Types, Sources, and Claims of COVID-19 Misinformation

Abstract

In this factsheet we identify some of the main types, sources, and claims of COVID-19 misinformation seen so far. We analyse a sample of 225 pieces of misinformation rated false or misleading by fact-checkers and published in English between January and the end of March 2020, drawn from a collection of fact-checks maintained by First Draft News.

Full text

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Types, Sources, and Claims of
COVID-19 Misinformation
Authors: J. Scott Brennen, Felix M. Simon, Philip N. Howard, and Rasmus Kleis Nielsen
Key ndings
In this RISJ factsheet we identify some of the main
types, sources, and claims of COVID-19 misinformation
seen so far. We analyse a sample of 225 pieces of
misinformation rated false or misleading by fact-
checkers and published in English between January
and the end of March 2020, drawn from a collection of
fact-checks maintained by First Dra News.
We nd that:
• In terms of scale, independent fact-checkers have
moved quickly to respond to the growing amount
of misinformation around COVID-19; the number
of English-language fact-checks rose more than
900% from January to March. (As fact-checkers
have limited resources and cannot check all
problematic content, the total volume of dierent
kinds of coronavirus misinformation has almost
certainly grown even faster.)
• In terms of formats, most (59%) of the
misinformation in our sample involves various
forms of reconguration, where existing and oen
true information is spun, twisted, recontextualised,
or reworked. Less misinformation (38%) was
completely fabricated. Despite a great deal of
recent concern, we nd no examples of deepfakes
in our sample. Instead, the manipulated content
includes ‘cheapfakes’ produced using much simpler
tools. The recongured misinformation accounts
for 87% of social media interactions in the sample;
the fabricated content 12%.
• In terms of sources, top-down misinformation
from politicians, celebrities, and other prominent
public gures made up just 20% of the claims
in our sample but accounted for 69% of total
social media engagement. While the majority
of misinformation on social media came from
ordinary people, most of these posts seemed to
generate far less engagement. However, a few
instances of bottom-up misinformation garnered
a large reach and our analysis is unable to capture
spread in private groups and via messaging
applications, likely platforms for signicant
amounts of bottom-up misinformation.
• In terms of claims, misleading or false claims
about the actions or policies of public authorities,
including government and international bodies
like the WHO or the UN, are the single largest
category of claims identied, appearing in 39% of
our sample.
• In terms of responses, social media platforms
have responded to a majority of the social media
posts rated false by fact-checkers by removing
them or attaching various warnings. There is
signicant variation from company to company,
however. On Twitter, 59% of posts rated as false
in our sample by fact-checkers remain up. On
YouTube, 27% remain up, and on Facebook, 24%
of false-rated content in our sample remains up
without warning labels.
•
FACTSHEET
April 2020

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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General overview
In mid-February, the World Health Organization
announced that the new coronavirus pandemic was
accompanied by an ‘infodemic’ of misinformation
(WHO 2020).
Mis- and disinformation1 about science, technology,
and health is neither new nor unique to COVID-19.
Amid an unprecedented global health crisis, many
journalists, policy makers, and academics have echoed
the WHO and stressed that misinformation about the
pandemic presents a serious risk to public health and
public action.
Cristina Tardáguila, Associate Director of the
International Fact-checking Network (IFCN), has
called COVID–19 ‘the biggest challenge fact-checkers
have ever faced.’ News media are covering the
pandemic and responses to it intensively and platform
companies have tightened their community standards
and responded in other ways. Some governments,
including in the UK, have set up various government
units to counter potentially harmful content.
This fact sheet uses a sample of fact-checks to identify
some of the main types, sources, and claims of
COVID-19 misinformation seen so far. Building on other
analyses (Hollowood and Mostrous 2020; EuVsDIS
2020; Scott 2020), we combine a systematic content
analysis of fact-checked claims about the virus and
the pandemic with social media data indicating the
scale and scope of engagement.
The 225 pieces of misinformation analysed were
sampled from a corpus of English-language fact-
checks gathered by First Dra News, focusing
on content rated false or misleading. The corpus
combines articles to the end of March from fact-
checking contributors to two separate networks:
the International Fact-Checking Network (IFCN)
and Google Fact Checking Tools. We systematically
assessed each fact-checked instance and coded it
for the type of misinformation, the source for it, the
specic claims it contained, and what seemed to be
the motivation behind it. Furthermore, we gathered
social media engagement data for all pieces of
content identied and linked to by fact-checkers in
the sample to get an indication of the relative reach
of and engagement with dierent false or misleading
claims. A majority (88%) of the sample appeared
on social media platforms. A small amount (also)
appeared on TV (9%), was published by news outlets
(8%), or appeared on other websites (7%). Throughout
the factsheet, when we speak about misinformation,
it is on the basis of this sample of content rated false
or misleading by independent professional fact-
checkers. Please see the methodological appendix for
a fuller description of the methods and sample.
While fact-checks provide a reliable way to identify
timely pieces of misinformation, fact-checkers
cannot address every piece of misinformation and
their professional work necessarily involves various
selection biases as they focus scare resources (Graves
2016). Fact-checkers also have limited access to
misinformation spreading in private channels, by
email, in closed groups, and via messaging apps
(and in oine conversations). Similarly, engagement
data for social media posts analysed here is only
indicative of wider engagement with and exposure to
misinformation which can spread in many dierent
ways, both online and oine. In many cases, it is
likely that claims were repeated and spread by many
accounts across platforms not included in these data.
Still, engagement data provide some indication of the
relative reach of dierent claims.
Thus, the analysis is neither comprehensive (we do not
systematically examine misinformation in search, via
photo-sharing platforms and messaging applications,
or sites like Reddit, or for that matter via news media
or government communications), nor is it exhaustive
(we look only at a sample of English-language fact-
checks). We still believe it takes a step towards better
understanding the scale and scope of the problems
we face.
Below, we present ve ndings that describe the
makeup and circulation of misinformation about
COVID-19 based on our content analysis, nalised by
31 March.
1 Many dene disinformation as knowingly false content meant to deceive. Given the diculty in knowing or assessing this, we use the term
misinformation throughout this factsheet to refer broadly to any type of false information – including disinformation.

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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Scale: massive growth in fact-checks about COVID-19
In response to growth in the volume and diversity
of misinformation in circulation, the number of
fact-checks concerning COVID-19 has increased
dramatically over the last three months (see Figure 1).
Many fact-checking outlets around the world appear
to be devoting much – if not most – of their time and
resources to debunking claims about the pandemic.
Even so, that fact-checking organisations continue
to nd new claims to investigate speaks to the large
amount of misinformation circulating.
0
200
400
600
800
1,000
1,200
1,400
January
February
March
Cumulative
fact-checks
in corpus
Figure 1: All English-language fact-checks in corpus
Figure 1 plots all English-language entries in the full corpus of fact-checks (N=1253) by day from January to March 2020.

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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Formats: little coronavirus misinformation is completely fabricated. All of it is
technologically simple
Rather than being completely fabricated, much of
the misinformation in our sample involves various
forms of reconguration where existing and oen
true information is spun, twisted, recontextualised,
or reworked (see Figure 2) (Wardle 2019). Judging
from the social media data collected, recongured
content saw higher engagement than content that
was wholly fabricated.2 Our analysis recognised
three dierent sub-types of misinformation that
recongured existing information. The most common
form of misinformation, ‘misleading content’ (29%),
contained some true information, but the details were
reformulated, selected, and re-contextualised in ways
that made them false or misleading. One very widely
shared post oered medical advice from someone’s
uncle, combining both accurate and inaccurate
information about how to treat and prevent the spread
of the virus. While some of the advice, such as washing
one’s hands, aligns with the medical consensus, other
suggestions do not. For example, the piece claims:
‘This new virus is not heat-resistant and will be killed
by a temperature of just 26/27 degrees. It hates the
sun.’ While heat will kill the virus, 27 degrees Celsius is
not high enough to do so.
A second common form of misinformation involves
images or videos labelled or described as being
something other than they are (24%). For example,
one post shows a picture of a selection of vegan foods
untouched on an otherwise empty grocery shelf and
suggests that ‘Even with the Corona Virus (sic) panic
buying, no one wants to eat Vegan food.’ AFP Australia
observed this image is of a grocery store shelf in Texas in
2017, ahead of Hurricane Harvey. This is also an example
of what some call ‘malinformation’ (Wardle 2019).
Our sample includes a small number of manipulated
images and videos. Every example of doctored or
manipulated content in this sample employed simple,
low-tech photo or video editing techniques. One video
includes images of bananas edited into a news segment
to suggest that bananas can prevent or cure COVID-19.
Despite a great deal of recent concern, we saw no
examples of misinformation employing deepfakes or
other AI-based tools. Rather, manipulated content are
‘cheapfakes’ (Paris and Donovan 2019) produced using
techniques that have existed as long as there have
been photographs and lm.
Figure 2: Recongured vs fabricated misinformation
Figure 2 shows the proportion of recongured (N=133) and fabricated (N=86) misinformation in the sample (N=225) and the types of
misinformation that constitute both recongured and fabricated misinformation.
59%
38%
Recongured Fabricated Satire/parody
Of this:
Misleading content 29%
False context 24%
Manipulated content 6%
Of this:
Fabricated content 30%
Imposter content 8%
3%
2 An independent samples t-test showed a signicant dierence in engagement between recongured and fabricated content t(137)=1.241,
p < 0.05.

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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Sources: misinformation moves top-down as well as bottom-up
High-level politicians, celebrities, or other
prominent public gures produced or spread only
20% of the misinformation in our sample, but that
misinformation attracted a large majority of all
social media engagements in the sample. While
some of these instances involve content posted on
social media, 36% of top-down misinformation also
includes politicians speaking publicly or to the media.
As an example, the New York Times and others have
documented that President Donald Trump has made
a number of false statements on the topic at events,
on Fox News, and on Twitter. While our data do not
capture the reach of misinformation spread via TV,
top-down misinformation on social media accounted
for 69% of total social media engagements in our
sample3 (see Figure 3), driven in part by very high
levels of engagement with misinformation posted or
spread by high-level elected ocials, celebrities, and
other prominent public gures (including a US-based
technology entrepreneur).
Despite this, it is important not to underestimate the
amount (or inuence) of bottom-up misinformation
produced and spread by members of the broader
public. Not only did this content make up the vast
majority of our sample in terms of volume, some
individual pieces, such as one about saunas and
hair dryers preventing COVID-19, also occasionally
generated large volumes of engagement. It is dicult
to assess motivation from content alone as members
of the public oen engage in highly ambiguous
practices online (Philips and Milner 2017). Members
of the public appear to have many reasons for sharing
pieces of misinformation, including a desire to ‘troll’,
the legitimate belief information is true, and political
partisanship.
It is also notable how few pieces of misinformation
across the sample appeared intended to generate a
prot. Only six (3%) pieces of content were obviously
linked to supposed cures, vaccines, or protective
equipment for sale, and eight (4%) were posted on
advertising-heavy websites and meant to generate
clicks.4 (This may reect the priorities of professional
fact-checkers rather than the wider universe of
misinformation, as there is almost certainly a
large volume of low-grade for-prot coronavirus
misinformation being published by those trying to
generate advertising revenues that may evade the
attention of fact-checkers).
Figure 3: Top-down vs bottom-up misinformation
The le chart in Figure 3 shows the share of content that was produced or shared by prominent people in the whole sample (N=225).
The right chart shows the percent of total engagements of content from prominent people out of the sub-sample of social media posts with
available engagement data (N=145).
69%
Top down Bottom up
20%
Top down Bottom up
Share of total sample
(both social and traditional
media content)
Share of all social
media engagements
(within social media content)
3 As discussed in the appendix, we were able to nd engagement data for only 145 articles. Top-down claims constituted 15% of this reduced
sample.
4 Beyond the issues discussed here it is worth recognising that some governments globally are arguably withholding public interest
information about the pandemic and in some cases actively misinforming the public about the health situation and the actions taken to
address it.

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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Claims: much misinformation concerns the actions of public authorities
Across the sample, the most common claims within
pieces of misinformation concern the actions or
policies that public authorities are taking to address
COVID-19, whether individual national/regional/local
governments, health authorities, or international
bodies like the WHO and UN (see Figure 4). The
second most common type of claim concerns the
spread of the virus through communities. This ranged
from claims that geographic areas had seen their rst
infections, to content blaming certain ethnic groups
for spreading the virus.
Notably, misinformation about government action
and about the public spread of the virus generally
challenge information oen communicated by various
public authorities: whether that is communicating
their direct policies or providing pressing public
information. While the prominence of these topics
may be a function of being easier for fact-checkers to
validate, they could also indicate that governments
have not always succeeded in providing clear, useful,
and trusted information to address pressing public
questions. In the absence of sucient information,
misinformation about these topics may ll in gaps in
public understanding, and those distrustful of their
government or political elites may be disinclined to
trust ocial communications on these matters.
Figure 4: Proportion of sample containing types of claims
Figure 4 shows the proportion of the sample (N=225) containing each type of claim. Pieces of misinformation may contain multiple claims.
See Table 1 in methodological appendix for full description of each claim type.
39%
24% 24% 23%
17% 16%
12%
6% 5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
public authority action
community spread
general medical
prominent actors
conspiracy theories
how virus transmits
virus origins
public preparedness
vaccine development
Proportion of
sample containing
each claim

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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Responses: platforms have responded to much, but not all, of the
misinformation identied by fact-checkers
Several of the major social media platform companies
have taken steps to try to limit the spread of
misinformation about COVID-19. While policies vary,
some platforms, including Facebook, Twitter, and
YouTube, say they have begun to remove fact-checked
false and potentially harmful posts with reference to
community standards that have in several cases been
tightened in response to the pandemic. Facebook
also now in some cases includes warning labels on
content that has been rated false by independent fact-
checkers.
Social media platforms have responded to a majority
of the social media posts rated false in our sample.
There is nonetheless very signicant variation from
company to company (see Figure 5). While 59% of
false posts remain active on Twitter with no direct
warning label, the number is 27% for YouTube and 24%
for Facebook. Please also note that each false claim
may exist in many slightly dierent permutations on
any given platform, and our analysis only captures if
the platform in question has acted against the rst or
main piece identied as false by fact-checkers.
There is no directly comparable data available,
but background conversations with fact-checkers
suggest COVID-19-related misinformation is more
likely to be actioned by platforms than, for example,
political misinformation. If this is so, it may reect
the combination of the clear and present danger of
the pandemic, less partisan disagreement, and the
fact that there is expertise and evidence to determine
more clearly what is false and what is not than is the
case in many political discussions (Vraga and Bode
2020).
Figure 5: Percentage of active false posts with no direct warning label in sample
Figure 5 shows the percentage of posts rated as false that were still active and did not have a clear warning label at the end of March.
(Twitter: N = 43; YouTube: N= 6; Facebook: N = 33) out of the total number of posts on each platform in the sample (Twitter: N = 73; YouTube:
N = 22; FB: N = 137).
59%
27% 24%
0%
10%
20%
30%
40%
50%
60%
70%
Twitter YouTube Facebook
Percentage
of posts in sample
on each platform

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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Conclusions and recommendations
Our analysis suggests that misinformation about
COVID-19 comes in many dierent forms, from many
dierent sources, and makes many dierent claims.
It frequently recongures existing or true content
rather than fabricating it wholesale, and where it is
manipulated, is edited with simple tools.
Given the scope and seriousness of the pandemic,
independent media and fact-checkers and actions
by platforms and others play an important role in
addressing virus-related misinformation. Fact-checkers
can help sort false from true material, and accurate
from misleading claims. Our nding that much
misinformation directly or indirectly questions the
actions, competence, or legitimacy of public authorities
(including governments, health authorities, and
international organisations) suggests it will be dicult
for those institutions to address or correct it directly
without running into multiple problems. How many
people will accept as credible a government trying
to debunk or refute misinformation that casts that
very same government in a negative light? In contrast,
independent fact-checkers can provide authoritative
analysis of misinformation while helping platforms
identify misleading and problematic content, just as
independent news media can report credibly on how
governments and others are responding (with varying
degrees of success) to the pandemic.
Our analysis also found that prominent public
gures continue to play an outsized role in spreading
misinformation about COVID-19. While only a small
percentage of the individual pieces of misinformation
in our sample come from prominent politicians,
celebrities, and other public gures, these claims
oen have very high levels of engagement on various
social media platforms. The growing willingness of
some news media to call out falsehoods and lies from
prominent politicians can perhaps help counter this
(though it risks alienating their strongest supporters.)
Similarly, the decision by Twitter, Facebook, and
YouTube in late March to remove posts shared by
Brazilian President Jair Bolsonaro because they
included coronavirus misinformation was in our view
an important moment in how platform companies
handle the problem that a lot of misinformation
comes from the top.
Although our data do not capture it, misinformation
from prominent public gures can also spread widely
through other channels such as TV. While fact-checks
rarely spread either as widely or in the same networks
(Bounegru et al. 2017) as the misinformation it corrects,
it is imperative that trusted fact-checking and media
organisations continue to hold prominent gures to
account for claims they make across all channels and
nd new ways to distribute and publicise their work.
That being said, fact-checking is a scarce resource.
Our ndings demonstrate the degree to which fact
checking organisations have redeployed their limited
resources to address misinformation surrounding
COVID-19. It is important that fact-checkers continue
to increase coordination to limit overlap in the claims
they assess and validate. At the same time, the pressing
imperative to validate coronavirus information does
not also mean that misinformation about other topics
has become less prominent or important. Given
some initial indication that news about COVID-19 is
supplementing rather than replacing existing news
use, there is reason to suspect there remains a diverse
landscape of misinformation circulating globally.
It remains unclear what eect this rapid shiing
of fact-checking resources and attention will have
on the larger information environment. Given the
importance of independent fact-checkers, we can only
hope that more funders will be willing to support such
work going forward.
While describing the landscape of COVID-19
misinformation as an ‘infodemic’ captures the scale,
our analysis suggests it risks mischaracterising the
nature of the problems we face. As we have shown,
there is wide variety in the types of misinformation
circulating, the claims made concerning the virus,
and motivations behind its production. Unlike the
pandemic itself, there is no single root cause behind
the spread of misinformation about the coronavirus.
Instead, COVID-19 appears to be supplying the
opportunity for very dierent actors with a range of
dierent motivations and goals to produce a variety of
types of misinformation about many dierent topics.
In this sense, misinformation about COVID-19 is as
diverse as information about it.
The risk in not recognising the diversity in the
landscape of coronavirus misinformation is assuming
there could be a single solution to this set of problems.
Instead, our ndings suggest there will be no silver
bullet or inoculation – no ‘cure’ for misinformation
about the new coronavirus. Instead, addressing the
spread of misinformation about COVID-19 will take
a sustained and coordinated eort by independent
fact-checkers, independent news media, platform
companies, and public authorities to help the public
understand and navigate the pandemic.

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
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References
Bounegru, L., Gray, J., Venturini, T., Mauri, M. 2018.
A Field Guide to ‘Fake News’ and Other Information
Disorders. Amsterdam: Public Data Lab. (Accessed
Mar. 2020). https://fakenews.publicdatalab.org/
EUvsDISINFO. 2020. ‘EEAS Special Report Update:
Short Assessment of Narratives and Disinformation
around the COVID-19 Pandemic’. EUvsDISINFO.
(Accessed Mar. 2020). https://euvsdisinfo.eu/
eeas-special-report-update-short-assessment-of-
narratives-and-disinformation-around-the-covid-
19-pandemic/
Graves, L. 2016. Deciding What’s True: The Rise of
Political Fact-Checking in American Journalism. New
York: Columbia University Press.
Hollowood, E., Mostrous, A. 2020. ‘Fake news in the
time of C-19’. Tortoise. (Accessed Mar. 2020). https://
members.tortoisemedia.com/2020/03/23/the-
infodemic-fake-news-coronavirus/content.html
Paris, B., Donovan, J. 2019. Deepfakes and Cheap Fakes:
The Manipulation of Audio and Visual Evidence. Data
& Society. (Accessed Mar. 2020). https://datasociety.
net/wp-content/uploads/2019/09/DS_Deepfakes_
Cheap_FakesFinal-1-1.pdf
Philips, W., Milner, R. 2017. The Ambivalent Internet:
Mischief, Oddity, and Antagonism Online. Cambridge,
UK: Polity Press.
Scott, M. 2020. ‘Facebook’s Private Groups are Abuzz
with Coronavirus Fake News.’ Politico. (Accessed
Mar. 2020). https://www.politico.eu/article/
facebook-misinformation-fake-news-coronavirus-
covid19/
Vraga, E., Bode, V. 2020. ‘Dening Misinformation and
Understanding its Bounded Nature: Using Expertise
and Evidence for Describing Misinformation’
Political Communication 37(1): 136-144. https://doi.
org/10.1080/10584609.2020.1716500.
Wardle, C. 2019. ‘First Dra’s Essential Guide
to Understanding Information Disorder’. UK:
First Dra News. (Accessed Mar. 2020). https://
rstdranews.org/wp-content/uploads/2019/10/
Information_Disorder_Digital_AW.pdf?x76701
Wardle, C. 2017. ‘Fake news. It’s complicated.’ UK:
First Dra News. (Accessed Mar. 2020). https://
rstdranews.org/latest/fake-news-complicated/
Acknowledgements
We would like to thank Claire Wardle and Carlotta
Dotto at First Dra News for sharing their corpus of
fact-checks. We are also grateful for the guidance,
feedback, and support that Richard Fletcher, Simge
Andi, Seth Lewis, Sílvia Majó-Vázquez, Anne Schulz,
and the rest of the research, communications, and
administration teams at the Reuters Institute for the
Study of Journalism provided throughout the process
of preparing this report.
A  
J. Scott Brennen is a Research Fellow at the Reuters Institute for the Study of Journalism and the Oxford Internet Institute at the
University of Oxford.
Felix M. Simon is a Leverhulme Doctoral Scholar at the Oxford Internet Institute and a Research Assistant at the Reuters Institute for
the Study of Journalism.
Philip N. Howard is the Director of the Oxford Internet Institute and a Professor of Sociology, Information and International Aairs at
the University of Oxford.
Rasmus Kleis Nielsen is the Director of the Reuters Institute for the Study of Journalism and Professor of Political Communication at
the University of Oxford.
Published by the Reuters Institute for the Study of Journalism as part of
the Oxford Martin Programme on Misinformation, Science and Media, a
three-year research collaboration between the Reuters Institute, the Oxford
Internet Institute, and the Oxford Martin School.

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
| 10 |
Methodological Appendix
M
The ndings described here derive from a systematic
analysis of a corpus of 225 pieces of misinformation
about the new coronavirus rated false or misleading
by international fact-checking organisations. Fact-
checks were sampled from a corpus of 2,871 articles
provided to the authors by First Dra News that
consolidates virus-related fact-checks from the
Poynter’s International Fact-Checking Network (IFCN)
database and Google’s Fact Check Explorer tool
between January and March 2020.
Aer excluding all non-English entries, a sample
of 18% of articles was drawn at random from the
remaining corpus (N=1253) and a secondary sample
of an additional 20% was drawn at random. Duplicate
articles and those that had a ‘true’ rating in the primary
sample were replaced from the secondary sample.
Importantly, no articles from 31 March 2020 were
included in the corpus. The % increase in fact-checks
between March and January quoted in the factsheet
may be slightly under-estimated.
False claims circulating in messaging apps and
private groups on social media platforms are likely
to be underrepresented in international fact-checks.
Similarly, fact-checkers must make choices of how
to use limited time and resources. Many of the
fact-checking organisations in this corpus have an
agreement with Facebook in which they regularly
complete fact-checks of Facebook content. While
there is no guarantee that fact-checkers assess a
representative sample of misinformation, this sample
provides a means of understanding in general the types
of misinformation in circulation and some of the most
common false claims made about COVID-19. Similarly,
owing to the individual styles and approaches of
the many fact-checking organisations included in
this sample, the fact-checks analysed here dier in
terms of format and detail, ranging from in-depth
descriptions that included links and screenshots to
those providing only very basic information about the
piece of misinformation in question. As a result, it was
not always possible to determine certain variables.
Articles were analysed by two coders based on a pre-
dened coding scheme. The coding scheme included
a series of descriptive variables (see full codebook
below), as well as measures of misinformation
type, apparent motive, and types of claims within
pieces of misinformation. The typology for types of
misinformation was adapted from Wardle’s (2019)
popular 7-part typology. The measure of apparent
motivation adapted Wardle’s 8-part typology into six
motivations (poor journalism, parody/satire, trolling
or true belief, politics, prot, other) (Wardle, 2017).
The typology of claims within misinformation was
inductively produced to be specic to misinformation
about COVID-19. A rough typology was generated based
on existing scholarship on health misinformation
and an initial review of COVID-19 claims. Next, both
reviewers coded the same 10 pieces of misinformation
identied through AFP fact-checks, discussed the
coding, and rened the typology. See Table 1 for the
nal inductively generated typology with descriptions.
In coding this variable, coders selected all claims that
appeared in a piece of content.
10% of entries were coded by both coders to
assess intercoder reliability. Cohen’s kappa for
misinformation type (0.82) and misinformation
claims (0.88) were acceptable. Cohen’s kappa for
apparent motivation (0.68) was marginal and reects
the diculty assessing motivation from content alone.
Findings reported have reected this diculty.
Coders also assessed if the original misinformation
claim was produced or spread by high level politicians,
celebrities, and other prominent public gures (top-
down) or by members of the general public (bottom-
up).
Given the well-publicised eort by social media
platforms to address COVID-19 related misinformation,
coders recorded if debunked content from Facebook,
Twitter, and YouTube had been labelled as ‘false’,
removed (by platform or submitter), or remained active
on the platform. Rather than count every repeated
posting on a platform, coders recorded the status of
the rst or main piece identied for each platform for
a given fact-check. All coded instances of active posts
without warning labels were re-checked at the end of
March. It is possible that posts included in this corpus
have been removed or labelled since then. Please also
note that each false claim may exist in many slightly
dierent permutations on any given platform, and our
analysis only captures if the platform in question has
acted against the rst or main piece identied as false
by fact-checkers.
Coders also gathered engagement metrics (likes,
comments and shares) for all pieces of misinformation
linked or archived by fact-checks. Recorded likes,
comments, and shares were summed into a single
engagement metric. Views were not included in the

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
| 11 |
total engagement metric. It should be noted that
some social platforms actively down-rank posts once
they have been agged by fact-checkers. By basing
engagement scores on archived/screenshotted
posts, these data are indicative of a post’s popularity
before being agged. Even so, this engagement score
likely underestimates the true engagement for a
misinformation claim, which is oen repeated and
spread by many separate accounts. Of the 225 fact-
checks in the sample engagement data were found for
145.
Table 1: Inductive typology of claims made within pieces of COVID-19-related misinformation
Type Description
Public authority action/policy Claims about state policy/action/communication, claims about
WHO guidelines and recommendations, etc.
Community spread Claims about how the virus is spreading internationally, in
nations/states, or within communities. Claims about people,
groups or individuals involved/aected, etc.
General medical advice and virus
characteristics
Health remedies, self-diagnostics, eects and signs of the
disease, etc.
Prominent actors Claims about pharmacy companies or drug-makers, companies
providing supplies to the health care sector, or other companies.
Or claims about famous people, including claims about which
celebrities have been infected, claims about what politicians
have said or done (but not if the misinformation is coming from
politicians or other famous people).
Conspiracies Claims that the virus was created as a bioweapon, claims
about who is supposedly behind the pandemic, claims that the
pandemic was predicted, etc.
Virus transmission Claims about how the virus is transmitted and how to stop the
transmission, including cleaning, the use of certain types of
lights, appliances, protective gear, etc.
Explanation of virus origins Claims about where and how the virus originated (e.g. in
animals) and properties of the virus.
Public preparedness (Normative) claims about hoarding, buying supplies, social
distancing, (non)-adherence to measures, etc.
Vaccine development and availability Claims about vaccines, the development and availability of a
vaccine.

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
| 12 |
Codebook
A. FORMAL VARIABLES
Fill in the information specied below for each piece of misinformation:
1. Fact-check organisation
2. Fact-check country
3. URL of the fact-check
4. Date of fact-check
5. Fact-check outcome (false; misleading/half-true)
6. Misinformation in English or other language
7. Date of misinformation (if identiable)
8. Country of origin of misinformation (if identiable)
9. URL of misinformation (if identiable)
10. Evidence of platform action on misinformation: Facebook, Twitter, YouTube (warning label present;
warning label absent; content removed)
B. MISINFORMATION MEDIA CONTENT TYPE
What is the format of the piece of misinformation?
(Using the main source of the fact-check, select all that apply.)
1. News-type article (digital or print, including blogs)
2. Social media text
3. Social media picture (with or without caption) including picture memes
4. Social media video (including YouTube, TikTok)
5. TV appearance or public speech (political gure, CEO, etc.)
6. Other
If platform, on which platform does the content referenced by fact-check appear?
(Select all that apply.)
1. Facebook
2. Facebook Messenger
3. Twitter
4. YouTube
5. Instagram
6. WhatsApp
7. Reddit
8. TikTok
9. Gab
10. Other
If ‘news outlet’, ‘TV programme’, or ‘speechs’, provide the name
C. MISINFORMATION TYPE
To the best of your ability, what type of misinformation is it?
(Select one that ts best.) (Adapted from Wardle 2019.)
1. Satire or parody
2. False connection (headlines, visuals or captions don’t support the content)
3. Misleading content (misleading use of information to frame an issue or individual, when facts/
information are misrepresented or skewed)
4. False context (genuine content is shared with false contextual information, e.g. real images which have
been taken out of context)
5. Imposter content (genuine sources, e.g. news outlets or government agencies, are impersonated)
6. Fabricated content (content is made up and 100% false; designed to deceive and do harm)
7. Manipulated content (genuine information or imagery is manipulated to deceive, e.g. deepfakes or
other kinds of manipulation of audio and/or visuals)

TYPES, SOURCES, AND CLAIMS OF COVID-19 MISINFORMATION
| 13 |
D. APPARENT MOTIVATION
Using your best judgement and the fact-checking article, what was the motivation behind the
misinformation? (Select one that ts best.)
1. Poor journalism (a mistake made by news outlet)
2. Parody or satire
3. To troll or provoke with no discernible political motive – or an expression of legitimate belief
4. Political motives (partisanship or inuence)
5. Prot
6. Other/unclear
E. MISINFORMATION CLAIMS
Which of the following claims appear in the piece of misinformation? (Select all that apply.)
1. General medical advice and virus characteristics (health remedies, diagnostics, eects of the disease,
etc.)
2. Virus transmission (how the virus is transmitted, how to stop virus transmission, including cleaning,
certain types of lights, protective gear, etc.)
3. Vaccine development and availability
4. Explanation of virus origins
5. Community spread (how the virus is spreading in nations/communities: people, groups involved, etc.)
6. About public authority action or policy (e.g. state policy/action/communication, WHO guidelines and
recommendations)
7. Public preparedness (e.g. (normative) claims about hoarding, buying supplies, social distancing,
appropriate measures, etc.)
8. About prominent actors: either companies (e.g. pharmacy companies and drug-makers, companies
providing supplies to the health care sector) or famous people
F. ‘TOP DOWN’ OR ‘BOTTOM UP’
For each piece of content select one of the following. (Select the one that ts best.)
1. The misinformation originated from a prominent person (e.g. politician, celebrity, well-known expert)
2. The misinformation originated elsewhere, but has been shared by a prominent person
3. The misinformation originated with a non-prominent person and has not been shared by a prominent
person
G. ENGAGEMENT
For each piece of content answer:
1. How many likes, shares, comments recorded for versions shared on:
a. Facebook
b. Twitter
c. YouTube
References
Wardle, C. 2019. ‘First Dra’s Essential Guide to Understanding Information Disorder’. UK: First Dra News.
(Accessed Mar. 2020). https://rstdranews.org/wpcontent/uploads/2019/10/Information_Disorder_Digital_
AW.pdf ?x76701
Wardle, C. 2017. ‘Fake news. It’s complicated.’ UK: First Dra News. (Accessed Mar. 2020). https://rstdranews.
org/latest/fake-news-complicated/