How we can protect truth in the age of misinformation

Fake news can sway elections, tank economies and sow discord in everyday life. Data scientist Sinan Aral demystifies how and why it spreads so quickly -- citing one of the largest studies on misinformation -- and identifies five strategies to help us unweave the tangled web between true and false.

So, on April 23 of 2013,

the Associated Press
put out the following tweet on Twitter.

It said, "Breaking news:

Two explosions at the White House

and Barack Obama has been injured."

This tweet was retweeted 4,000 times
in less than five minutes,

and it went viral thereafter.

Now, this tweet wasn't real news
put out by the Associated Press.

In fact it was false news, or fake news,

that was propagated by Syrian hackers

that had infiltrated
the Associated Press Twitter handle.

Their purpose was to disrupt society,
but they disrupted much more.

Because automated trading algorithms

immediately seized
on the sentiment on this tweet,

and began trading based on the potential

that the president of the United States
had been injured or killed

in this explosion.

And as they started tweeting,

they immediately sent
the stock market crashing,

wiping out 140 billion dollars
in equity value in a single day.

Robert Mueller, special counsel
prosecutor in the United States,

issued indictments
against three Russian companies

and 13 Russian individuals

on a conspiracy to defraud
the United States

by meddling in the 2016
presidential election.

And what this indictment tells as a story

is the story of the Internet
Research Agency,

the shadowy arm of the Kremlin
on social media.

During the presidential election alone,

the Internet Agency's efforts

reached 126 million people
on Facebook in the United States,

issued three million individual tweets

and 43 hours' worth of YouTube content.

All of which was fake --

misinformation designed to sow discord
in the US presidential election.

A recent study by Oxford University

showed that in the recent
Swedish elections,

one third of all of the information
spreading on social media

about the election

was fake or misinformation.

In addition, these types
of social-media misinformation campaigns

can spread what has been called
"genocidal propaganda,"

for instance against
the Rohingya in Burma,

triggering mob killings in India.

We studied fake news

and began studying it
before it was a popular term.

And we recently published
the largest-ever longitudinal study

of the spread of fake news online

on the cover of "Science"
in March of this year.

We studied all of the verified
true and false news stories

that ever spread on Twitter,

from its inception in 2006 to 2017.

And when we studied this information,

we studied verified news stories

that were verified by six
independent fact-checking organizations.

So we knew which stories were true

and which stories were false.

We can measure their diffusion,

the speed of their diffusion,

the depth and breadth of their diffusion,

how many people become entangled
in this information cascade and so on.

And what we did in this paper

was we compared the spread of true news
to the spread of false news.

And here's what we found.

We found that false news
diffused further, faster, deeper

and more broadly than the truth

in every category of information
that we studied,

sometimes by an order of magnitude.

And in fact, false political news
was the most viral.

It diffused further, faster,
deeper and more broadly

than any other type of false news.

When we saw this,

we were at once worried but also curious.

Why?

Why does false news travel
so much further, faster, deeper

and more broadly than the truth?

The first hypothesis
that we came up with was,

"Well, maybe people who spread false news
have more followers or follow more people,

or tweet more often,

or maybe they're more often 'verified'
users of Twitter, with more credibility,

or maybe they've been on Twitter longer."

So we checked each one of these in turn.

And what we found
was exactly the opposite.

False-news spreaders had fewer followers,

followed fewer people, were less active,

less often "verified"

and had been on Twitter
for a shorter period of time.

And yet,

false news was 70 percent more likely
to be retweeted than the truth,

controlling for all of these
and many other factors.

So we had to come up
with other explanations.

And we devised what we called
a "novelty hypothesis."

So if you read the literature,

it is well known that human attention
is drawn to novelty,

things that are new in the environment.

And if you read the sociology literature,

you know that we like to share
novel information.

It makes us seem like we have access
to inside information,

and we gain in status
by spreading this kind of information.

So what we did was we measured the novelty
of an incoming true or false tweet,

compared to the corpus
of what that individual had seen

in the 60 days prior on Twitter.

But that wasn't enough,
because we thought to ourselves,

"Well, maybe false news is more novel
in an information-theoretic sense,

but maybe people
don't perceive it as more novel."

So to understand people's
perceptions of false news,

we looked at the information
and the sentiment

contained in the replies
to true and false tweets.

And what we found

was that across a bunch
of different measures of sentiment --

surprise, disgust, fear, sadness,

anticipation, joy and trust --

false news exhibited significantly more
surprise and disgust

in the replies to false tweets.

And true news exhibited
significantly more anticipation,

joy and trust

in reply to true tweets.

The surprise corroborates
our novelty hypothesis.

This is new and surprising,
and so we're more likely to share it.

At the same time,
there was congressional testimony

in front of both houses of Congress
in the United States,

looking at the role of bots
in the spread of misinformation.

So we looked at this too --

we used multiple sophisticated
bot-detection algorithms

to find the bots in our data
and to pull them out.

So we pulled them out,
we put them back in

and we compared what happens
to our measurement.

And what we found was that, yes indeed,

bots were accelerating
the spread of false news online,

but they were accelerating
the spread of true news

at approximately the same rate.

Which means bots are not responsible

for the differential diffusion
of truth and falsity online.

We can't abdicate that responsibility,

because we, humans,
are responsible for that spread.

Now, everything
that I have told you so far,

unfortunately for all of us,

is the good news.

The reason is because
it's about to get a whole lot worse.

And two specific technologies
are going to make it worse.

We are going to see the rise
of a tremendous wave of synthetic media.

Fake video, fake audio
that is very convincing to the human eye.

And this will powered by two technologies.

The first of these is known
as "generative adversarial networks."

This is a machine-learning model
with two networks:

a discriminator,

whose job it is to determine
whether something is true or false,

and a generator,

whose job it is to generate
synthetic media.

So the synthetic generator
generates synthetic video or audio,

and the discriminator tries to tell,
"Is this real or is this fake?"

And in fact, it is the job
of the generator

to maximize the likelihood
that it will fool the discriminator

into thinking the synthetic
video and audio that it is creating

is actually true.

Imagine a machine in a hyperloop,

trying to get better
and better at fooling us.

This, combined with the second technology,

which is essentially the democratization
of artificial intelligence to the people,

the ability for anyone,

without any background
in artificial intelligence

or machine learning,

to deploy these kinds of algorithms
to generate synthetic media

makes it ultimately so much easier
to create videos.

The White House issued
a false, doctored video

of a journalist interacting with an intern
who was trying to take his microphone.

They removed frames from this video

in order to make his actions
seem more punchy.

And when videographers
and stuntmen and women

were interviewed
about this type of technique,

they said, "Yes, we use this
in the movies all the time

to make our punches and kicks
look more choppy and more aggressive."

They then put out this video

and partly used it as justification

to revoke Jim Acosta,
the reporter's, press pass

from the White House.

And CNN had to sue
to have that press pass reinstated.

There are about five different paths
that I can think of that we can follow

to try and address some
of these very difficult problems today.

Each one of them has promise,

but each one of them
has its own challenges.

The first one is labeling.

Think about it this way:

when you go to the grocery store
to buy food to consume,

it's extensively labeled.

You know how many calories it has,

how much fat it contains --

and yet when we consume information,
we have no labels whatsoever.

What is contained in this information?

Is the source credible?

Where is this information gathered from?

We have none of that information

when we are consuming information.

That is a potential avenue,
but it comes with its challenges.

For instance, who gets to decide,
in society, what's true and what's false?

Is it the governments?

Is it Facebook?

Is it an independent
consortium of fact-checkers?

And who's checking the fact-checkers?

Another potential avenue is incentives.

We know that during
the US presidential election

there was a wave of misinformation
that came from Macedonia

that didn't have any political motive

but instead had an economic motive.

And this economic motive existed,

because false news travels
so much farther, faster

and more deeply than the truth,

and you can earn advertising dollars
as you garner eyeballs and attention

with this type of information.

But if we can depress the spread
of this information,

perhaps it would reduce
the economic incentive

to produce it at all in the first place.

Third, we can think about regulation,

and certainly, we should think
about this option.

In the United States, currently,

we are exploring what might happen
if Facebook and others are regulated.

While we should consider things
like regulating political speech,

labeling the fact
that it's political speech,

making sure foreign actors
can't fund political speech,

it also has its own dangers.

For instance, Malaysia just instituted
a six-year prison sentence

for anyone found spreading misinformation.

And in authoritarian regimes,

these kinds of policies can be used
to suppress minority opinions

and to continue to extend repression.

The fourth possible option
is transparency.

We want to know
how do Facebook's algorithms work.

How does the data
combine with the algorithms

to produce the outcomes that we see?

We want them to open the kimono

and show us exactly the inner workings
of how Facebook is working.

And if we want to know
social media's effect on society,

we need scientists, researchers

and others to have access
to this kind of information.

But at the same time,

we are asking Facebook
to lock everything down,

to keep all of the data secure.

So, Facebook and the other
social media platforms

are facing what I call
a transparency paradox.

We are asking them, at the same time,

to be open and transparent
and, simultaneously secure.

This is a very difficult needle to thread,

but they will need to thread this needle

if we are to achieve the promise
of social technologies

while avoiding their peril.

The final thing that we could think about
is algorithms and machine learning.

Technology devised to root out
and understand fake news, how it spreads,

and to try and dampen its flow.

Humans have to be in the loop
of this technology,

because we can never escape

that underlying any technological
solution or approach

is a fundamental ethical
and philosophical question

about how do we define truth and falsity,

to whom do we give the power
to define truth and falsity

and which opinions are legitimate,

which type of speech
should be allowed and so on.

Technology is not a solution for that.

Ethics and philosophy
is a solution for that.

Nearly every theory
of human decision making,

human cooperation and human coordination

has some sense of the truth at its core.

But with the rise of fake news,

the rise of fake video,

the rise of fake audio,

we are teetering on the brink
of the end of reality,

where we cannot tell
what is real from what is fake.

And that's potentially
incredibly dangerous.

We have to be vigilant
in defending the truth

against misinformation.

With our technologies, with our policies

and, perhaps most importantly,

with our own individual responsibilities,

decisions, behaviors and actions.

Thank you very much.

(Applause)