Monitoring and Understanding the Co-Spread of COVID-19 Misinformation and Fact-checks”. Using more than 3 years of data collected from Twitter and fact-checking organizations and a combination of spread variance analysis, impulse response modeling, and causal analysis, we will highlight the weak causal relationships between the spread of misinformation and fact-checks and discuss what topics are the less likely to be affected by fact-checks. We will also show how the proposed observatory can be used for tracking demographics, fact-checks, and topics over time.
Monitoring and Understanding the Co-Spread of COVID-19 Misinformation and Fact-checks
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Monitoring and Understanding the
Co-Spread of COVID-19
Misinformation and Fact-checks
Grégoire Burel
Knowledge Media Institute, The Open University, UK
Global Fact 9, Oslo, 22-25 June 2022
2. ► Does fact-checking impact misinformation spread?
► How misinformation and fact-checks? Spread for different topics and
demographics?
Misinformation and Fact-Checking
during COVID-19
We monitor and analyse the propagation of misinformation and
fact-checks on social media.
More than 16k fact-checks were
published during COVID-19
Misinformation still spreads
twice as much…*
*For fact-checked content. 2
3. Reporting and
Visualising
3
We create weekly reports about
the reach of misinforming posts
and their corresponding fact-
checks on Twitter.
- What misinformation
persists?
- What fact-checks spread
successfully?
Fact-checking
Observatory
Automatically generated
weekly reports on the
spread and impact of
fact-checks and
misinformation.
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.
3
twitter
*We also track Russo-Ukrainian war misinformation.
A continuously
updated database of
misinformation and
fact-checked URL
mentions on Twitter.
4. Should all misinformation be fact-checked in the same way?
What is the relation between
misinformation and fact-
check spread?
5
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 May 2020
Jan 2020 Apr 2020
Understanding
2
6. ► Already fact-checked content re-spreading.
► Conspiracies and causes need to be addressed
differently than other topics.
► Fact-checkers republishing/reposting policy?
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.
7
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.
7. Short-term demographics differences.
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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.
8. 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
2
9
► 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..
9. Short term success in reducing misinformation spread. Hard to affect irrational misinformation spread.
10
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
11. ► What misinformation keep 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.
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Reporting and Visualising
2
12. 13 13
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?
Reporting and Visualising
2
Spread evolution
What else?
13. ► 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: What’s next?
14 14
- 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.
What’s next?
- Making the FC observatory more useful to fact-
checkers .
- A social media bot for spreading fact-checks to
misinformation spreaders..
As I just mentioned, the data collection is relatively simple:
First, we collect claim reviews related to COVID.
Then, we collect twitter mentions of these URLs
For each user, we then extract demographic information using Machine Learning like account type, gender, age group… We only keep account type and gender as they are more reliable. (Wang et al. 2019, webconf)
We then add topics as identified by fact-checkers from the Poynter database and we obtain the database.
We update this database continiousely.