How We Can Protect Truth in the Age of Misinformation

Summary

The video script discusses the alarming spread of fake news and its impact on society, economies, and democracies. It recounts the 2013 AP hack that falsely reported an explosion at the White House, causing a stock market crash. It also details Russia's Internet Research Agency's misinformation campaigns during the 2016 US election. The script explores why false news spreads faster than the truth, driven by novelty and human psychology. It warns of the impending threat of synthetic media, powered by generative adversarial networks, which can create convincing fake videos and audio. The speaker calls for vigilance and reliance on trusted news sources to navigate the era of misinformation.

Takeaways

• 🚨 A false tweet by the Associated Press in 2013 about explosions at the White House led to a stock market crash, demonstrating the impact of misinformation on financial markets.
• 🌐 The Internet Research Agency, linked to the Kremlin, was involved in spreading misinformation on social media platforms to influence the 2016 US presidential election.
• 📊 A study by Oxford University revealed that a third of social media content about the Swedish elections was fake or misinformation.
• ⏰ Misinformation on social media can have severe consequences, such as delaying first responders in emergency situations and leading to loss of lives.
• 📉 The spread of fake news is more rapid and extensive than true news, with false political news being particularly viral.
• 🤖 Bots play a role in spreading misinformation, but they are not solely responsible for the differential diffusion of truth and falsity online.
• 🧠 Humans are more likely to share novel information, which often includes false news, as it makes them appear knowledgeable and increases their perceived status.
• 🎭 Synthetic media, powered by technologies like generative adversarial networks, is becoming increasingly convincing and poses a threat to the authenticity of visual and audio content.
• 🛡️ Addressing the spread of misinformation requires a multifaceted approach, including labeling, incentives, regulation, transparency, and the development of algorithms to detect fake news.
• 🌟 The speaker emphasizes the importance of defending the truth against misinformation through technology, policy, and individual responsibility.

Q & A

What was the impact of the false tweet by Syrian hackers on the Associated Press in 2013?

The false tweet about explosions at the White House and injuries to President Barack Obama led to a stock market crash, wiping out $140 billion in equity value in a single day.

Who was indicted by Robert Mueller for meddling in the 2016 U.S. presidential election?

Three Russian companies and 13 Russian individuals were indicted for a conspiracy to defraud the United States through social media manipulation.

What was the role of the Internet Research Agency during the 2016 U.S. presidential election?

The Internet Research Agency, a shadowy arm of the Kremlin, created and propagated fake news and misinformation on social media platforms to sow discord during the election.

How did the spread of fake news affect the Swedish elections according to an Oxford University study?

A third of all information spreading on social media about the Swedish elections was found to be fake or misinformation.

What are the potential consequences of misinformation during emergency situations like terrorist attacks or natural disasters?

Misinformation can lead to lost minutes and lives during emergency responses, as it can misguide first responders about the location of terrorists or trapped individuals.

How did the study published in Science in March 2017 analyze the spread of true and false news on Twitter?

The study analyzed verified true and false news stories on Twitter from 2006 to 2017, comparing their diffusion, speed, depth, and breadth of spread.

What was the 'novelty hypothesis' proposed to explain why false news spreads more widely on social media?

The 'novelty hypothesis' suggests that humans are drawn to and share novel information because it makes them seem knowledgeable and increases their perceived status.

What role do bots play in the spread of misinformation according to the speaker's research?

Bots accelerate the spread of both false and true news online, but they are not responsible for the differential diffusion of truth and falsity, as humans are the primary agents in spreading misinformation.

What are generative adversarial networks and how do they contribute to the rise of synthetic media?

Generative adversarial networks are machine learning models that consist of a generator creating synthetic media and a discriminator that tries to determine the authenticity of the media. This technology is contributing to the creation of very convincing fake videos and audio.

What are the potential dangers of synthetic media as described in the script?

Synthetic media can be used to create convincing fake videos and audio, potentially making it look like anyone is saying anything, which can be dangerous for trust in information and the truth.

What are the five potential paths to address the problem of misinformation as outlined in the script?

The five potential paths are labeling, incentives, regulation, transparency, and algorithms and machine learning technology to detect and dampen the spread of fake news.

Why is it important for humans to be involved in the technology designed to combat fake news?

Humans must be involved because any technological solution is underpinned by ethical and philosophical questions about truth and falsity, and who has the power to define them.

Outlines

00:00

🚨 The Impact of False News on Society and Economy

The paragraph discusses the significant impact of false news, exemplified by a 2013 tweet from the Associated Press' hacked Twitter account about explosions at the White House injuring President Obama. This false news quickly went viral, causing a panic reaction in financial markets that led to a loss of $140 billion in equity value. The narrative then shifts to the role of the Internet Research Agency in spreading misinformation during the 2016 U.S. presidential election, reaching millions through social media platforms. The paragraph concludes with examples of how misinformation can have severe consequences, including influencing elections and even leading to loss of lives in emergency situations.

05:02

📊 The Virality of False News vs. True News

This section delves into the study of the spread of false versus true news on Twitter, as published in the journal Science. It highlights how false news stories spread more rapidly and extensively than true ones, sometimes by an order of magnitude. The study controlled for various factors such as the number of followers, activity levels, and credibility of the users spreading the news. A key finding was that false news was 70% more likely to be retweeted than true news, despite being shared by users who were less influential and active on the platform. The paragraph introduces the 'novelty hypothesis,' suggesting that the novelty of information plays a significant role in its spread, as people are drawn to and share new and surprising information.

10:04

🤖 The Role of Bots in Spreading Misinformation

The paragraph explores the role of bots in the dissemination of false news. It clarifies that while bots do accelerate the spread of false news, they also do the same for true news, implying that bots are not the primary reason for the differential spread of truth and falsehood. The speaker emphasizes that the responsibility for the spread of misinformation lies with human users. The paragraph also foreshadows the increasing challenges posed by emerging technologies that can create highly convincing synthetic media, which can further exacerbate the problem of misinformation.

15:06

🎭 The Threat of Synthetic Media and Deepfakes

This section warns of the impending threat of synthetic media, including deepfakes—highly realistic fake videos and audio generated using machine learning models like generative adversarial networks (GANs). The speaker explains how these technologies can be used to create convincing fake content, making it difficult to distinguish between real and fake information. Examples are given of how such technology could be misused to create damaging fake statements by public figures. The paragraph underscores the need for vigilance and reliance on trusted news sources in the face of such challenges.

20:07

🛡 Combating Misinformation: Potential Solutions

The final paragraph discusses potential strategies to combat the spread of misinformation. It suggests labeling information with credibility indicators, adjusting economic incentives to discourage the spread of false news, implementing regulations that balance transparency with privacy, and demanding transparency from social media platforms about their algorithms. The speaker also mentions the need for algorithms and machine learning to help identify and mitigate the spread of fake news. However, it emphasizes that technology is not a panacea and that ethical and philosophical considerations are crucial in defining truth and managing the flow of information.

Keywords

💡Fake News

Fake news refers to false information or propaganda that is presented as genuine news. In the video, it is highlighted as a significant issue that can disrupt societies, influence elections, and even cause economic turmoil, as exemplified by the false tweet about explosions at the White House causing a stock market crash.

💡Misinformation

Misinformation is the communication of false or inaccurate information, often unintentionally. The video discusses how misinformation can have severe consequences, such as causing panic during emergencies or contributing to violence, as seen in the examples of the Rohingya in Burma and mob killings in India.

💡Viral

Viral in the context of the video refers to the rapid and extensive spread of information, particularly through social media platforms. The term is used to describe how quickly false news can propagate, as illustrated by the tweet about Barack Obama being injured that was retweeted thousands of times in minutes.

💡Internet Research Agency

The Internet Research Agency is a Russian organization mentioned in the video, known for creating and spreading fake news and misinformation on social media platforms to influence public opinion and political outcomes, such as during the 2016 U.S. presidential election.

💡Novelty Hypothesis

The Novelty Hypothesis in the video suggests that people are more likely to share information that is new or surprising. This concept is used to explain why fake news, which is often more novel, spreads more quickly than true news. The video provides evidence that false news tweets were found to be more novel than true news tweets.

💡Information Cascade

An information cascade in the video refers to the pattern of information spread on social media, where a piece of news, true or false, is shared rapidly among users. The video describes how false news cascades can grow exponentially, leading to widespread dissemination of misinformation.

💡Synthetic Media

Synthetic media, as discussed in the video, involves the creation of fake audio and video content that appears real. The video warns about the rise of technologies like generative adversarial networks that can produce highly convincing synthetic media, which poses a threat to the authenticity of information.

💡Generative Adversarial Networks (GANs)

GANs are a type of machine learning model mentioned in the video that consists of two parts: a generator that creates fake media and a discriminator that tries to distinguish between real and fake media. The video highlights how GANs can be used to create increasingly convincing synthetic media, contributing to the spread of fake news.

💡Economic Incentive

The video discusses the economic incentive as a driving force behind the creation and spread of fake news. It explains how the viral nature of false information can lead to more advertising revenue, as it attracts more viewers and engagement, thus motivating the production of fake news.

💡Transparency

Transparency in the video refers to the need for social media platforms to be open about their algorithms and data practices to understand and potentially mitigate the spread of misinformation. The video points out the challenges of achieving transparency without compromising data security and user privacy.

💡Algorithms

Algorithms in the context of the video are the processes used by social media platforms to curate and display content to users. The video suggests that understanding and potentially modifying these algorithms could be a way to control the spread of fake news, but it also acknowledges the complexity of balancing this with ethical considerations.

Highlights

On April 23rd, 2013, a false tweet by Syrian hackers caused a stock market crash, demonstrating the impact of misinformation.

The Internet Research Agency, a Kremlin-linked organization, was involved in spreading misinformation during the 2016 U.S. presidential election.

Misinformation campaigns can have severe real-world consequences, including influencing elections and even inciting violence.

A study by Oxford University revealed that a third of social media information during the Swedish elections was fake.

False news spreads more rapidly and widely than true news, with political news being particularly viral.

Contrary to expectations, those who spread false news on Twitter tend to have fewer followers and are less active, not more.

The novelty hypothesis suggests that people are more likely to share surprising and new information, which often includes false news.

False news often elicits more surprise and disgust in reactions, indicating its novelty and impact on audiences.

Bots accelerate the spread of both true and false news, but they do not account for the differential diffusion of truth and falsity.

Generative adversarial networks and AI democratization are enabling the creation of convincing fake videos and audio.

The rise of synthetic media poses a threat to the ability to discern truth from falsehood, potentially leading to a 'post-truth' era.

Labeling information with credibility and source transparency could be a way to combat the spread of misinformation.

Economic incentives play a role in the spread of false news, and addressing this could reduce its prevalence.

Regulation of social media platforms could help, but it also carries risks of suppressing minority opinions in authoritarian regimes.

Transparency in algorithms is needed to understand their impact on society, but it conflicts with privacy and security concerns.

Algorithms and machine learning can help identify and limit the spread of fake news, but ethical considerations are crucial.

Defending the truth against misinformation requires vigilance, technological solutions, policy changes, and individual responsibility.

Transcripts

[Music]

on April 23rd of 2013 The Associated

Press put out the following tweet it

said breaking news two explosions at the

White House and Barack Obama has been

injured this tweet was retweeted more

than four thousand times in less than

five minutes and it went viral

immediately thereafter but this tweet

was not real news this was false news

that was propagated by Syrian hackers

that had infiltrated the AP 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 a hundred and

forty billion dollars in equity value in

a single day earlier this year robert

muller 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 housed in this nondescript

building in st. petersburg with four

stories dedicated to the creation and

propagation of fake news and

misinformation on twitter facebook and

all other social media platforms 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 forty three hours worth of YouTube

content all of which was fake

misinformation designed to sow discord

in the u.s. presidential election a

recent study by Oxford University showed

that in the recent Swedish elections a

full third one-third of all of the

information spreading on social media

about the election was fake or

misinformation and these types of

misinformation don't just affect

economies and democracies but when we

talk about first responders that are

responding to a terrorist attack or

responding to a natural disaster

misinformation spreading about where the

terrorists are or which building people

are trapped in can mean minutes lost and

therefore lives lost in addition these

types of social media misinformation

campaigns can spread what has been

called genocide or propaganda for

instance against the Rohingya in Burma

or recently on whatsapp triggering mob

killings in India if you see the quote

at the bottom of the page it says fake

news is blamed for influencing elections

in the West but in India it's killing

people

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 the present day

2017 in fact 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 this is an

example of the type of information we

have in red you see the rise and fall of

false news stories over time in green

new stories over time and in yellow an

insidious category that we called mixed

news which contained information that

was partially true and partially false

some of the most difficult to root out

and some of the most difficult for

people to discern this is a graph of the

false political news that was spreading

during this period and you see it rise

and fall you see spikes of false news

during the u.s. presidential elections

on Twitter and you see one massive spike

of mixed information during the

annexation of Crimea it begins and ends

during that one and a half month period

that Crimea was annexed I tell this

story of the Crimean annexation and the

role of misinformation and fake news in

my upcoming book the hype machine I will

save that story for you in the book this

is what these cascades look like these

are cascades of true and false

information on Twitter the larger red

cascade is a false news tweet the green

one a true news tweet and it begins with

a starburst pattern of retweets at the

beginning of the Cascade and then you

see these tendrils like jellyfish

emanating from the starbursts one person

retweeting another and retweeting

another and retweeting another and these

types of structures have mathematical

properties 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 these results 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% 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 what 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 because

it makes us seem like we're in the know

it makes us seem like we have access to

inside information and we've gained 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 so we used information

theoretic measures of the information

content in these true or false tweets

compared to all of the information that

they had seen in the 60 days prior to

this incoming true or false tweet and we

measured information novelty across

three distinct measures and across all

of these measures false news was much

more novel than the truth 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 report

lies 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 exhibit 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

measurements when we remove the bots and

when we put them back in 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 be powered by two

technologies the first of these is known

as generative adversarial networks this

is a machine learning model with two now

works 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 then the generator sees what

the discriminator does and optimizes a

function to generate more and more

convincing video and audio 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 a 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 meit

media makes it ultimately so much easier

to create videos like this one we're

entering an era in which our enemies can

make it look like anyone is saying

anything at any point in time even if

they would never say those things so for

instance they could have me say things

like I don't know kill monger was right

or ben Carson is in the sunken place or

how about that simply President Trump is

a total and complete now you see

I would never say these things at least

not in a public address but someone else

would someone like Jordan Peele this is

a dangerous time moving forward we need

to be more vigilant with what we trust

from the internet that's a time when we

need to rely on trusted news sources

it may sound basic but how we move

forward age of information is going to

be the difference between whether we

survive

or whether we become some kind of

fucked-up dystopia thank you I'm stay

woke pitches in fact just recently 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 stunt

men 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

reporters 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 promised 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

how many trans fats it has whether it's

been produced in a facility that

produces wheat or peanuts if you have an

allergy and yet when we consume

information we have no labels whatsoever

what is contained in this information is

it true or false

does this source typically put out true

information or false information is the

source credible where is this

information gathered from how many

reporters worked on this story what is

the policy of this journal in terms of

running with a fact do they need to have

two independent sources or three 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 government's 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

u.s. 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 recently in Europe the GDP are

went into the effect at the end of May

instituting strict privacy policies and

uses of data and algorithmic

transparency requirements but this type

of regulation 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

combined 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 medias 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 to

not give data to third parties like

Cambridge University who then gave the

data to Cambridge analytical that

created that scandal 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]