>> Hi everybody. I'm Catenya McHenry, Communications Director here at
the McCombs School of Business. And you are joining us on our Big Idea show,
where we look deeper into faculty research happening here on campus.
Joining me today is Tricia Moravec, she is a new professor here in the
McCombs School of Business. She is an assistant professor of information
management. We are focusing on her new paper today, which everyone will
be very interested in. It's focusing on fake news. The title of the paper is
"Fake News on Social Media: People Believe What They Want to Believe
When it Makes No Sense At All." Thank you so much for joining us today.
I've-- ever since you got here, we've been talking about this paper and fake
news. I've been very interested in this because now it feels like fake news has
become a household buzz term, and it's used constantly.
>> Right. But unfortunately, it's not being used in the correct way.
So the way that I'm using fake news and the way fake news should be used
is that it's used to describe information that's verifiably false. And how I also
describe it is it's intentionally created to mislead.
So that's my understanding of fake news, but unfortunately, it's currently
being used in ways that don't actually describe false information.
>> Right, right. I think what's most offensive, as a journalist, is the misuse
of the term. So let's talk a little bit about just your motivation for
wanting to even look into this topic. >> Right. So it was in 2016.
And if you'll remember, 2016, that was when our presidential election happened.
And so leading up to the 2016 presidential election, two of the three most
engaged with pieces of news on social media were false.
And so-- >> Which were those?
>> So it was one about "Pope endorses Donald Trump," and that Hillary sold
some guns to ISIS. Something like that. And so that was a first.
And then the third most engaged with. So that means shared, liked, whatever.
But people saw it. And likely believed it for a bit. And so,
and you know, Facebook had this problem and continues to, where they're
profit-driven. They want us to stay online, and to do that, they show us
information that we like. And that means our confirmation bias is
driving what we're doing online and everything.
>> So information that we like, whether it's true or not. It doesn't really matter
to Facebook and you guys just focused on Facebook in the research.
>> We just focused on Facebook in the research just to kind of constrain it.
But really, it was at this time. It was in 2016 when I needed a new research
project and I was very motivated by this relevant issue that was occurring,
and so I had this research question of well, can people detect fake news on
social media? And does this disputed flag that Facebook debuted, which
was-- it said, it had a little caution sign and it said "disputed by third-party
fact checkers." And so they were going to put this on fake articles after
enough people had said "this might be fake." And we won't go into the issues
with that design in general-- >> Yeah, because we'll be here all day
talking about that. (laughs) >> Yeah, I could talk about this for
hours. But I was curious, like "does this work? Can people detect fake news?"
And so I brought this to my adviser and we just took off and studied it
in a lab with students and using electroencephalography or EEG, which
is measuring brain activity. So the flag was on particular articles that
could have been real or not, but it was Facebook's way of disclaiming
that it may or may not be true, and it's really up to you to decide
what you believe about that information. >> So I think that's one of the reasons
why it didn't work. Because what people actually need is a flag that says,
"this is fake." Not something that says, "this may or may not be fake."
So the issue with the phrase "disputed by third-party face checkers," is that
it's kind of soft. It's very soft language that doesn't really get to the fact
that this is fake news, and you shouldn't believe it. But that was the
intention, as far as I know, of the flag, was to say "this is inaccurate
information and you should not be consuming it as truth."
>> So when you brought the group in to test whether or not your research
would or wouldn't work, what happened?
>> So it was one person at a time, and this is one of the constraints of EEG
research is because you have to put this headset on people, and so that's
one interesting factor in getting enough subjects and actually doing research
like this. But what happened is that they came in the lab, they went through
some demographic questions, and then I got the headset put on them.
And then they went through 50 articles that were either true or false,
and they were actual articles that were circulating around December, January
of 2016, 2017. And so they were actually verifiably true or false. They were real
articles and people only saw the headlines, and then the headlines were
either flagged as false or not, which was the control condition.
And so what we ended up seeing is that when you ask people, you know
"how believable is this article? How credible is this headline?"
People's belief didn't change when the flag was present.
>> Really? >> So their stated belief didn't change
whether it was flagged or not. And so that was an initial, wow. So the flag
doesn't work. Facebook had also figured this out, but I think we end up
getting more into the why it doesn't work. And so we found out that the
flag doesn't work, but then, alarmingly, we also checked whether people were
correct in whether they-- >> And when you say "correct,"
>> In whether they believed it or not. >> Okay. So not matter how outrageous
the headline was, your determination was whether or not people believed what the--
>> The headline. >> The headline, yeah. Yeah.
>> And so the way I measured this was that the items-- there are three items
on seven point Likert scale, with the middle being "I don't know, unsure."
And so for someone to be classified as correct, if it was a false article,
they needed to say either "I -- it's extremely unbelievable, not believable,
not very believable." And if they said any of those,
then they would be correct. And likewise for truth, if it was true, as long as they
said "this is extremely believable, moderately believable, slightly
believable--" even if they said slightly. I mean that's not a very big commitment.
But even if they said slightly, I would count them correct. But if
someone was unsure or in the opposite direction, they were incorrect.
And what I ended up finding is that I think 17% of my sample was able
to detect fake news better than chance. So that means they were correct on
more than 50%. Which is really, really bad.
>> Yeah. Only 17%! That's just-- >> The best person did 66% correct.
And that's a D, or an F. That's essentially failing.
>> That's bad! So that-- what does that tell you? Does that tell you
that people will-- I mean, obviously in the title of your paper, people believe
what they want to believe, but was it surprising to you that people
believe what they already felt in their moral being?
>> So to capture that, we used a measure of confirmation bias.
So assessing what people had already, what people believed based on some
conservatism items and whether they self-identified as a Democrat
or Republican. So combining these two. And using that, we're able to see that
if the headline was something that aligned with your confirmation bias,
then people believed that more. So yes, essentially people were unable
to detect fake news. The flag did not work as Facebook had intended.
And it was essentially just this confirmation bias where people really
just believed whatever they want to believe, and that's not entirely new.
But the extent to which confirmation bias drives what we do on social media
and what we believe, that is a new topic to be studied.
>> So on the back end, did you look at just Facebook and how they infiltrate
information into their platform and how they sort of categorize certain stories
to target certain people because they have those algorithms to figure out
who people are and what their interests are, depending on part
of the country and all these other variables.
>> Right. And so that creates these echo chambers where, you know,
Facebook is financially motivated to keep us online and keep us happy.
And so they already know that they want to show us information that we
like so that we stay online because they're doing hundreds of
micro-experiments every week. And so they know what they're doing.
But the problem is that, you know, with their financial motivation, they don't
really care about whether they're showing us true information or false.
And I didn't go into the details of Facebook's algorithm because it really
is a black box. None of us really know about it and Facebook doesn't want
to share those details. >> Of course.
>> But what is just... alarming about this whole thing is that we really
don't know what we're going to see next. And so we are very susceptible to
believing fake information. And Facebook did not want to take
responsibility for their part in the spread of fake news for pretty much
all of 2017. So when this started coming out about the top 2--
or two of the three most engaged with articles were fake in the three months
leading up to presidential election, Mark Zuckerberg pretty much would just say,
"this isn't my problem. This isn't Facebook's problem. You know,
I shouldn't have to-- we shouldn't be censoring information." And so
slowly throughout the past three years since then, they've come to take
more responsibility for this because they realized that as a platform, they
need to be providing people with actually valuable information, rather than
false information and misleading us. And especially with the
Cambridge Analytica scandal, I think that's also helped in helping Facebook
to take responsibility, but truly one of the worst problems about this and
as it's been getting worse and worse is that there just hasn't been anyone
taking responsibility for it. >> But the information, it's still coming.
It's still out there. And people are still engaging with it.
>> And people aren't -- one thing I'll say is that, we could train everyone
in the world to use Facebook responsibly and then it could be an issue of our
own that we're taking responsibility for, but that's impossible.
And people use social media in a hedonic mindset. They use it to escape.
This isn't utilitarian. This isn't work- oriented or goal-oriented.
People are using it as a passive source of entertainment.
>> And one of your stats in the paper, you say "social media has become a
common source of news, more than 50% of American adults read news on
social media." >> Yeah.
>> That's amazing. >> And I think it's actually gone up
since then. And so, and that's people who get some amount of information.
And then I don't have a statistic for this, but when I talk to people,
and some people say "oh, I get all of my news on social media."
That happens! There are people who use social media as a kind of source
to curate information and rather than going out to actual news sources
themselves, they're using Facebook and at the end of all this, and I've got a
number of other studies looking at fake news and how we can really help
people become more responsible in their social media use. So since this
flag doesn't work, can we design it with stronger language to make
it work? How else can we design it? What are our options?
And since Facebook seems loathe to add these to the platform and really
take more responsibility in this way, what people need to do is take
responsibility for themselves. And since we can't force people to use social
media with a goal in mind, we can't force them to think critically, and
it's also very hard for us to think critically. We expend a lot of mental
effort when we're trying to think about difficult problems and that tires us out,
and then we switch back to our automatic or gut-level cognition, which drives
what we do. And so we can't force people to use social media in a
different way. But what people can do is go to actual news sites and get
their news there. Try to be more-- >> So examples. Examples. Give examples
of actual news sites. >> Well, look. There are biases in the
news. And I don't really cover that. But at least if you're on a news site,
you know you're consuming news. And so if you're on CNN or even if you're
on Fox News, or NBC, whatever you're on, at least you know you're consuming
news. And so-- >> Do you think it's more difficult
for people these days-- so I have two more questions.
One, when you have a president who's saying that these reliable news
sources that we've known have been reliable for many, many years, are fake
news and when he is very critical of certain sites. He goes after New York
Times, he goes after all of them who don't have positive things to say
about him, how do you think that makes it more difficult for people
to make a cognitive judgement about what they believe is real or not.
>> So people who believe what our president is saying, of course that
makes it harder for them to trust the news site that he says isn't true.
And that is a problem because it is good to get news from a variety of
different news sources with a variety of biases. You want to do that so that
you have a more neutral opinion because you've been able to take
information from all sides. And so when we have this instance where people are
encouraged to only get news from a couple news sites and the other
reputable sources are being bashed and said that they're not reputable and
that they're all fake news, that's a problem for people who are in the
camp of believing the president because then they might discount these actually
reputable news sites when it would benefit them to get news from a variety
of different news sources. >> So what's next?
What is the next step? Because obviously there is so much more to uncover
about just the idea of fake news. >> Right.
>> What is next? What's your next piece of research?
>> So currently I am setting up the behavioral lab in McCombs with EEG
equipment, so that's going to enable me to do more of these EEG studies
to see brain cognition as people are on social media, as they're looking
at different types of news. But additionally looking at a stronger flag
to see how that works and whether increased cognitive dissonance can
really help people to better detect fake news. Just different types of flags.
I'm fascinated by deep-fakes. I think that with our current society and our
technology and the technology growth is just amazing, how quickly we're
learning to do different things. And so these deep-fakes, these videos that
people are very well able to make it look like someone's saying something
else or putting someone else's face on the person who's actually doing the
talking and these things, I think that's going to be fascinating to study as
well because since it's a video, I think we've all learned to trust video more.
Because it seems more credible. And so what happens in the era of fake news
and deep-fakes and how all of this is just going to keep progressing.
The information credibility issue online is just going to keep getting
more interesting as we continue to do more of our lives online.
And so I see just unlimited potential. >> It's endless!
Yeah, I mean I feel like there's really no end to it. As technology continues
to advance and we spend more time online, there's really no cap!
And there's no threshold. >> I mean this is what I love about
studying information systems and information management. It's just
that as technology continues to advance and improve, my opportunities for
research just continue to expand as well. And so I think this is
fascinating. People are fascinating. We are not the rational actors that
is often assumed. We're really-- >> It's not like in the movies.
>> Right. And so, I love doing this behavioral style research where I
really understand what's going on with us and the way that we're
using social media. And yeah, so I just plan to continue in that
trajectory until I get tired of it, and then pivot to something else.
>> I don't think you ever will. There'll be more to look at.
>> I think so. >> One last question. What has been
the most interesting or surprising discovery as part of your research
with this particular paper? >> With this paper?
>> Yes. >> I think -- one of the things that
was very interesting was that the flag didn't work, but cognitively, people
had more activation in their frontal cortex when they were shown news
headlines that aligned with their beliefs but were flagged as false.
And additionally, they spent a lot more time thinking about those.
So even though we're bad at detecting fake news and the flag didn't work,
but behind that, people thought about it more. So what's really going
on there? Do we just discount it? Are we taking it in and then saying,
"mm. Okay. But could this be true or false? Okay, well it doesn't align with
my beliefs so I think it's false." >> Yeah. Wow.
>> So just the people believing what they want to believe when it makes
no sense at all. I think was the most fascinating part for me.
>> Well, this has been so interesting. I mean, like I said, we could talk about
this for a whole day. (laughs) >> Right. I could. I could talk about
this forever. >> Yes! It's very fascinating. So
thank you so much for joining us today. I really appreciate your time and just,
digging deep into what this is and what it's all about. So thank you so much.
If you want to read Tricia's paper, it's called "Fake News On Social Media:
People Believe What They Want to Believe When It Makes No Sense at All."
Thank you for joining us on our Big Ideas. We'll have a link to the full paper,
right there in the bottom of the video. We'll see you next time on our
Big Idea show.