In the context of the Covid-19 pandemic, the consequences of misinformation are a matter of life and death. Correcting misconceptions and false beliefs are important for injecting reliable information about the outbreak. Fact-checking organisations produce content with the aim of reducing misinformation spread, but our knowledge of its impact on misinformation is limited. In this paper, we explore the relation between misinformation and fact-checking spread during the Covid-19 pandemic. We specifically follow misinformation and fact-checks emerging from December 2019 to early May 2020. Through a combination of spread variance analysis, impulse response modelling, and causal analysis, we show similarities in how misinformation and fact-checking information spread and that fact-checking information has a positive impact on reducing misinformation. However, we observe that its efficacy can be reduced, due to the general amount of online misinformation and the short-term spread of fact-checking information compared to misinformation.

1. Co-Spread of Misinformation
and Fact-CheckingContent
during the Covid-19 Pandemic
Grégoire Burel, Tracie Farrell, Martino Mensio,
Prashant Khare, and Harith Alani
SocIinfo 2020, 6-9 October 2020

2. • During the Covid-19 pandemic the amount of misinformation shared on
social media has risen significantly with some tragic results.
• Fact-checking organisations have been correcting misinforming content.
However, the effectiveness of corrective information remains largely
unknown.
Misinformationand Fact-Checkingspread duringCovid-19
Amount of new fact-checks per day. (Source: Poynter)
Peak: 143 per day.
“Rumours about
coronavirus – that it
can be prevented by
drinking alcohol; that it
is killed by cold or
heat – pose serious
risk.”
The United Nations
17 Apr 2020
Did fact-checking during the Covid-19 pandemic have a positive impact in reducing misinformation spread?
2

3. Co-Spread of Misinformationand Fact-
Checking
• Research Questions:
1. Are misinformation and fact-checking
information spread similarly?
2. How do misinformation and fact-checking
spread patterns vary in relation to each other,
and to the pandemic level?
3. Does fact-checking spread affect the diffusion
of misinformation about Covid-19?
Misinformation is false or inaccurate
information. Examples of misinformation
include false rumours or more deliberate
disinformation such as malicious content
such as hoaxes and computational
propaganda.
Fact-checking is the process of verifying
information in non-fictional text in order to
determine its veracity and correctness.
3

4. Data collection
• We collect tweets mentioning misinforming and fact-
checking URLs.
• Fact-checks and misinforming URLs are obtained from the
misinfo.me tool that collect websites belonging to the
International Fact-Checking Network (IFCN).
• ClaimReview is a tagging system that fact-checkers use
to identify their articles (i.e., claims, ratings fact-checked
content and fact-checking URL).
twitter
misinfo.me
Stacked cumulative spread of misinforming and corrective information.
2,830 Misinforming URLs
734 Fact-checking URLs
21,394 Tweets
Data collected until 4th May 2020
4

5. Methodology
1. Misinformation and fact-checks spread analysis over
time:
• Relative and pandemic-level analysis.
• Non-parametric MANOVA/ANOVA (Analysis of Variance)
→ Identifies if there is a significant variance in means
between the misinformation and fact-check spreads.
• Identifies if there are significant differences in how
misinformation and fact-checking information
spreads in different time periods.
2. Relational analysis between misinformation and fact-
checking spread:
• Relative analysis.
• Causation analysis (Granger causality) model) for
estimating if the spread of a given information type can
be used to predict the spread of another type of
information.
• Impulse response analysis and Forecast Error
Variance Decomposition (FEVD) to evaluate the
spread response of a given information type
depending on change in misinformation or fact-check
spread.
Relative analysis: Data is aligned based on
their initial sharing date and then divided in
initial, early and late periods (using linear
regressions and inflection points).
Pandemic-level analysis: Divide pandemic
in the same three different time periods
using worldwide cases.
AnalysisLevels
0 – 2 days 2 – 14 days 14+ days
initial early late
< 14th March 14/3 – 2/4 > 2nd April
initial early late
5

6. • Results:
• Pandemic-level analysis:
• Misinformation and Fact-checks spread
differently globally (MANOVA, p = 0.01)
• At individual periods, significant
differences are observed for the initial
and late periods.
• Misinformation is shared much more than
fact-checks.
Stacked cumulative spread of misinforming and corrective information.
Multivariate Analysis
Initial onset period
until 14th March .
• Relative analysis:
• There are significant differences in how
misinforming URLs and fact-checking
URLs spread globally (MANOVA, P <
0.001).
• Significant differences are only
observed in the early and late periods.
• The highest difference in terms of
mean and standard deviation between
the different URL types appears to be
mostly during the initial phase.
Late period from
2nd April
Ramp-up period from
14th March until 2nd
April .
6

7. Fact-checking
delayed spread
response
Downward
misinformation spread
trend
Self initial response
(spread drop soon
after initial increase)
• Results:
• Misinformation spread can be predicted from
fact-checking spread (Granger causality, p = 0.02
vs. p = 0.93).
• Fact-checking impulse generates a slow
downward trend in misinformation spread.
• Misinformation impulse generates a delayed
fact-checking spread response.
• FEVD analysis confirms that misinformation
spread is influenced by fact-checking.
Causalityand Impulse Analysis
Misinformation
spread is affected by
Fact-checks.
Fact-checks spread
marginally affected
by misinformation.. 7

8. Conclusions and FutureWork
8 8
• Limitations and future work:
• Limited but accurate data sample (restricted by
Fact-checking data) → Automatic identification of fact-
checked claims.
• Granular features necessary for better
understanding of spread variance (e.g.,
demographics and topics) → Topic analysis and
demographics extraction.
• Conclusions:
• Fact-checking spread has a positive impact in
reducing misinformation.
• However, the impact of fact-checking is seriously
impeded by: 1) the amount of shared
misinformation, and; 2) the short period of time in
which fact-checks are likely to spread.
• Creating fact-checking content that is more
spreadable may be the key to reduce misinformation
spread.
- Improving Covid-19 fact-
check database (7,100+
fact-checks) and data
collection (226,000+
tweets).
- Topic and demographics
analysis.
- Automatic tracking and
reporting (fc-observatory).
FC-Observatory
prototype.
CurrentWork

9. Thank you.
Grégoire Burel.
@evhart
github.com/evhart
g.burel@open.ac.uk
Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic
Background illustrations: Myth busters created by Redgirl Lee for United Nations Global Call Out To Creatives.