Bot Campaigns Turned Taylor Swift Into a Nazi and Cracker Barrel Into a Culture War

Episode Transcript
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Speaker 1 (00:04):
There Are No Girls on the Internet, as a production
of iHeartRadio and Unbossed Creative. I'm Bridget Todd, and this
is there Are No Girls on the Internet. A pop
star accused of being a secret Nazi. A family restaurant
accused of going woke. Now, at first glance, these may

(00:25):
seem like two completely unrelated Internet controversies, but dig a
little bit deeper and they share something important in common.
Both controversies were amplified by coordinated bot campaigns that sought
to rile up ordinary users and draw them into conversation.
In this episode, we're breaking down two reports on coordinated
bot campaigns, one aimed at Taylor Swift, another aimed at

(00:49):
Cracker Barrel to talk about what happens when online outrage
is manufactured, amplified, and weaponized, as well as what it
means for the authentic human voices trying to be heard
against the algorithmic roar. Because if bots can create the
appearance of consensus and generate real backlash or controversy at scale,
how do any of us know when we're reacting to

(01:10):
real people or just reacting exactly the way somebody wanted
us to. This is what Molly Dwyer has spent her
career researching. She describes herself as a kind of professional
Internet vibe checker, but her real title is Director of
Insights at Peak Metrics. And before the Internet was talking
about bots manipulating conversations around Taylor Swift, Molly had been

(01:31):
looking into how similar kinds of unauthentic behavior was driving
the discourse around the old timey country themed chain restaurant
Cracker Barrel. Now. I know it may seem wild to
think that anybody would care deeply about this, but Molly
says it's yet another instance of how easily the Internet
can be manipulated to change people's views. Her company, Peak Metrics,

(01:53):
was not involved with the report about Taylor Swift, but
a few months earlier they published a report about the
Cracker Barrel controversy that had a lot of similar findings.
So we asked her about what was happening with the
Cracker Barrel discourse and got her take on the recent
Taylor Swift report.

Speaker 2 (02:09):
I spent a number of years living in Russia on
various US State Department programs to help young people study
languages that are critical for national security. I say that's
relevant because my first kind of experience of how the
Internet can manipulate and change people's view on the world
was my experience sort of pre and post twenty fourteen

(02:31):
in Russia, where I watched people that I knew personally
go through quite a transformation on their perspective on Ukraine
and the US in a very short period of time.
So that really kicked off kind of my interest in
this idea of like information operations, how did the things
that we consume on the Internet impact our worldview, whether
we're aware of it or not. Immediately out of that,

(02:54):
I went to work for a small startup that does
open source intelligence, which is really just another way of
talking about all of the data that is publicly available
on the Internet. There's a pretty big tool set out
there nowadays for different technologies that will help you quantify
and qualify what is being said, what is being researched
on the Internet. And I've had a decade of experience

(03:16):
in that open source intelligence tool space.

Speaker 3 (03:19):
At this point.

Speaker 2 (03:20):
Now, the Internet of we're going into twenty twenty six.
The Internet of twenty twenty six is very different than
the Internet of twenty sixteen. When I first started out
in this industry. Traditional metrics that we were used to,
like volume or sentiment, or even just taking for granted
the idea that a verified page was who they say

(03:41):
they are are just no longer applicable nowadays. And I
think that the Internet has fundamentally changed from under us.
I think especially obviously since the rise of llms and
agentic AI, and we're all just sort of grappling and
trying to figure out what this means for us. And
my role at Peak Metrics is to help companies and

(04:02):
other organizations figure out what the Internet means for them.

Speaker 1 (04:06):
Before we started recording, you said that you basically were
an Internet vibe checker, that it's one of the reasons
I wanted to talk to you is that I've had
the experience of seeing a conversation kind of take hold
of the Internet very quickly, much more quickly than other
kind of organic conversations. And maybe you don't know something

(04:29):
is going up, and you suspect right things where I
would think, who really is dedicating their time to writing
a bunch of posts about this? Who really cares about this?
These conversations where your spidey senses kind of get tingling.
Is that sort of what you mean exactly?

Speaker 2 (04:43):
I think we all have an innate sense of the Internet,
but because we don't have easily understandable metrics to understand
how big is this conversation? Am I seeing this because
of my own algorithmic bubble? Are other people seeing this?
And like, what are the mo motivations for people to
participate in this conversation. I'll give you an example of

(05:04):
maybe sort of the algorithmic bubbles that we live in.
Last year, I remember there were a lot of media
inquiries to look at the impact of I was the
first or second presidential debate with Kamalairis as the candidate,
and I was looking at the scale of conversation to
that relative to something that I had not heard of,

(05:25):
but someone on my team brought forward to me, which
was the plight of this squirrel that was like a
pet squirrel that was going to be put down in
New York State that Trump entered the conversation on, and I,
in my algorithmic bubble had not heard of this. I
was focused on reporting on the metrics around discussions around
the debate, and when I actually went to go look

(05:46):
at just the overall volume of conversation around this two
topics on X, the volume of conversation around the squirrel
was multiple x larger than the volume of the conversation
around around the presidential debate. So I think that anecdote
illustrates sometimes both the our algorithmic bubbles and just like
how big these conversations can get quickly when certain players

(06:09):
are involved, like when certain big influencers enter the conversation,
and then also thinking about like when bots are involved, right,
like what type of content are they incentivized to post
about and to amplify for you know, their ultimate goals
probably of monetization.

Speaker 1 (06:29):
Molly's background in Russian makes sense here because there was
a time that when you talked about bat campaigns online,
the assumption was was being done by foreign bad actors,
the kind of sophisticated manipulation campaign that we associate with
an adversarial foreign country. But today things have really changed.

Speaker 2 (06:46):
So I think if you take the Okham's Ranger approach
to the Internet, which is just assuming that everything is
a money making drift, you'll probably be correct in like
ninety percent of cases, and I think that that applies here.
I'll back up a little bit to talk about like
bot networks in general, and I think that this is
important to cover before we get into who is behind it.

(07:08):
So traditionally when we would talk about bot networks and
who was operating them, you know the degree of sophistication
required to set up these campaigns, to run these accounts simultaneously,
to get them to stay on topic with their posting.
You were looking at a relatively limited set of essentially
state adversary actors like the big bads that were used

(07:29):
to Russia, China, Iran, North Korea, right, that had the
sophisticated like cyber teams that could run these campaigns. And
that's why I think in our minds were still a
little bit stuck in this idea of like, okay, big
bad foreign actors manipulating the conversation. I think there are
still big bad foreign actors manipulating the conversation, and they
have a lot of incentives to do so. But the

(07:50):
world changed with with AI and it's now easier than
ever to run these types of campaigns. The other side
of that was you had made be like traditional non
state cyber actors like threat network, you know, a non
type people who were super sophisticated could do this right.
And now you know, smaller governments can like run a

(08:12):
campaign to prop up their dictator. Right, average people who
were not part of like a threat network can stand
up campaigns like this thanks to AI, you know, run
them simultaneously. And I think that's where we get into
this explanation of Okay, well what if it's not ideological
the way that like a state adversary actor would be, Well,
why are they doing this? I think shits and giggles

(08:34):
is always a reasonable guess. But I think that money
is the thing that makes the most sense. And I'll
tell you why. I think that worldview I think is
bearing out recently. So I don't know if you saw
in the past few weeks that X made an update
where user locations were published, and what we saw this

(08:54):
is again anecdotal. I don't necessarily have the data on this,
but what we saw anecdotally was that a lot of
accounts that we're posting incendiary political content on both the
left and the US right. We're coming from places like India,
the Global South, places where you know, our relatively low income.

(09:15):
The money that you could make from this is not insignificant.
We don't necessarily think that these people have ideological reasons
to fan discourse in the US. It just so happens
that that's maybe a good way to make a side
hustle online because of the way that you know, engagement
is monetized. So that's my overview answer of like the

(09:36):
types of people who could be behind these it's a
much larger set of actors than it was maybe four
years ago, But I still think that money is probably
the guiding principle of what's going on here.

Speaker 1 (09:51):
Yeah, We've been having a lot of conversations about that
change at X, and I think it really helped me.
See I knew this sort of inside, but it helped.
It was just like another way to sort of crystallize
exactly what you said that we're so used to thinking
about nefarious actors, bad actors who are manipulating our online

(10:11):
discourse to foment chaos and confusion and political division. But
also in making this, in changing the financial payouts of X,
they certainly have incentivized people to post inflammatory, insidiary content,
rage b right content that we know works as a
revenue stream, And part of me can't even really blame

(10:33):
them for being like, oh, they set up this incentivized
reason for me to do this to make a little
money I need money, I'm going to do it.

Speaker 2 (10:42):
Yeah, we just happened to be a big, very flammable
target for people to go after on the internet US society.

Speaker 1 (10:50):
Yes, if you haven't been on a long car trip
in a while, I bet you haven't spent a lot
of time thinking about the restaurant Cracker Barrel. So here's
the content. Earlier this year, Cracker Barrel rolled out a
new logo. Previously, the logo featured an old white man
in overalls casually leaning over a barrel. But this new

(11:11):
logo removed the old man and just kept the name
Cracker Barrel in an ever so slightly sleeker font, one
that maybe jibed with a younger audience. To be sure,
this is the kind of boring corporate marketing change that
most people wouldn't even notice, But in today's climate, where
every little thing can be turned into evidence of a
woke agenda, it became an entire news cycle. Okay, so

(11:34):
you really set the stage wonderfully. And I think I
mentioned this too, that independently we had been wanting to
do an episode about bots and sort of the general
grip that they have on our discourse, and one of
the conversations that I saw that. I was like, this
is very weird conversation happening. Was cracker Barrel changing their logo?
I I grew up in the South and my parents

(11:57):
went to cracker barrel. We had one in our town.
They went every week. But other than my parents, I
don't think I've ever heard anybody mention cracker Barrel. It
wasn't like a big part of the discourse. So imagine
my surprise when one day I wake up and everybody's
freaking talking about cracker Barrel. Then I saw the Peak
Metrics report about the way that bots might have helped

(12:17):
shape the way that conversation spread online. How did that
come to be something that Peak Metrics was looking at.

Speaker 2 (12:24):
Yeah, I too can't believe that we're still talking about
cracker Barrel, but here we are.

Speaker 3 (12:28):
I think.

Speaker 2 (12:29):
I think why media latched onto it when we put
out some initial findings was this sense of why are
we talking about this? And we gave them some numbers
to chew on to help them understand what degree of
this conversation is potentially inorganic or automated. That might explain

(12:49):
why it's staying in the discourse longer than we would
expect it to be or why it rose to the
top of the discourse in the first place. And I
think our key finding there was that, you know, within
the first twenty four hours of this like rage cycle,
we found that the original posts that seeded this idea

(13:11):
of a boycott and you got to rewind it back
because I think that's also another principle that's hard to
do on the modern Internet is how did something start?
There's not necessarily an easy answer to that. You have
to bring in a lot of data, search for the
right stuff to figure out how a trend even started.
So from what we could see how the idea of
a boycott started, it did start from what our tech

(13:33):
would classify as organic accounts, but very quickly those calls
for a boycott were amplified by accounts that we flagged
as automated or inorganic. So there was this sort of
like seeding introduction of a claim that immediately got amplified

(13:54):
and at the height of the discourse, Like within the
first twenty four hours, we found that basically half of
the posts that we're calling for a boycott we're coming
from accounts that we flagged as automated. Half is a
big number, but we also don't necessarily at the time
that we publish the report, we didn't have a lot
of comparison points. I think that's key to maybe understanding

(14:15):
what's going on with the Taylor Swift discourses. I think
we're all across the industry like finding these numbers and
trying to figure out what they mean. If I were
to give you a spidy sense now several months after
Cracker Barrel is that when there's an incendiary conversation online,
I would consider a normal baseline for the amount of
automated activity in that conversation to be somewhere between twenty

(14:37):
to thirty percent of the conversation wow, And it typically
will go higher than that when we hit like a
crisis point. So like the Cracker Barrel changes its logo,
organic accounts seed this call for a boycott, and then
it jumps up. But that may just be a symptom
of how the internet works, and not necessarily a symptom

(15:00):
of a coordinated campaign to target chain restaurants that are
abandoning traditional values.

Speaker 1 (15:08):
Right.

Speaker 3 (15:09):
I think we're grasping these numbers.

Speaker 2 (15:10):
We're putting a number to this thing for the first time,
and so we're trying to figure out what is baseline
at this point.

Speaker 1 (15:16):
So what's the takeaway that you all found from the
Crocker Bill report.

Speaker 2 (15:19):
I think the takeaway here is one into being automated.
That's a big number, but that's still a lot of
people that have potentially big, real feelings about this. The
other element that's at play here, though, is because of
how algorithmic boosting works, how many of those people who
were real people posting outrage would have even seen this

(15:40):
to post their outrage had it not first been amplified
by inorganic accounts that brought it to the top of
their feed. So even within that number of people with big,
real human feelings, there is an element of being shaped
by this inorganic discourse bringing that conversation to them in

(16:00):
the first place.

Speaker 1 (16:01):
I saw an analogy to this that if you were
cutting vegetables in your kitchen and then you cut your finger,
and you ask somebody next to you hand me that towels,
why could apply pressure if they squeezed it instead so
that you bled faster. They didn't start the cut, but
they certainly made it worse. They certainly brought the problem,
you know, to a different level. And that's sort of
a good way to think about how bots and inauthentic

(16:22):
activity can shape a conversation online that might actually be
seated with organic people and their big feelings they're sharing
on the internet.

Speaker 2 (16:30):
Now, I'm just thinking in my head of like what
all of the potential bots would say if you ask
them to hand you a towel. So I find that
you can, like you can spot them in the commons
section of like they'll they'll take things too literally sometimes,
so it would be, you know, like something about the
something about the towel, something about like not being able
to squeeze your finger. The I think there was a

(16:51):
key point in time where we saw some of the
bot networks, like short Circuit with their instructions about what
to post at the peak of the Trump Epstein files controversy,
where in real time, I'm not the only person who
saw this. I don't know if there's any anything written
about this, but other people were observing this that like
a lot of these seemingly maga accounts were turning on

(17:14):
Trump in the context of like calling for the Epstein
files to be released, because you can tell that they're
like pre programmed instructions were to like non stop call
for the Epstein files to be released, and they short
circuited a little bit based on their previous instructions for
the world. Before you know, they're they're they got updated
to the version of the world that existed at that

(17:34):
point in time.

Speaker 1 (17:36):
It's part out there for a bot. I don't find
the boss. I'm not a bue. I get confused. It's
I feel bad that these bots that are like, I
don't even know what the post anymore.

Speaker 2 (17:45):
Yeah, yeah, I think if people are wondering, you know,
like what is what is a good way to confirm
your spidy sense of something in surreal I mean, I
think we were all used to like those account handles
that are like John two four, five, six, oh whatever, right,
Like we know that's a that's a common way that
those account.

Speaker 3 (18:02):
Names are formulated. We do still see that.

Speaker 2 (18:05):
But I think even without any other technology to flag
automated behavior, what you can do is if you're like
on your phone and just scroll a couple of times.
If you're still scrolling and you have not reached yesterday
in that person's posting, you know, that's probably a sign
that they are posting at a frequency that is just
not probably humanly possible and is probably you know, an

(18:29):
automated posting rate that's that's going out there. So that's
one of my tip of one of the simplest ways
to be like, eh, is it a bot? Yeah, just
scroll back and see how far it takes you to
get to yesterday.

Speaker 1 (18:41):
That is a great tip. And yeah, don't spend don't
dedicate your time and energy to getting into a back
and forth with a bot or like worrying yourself with
what a bot is saying, unless you're doing it because
you're a researcher and you're interested. Don't have it like
fuck up your day what a bot left in your
comments on Instagram or something.

Speaker 3 (19:00):
Yeah, exactly.

Speaker 2 (19:01):
I mean I think we're talking about X, and I
do think that X is pretty central to like the
discourse in the sense that it really is like the
most fertile ground for bot behavior. Like it's very text based,
which means it's like easier to you know, produce content there,
like it's a little bit harder to automate like posting
of pictures if you think about it, like just logistically,

(19:23):
So I think we do still see a lot of
bought activity on X, where I think researchers are less
familiar with what bot activity looks like on other platforms.
I will say that, you know, I'm seeing a lot
of folks going to more niche communities like Facebook groups
or discord or Reddit, where I think that there's a

(19:47):
desire for people to like be in conversation with real
people on the Internet, and when they're finding that that's
not possible in certain forums anymore, they're they're moving to
different places to try to be assured that when they're
engaging in conversation with someone that yeah, it's not like
a bot who's arguing back at them.

Speaker 1 (20:06):
Especially in this age of AI, I want to know
even if we're having a spicy conversation a record argument,
I want it to be with a real person, like
in des Moines, iware or something. I don't want to
have the feeling of like what am I wasting my
time going back and forth on Reddit with a bot? No,
thank you, Let's take.

Speaker 4 (20:25):
A quick break at our back.

Speaker 1 (20:39):
Since Molly is an Internet Vibe checker, I wanted to
know her thoughts on the report around the controversy surrounding
Taylor Swift, things like accusations that Swift is a secret
trad wife or even secretly a Nazi. So I want
to talk about the report that I'm sure you've seen
by now that was put out by a company called Gooday,

(21:00):
that was then reported about in Rolling Stone that looked
into social media commentary around Taylor Swift's latest album and
whether or not that commentary was in part driven by
inauthentic coordinated accounts. What was your reaction, just as somebody
who puts together reports like this, somebody who was in
this space, what was your reaction to that report?

Speaker 2 (21:19):
I mean, I think it was a spidy sentence check.
I am not a swifty, please don't come after me swifties.
My wife is a swifty. But I, as a person
on the internet, had had seen the reactions to Taylor
Swift's album Now. At the time, I was not discriminating
whether I thought that those reactions were organic or inorganic,

(21:40):
but I was aware of the pushback to the most
recent album, and looking through the methodology, it makes sense
to me. I think it sound in certain aspects of it,
and I think maybe where sometimes the nuance gets lost
is you know, we talk about how one part of
a conversation is automated or manipulated. And that's not to

(22:04):
discount the other part of the conversation that has real
people having real, big human feelings here. And inherently, in
whether it's data sampling or you know, the way that
your technology, whether it's by keywords or something else, is
identifying the parts of the discourse, you're going to more

(22:24):
easily identify the parts of the discourse that like sound
automated to begin with, because they're flagging like the right
keywords or the right markers for what's going on. And
inherently you're going to miss the more nuanced conversations because people,
when real people talk about issues, they're all using slightly
different language to describe what's going on. They're maybe not referencing,

(22:46):
like Taylor Swift's full name in the conversation they're replying.
So inherently, in the work that we do, based on
how you're going to collect this data, it's already going
to veer a little bit more towards missing some of
those more organic conversations because of how humans talk about things.
So something got lost down the road. I in terms

(23:07):
of the real people having real human feelings about this,
I don't disagree based on the methodology that there are
certainly indicators of automated activity here, but we don't necessarily know.
Going back to the earlier question, you know these bot
networks that were you know, jumping on the bandwagon here
that also we're attacking Blake Lively. You know, we don't

(23:30):
necessarily know what the incentives are of these butt networks.
Are they just hopping on the latest celebrity trend to
gain traction and monetization, or are they targeted against certain
celebrities like these are unknown questions at this point, so
I think we want to make sure to not jump
too many steps forward to claim that billionaire Taylor Swift

(23:52):
is being targeted and we need to protect her from
attacks online. There are a lot of other people who
are being targeted who don't necessarily have of Taylor Swift's
pr arsenal at their back.

Speaker 1 (24:04):
Yes, I'm so glad that you brought that up, because
I mean, I don't know if you've I mean, I'm
sure you've seen the way that conversation around this report
has spread online. And one of the things that I've
been a little frustrated to see is people saying, oh, well,
this report confirms that everybody who was critical of Taylor
Swift was just a bot? They can they can or

(24:25):
people who were you know, manipulated by bots, all of
that discourse can be dismissed. So, first of all, the
report does not say that, not even a little. And
in fact, something that I think is getting lost in
the sauce of the report is that, in fact, the
report is talking about authentic accounts having authentic discourse alongside
what they have what they have seen as like perhaps

(24:46):
inauthentic discourse. So like, nowhere in that report are they
saying everybody who was critical of Taylor Swift can just
be dismissed as a bot. And I think that there's
something about the way it was reported that is not
giving enough credence to the that were there were and
are real people in this conversation who are not bots,

(25:06):
who have been talking about Taylor Swift critically for a
long time. And so even though you know, I'm the
data scientist, but looking at their methodology, I don't think
I don't think they've made this up. I don't think
Taylor's Swift paid them to put this reporting together or
something like that. But I understand what people are saying
when they're saying this report does not actually reflect the
fact that there are so many people authentically being critical

(25:30):
of Taylor Swift on the internet. We're not bots. I
think something about the way the report was framed sort
of gave credence to this idea that you could just
be dismissive of all of these critical voices exactly.

Speaker 2 (25:41):
I mean, I think on the flip side, we could
look at any other recent controversial issue and perhaps come
to a similar conclusion of, Wow, this conversation is bot driven,
because I think that that may just be a symptom
of like how the Internet functions nowadays, that may not
necessarily be specific to the dynamics of Taylor Swift. I

(26:03):
think the other thing that you can do, and I
mean I'm taking lessons from this, you know, working and
researching in this space, is you know, one of the
one of my favorite ways to set folks up to
analyze conversations online with Pea metrics technology is all say,
you know, look at the data and ask the same
question filtered to the organic activity and the non organic activity,

(26:25):
so that you can see the nuance and the difference
and maybe like the specific themes or narratives that they're
talking about. So an interesting question to ask of this
data might have been within the controversial conversation around the
latest album, which one specifically were the bots trying to push?

(26:46):
And then when it comes to real human people, which
aspects were they focused on presenting. I think maybe those
two things side by side gives us better context for
what those people who were having real human feelings about this.
We're thinking, which aspects of the album controversy were they
latching onto the most, and how did that perhaps differ
from what the boss were focused on.

Speaker 1 (27:08):
And I think there's also I mean, I can't not
talk about the way that this seems too the conversation
seems to be happening along some clear racial lines to me,
where a lot of the voices who were critical of
Taylor Swift, not all, but a lot were women of color,
black women, people of color, and a lot of the
Swifty community appeared to be again not all, but a

(27:29):
lot of white ladies, white people. And so I think
it's just one of those issues that will always sit
at these tension points of the tensions that we know
exist in our society. Something about the report giving credence
to the idea that you could just discount a largely
like the voice the critical voices of largely minoritized people

(27:52):
because you love Taylor Swift and you don't have to
think critically about the points these people are making because
they're bots. I can see why that this hit people sideway.

Speaker 2 (28:01):
Yeah, And I think to keep the organic voices anonymized here,
I think that might have been a great opportunity to
look at the people on the organic side of the
conversation and see, like, who were the biggest influencers in
this space, so within people who were criticizing the album
or the aesthetics of Taylor Swift and they were organic,
you know, who were some of the biggest accounts that

(28:21):
weighed in, Like there are you know, I'm aware that
like black Twitter is a thing, like who were some
of the biggest voices that were that were shaping the
conversation from the human side. What we may uncover if
we looked at like, you know, there might have been
a post from someone who has like five hundred thousand followers,
and arguably that might have shaped the discourse a lot more.

(28:42):
That single post from that person with a lot of
reach might have shaped the discourse more than the you know,
fifty account fifty posts from bot accounts that have like
one hundred followers. So maybe there's some nuance to be
parsed out here too. That volume doesn't necessarily equal impact
or influence on the Internet, and what bots are inherently

(29:05):
trying to do is boost the volume of the conversation.
But where humans have an impact, And there are also
like automated accounts that look like influencer accounts that have
lots of followers. But I'd like to pull the threat
a little bit more about who are the influential people
on the human side of the conversation.

Speaker 1 (29:22):
That makes a lot of sense, and I guess I
would have liked I also think that listen, we have
said this put it on the show a million times.
When you talk about something, there's something about Taylor Swift
that is like, once you start talking about her, big
feelings come out on all sides, even people who don't
like Kidler. Swift is like, there's just something about her
that gets people talking. And so I think if you're
going to be putting out a report that is about

(29:44):
Taylor Swift, it would behove you to make some of
this clear, right, It would behove you to spend a
little time explaining like, oh, well, who were the voices
that we're seeing on the like, who are the authentic
voices that we're seeing that are talking about her in
this way and weighing in this way? Because you have
to imagine it's it's gonna get a lot of eyes
on it. By now, everybody knows that when you talk

(30:04):
about Taylor Swift, it's something that gets a lot of
eyes or a lot of ears or whatever medium you're using.

Speaker 2 (30:09):
Yeah, if I maybe have three guideposts that I'm making
up on the fly as as an Internet person having
been on you know, Tumbler back in like the early
twenty tens, you knew not to mess with the swifties, right,
Like that's so the guideposts of the Internet is, like,
what do we established, like, assume it's a grift?

Speaker 3 (30:30):
What is the other one from that Netflix documentary.

Speaker 2 (30:33):
Don't fuck with don't don't fuck with cats? And I
think maybe the third guidepost of the Internet is do
not touch the tailor swift discourse with a ten foot pole.
If you follow these things, you can't go wrong on
the Internet.

Speaker 1 (30:48):
What do you think about the methodology that they used
in this report to define a bot accounts.

Speaker 2 (30:52):
I think it's challenging that we're using a common word
that you could define in a lot of different ways.
I think that there's no you know, set methodology. I
think a lot of different tech companies have different definitions
and have their own sort of secret sauce when it
comes to you know, what available data they're using to

(31:14):
come to these conclusions. I can tell you that, you
know from the way that we think about it, coming
from sort of a framework of you know, degrees of
confidence that you would see in like the intelligence community,
right like you're never going to say I'm one hundred
percent sure that you know something is something. You're going
to say, okay, well, the available data that I has

(31:34):
that I have shows, you know, it's highly likely that
this is a bot account. Even when you're getting into
the highest degree of confidence that something is a bought account,
the way that we frame it is that it's almost
certainly a bought account, which is still you know, by
verbiage a little bit short of saying you know for sure,

(31:54):
because I think the only way to know for sure
that something is a bought account is to do a
lot of honestly like manual forensic analysis of that account,
and the issue is that you can't do that at scale,
so when you're looking at large sets of data and
you're looking at, you know, a limited availability of metrics
that you have on these accounts. Honestly, I don't think
it's necessarily the way that you know, the bots are classified.

(32:18):
It's the language that you're using to describe the confidence
that these are bots. And again, I think there's something
that's getting lost in translation between like a methodological report,
journalistic reporting, and then where that journalistic reporting goes in
the discourse. So we have you know, things like account history,
like when was the account created, We have the posting frequency.
You have like the the you know profile image, right,

(32:42):
is that like a recycled image or an AI generated image?
You know, we talked about like the obvious indicator of
the way that the user name is formulated. Are there
other usernames like that that appear on other social media sites?
What type of content is this account posting? Is it
mainly act as an amplifier and just resharing reposting, or

(33:04):
is it doing a lot of original posts, which is
just like mechanically and also computationally from like a energy perspective,
going to be harder for like a bought run account
to maintain posting original content.

Speaker 3 (33:17):
So I throw out.

Speaker 2 (33:18):
All of these metrics to say that I think that
the answer lies in probably a combination of all of
them to get to the best answer. But I think
we need to be really careful about the language that
we're using when we say, like the confidence level that
we have that an account is automated.

Speaker 1 (33:35):
One of the criticisms, and I mean, you've been, I
think helpfully kind of critical, good critical of the report.
But some of the criticisms I'm seeing to people sort
of just trying to trash this report and say that
it's a lie. Is that on their site they have
a disclaimer that's like, oh, we cannot we're not saying
that we're not guaranteeing that what we say in these

(33:57):
reports is true. And I thought to myself, well, this
seems like standard cover your ass language to me that like,
of course, I'm not going to say with certainty like
what we're saying you could take it to the bank,
take it to your grave, one hundred percent true. I
didn't feel like that was a necessarily a fair criticism
of saying this report is full of lies it's bunk.

Speaker 2 (34:17):
Yeah, I mean, I think when you're stepping foot into
measuring the Internet, I think the issue is just that, like,
what I'm finding is that media outlets are really hungry
for any data that quantifies what's going on. Don't necessarily
have folks in house who can fact check or verify

(34:38):
that data. So you're really being you know, it's a
pretty big responsibility to be the person who is doing
the research and the person who is presenting that research
to an organization that you know doesn't necessarily have the
same tools at their disposal to interrogate it. I feel

(34:58):
that responsibility someone working in the space, which is why
I will not touch the Taylor Swift discourse with the
ten football This is the closest that I will get.
But it's a lot of responsibility, and I think all
of the players need to be aware of that.

Speaker 4 (35:15):
More.

Speaker 1 (35:15):
After a quick break, let's get right back into it.
In my opinion, the reason why this conversation started out
from such a volatile place was that piece and Rolling Stone. Right,

(35:38):
so I read the report, I have a good sense
of what's it. It exactly what you were just saying, right, Like,
they're not necessarily saying this is guaranteed, you know, all
bots whatever. Whatever. The headline of the Rolling Stone report,
which I believe is what most people read, I don't
think most people were going to the actual report. The
headline was Taylor Swift's last album sparked bizarrec you's of Nazism.

(36:01):
It was a coordinated attack. Having read the report, that's
not even really what it said.

Speaker 3 (36:07):
It's so many steps too far.

Speaker 2 (36:10):
And I don't know how many people are aware that,
right like, headlines are written by different people who write
the article. You know, It's it's almost like we circled
back to the thing that people have said for you know, decades,
that they don't like about the media, right Like, So
this isn't necessarily an Internet problem at this point that
we're talking about. This is a problem of like media

(36:31):
and common grievances that people have about how things are clickbaity.

Speaker 1 (36:36):
Yeah, I don't know that people know how pieces like
this come to be. And I also think Rolling Stone,
like I saw people going back and saying, oh well,
when her album came out, Taylor Swift and a Rolling
Stone takeover, they gave her album a five out of five,
like be I don't think that that's I think that
that's fair to be part of a conversation is what
relationship did Rolling Stone have with Taylor Swift before? And

(37:00):
then like why were they chosen to get this exclusive
report about her being the target of this this coordinative attack.
I don't think those questions are are totally out of pocket,
but I think it really goes to show like why
you need to be so intentional and so careful when
you're going to be rolling out a report like this,
because people are going to run with it.

Speaker 2 (37:20):
Yeah, I mean coming from from this side, from the
from the industry side, I would say, you know, there
are two sides of how this could work. You know,
sometimes we find something internally that's interesting, and you know,
we shop it out to various media outlets and we say, like,
is anyone interested in this cool data that we found?

(37:41):
And sometimes it's the flip side. Sometimes it's an outlet
coming to us that they're asking this question of a
lot of different tech companies or researchers in the space
and saying, hey, does anyone have an answer to this question?
So that would be kind of my follow up from
like the mechanics of the industry perspective is, was this
report shopped around bund to a number of different outlets,

(38:02):
and just it happened to be that Rolling Stone was
the one who picked it up, or was rolling Stone
calling different companies asking if anyone had any data on this.
We didn't get a call from Rolling Stone asking if
we had Taylor Swift data, So I can tell you
that they did not call us for a consusation. But
it is something that happens frequently. And sometimes I will

(38:23):
exhaustively research something and you know, work with a media
organization about how to you know, write an article about it,
and sometimes that article doesn't even get published, right, So
you know, that's kind of the background of how this
all works.

Speaker 3 (38:37):
So I'm not sure which dynamic was at play here.

Speaker 5 (38:39):
If I can jump in with another question here, I
think this is, you know, raises some good questions about
the role of industry in making this kind of data
available to the public. So, like, I come from a
public health background where resources are pretty limited, right, and
with the platforms shutting down API access researchers a couple

(39:01):
of years ago, and you know, the Trump administration shifting
resources away from a lot of funding mechanisms that were
there in the past. The capacity of like nonprofit public
health researchers to do these kinds of investigations is really
pretty limited, and so, you know, I've seen criticism online

(39:23):
about this Tailor Swift report that like, oh, it's a
for profit company, we can't trust anything that they say.
But given like just the state of the world and
how complicated the internet is and the limited resources and
capacity of people in public health, I'm really like skeptical

(39:43):
of an approach that says like, well, we just can't
trust anything that comes out of industry whatsoever. It just
feels like so limiting to shoot ourselves in the foot
when you know, I wish that we had companies like
yours doing these kinds of analyzes of public health topics
instead of brands or celebrities, but you know, it's just

(40:07):
not there. And so I'm just, yeah, curious what you
think about, you know, what is that relationship of for
profit companies that are acting in private space but then
contributing to public conversation. How should members of the public
think about to think about that, That's a great question.
We're getting like into the nitty gritty of this industry,

(40:29):
and I really like that so I can tell you
that I'm working on public health problem sets for our customers, right,
but that's not necessarily something that we're being asked to
report publicly about. So I think maybe to lift the
hood a little bit for the industry side, is, you know,
I'm working on a huge number of different customers from

(40:51):
Fortune five hundred to government to commercial to big pr
and entertainment, and the only things that I end up
being able to talk public about are if that customer
wants to, you know, do a public facing report, or
if media asks us. And so that's why in the
industry we'll say yes to media asks because that's a

(41:14):
way to highlight our capabilities. And what does media typically
ask about. It's typically politics or celebrities. So if you
were to look at, you know, what are the issues
that I've commented on in my in my current role
over the past two years, you would come to sort
of the like an odd conclusion that I am very

(41:37):
into only pop culture and politics and this is one
hundred percent of my work. Those just happen to be
the issues that I'm able to talk about publicly because,
to be frank like a lot of these tech companies
were small I know, Peak Metrics is still in like
a startup role, and so we don't necessarily have the
timer resources to pull together the most interesting public facing

(42:01):
report on the most pressing issue. If we can't guarantee
that it's going to get placement somewhere, that's a lot
of energy and resources to expend on something. So that's
why companies, you know, answer the call from media. So
media gets basically this free data and companies get free

(42:21):
you know, advertising of the types of things that they
can do so that you know, customers who have other
types of problems that are not maybe pop culture politics related,
call us up to say, hey, can you take a
look at this issue for me. So that's the mechanics
of like how this works. And I think the incentives
I don't think. I think everyone should be skeptical of

(42:43):
things that are coming always from private industry versus like
academia and researchers. But we also don't really have like
an established discipline of internet researchers I think in academia
to even to even call from. I mean, you know,
there's like the AM I thinking correctly of the Shortenstein
Center at Harvard, you know, love all of their stuff

(43:05):
that they publish. But I mean, it's an emerging field.
And I think that's maybe why you have a sense
that industry is leading the way is because I think
that academia has not figured out where to carve this
out yet.

Speaker 1 (43:18):
Oh Molly, you just hit my I'll just say I agree,
because I'll if you get me going. I so. Formerly
I was a research fellow at brookmanklin which is sort
of a cousin to the Short Shortenstein Center at Harvard.
People who do research on the Internet in from in
an academic way attached to universities are having a rough time,

(43:42):
I'll just put it that way, and they're us it
used to Things used to be better. There used to
be more funding. But if you do that kind of
work when you're doing it today in twenty twenty five,
god bless you. I am happy that you've got it
figured out. But we have really hollowed out an entire
space of researchers who are interested in what's happening on
the Internet. It's not a robust field any longer. I

(44:03):
can definitely confirm that. But I'm really glad that you
that you made this point about private companies versus you know,
Academia and another spaces because one of the criticisms I've
seen online about this report was that it's essentially Taylor
Swift PR right that you know, and they're they're talking
about this in an almost nefarious way that oh, this

(44:26):
is just a company whose whole job is to get
good positive press hits for brands and celebrities and things
like that. And what's interesting to me is that from
what you've said, it doesn't sound like that criticism is
like totally wrong because in a kind of way, that's
how these companies get their name out there so that
they can continue to fund the other important work that

(44:48):
they're doing. But that like it's sort of missing the
forest for the trees of what's actually going on in
terms of how this research comes to get to the public.
Do I have that sort of.

Speaker 3 (44:56):
Right on your person?

Speaker 2 (44:58):
And I mean I think if they were successfully doing
PR plants for Taylor Swift, they would have a much
more you know, robust company at this point, and we
would be able to tell that the roof would be
in the putting. I think I guess one other element
to talk about, like, while we're just talking about, you know,
the vibes of the Internet is. You know, you talked

(45:19):
about the hollowing out of you know, academia.

Speaker 4 (45:22):
On this.

Speaker 2 (45:24):
There's you know, maybe fewer people in house that news
and media organizations have that are like Internet experts and
are are given kind of like the time and resources
to dig into this. I mean, if we're talking about
like Internet vibes and reporting on the Internet as a beat,
I feel like I have to mention Brandy Seedrosni who.

Speaker 3 (45:44):
I respect her work so greatly.

Speaker 2 (45:46):
Getting to getting to pitch Peak Metrics data to her
and interact with her has like been one of the
highlights of my career because that was that was the
first time that I really saw the Internet being reported
on as like, hey, we're sending a reporter out to
this location. Now we're sending a reporter like into the
bowels of the Internet to come back and tell us

(46:07):
what's going on. And you know, not every news organization
news organization has a brand news adrosneens, So that's why
they they call up companies like Peak Metrics to you know,
give them a sense of what is going on on
the other side, which is, you know, this thing that
we're all a part of. But you can't just walk

(46:28):
away and like put your finger up in the air
and say, like, as a matter of fact, this is
what's happening.

Speaker 1 (46:32):
One of the questions I was going to ask is,
you know, what's the solution to this, to the way
that bots are disrupting our discourse? And I was thinking
of it as a question of like what should platforms
be doing or not doing? But it sounds like the
stepping back, the problem is so much more layered than
platform X needs to do specific thing. It's a it's
a problem of media outlets, it's a problem of coverage.

(46:55):
It sounds like a much more a complex problem than
just saying, oh, must decide to do this, that'll fix
the problem.

Speaker 6 (47:02):
Yeah.

Speaker 2 (47:03):
I mean we're going to continue, probably to be in
like an outrage clickbait cycle of hey, the bots were
this big of a part of this conversation and what
implications does that have for the subject of this conversation.
But you know, until we get a sense of like
what is the normal baseline level of bot activity, We're

(47:23):
just going to kind of keep spinning on this wheel.
So I'm hopeful that maybe this time next year, I
feel like this is this bot activity discourse has really
hit maybe in twenty twenty five, we're all a little
bit more aware of it now, we're building out, you know,
a repertoire of research on it. You know, hopefully if
we were to be coming back together in December twenty

(47:44):
twenty six, maybe we'll have like a better understanding of
this that will get us less locked into these outrage cycles.

Speaker 1 (47:52):
Molly, is there anything else that I have not asked
you about that you want to make sure gets included
in this conversation?

Speaker 3 (47:59):
I guess is there a part of the Internet that
makes you happy? Like why are we all? Why are
we all still here on the internet? Like why are
we doing this?

Speaker 1 (48:07):
What are you? What's your Internet happy place? What make
what fills you with hope for the future? And or
just you dislike spending time online?

Speaker 2 (48:14):
Yeah, well, I mean, I guess I mentioned that I
was like a twenty tens Tumblr girly, and I still
have close friends that I met on Tumblr that I
am talking to, you know, fifteen years later. So I
think that that's like the power of Internet subcommunities when

(48:34):
they're working well, And I think that people use the
Internet to find connections like that.

Speaker 1 (48:42):
I'm so used to talking about the fucking drugs of
the Internet that I forget that. Actually it's a place
that I like and I'm there every day voluntarily and
like it was hugely informative to me as a youth,
and you know, that's why I keep coming back. It's
a good it's good of a reminder to be like,
you actually enjoy technology, right, like you actually voluntarily keep
it in your life.

Speaker 2 (49:02):
No, yeah, you're not like a complete let in at
this point, right right, exactly.

Speaker 1 (49:11):
More after a quick break, let's get right back into it.
Social media platforms only work when they actually help people
make sense of the world around them. That breaks down

(49:32):
when they're overrun with inauthentic narratives and bots engineered to
bait engagement. But understanding all this is where the work
of people like Keith Presley comes in.

Speaker 6 (49:43):
We started as a nonprofit trying to identify how information
moves across the Internet, So where are those breeding grounds
of information campaigns? And then how does it get to
the broader network and impact people? Just mapping how information moved,
So we went ahead and did that. Turns out there's
a lot more use cases than the limited one we
had in the nonprofit. So we then spun out into

(50:05):
a company.

Speaker 1 (50:06):
Keith's company, Kadeta, was all over the Internet thanks to
a report they put out looking at how coordinated bot
campaigns influenced discourse around the release of Taylor Swift's latest album,
Life of a show Girl. You might recall that after
the album was released, Taylor put out a necklace featuring
lightning bolts, an image that some felt bore us striking
resemblance to not the ss iconography. The report examined what

(50:30):
sparked those claims and how those claims spread across the
Internet like wildfire. The report was published in Rolling Stone magazine.

Speaker 6 (50:38):
We like to consider ourselves the stormtrocker for the Internet. So,
as a meteorologist uses patterns to predict the weather, we
use patterns to predict information online. So we take in
information from over four hundred and eighty different platforms, and
then we have a patented process where we run graphnal
networking through that data to see how people behave with it,

(50:58):
and then use AI to lift out those behavioral patterns
and summarize for our clients.

Speaker 1 (51:03):
I have seen people say that what your company does
is essentially pr that celebrities and brands like Taylor Swift,
just give you all money to print nice things about them.
What do you say to somebody who feels that way
about what you're doing. I would say one this report
we did on our own. We were just interested.

Speaker 6 (51:21):
I had a gut feeling that there was something funny
going on, ran the report, and then found something. A
big reason of why we wanted to do this though
and released it is a lot of this work is
behind closed doors.

Speaker 1 (51:35):
Right.

Speaker 6 (51:36):
We do work with companies and report on what is
happening around their narratives. Public doesn't get to see that,
and so this was a way for us to get
something out there so that people could have a better
understanding of how online information environments are actually being manipulated.

Speaker 1 (51:52):
It's true that most of the social listening work happening
in twenty twenty five stays behind closed doors. And even
though the headline and Rolling Stone made it seem like
something extraordinary had happened with a Taylor Swift narrative, to
analysts who work on this kind of stuff all the time,
this kind of influence campaign was basically just another Tuesday.
It's funny that you say that this report started with

(52:14):
just sort of a gut feeling months ago, back in October,
we did an episode about the conversation links to the
launch of Taylor Swift's latest album, and I said the
exact same thing. I said, I don't have any proof.
I'm not a data scientist. Do My producer is a
data analyst, but like I do that It's not a
skill set that I have. But I have been on

(52:34):
the internet for a very long time, and I'm particularly
pretty plugged in with how conversation about marginalized people, so
like women, especially women in the public eye, I'm pretty
cluded to have those conversations move. And the way that
I would describe the conversation specifically around the Taylor Swift

(52:55):
Nazi necklace thing, it was like it came on strong
out of no where, was such an intense conversation, and
then it kind of stopped just as abruptly as it started.
I just something about that, I said, I don't know
if this is this is all authentic, Something about the
way that it came on so suddenly just gave me pause.

(53:16):
I guess I will say, it sounds like you all
were in the same boat exactly.

Speaker 6 (53:20):
It's there are some tall tale signs of things are fishy, right,
and this is when you see such huge surges out
of nowhere that it is normally a leading indicator that
there are some sort of some sort of coordinated activity
to get that information out there to then impact how
a normal person is going to talk about it. So

(53:43):
get them to interact with that negative or illicit content,
right that it's the goal they're rage baiting.

Speaker 1 (53:50):
So is that really kind of how this report came
to be? Just this inkling of something fishy is going
on here, let's find out what it is?

Speaker 6 (53:57):
Yeah, truly, I know in the article, Georgia Paul says,
you know, she just had a feeling. It really came
from that. It was we were like in you know,
one of our early morning meetings and we're like, hey,
let's do it. Let's investigate and see what's going on there.

Speaker 1 (54:14):
So what did the report find?

Speaker 6 (54:16):
So essentially that the Nazi, that Taylor Swift is a Nazi,
and that the necklace with the lightning bolt necklace and
that was alluding to the SS from Germany. And what
they were doing is so they laid down that content,
a large quantity of that content to then impact influencers
who would then pick that up and then spread it

(54:36):
more broadly to normal individuals. At that point, the narrative
transforms to actually then being a comparison of Taylor to Kanye.
Having something like that happen is the end goal, right,
they want because that was actually real people now having
that conversation, not driven by this inauthentic activity.

Speaker 5 (54:56):
Yeah, thank you for the summary there. I'm kind of
jealous of the position that you guys are in, like
having this data, because, like Bridgie was saying, we had
a similar like feeling and conversation that something felt off
about this conversation. But that's where it ended with us, right,
because we did not have the tools readily available to
look into it. And one of the things that has

(55:19):
come up for me as we were researching this episode
and talking to people is just how valuable it is
for the public to get glimpses like this using data
to like actually bring some data to help us understand
what can seem very random but often is not. In
terms of like conversation happening on the Internet.

Speaker 6 (55:41):
It's almost kind of like a black box the Internet,
especially the social media platforms, where there's millions of posts,
billions of posts being made daily, lots of different coordinated
activities to either you know, from crypto schemes to just
influencers wanting to get clicks. There's a lot of competing

(56:01):
priorities that just you can't see normally. You can't get
that bigger picture, especially as a normal person, right, we
can only really see what our feeds are.

Speaker 1 (56:12):
Yeah, and I always make this point on the show.
There are so few spaces or industries where the public
is coming into contact with it regularly, where you have
such limited information or data about how it's impacting people. Right,
if there was a car company that was killing people
and we weren't able to get that information, if there

(56:32):
was a pharmaceutical company. There aren't really a lot of
companies or industries where we've just accepted this is a
black box of information where the public is interacting with
this every day but has no idea what that interaction
actually means or looks like.

Speaker 6 (56:46):
On our end. This industry is really new too, just
trying to actually understand how the Internet works, or information
moving on the Internet, or how it's impacting people. There's
some academic research that's been happening recently, you know, and
then companies like ours that are trying to figure this out.
To help society, but it's still a pretty small pool

(57:08):
of people trying to do this.

Speaker 5 (57:09):
I'd like to get into the methodology of your report
just a little bit. Like one of the things that
you that you did was classify accounts according to whether
they were typical or atypical, and then further subdivideed the atypical.
And that seemed like an interesting approach to this problem
of trying to make sense out of all these different
actors and like different types of actors, where you know,

(57:32):
the binary of ordinary human versus bot. As we've been
researching this, it's I'm starting to feel like that's not
perhaps a super useful distinction, a binary distinction. And so
you guys used I think five different categories. Could you
talk a little bit about that decision and how you
defined those?

Speaker 6 (57:52):
Yeah, and you're kind of hitting the nail on the
head for how we were thinking about this that one
or zero. If you're looking at the what is trying
to what are people trying to do online as either
bought or not bought, typical or atypical, you're kind of
missing the point. These all of these actions are coordinated

(58:14):
to make information go viral, right, That's the end goal
they want to get in front of eyeballs because for
either illicard reasons or making money, et cetera. To do that,
you can't just here's a bot, right, it's going to
post a lot. That's not going to get something to
go viral, as we've seen, right, you've got to impact people.
And so we've observed and modeled five different behavioral patterns

(58:39):
from the typical user all the way down to what
you would consider that through you know, the bot, and
are working to understand how they're used online to actually
get information to go viral. So, you know, an influencer
has their own specific behavioral pattern that they're going to
be using what we call outliers, facilitators, and then power players,

(59:03):
and so all of them work together in some instance
to push strategies and tactics to make that information get
in front of people.

Speaker 1 (59:12):
How do you know when this behavior is coordinated? That
was one of the points the takeaways in the report
was this is these are not just inauthentic accounts acting
on their own. They're all coordinated in a kind of
way as a network. How did you determine that?

Speaker 6 (59:27):
Yeah, so that's part of our patented process where so
that graphnal networking through the information reveals patterns of behavior,
and so it really does. It's like a fingerprint within
the data. It actually has a very distinct pattern that
we can then see and lift out and then make
those determinations. And so a lot of that can be

(59:48):
you know, how are they interacting with like accounts, how
are they interacting with peers, the language that they are using,
the timing, there is a slew of clues that we
are at a that we then use to make these determinations.

Speaker 1 (01:00:03):
One of the criticisms that I've seen of the study
is that the data set, the data that you used
for this was not made available right, and so if
I wanted to take that data set and crunch the
numbers myself, I could not do this. We had a
conversation with somebody in it from a socialisting organization and
they said, oh, well, that's actually kind of commonplace for

(01:00:25):
private companies. They might have proprietary things they don't want
out there. How much did that impact not making that
data set available for the public.

Speaker 6 (01:00:36):
That two fold, So proprietary processes and then like data
licenses that we're not actually can make some data available
right because we do have API access to various platforms,
so you just can't give away their information.

Speaker 1 (01:00:54):
Okay, this is such a good point because and this
is a frustration of mine. It used to be that
if you wanted API access to X, right, that was
not something that will be difficult to get. In twenty
twenty five, things have really changed, and so yeah, I
don't know if people who are not sort of in
this world really know the ways that a lot of

(01:01:14):
the Internet and how it works is has been turned
into a black box, and so it's incredibly difficult to
get that kind of access for most of us these days,
and we simply, you know, just don't have it.

Speaker 5 (01:01:26):
And just to underscore that, you know, it's it's not
a black box because it is so mysterious, no one
could know it. It is a black box because the
people who run the platforms have decided that they want
the box to be black.

Speaker 6 (01:01:37):
Yeah, you know, monetizing the information that they have. And
I think it's become even probably even worse now, you know,
with training llms. You know, a good place to get
information is from these social media platforms, which then you know,
aren't they're not getting any value off of that. Those

(01:01:58):
companies training off of their so I think it's kind
of become a positive feedback loop.

Speaker 5 (01:02:04):
I was also really interested in the sort of temporal
analysis that you guys did, looking at not just the
mix of the types of accounts, but how it changed
over time pretty rapidly in the you know, immediate days
after you know, the beginning of the study period, which
I think was like the day after the album drop.
Can you talk a little bit about what that temporal

(01:02:26):
analysis and how the shifting mix of actors what that
tells us. It wouldn't be available if you just looked
at a single snapshot in time.

Speaker 6 (01:02:36):
It's actually important to look at these as snapshot in time,
so we can go down from like the microsecond all
the way up to you know, centuries. We don't have
that much data yet though, But the key here is,
so let's say the Taylor Swift report, it's actually about
three point seven percent of the total users were these
inauthentic type users that contributed twenty eight percent of the

(01:02:59):
total content. That's the big snapshot, but that doesn't tell
you the whole picture when you then look at it
how it happened over the course of hours, where that
three point seven percent. At the first instance of this narrative,
Spiking contributed, it was about fifteen to twenty percent of
the total audience and contributed I think it was seventy
eight percent of the total volume of right. So if

(01:03:20):
you only look at one overall snapshot, you are going
to you're not going to see the force from the trees.

Speaker 1 (01:03:26):
I'm sure you know that the reaction to this has
been quite aware. Yeah, it's been big. I'm sure y'all
have had a wild week and it's been really interesting
and I think kind of weirdly telling to engage with
some of the criticisms that people have made or like
just responses that people have had, they've been like deeply

(01:03:47):
emotional responses, I guess is how I'll put them. And
one of the things I've seen i've seen the takeaways
from the report. I don't want to say misrepresented, but
I would summarize it as one camp being like, see,
one hundred percent of the people who push this narrative
were bots, and this was a narrative that we did

(01:04:07):
not exist for real. If you if you bought this
was real, you got taken. And then I saw people
like black women on social media being like, I'm on
a bot, I'm a real person. I felt x y
Z about Taylor Swift. And what's interesting to me is
that when you actually read the report, neither the report
does not make either of those claims. And so I
guess I wonder, you know, what do you think accounts

(01:04:28):
for the fact that people are using the report to
say something that I think the report patently did not suggest.

Speaker 6 (01:04:37):
Yeah, we've definitely noticed and had discussions about that too,
where like, where are you guys getting this from our
whole goal? So we're not the arbitras of truth, right,
that is not what we do as a company. What
we're trying to do is tell you who and what
and why is that narrative happening. That's what we're trying

(01:04:59):
to do, is like how did that come to be?
And so, yeah, you know, in this instance, even though
there's a high percentage of nontypical actors, there were still
normal people that did engage with it. So we're not
discounting that there was some genuine engagement at the start.
It's just that that first layer came from non typical users, right,

(01:05:21):
and then people got brought into it, and then it
morphed and as it grew over the course of the
couple days.

Speaker 1 (01:05:28):
And I feel like that is part and parcel of
these online manipulation campaigns, where the point is to get real,
authentic people talking about stuff that otherwise they probably wouldn't
be talking about. And so this is not inauthentic discourse
from bots. I've personally gotten myself pulled into conversations around
like why am I all caps rage tweeting about cracker

(01:05:51):
Barrel right now, a restaurant I have not eaten at
in twenty years, Like the ways that they can get
you to pull in.

Speaker 6 (01:05:57):
And engage, like that's the point that is, Yeah, nail
on the head, that literally is the point they want
you to get engaged with that content.

Speaker 1 (01:06:06):
The report makes it clear that there was overlap between
some of this Taylor Swift and authentic behavior and Blake
Lively what's going on there? Like like, what's the what
are the implications for that? Yeah?

Speaker 6 (01:06:19):
I would say highly suspicious?

Speaker 1 (01:06:23):
Where is us? You know?

Speaker 6 (01:06:25):
Especially that far apart right?

Speaker 4 (01:06:30):
Uh?

Speaker 6 (01:06:30):
And the same classification like the facilitator accounts that those
are the ones that typically posted high volumes in short
and short stints. The fact that there were so many
overlapping that far apart tells me that that was more
of a coordinated uh activity.

Speaker 1 (01:06:47):
I mean that kind of takes me back to a
stepping back question. Why would someone be invested in manipulating
the conversation around Taylor Swift on the internet like like
and why?

Speaker 6 (01:07:01):
So we've also talked about that internally and trying to
you know, so we work with brands and we see
this all the time where you know, either corporate espionage
or you know, targeted campaigns to hurt market relevance on
the Taylor Swift side and play clively either. I guess

(01:07:22):
we have two theories. One testing right, So Taylor Swift
is a huge brand. She drives economies. You know, you
can almost say that she's a political figure. If you
can impact how Swift eas or that conversation online around her,
that means those strategies and tactics would do then work
for others. And then the other one is around the

(01:07:45):
economic Uh. You know again, she drives economies, so if
you can hurt her reputation that allows others to fill
that void.

Speaker 1 (01:07:54):
Was that meant to be a bit of a tailor
Swift pun? Doesn't she have an album called reputation I think,
don't quote me on that more. After a quick break,

(01:08:18):
let's get right back into it. How did the report
end up in being covered in Rolling Stone?

Speaker 6 (01:08:25):
We went out and we shopped it around and Rolling
Stones was interested in writing about it. Like again, we
were also equally giddy about that one. We did not
anticipate such a reaction.

Speaker 1 (01:08:37):
I'm gonna be honest, Yeah, I mean the reaction has
been absolutely wild. And I understand that like companies like yours,
part of publishing studies like this, like especially like flashy ones,
ones that people are gonna actually you know, want to
be reading. Part of it is like getting publicity for
the work that they were able to do, and so like,

(01:08:57):
to that end, do you consider this to be a success, Like,
I don't know the last time at a report. I mean,
we look internally, we're reading reports about the Internet all
the time. Typically my cousin is not texting me about them.
You know what I'm you know what I'm saying.

Speaker 6 (01:09:14):
Yeah, So yes, definitely would consider it a success. To
that point, I've had family that I haven't spoken to
in years and we're like, oh my god, Yeah, it
was crazy, but you know it was I guess to
the point, we were just trying to demonstrate the ability

(01:09:34):
that we have, right, that was really what we wanted
to show people what is happening online and how we
can help.

Speaker 5 (01:09:41):
I mean, one possible takeaway here is that, like there
is a lot of appetite among the public and demand
for this kind of information.

Speaker 6 (01:09:49):
Yeah, that's uh, I was kind of kind of we're
thinking that too. And then what else could we look
into that could be helpful for people?

Speaker 1 (01:09:56):
Can you give us a sense of when somebody gets
on the internet, how much conversation online is perhaps being
impacted by inauthentic behavior, because you know, from Cracker Barrel
to Blake Lively the Taylor Swift, it seems like these
conversations that you might have thought of as innocuous are
now actually being manipulated by inauthentic actors.

Speaker 6 (01:10:19):
Yeah, so my whole quote the Internet is fake, it
kind of really is. So since we've been doing this,
we have yet to find a narrative that didn't have
ood activity or inauthentic activity, Like, not a single one.

Speaker 1 (01:10:37):
I mean, it's sort of I I mean, I guess
you when I read that quote the Internet is fake,
I didn't realize you.

Speaker 6 (01:10:43):
Meant it quite so literally, it's just kind of scary.
Like we you know, we were a little bright eyed
and bushy tailed when we got into this.

Speaker 4 (01:10:52):
Uh.

Speaker 6 (01:10:53):
And then over the course of you know, doing this work,
we've seen just how much inauthentic activity there really is
happening online.

Speaker 1 (01:11:00):
How are we meant to use our social media platforms
and platforms for discourse? How are we meant to use
them effectively? If that's the case, Like, can they be
used effectively anymore? That it's a really hard question too.
And so if I could wave a magic wand the like,
how I would think or fix this? So humans have

(01:11:24):
had thousands of years to figure out the etiquette of
behaving with each other in real life, right, there's so
many different norms that we have that have just come
from those centuries. The internet, like in the current state
that we use it, maybe twenty years, twenty five, right,
My gut is that we probably need to figure out
what the etiquette is when interacting with people online because

(01:11:49):
you know, right now there is none you just just
the wild West. Yeah, the wild West is a good
way to describe it, and especially for these conversations that
frankly I think even fifteen years ago did not feel
this heated online. It's incredibly difficult to have a conversation,
even a conversation is about something as simple and Innocubus

(01:12:09):
has an album, a movie, something like that without it
feeling the triolic. And I have to imagine this inauthentic
activity is adding to that.

Speaker 6 (01:12:19):
Oh yeah, absolutely Again, that type of content gets the
most clicks, so it gets lifted up the fastest. It's
actually been really really interesting and meta for us to
watch us become the conspiracy that is. Yeah, yeah, that
one's going to be a fun white paper. We're going
to do in ourselves now, Oh my.

Speaker 1 (01:12:40):
God, you absolutely should. Well we'll have you back. And
I'm guess something. When I was preparing for this, I
was going through social media and I was trying to
pull out some of the I guess criticisms. Maybe isn't
the right word, but some of the things people have
been saying about the report about your company, some of
them for my own background, I know to be incorrect, right,
like saying, oh, this is just PR Taylor's that probably

(01:13:00):
paid to have them print this, And I'm like, well,
it's not really what these companies do and I guess
how can I even ask this. Let's say it like,
I understand why and how a lot of people who
are saying, hey, I'm not about I feel offended and
unseen and erased by a report that makes me feel

(01:13:21):
like my voice isn't a real voice. Right, that's not
what the report said, But I get how that's the
conclusions that they're coming to. My thing is this, If
you are someone who really can't stand Taylor Swift, you
think that Taylor Swift is a literal Nazi. Let's say
that for the sake of argument, I would think that
you would want reports like this to make clear how

(01:13:41):
difficult it is for your message to break through online,
how much that message, even if that's a message that
you authentically feel, how easily exploited it is, and the
fact that it is being disrupted. And so I would
imagine even people who want to use internet platforms to
authentically engaged in like critical discourse, they, more than anybody,

(01:14:04):
should want to have a media landscape where that is
possible without this kind of inauthentic interference and manipulation.

Speaker 6 (01:14:12):
No, I completely agree, not only that, but those individuals
that do have those true feelings and you know, want
to use it as a platform to voice those feelings
often get taken advantage of by the inauthentic activity accounts.
So a very very common thing is they're not creating
the narrative that they want to push. They're pulling it

(01:14:33):
from these small populations that they're like, oh, that one,
that one will really make people mad, and then they
push it inauthentically. So essentially they're actually getting taken advantage
of by these accounts.

Speaker 1 (01:14:45):
I've actually seen this, or at least suspected that I've
been a target at this kind of thing, because you
could just be being a garden variety hater on the internet,
you know, like oh I didn't like this, I didn't
like that. Then you're going to comment from someone who's like, yeah,
let's boycott it, or like yeah, let's like get to
a level where you're like, well, I was just trying
to engage in a little low level snark. I didn't
mean it like this. But if you're already sort of

(01:15:07):
riled up and you're not necessarily thinking super critically about it,
it is this difficult to not engage. I guess that's
what I'm saying.

Speaker 6 (01:15:15):
No exactly, and Again, that's the point. It's really hard
not to engage with this when either you're really do
believe in it or you're really really opposed to it.
You want to say something that's just human nature.

Speaker 1 (01:15:28):
Do you see any solutions to this? Is there something
that I mean, I don't. It's like like what, like,
is there something that platforms could be doing that they're
not or you know, shouldn't be doing what they're currently doing.

Speaker 6 (01:15:39):
Yeah, and that's a really tough question that I don't
know if we're even equipped to say, like what the
solution would be here. There are certain things that are
really clear indicators that somebody is doing something to faries,
Like people that are really trying to push illicit content
will change their handle us a lot over the course

(01:16:01):
of just a short time span, Right, how many times
did you change your handle like whatever? Rarely people write
that's and then these accounts are doing it, you know,
three times a day, Like you know, there are there's
key indicators that they could be looking for that would
allow them to mitigate some of this uh and authentic activity.

Speaker 1 (01:16:25):
My favorite thing is when I see an account that
says it has a that the images of like a
white person and then the post will be like, well
as a black woman, and I'm like, oh, did someone
switch up their grift? What happened?

Speaker 6 (01:16:39):
Those are our favorite too. We passed those around where
it's like, wow, you're your a LLLM model here really
fails you.

Speaker 1 (01:16:47):
Yeah, so it's it's tough. I mean, it sounds because
a lot platforms could be doing. But obviously I don't
have a direct line to Elon Musk or anything. But
what about individuals, like, while we are in the absence
of platforms really doing what they can to crack down
on this kind of thing? Do you have advice for people,

(01:17:08):
especially as we navigate what feel like increasingly volatile times
where conversation just reflecting the times like it just seems
more volatile. Do you have advice for folks as they
wade through that to not be impacted or manipulated by
this kind of an authentic behavior.

Speaker 6 (01:17:26):
Yeah, first, like, take a breath, right, don't don't respond immediately.
But then I think it's there. It is because people
are still going to want to respond, So how do
you do that?

Speaker 3 (01:17:39):
Right?

Speaker 6 (01:17:39):
How do you engage with this content without actually engaging
the algorithm that then is going to boost it to
even more eyes. So our goal is to try to
help people understand how to navigate these narratives. And so
in this instance, it's kind of like a three step process.
So observe but don't interact, so you know, you can
see the content, but don't like, get, don't share it,

(01:18:01):
don't do anything that would boost the algorithm. Then if
you want to counter, you post separately, but don't reply
in the comments, right, so you can make your own
post about it, but don't be replying to it in
the comments, because again that's going to boost it algorithmically.
And then try to redirect to a more positive narrative.
So in your post, talk about you know, don't don't

(01:18:24):
engage what they're talking about directly, try to redirect it
into a more positive sense without using any of the
same hashtags or the like of that original negative content.
That way, you can be trying to change the conversation
instead of boosting something algorithmically.

Speaker 1 (01:18:41):
What is next for you all? What's your do you
have it? Can you give us a little preview about
what the next big yeah and authentic conversation online might be?

Speaker 6 (01:18:53):
Sadly no, right, because you never know what's going to
happen online. I mean, we definitely are going to do
one on ourselves because and did that go crazy?

Speaker 4 (01:19:01):
Uh?

Speaker 6 (01:19:02):
But I don't know, like if we wanted to do
another one, maybe k pop could be a fun one.

Speaker 1 (01:19:08):
Oh, we were just having a conversation with one of
our producers, Joey, about just K pop fandom in general.
I had no idea. I had no idea. Oh we have.

Speaker 6 (01:19:20):
We've worked with clients where like they're they're just their
online activity have broken our systems. And so the K
pop community is real and large.

Speaker 1 (01:19:31):
And it just goes to show exactly what you were
talking about that these might sound like quote celebrity stories,
but you know k pop, it's it's such a big
fandom that it says so much about how not just
celebrity and fandom and how we live, but like how
we live our lives politically, class issues, gender issues, these
are real things that really motivate people in our world.

(01:19:54):
And so it's not just celebrities and fluff. It's conversations
that have actual influence.

Speaker 6 (01:20:01):
When we see big fandoms getting involved with something normally
it's actually in a positive sense, really lifting up a
new album or saying how much they like something. Actually,
the fandoms really do tend to be like I want
to say, wholesome community, Like they share nice content.

Speaker 1 (01:20:24):
As someone who studies the Internet and this does spends
a lot of time making reports about what's happening there,
are you does it give you hope? Like this, like
the conversation about fandoms and them making wholesome content. You
had a little smile when you mentioned that talking about
technology and the Internet, that like, these are things that
we like, spaces that we like. I find myself hating
on them and lifting the bad stuff. But do you

(01:20:46):
feel hopeful and good about the Internet given all of this?

Speaker 6 (01:20:49):
No, I think there's still so much to be gained
from ever increasing connections, right because in all of these
social media platforms really were about connecting individuals together that
had like interests. And I think there when it became
like social media and it's now about media content that

(01:21:12):
has driven a little bit more of the negativity. Uh,
you know, it really did. At its core, it is
about people engaging with communities that have like interest and
I think that's great. Our whole goal is to help
people have a better understanding of the world they live in,
and especially that information environment where it's just really hard
to know what's real or not.

Speaker 1 (01:21:36):
Got a story about an interesting thing in tech, I
just want to say hi. You can be just at
hello at tengodi dot com. You can also find transcripts
for today's episode at tengody dot com. There Are No
Girls on the Internet was created by me Brigittad. It's
a production of iHeartRadio and Unbossed. Creative Jonathan Strickland is
our executive producer. Terry Harrison is our producer and sound engineer.
Michael Amata is our contributing producer. I'm your host, Bridgeitad.

(01:21:59):
If you want to help, let's grow, rate and review
us on Apple Podcasts. For more podcasts from iHeartRadio, check
out the iHeartRadio app, Apple Podcasts, or wherever you get
your podcasts.

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