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Monitoring, Understanding, and Influencing
the Co-Spread of COVID-19 Misinformation
and Fact-checks
Grégoire Burel
Knowledge Media Institute, The Open University, UK
Milton Keynes, 31 July 2022

2.
► Does fact-checking impact misinformation spread?
► Can we affect spreading behaviour?
► How to present the evolution of spread over time automatically?
Misinformation and Fact-Checking
during COVID-19
We monitor and analyse the propagation of misinformation and fact-checks on social
media and investigate methods for influencing spreading behaviour.
More than 16k fact-checks were
published during COVID-19
Misinformation still spreads
twice as much…*
*For fact-checked content.
2

3.
Influencing
3
We push fact-checks to
misinformation spreaders and
check who might be influenced.
- What types of messages are
the most successful?
- What are the reactions of
misinformation spreaders?
Identify misinformation
spreaders and push fact-
checks.
Understanding
2
We analyse if fact-checks impact
the spread of misinformation.
- Who spreads
misinformation/fact-checks?
- What topics are resistant to fact-
checking?
- Does fact-checking impacts
misinformation spread?
A large-scale study on
the effectiveness of fact-
checking across topics,
demographics and time.
Monitoring
1
We track the spread of COVID-
19* misinformation and fact-
checks on Twitter using claim
reviews and create weekly reports
about the reach of misinforming
posts and their corresponding
fact-checks on Twitter.
twitter
*We also track Russo-Ukrainian war misinformation.
A continuously updated
database of
misinformation and fact-
checked URL mentions on
Twitter and weekly
spread reports.
Fact-checking
Observatory
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4.
The Misinformation and Fact-checks Database
/ The Fact-checking Observatory
Monitoring and Visualising
1

5.
The Misinformation and Fact-checks mentions
database.
Monitoring and Visualising
1
In order to track the spread of COVID-19*
misinformation and fact-checks on Twitter we
need to:
1. Identify and collect URLs pairs that
connect misinforming content to fact-
checks.
2. Track their mentions on social media.
We use the Claim Reviews published
IFCN signatories found in the Corona
Virus Facts alliance database.
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6.
The Misinformation and Fact-checking mentions
database.
Monitoring and Visualising
1
Fact-checking URLs
Data Collection
Twitter Data
Collection Tweets
Claim
Reviews
Demographics
Extraction
FCO
Database
Topics
Continuous
tracking
Misinf0/
FC URLs
16,460+ COVID-19 Fact-checks
491,400+ COVID-related Tweets
twitter
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7.
Weekly Misinformation and Fact-checking Reports.
Monitoring and Visualising
1
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8.
► What misinformation keeps spreading?
► What fact-check spreads the most?
Weekly automatically generated
reports on the spread of misinformation
and fact-checks that include:
1. Key content and topics.
2. Fact-checking coverage.
3. Demographic impact.
The Fact-checking
Observatory.
The weekly reports help identifying the
evolution of key topics and key
misinforming content.
Monitoring and Visualising
1
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9.
Spread ratio Fact-checking delay
Topic spread
Fact-checker reports
Claim trend
Claim reports
Top spreaders
First/last spread
Most/least successful claims Associated claims/fact-checks
Demographic impact
Sharers locations
What’s next?
Monitoring and Visualising
1
Spread evolution
9

10.
Demographics and topics impact on the co-
spread of COVID-19 misinformation and fact-
checks on Twitter
Understanding
2

11.
Should all misinformation be fact-checked in the same way?
What is the relation between
misinformation and fact-check
spread?
1. Do misinformation and fact-checking
information spread similarly?
- Non-parametric MANOVA/ANOVA.
2. Does fact-checking spread affect the
diffusion of misinformation about Covid-
19?
- Weak causation analysis.
- Impulse response analysis and FEVD.
Who is most likely to spread misinformation / facts ?
Do fact-checks reduce misinformation spread?
7,370 Misinforming URLs
9,151 Fact-checking URLs
358,776 Tweets
Analysis on data
collected until
4th January 2021
Jan 2020 Apr 2020
Understanding
2
11
0 – 3 days
4 – 10 days
10+ days
initial
early
late
Analysed Periods

12.
Understanding
2
Vaccines
Symptoms
Spread
Other
Cures
Conspiracy
Theories
Causes
Authorities
Fact-check URLs
shares
Misinformation URLs
shares
Topical misinformation and fact-checks spread (log scale).
Gender*
Male
Female
Account type
Non-org.
Org.
User Age
≥40
30-39
19-29
≤18
Demographic misinformation and fact-checks spread
(log scale).
Topics and Demographics
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13.
► Already fact-checked content re-spreading.
► Conspiracies and causes need to be addressed
differently than other topics.
► Fact-checkers republishing/reposting policy?
Stacked cumulative spread of misinforming and corrective information.
Global and topical spreading differences.
Initial onset period
until mid-March.
Late period from
mid-September.
Ramp-up period from
mid-March until mid-
September.
Jan 2020 Apr 2020 Jul 2020 Oct 2020 Jan 2120
2x more
misinform
ation.
Understanding
2
0 – 3 days
4 – 10 days
10+ days
initial
early
late
Global Topics
≠
“Converging”
behaviour.
=
≠
≠
≈/
≠
=/
≠
Period
Causes and conspiracies still spreading differently in
the late phase.
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Short-term demographics differences.
Individuals vs. Organisations Gender*
Understanding
2
- Individuals spread more misinformation
than organisations.
- Most organisation-driven spread occurs in
the initial period.
- Individual spread of misinformation
continuing over long periods.
Individuals exposure to fact-checked content
over long periods is key.
- Females spread less misinformation than
males (but represent 40% of Twitter
userbase)
- Misinformation spread is independent of
gender.
- Same spreading behaviour → ∞.
Gender is not important when dealing with
misinformation spread.
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Fact-checking fast
spread response
Inconclusive
misinformation
response trend
Self initial response
(spread drop soon
after initial increase)
- Bidirectional weak causation between
misinformation and fact-checks spread.
- Fact-checking spread not clearly impacting
misinformation spread (impulse response and
FEVD).
- Fact-checks are quick to respond to
misinformation spread.
Weak impact of fact-checking spread
on misinformation spread*.
Understanding
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► Make fact-checking content more sharable?
► Keep spreading fact-checks?
How to increase the impact/spread of fact-
checking content?
*globally for fact-checked content but not for all the topics..
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Short term success in reducing misinformation spread. Hard to affect irrational misinformation spread.
Virus Causes
Misinformation spread
increasingly dependent on
fact-checks spread
Fact-checking
spreading initially
independently
Fact-checking
initially affecting
misinformation.
Fast fact-
checking
response
Inconclusive
misinformation response
trend
Understanding
2
Conspiracy Theories
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► Conspiracies and causes need to be addressed
differently than other topics.
Topic Co-Spread
► No need to target gender specifically.
► Targeting long individual exposure to
misinformation.
► Make fact-checking content more sharable?
► Keep spreading fact-checks?
Misinformation and fact-checking spread.
- Misinformation spreads more than fact-checks.
- Fact-checking is fast to spread initially in response to misinformation spread.
- Weak bi-directional relation between fact-checks and misinformation spread.
Demographic Co-Spread
Overall Misinformation and Fact-checking Spread
- Misinformation spreads independently from
gender.
- Individuals spread more misinformation over
long periods.
- Misinformation topics continue spreading
over long time periods .
- Fact-checking spread impact on individual
topics tend to be short-term.
Understanding
2
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18.
Automatically Pushing Fact-checks to
Misinformation Spreaders
Influencing
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Preliminary Work
Can we influence misinformation
spreading behaviour by pushing fact-
check content?
Influencing
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- Individuals share more misinformation compared to
organisations.
- Users spread misinformation and credible information at
different rates (e.g., some users share little misinformation while
other share a large amount). User behaviour does not impact
others at the same level (e.g., celebrity have high reach).
- Pushing corrective information may be helpful in reducing
misinformation spread.
How do we measure influencing/influence behaviour?
How consistent is user behaviour?
Can we change user behaviour toward misinformation by exposing them
to fact-checks?

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- Most users spread both credible information and
misinformation → This population should be the focus of
behaviour altering methods.
- Purely informing and misinforming users seems to
account for a similar proportion of the population.
Influencing Behaviour - Overlapping
Behaviour
Informing (13%)
Misinforming (15%)
Informing - Misinforming
(61%)
Neutral (11%)
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Influencing
2
Dataset
100k Twitter seed posts containing
misinformation or fact-checks and 52k
users. Data sample based on 7.5% of
the retrieved users:
3,809 Users (≈ 10M tweets)
Measuring Behaviour
- Adjusted behaviour score between
0 and 1.
- Inspired by Hochschild and Einstein
(2015) behaviour states.
- Uses user timelines and historical
tweets over a 25 days period.
- Fitted Adjusted Percentile Ranks
rather than min-max normalisation
(better at identifying standard behaviour
and outliers since it is based on
population behaviour).

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Influencing Behaviour - Behaviour Consistency
- Users that are more actively informed have
more consistent behaviour → always spread
credible information.
- Users with an adjusted score < 55% are highly
unstable → more opportunistic behaviour.
- As with informing behaviour, convergence occurs for
users that have a high score.
- Misinformation spreading behaviour is only stable for
users that share misinformation with an adjusted
score > 80% → only users with high misinforming score
spread misinformation consistently.
Informing Consistency Misinforming Consistency
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Influencing
2
Methods for altering user behaviour towards misinformation should
target users that elicit mixed behaviour.

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Misinformation Bot
How can we reach those not on the choir?
- Don’t think they need fact-checking tools…
- Don’t know about such tools...
- Not tech-savvy enough to install and use them...
Approach:
1. Search for mentions of misinformation on Twitter.
2. Use templates for notifying misinformation to user.
3. No installation required.
4. Corrections can be seen by anyone.
We need tools to push fact-checks when and
where necessary.
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Influencing
2

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Reply Templates Style
Please, note that the link you shared contains a claim that was fact-checked and appears to be <VERDICT>.
Fact-check: <FACT-CHECK-URL>
I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback.
Factual
Oops… it seems something might be wrong! The link you shared contains a claim that was fact-checked <FACT-CHECK-URL> and appears to be
<VERDICT>.
I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback.
Alerting
I’m a bot fighting misinformation spread. I noticed the link you shared contains a claim that was fact-checked <FACT-CHECK-URL> and appears to be
<VERDICT>.
Plz follow me & DM any feedback.
Identity
How about double-checking this? This link contains a claim that was fact-checked <FACT-CHECK-URL> and appears to be <VERDICT>.
I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback.
Suggestive
I know, it's hard to distinguish fact from fiction 😩. The link you shared contains a claim that was fact-checked and appears to be <VERDICT>. Fact-check:
<FACT-CHECK-URL>.
I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback.
Empathetic
Misinformation can be really harmful! 😬 Please, note that the link you shared contains a claim that was fact-checked and appears to be <VERDICT>. Fact-
check: <FACT-CHECK-URL>.
I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback.
Alarming
Hi there! Please note that the link you shared contains a claim that was fact-checked and appears to be <VERDICT>. Fact-check <FACT-CHECK-URL>.
I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback.
Friendly
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Distrust fact-checkers
“fact-checkers are paid by pharma industry”, “controlled by Facebook and government”, “they stop diversity of opinion”,
“who checks the fact-checker?”, “who’s paying them?”
Follow anti-fact-checking
sites
cite far-right websites that speak against fact-checkers - eg einprozent.de. (https://www.einprozent.de/correctiv-das-
zensurwerkzeug-der-elite )
Distrust governments
“if detox didn’t work why would money be paid into telling you NOT to do this after shot. Remember a billion $ was put
into #Vaccine “awareness & promotions” in US alone.”
Seek other supporting
articles
If there’s another article with similar claims that is not fact-checked then they feel they won the argument.
Refer to non-related
claims
Search claims to support their position, e.g., against a vaccine – “what about this, eh?”
Work in network They retweet and like each other’s tweets against the bot’s reply
Discrediting a source Point to officials who said something not entirely accurate to bring in doubt and reason to distrust everything
Accuse of censorship “Freedom of speech”, “a contested opinion is still an opinion”, “this is censorship”, “police state”, “ministry of truth”
Anti-bots “you are a bot” , “you are a big pharma bot”, “When ‘They’ send a fact bot after me …then i know I’m on to something”
Reactions… to 745 bot posts so far.
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Influencing
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Reactions… to 745 bot posts so far.
- Very few positive outcomes so far.
- Blocking and replying as a common pattern.
- People delete their posts but also block the bot.
- No clear relation between reply template and behaviour → more personal message may be necessary.
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Influencing
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Future Directions
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Monitoring - COVID-19 misinformation tracking
Understanding – Fact-checking impact
Influencing - Misinformation countering
- New types of reports (e.g., Individual claims reports and history).
- One-off reports (e.g., small detailed studies).
- Generic misinformation tracking.
- Updating the co-spreading analysis between fact-checks and
misinformation.
- Additional demographics (e.g., age groups, values, etc.)
- Personalisation of responses depending on user (e.g., conspiracy
theorist, influencer, etc.).
- Behaviour targeting.
- Visual templates.

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Thank you.
Grégoire Burel.
@evhart
fcobservatory.org
g.burel@open.ac.uk
Monitoring, Understanding and Influencing the Co-Spread of
COVID-19 Misinformation and Fact-checks
Background illustrations: Myth busters created by Redgirl Lee for United Nations Global Call Out To Creatives.
github.com/evhart
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