID by creating a new
account. And we know the lowest ID because it's zero. And then we know that there are
40 billion Facebook IDs. Now, in the next step, we took a sample, a random sample
from these 40 billion Facebook IDs. And inside of the sample, we checked if these
accounts exist, if this ID corresponds to an existing account. And we do that because
we obviously cannot check 40 billion accounts and 40 billion IDs, but we can
check a small sample of these accounts of these IDs and estimate, then, the number
of existing accounts on Facebook and total. So for this, we repeatedly access
the same sample of one million random IDs over the course of one year. And we also
pulled a sample of 10 million random IDs for closer analysis this July. And now
Dennis is going to tell you how we did it. Dennis: Yeah. Well, pretty interesting,
pretty interesting results so far, right? So we again implemented the crawler, the
second time for gathering public Facebook information, the public Facebook account
data. And, yeah, this was not so easy as in the first case. Um, yeah. As. It's not
surprising that Facebook is using a lot of measures to try to block the automated
crawling of the Facebook page, for example with IP blocking or CAPTCHA solving. But,
uh, we were pretty easy... Yeah, we could pretty easy solve this problem by using
the Tor Anonymity Network. So every time our IP got blocked by crawling the data,
we just made a new Tor connection and change the IP. And this also with the
CAPTCHAs. And with this easy method, we were able to to crawl all the Facebook,
and all the public Facebook data. And let's have a look at two examples. The
first example is facebook.com/4. So the, very, very small Facebook ID. Yeah, in
this case, we are, we are redirected and check the response and find a valid
account page. And does anyone know which account this is? Mark Zuckerberg? Yeah,
that's correct. This is this is a public account for Mark Zuckerberg. Number four,
as we see, as we already saw, the other IDs are really high. But he got the number
four. Second example was facebook.com/3. In this case, we are not forwarded. And
this means that it is an invalid account. And that was really easy to confirm with a
quick Google search. And it was a test account from the beginning of Facebook. So
we did not get redirected. And it's just the login page from Facebook. And with
these examples, we did, we did a lot of, a lot more experiments. And at the end, we
were able to to build this tree. And, yeah, this tree represents the high level
approach from our scraper. So in the, What's that?
Svea: Okay. Sleeping. *Laughing*
Dennis: Yeah. We have still time. Right. So what? Okay, so everyone is waking up
again. Oh, yeah. The first step we call the domain, www.facebook.com/FID. If we
get redirected in this case, then we check if the, if the page is an account page. If
it's an account page, then it's an public account like the example 4 and we were
able to save the raw data, the raw HTTP source. If we, if it's not an account page
then everything is OK. If it's not, it's not a public account and we are not able
to save any data. And if we call, if we do, if we do not get redirected in the
first step, then we call the second domain, facebook.com/profile.php?id=FID
with the mobile user agent. And if we get redirected then, then again, it is a
nonpublic profile and we cannot save anything. But, and if we get not
redirected, it is an invalid profile and it is most often a deleted account. Yeah.
And yeah, that's the high level overview of our scraper. And Phillip will now give
some more information on interesting results.
Phillip: So the most interesting result of this scraping of the sample of Facebook
IDs was that one in four Facebook IDs corresponds to a valid account. And you
can do the math. There are 40 billion Facebook IDs, so there must be 10 billion
registered users on Facebook. And this means that there are more registered users
on Facebook than there are humans on Earth. And also, it means that it's even
worse than that because not everybody on Earth can have a Facebook account because
not everybody, you need a smartphone for that. And many people don't have those. So
this is actually a pretty high number and it's very unexpected. So in July 2019,
there were more than ten billion Facebook accounts. Also, we did another research on
the timeframe between October 2018 and today, or this month. And we found that in
this timeframe there were 2 billion new registered Facebook accounts. So this is
like the timeframe of one year, more or less. And in a similar timeframe, the
monthly active user base rose by only 187 million. Facebook deleted 150 million
older accounts between October 2018 and July 2019. And we know that because we
pulled the same sample over a longer period of time. And then we watched for
accounts that got deleted in the sample. And that enables us to estimate this
number of 150 million accounts that got deleted that are basically older than our
sample. So I made some nice graphs for your viewing pleasure. So, again, the
older accounts were, just 150 million were deleted since October 2018. These are
accounts that are older than last year. And Facebook claims that since then, about
7 billion accounts got deleted from their platform, which is vastly more than these
older accounts. And that that's why we think that Facebook mostly deleted these
newer accounts. And if an account is older than a certain age, then it is very
unlikely that it gets deleted. And also, I think you can see the scales here. So, of
course, the registered users are not the same thing as active users, but you can
still see that there are much more registrations of, of new users than there
are active users. And there are new active users during the last year. So what does
this all mean? Does it mean that Facebook gets flooded by fake accounts? We don't
really know. We only know these numbers. What Facebook is telling us is that they
only count and publish active users, as I already said, that there is a disconnect
between this record, registered users and active users and Facebook only reports on
the active users. Also, they say that users register accounts, but they don't
verify them or they don't use them, and that's how this number gets so high. But I
think that that's not really explaining these high numbers and because that's just
by orders of magnitude larger than anything that this could cause. Also, they
say that they regularly delete fake accounts. But we have seen that these are
mostly accounts that get deleted directly after their creation. And if they survive
long enough, then they are getting through. So what does this all mean?
Svea: Okay, so you got the full load, which I had like over two or three months.
And what for me was, was a one very big conclusion was that we have some kind of
broken metric here, that all the likes and all the hearts on Instagram and the
followers that they can so easily be manipulated. And then it's it's so hard to
tell in some cases, it's so hard to tell if they are real or not real. And this
opens the gate for manipulation and yes, untrueness. And for economic losses, if
you think as somebody who is investing money and or as an advertiser, for
example. And in the very end, it is a case of eroding trust, which means that we
cannot trust these numbers anymore. These numbers are, you know, they are so easily
manipulated. And why should we trust this? And this has a severe consequence for all
the social networks. If you are still in them. So what can be a solution? And
Philip, you thought about that. Phillip: So basically we have two
problems. One is click workers and one is fakes. Click workers are basically just
hyper active users and they are selling their hyper activity. And so what social
networks could do is just make interactions scarce, so just lower the
value of more interactions. If you are a hyper active users, then your interaction
should count less than the interactions of a less active user.
*Mumbling* That's kind of solvable, I think. The real
problem is the authenticity. So if you if you get stopped from posting or liking
hundreds of pages a day, then maybe you just create multiple accounts and operate
them simultaneously. And this can only be solved by authenticity. So this can only
be solved if you know that the person who is operating the account is just one
person, is operating one account. And this is really hard to do, because Facebook
doesn't know who is clicking. Is it a bot? Is it a clickworrker, or is it one
clickworker for ten accounts? How does this work? And so this is really hard for
the, for the social media companies to do. And you could say, OK, let's send in the
passport or something like that to prove authenticity. But that's actually not a
good idea because nobody wants to send their passport to Facebook. And so this is
really a hard problem that has to be solved. If we want to use social, social
media in a meaningful way. And so this is what, what companies could do. And now...
Svea: But what do what you could do. Okay. Of course, you can delete
your Facebook account or your Instagram account and stop.
*Slight Applause, Lauthing* Svea: Yeah! Stay away from social media.
But this maybe is not for all of us a solution. So I think be aware, of course.
Spread the word, tell others. And if, if you, if you like, then and you get more
intelligence about that, we are really happy to dig deeper in these networks. And
and we will go on investigating and so at last but not least, it's to say thank you
to you guys. Thank you very much for listening.
*Applause* Svea: And we did not do this alone. We are
not three people. There are many more standing behind and doing this, this
beautiful research. And we are opening now for questions, please.
Herald: Yes. Please, thank Svea, Phil and Dennis again.
*Applause* And we have microphones out
here in the room, about nine of them, actually. If you line up behind them to
ask a question, remember that a question is a sentence with a question mark behind
it. And I think I see somebody at number three. So let's start with that.
Question: Hi. I, I just have a little question. Wouldn't a dislike button, the
concept of a dislike button, wouldn't that be a solution to all the problems?
Phillip: So we thought about recommending that Facebook ditches the like button
altogether. I think that would be a better solution than a dislike button, because a
dislike button could also be manipulated and it would be even worse because you
could actually manipulate the network into down ranking posts or kind of not showing
posts to somebody. And that, I think would be even worse. I imagine what dictators
would do with that. And so I think the best option would be to actually not show
off like, like counts anymore and to this, to actually make people not invest into
these counts if they become meaningless. Herald: I think I see a microphone 7, up
there. Question: Hello. So one question I had is
you are signed creation dates to IDs. How did you do this?
Phillip: So, we actually knew the creation date of some accounts. And then we kind of
interpolated between the creation dates and the IDs. So you see this black line
there. That's actually our, our interpolation. And with this black line,
we can then estimate the creation dates for IDs that we do not yet know because
they did, kind of fill in the gaps. Q: Follow up question, do you know why
there are some points outside of this graph?
Phillip: No. Q: No? Thank you.
Herald: So there was a question from the Internet.
Question: Did you report your findings to Facebook? And did they do anything?
Svea: Because this research is very new, we, we just recently approached them and
showed them the research and we got an answer. But I think we also already showed
the answer. It was that they, I think that they only count and publish active users.
They could, they did not want to tell us how many registered users they have, that
they say, oh, sometimes users register accounts, but don't use them or verify
them. And that they regularly delete fake accounts. But we hope that we get into a
closer discussion with them soon about this.
Herald: Microphone two. Question: When hunting down the bias of
the campaigns, did you dig out your own campaign line, Line below the line? No,
because they stopped scraping in August. And I, you stopped scraping in August. And
then I started, you know, the whole project started with them coming to us
with the list. And then we thought, oh, this is very interesting. And then the
whole journalistic research started. And, but I think if we, I think if we would do
it again, of course, I think we would find us. We all also found there was another
magazine, and they did, also a test, paid test a couple of years ago. And we found
their campaign. Phillip: So, so we we actually did another
test. And for the other test, I noted we also got like this ID, I think. And it
worked to plug it into the URL and then we also got to redirected to our own page. So
that worked. Q: Thank you.
Herald: Microphone three. Question: Hi. I'm Farhan, I'm a Pakistani
journalist. And first of all, I would like to say that you were right when you said
that there might be people sitting in Pakistan clicking on the likes. That does
happen. But my question would be that Facebook does have its own ad program that
it aggressively pushes. And in that ad program, there is also options whereby
people can buy likes and comments and impressions and reactions. Did you, would
you also consider those as a fake? I mean, that they're not fake, per se, but they're
still bought likes. So what's your view on those? Thank you.
Phillip: So, when you buy ads on Facebook, then, so, what you what you actually want
to have is fans for your page that are actually interested in your page. So
that's kind of the difference, I think to the, to the paid likes system where the
people themselves, they get paid for liking stuff that they wouldn't normally
like. So I think that's the fundamental difference between the two programs. And
that's why I think that one is unethical. And one is not really that unethical.
Svea: The very problem is if you, if you buy these click workers, then you have
many people in your fan page. They are not interested in you. They don't care about
you. They don't look at your products. They don't look at your political party.
And then often the people, they additionally, they make Facebook ads, and
these ads, they are shown, again, the click workers and they don't look at them.
So, you know, people, they are burning money and money and money with this whole
corrupt system. Herald: So, microphone two.
Question: Hi. Thanks. Thanks for the talk and thanks for the effort of going through
all of this project. From my understanding, this whole finding
basically undermines the trust in Facebook's likes in general, per se. So I
would expect now the price of likes to drop and the pay for click workers to drop
as well. Do you have any metrics on that? Svea: The research just went public. I
think one week ago. So, so what we have seen as an effect is that Facebook, they
excluded paid likes for, for a moment. So, yes, of course, one platform is down. But
I think there are so many outside. There are so many. So I think...
Q: I meant the phenomenon of paid likes, not the company itself. Like the value of
a like as a measure of credibility... Phillip: We didn't...
Q: ...is declining now. That's my, that's my...
Svea: Yes. That's why many people are buying Instagram hearts now. So, so, yes,
that's true. The like is not the fancy hot shit anymore. Yes. And we also saw in the
data that the likes for the fan pages, they rapidly went down and the likes for
the posts and the comments, they went up. So I think, yes, there is a shift. And
what we also saw in that data was that the Facebook likes, they, they went down from
2016. They are rapidly down. And what is growing and rising is YouTube and
Instagram. Now, everything is about, today, everything is about Instagram.
Q: Thanks. Herald: So let's go to number one.
Question: Hello and thank you very much for this fascinating talk, because I've
been following this whole topic for a while. And I was wondering if you were
looking also into the demographics, in terms of age groups and social class, not
of the people who were doing the actual liking, but actually, you know, buying
these likes. Because I think that what is changing is an entire social discourse on
social capital and, the bold U.S. kind of term, because it can now be quantified. As
a teacher, I hear of kids who buy likes to be more popular than their other
schoolmates. So I'm wondering if you're looking into that, because I think that's
fascinating, fascinating area to actually come up with numbers about it.
Svea: It definitely is. And we were all so fascinated by this data set of 90,000 data
points. And what we did was, and this was very hard, and was that we tried it, first
of all, to look who is buying likes, like automotives, you know, to to, this some,
you know, what, what kind of branches? Who is in that? And so this was this was
doable. But to get more into demographics, you would have liked to, to crawl, to
click every page. And so we we did not do this. What we did was, of course, that we
that we were a team of three to ten people and manually looking into it. And what we,
of course, saw that on Instagram and on YouTube, you have many of these very young
people. Some of them, I actually called them and they were like, Yes, I bought
likes. Very bad idea. So I think yes, I think there is a demographic shift away
from the companies and the automotive and industries buying Facebook fan page likes
to Instagram and YouTube wannabe- influencers.
Q: Influencers, influencer culture is obviously...
Svea: Yes. And I have to admit here we, we showed you the political side, but we have
to admit that the political likes, they were like this small in the numbers. And
the very, very vast majority of this data set, it's about wedding planners,
photography, tattoo studios and influencers, influencers, influencers and
YouTubers, of course. Q: Yes. Thank you so much.
Herald: So we have a lot of questions in the room. I'm going to get to you as soon
as we can. I'd like to go to the Internet first.
Signal Angel: Do you think this will get bit better or worse if people move to more
decentralized platforms? Phillip: To more what?
Svea: If it get better or worse. Dennis: Can you repeat that, please?
Herald: Would this issue get better or worse if people move to a more
decentralized platform? Phillip: Decentralized. decentralized,
okay. So, I mean, we can look at, at the, this slide, I think, and think about
whether decentralized platforms would change any of these, any of these two
points here. And I fear, I don't think so, because they cannot solve the interactions
problem that people can be hyperactive. Actually, that's kind of a normal thing
with social media. A small portion of social media users is much more active
than everybody else. That's kind of. You have that without paying for it. So
without even having paid likes, you will have to consider if social media is really
kind of representative of the society. But, and the other thing is authenticity.
And also in a decentralized platform, you could have multiple accounts run by the
same person. Herald: So, microphone seven, all the way
back there. Question: Hi. Do you know if Facebook even
removes the likes when they delete fake accounts?
Svea: Do you know that? Phillip: No, we don't know that. No, we
don't. We don't know. We know they delete fake accounts, but we don't know if they
also delete the likes. I know from our research that the people we approached,
they did not delete the click workers. They get...
Herald: Microphone two. Question: Yeah. Hi. So I have a question
with respect to this, one out of four Facebook accounts are active in your, in
your test. Did you see any difference with respect to age of the accounts? So is it
always one out the four to the entire sample? Or does it maybe change over the,
over the like going from a zero ID to, well, 10 billion or 40 billion?
Phillip: So you're talking about the density of accounts in our ID?
Q: Kind of. Phillip: So, so there are changes over
time. Yeah. So I guess I think now it's less than it was before. So now they are
less than for then, and before it was more and so I think it was. Yeah. I don't know.
Q: But you don't see anything specific that now, only in the new accounts, only
one out of 10 is active or valid and before it was one out of two or something
like that. Phillip: It's not that extreme. So it's
less than that. It's kind of... Dennis: We have to say we did not check
this, but there were no special cases. Phillip: But it changed over time? So
before it was less and, before it was more and now it is less. And so what we checked
was whether an ID actually corresponds to an account. And so this metric, yeah. And
it changed a little bit over time, but not much.
Herald: So, so number three, please. Question: Yeah. Thank you for a very
interesting talk. At the end, you gave some recommendations, how to fix the
metrics, right? And it's always nice to have some metrics because then, well, we
are the people who deal with the numbers. So we want the metrics. But I want to
raise the issue whether quantitative measure is actually the right thing to do.
So would you buy your furniture from store A with 300 likes against store B with 200
likes? Or would it not be better to have a more qualitative thing? And to what extent
is a quantitative measure maybe also the source of a lot of bad developments we see
in social media to begin with, even not with bot firms and anything, but just
people who go for the quick like and say Hooray for Trump and then get, whatever,
all the Trumpists is liking that and the others say Fuck Trump and you get all the
non Trumpists like that and you get all the polarization, right? So, Instagram, I
think they just don't just display their like equivalent anymore in order to
prevent that, so could you maybe comment on that?
Svea: I think this is a good idea, to, to hide the likes. Yes. But I you know, we
talked to many clickworkers and they do a lot of stuff. And what they also do is
taking comments and doing copy paste for comments section or for Amazon reviews.
So, you know, I think it's really hard to get them out of the system because maybe
if the likes are not shown and if and when the comments are counting, then you will
have people who are copy pasting comments in the comments section. So I really think
that the networks, that they really have an issue here.
Herald: So let's try to squeeze the last three questions now. First, number seven,
really quick. Question: Very quick. Thank you for the
nice insights. And I have a question about the location of the users. So you made
your point that you can analyze by the metadata where, uh, when the account was
made. But how about the location of the followers? Is there any way to analyze
that as well? Phillip: So we can only analyze that if
the users agreed to share it publicly and not all of them do that, I think often a
name check is often a very good way to check where somebody is from. For these
fake likes, for example. But as I said, it always depends on what the user himself is
willing to share. Herald: Internet?
Signal Angel: Isn't this just the western version of the Chinese social credit
system? Where do we go from here? What is the future of all this?
Svea: Yeah, it's dystopian, right? Oh, yeah, I don't, after this research, you
know, for me, I deleted my Facebook account like one or two years ago. So this
does you know, this did not matter to me so much. But I stayed on Instagram and
when I saw all this bought likes and abonnents and followers and also YouTube,
all this views, this, because the click workers, they also watch YouTube videos.
They have to stay on them like 40 seconds, it's really funny because they hate
hearing like techno music, rap music, all 40 seconds and then they go on. But when I
sit next to Herald for two hour, three hours, I was so desillusionated about all
the social network things. And and I thought, OK, don't count on anything. Just
if you like the content, follow them and look at them. But don't believe anything.
That was my personal take away from this research.
Herald: So very last question, microphone two.
Question: A couple of days ago, The Independent reported that Facebook, the
Facebook App was activating the camera when reading a news feed. Could this be in
use in the context of detecting fake accounts?
Svea: I don't know. Phillip: So, I think that that in this
particular instance that it was probably a bug. So, I don't know, but I mean that the
people who work at Facebook are, not all of them are like crooks or anything that
they will deliberately program this kind of stuff. So they said that it was kind of
a bug from from an update that they did. And the question is whether we can
actually detect fake accounts with the camera. And the problem is that current, I
don't think that current face recognition technology is enough to detect that you
are a unique person. So there are so many people on the planet that probably another
person who has the same face. And I think the new iPhone, they also have this much
more sophisticated version of this technology. And even they say, OK, there's
a chance of one in, I don't know, that there is somebody who can unlock your
phone. So I think it's really hard to do that with, do that with recording
technology, to actually prove that somebody is just one person.
Herald: So with that, would you please help me thank Svea, Dennis and Philip
one more time for this fantastic presentation! Very interesting and very,
very disturbing. Thank you very much. *Applause*
*postroll music*
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