https://doi.org/10.1177/20563051221150412
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons AttributionNonCommercial
4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction
and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
(https://us.sagepub.com/en-us/nam/open-access-at-sage).
Social Media + Society
January-March 2023: 1­–13 
© The Author(s) 2023
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/20563051221150412
journals.sagepub.com/home/sms
Special Issue: Multidisciplinary approaches to mis- and disinformation studies
Introduction
Concerns about misinformation are rising the world over.
Today, Americans are more concerned about misinformation
than sexism, racism, terrorism, or climate change (Mitchell
et al., 2019). Most of them (60%) think that “made-up news”
had a major impact on the 2020 election (Auxier, 2020).
Internet users are more afraid of fake news than online fraud
and online bullying (World Risk Poll, 2020). Numerous scientific
and journalistic articles claim that online misinformation
is the source of many contemporary sociopolitical issues
while neglecting deeper factors such as the decline of trust in
institutions (Benkler et al., 2018; Bennett & Livingston,
2020) or in the media (Newman et al., 2020).
Yet, the scientific literature is clear. Unreliable news,
including false, deceptive, low-quality, or hyper partisan
news, represents a minute portion of people’s information
diet (Allen et al., 2020; Altay et al., 2022; Cordonier & Brest,
2021); most people do not share unreliable news (Grinberg
et al., 2019; Guess et al., 2019); on average people deem fake
news less plausible than true news (Acerbi et al., 2022); social
media are not the only culprit (Benkler et al., 2018; Bennett &
Livingston, 2020; Tsfati et al., 2020); and the influence of
fake news on large sociopolitical events is overblown (Guess
et al., 2020; Mercier, 2020; Nyhan, 2020).
Because of this disconnect between public discourse and
empirical findings, a growing body of research argues that
alarmist narratives about misinformation should be understood
as a “moral panic” (Anderson, 2021; Carlson, 2020;
Jungherr & Schroeder, 2021; Mitchelstein et al., 2020). In particular,
these narratives can be characterized as one of the
1150412SMSXXX10.1177/20563051221150412Social Media <span class="symbol" cstyle="Mathematical">+</span> SocietyAltay et al.
research-article20232023
1
University of Oxford, UK
2
Fondation Nationale des Sciences Politiques (FNSP), France
3
Centre for Culture and Evolution, Division of Psychology, Brunel
University London, UK
*These authors contributed equally.
Manon Berriche is now affiliated to médialab, Sciences Po Paris, France
and Alberto Acerbi is also affiliated to Department of Sociology and Social
Research, University of Trento, Italy
Corresponding Author:
Manon Berriche, médialab, Sciences Po Paris, 1 place St Thomas d’Acquin,
75007, Paris, France.
Email: manon.berriche@sciencespo.fr
Misinformation on Misinformation:
Conceptual and Methodological
Challenges
Sacha Altay1*
, Manon Berriche2*
, and Alberto Acerbi3
Abstract
Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact
are overstated. Drawing on research examining the use of big data in social science and reception studies, we identify six
misconceptions about misinformation and highlight the conceptual and methodological challenges they raise. The first set of
misconceptions concerns the prevalence and circulation of misinformation. First, scientists focus on social media because it
is methodologically convenient, but misinformation is not just a social media problem. Second, the internet is not rife with
misinformation or news, but with memes and entertaining content. Third, falsehoods do not spread faster than the truth;
how we define (mis)information influences our results and their practical implications. The second set of misconceptions
concerns the impact and the reception of misinformation. Fourth, people do not believe everything they see on the internet:
the sheer volume of engagement should not be conflated with belief. Fifth, people are more likely to be uninformed than
misinformed; surveys overestimate misperceptions and say little about the causal influence of misinformation. Sixth,
the influence of misinformation on people’s behavior is overblown as misinformation often “preaches to the choir.” To
appropriately understand and fight misinformation, future research needs to address these challenges.
Keywords
misinformation, misperceptions, social media, conspiracy theories, big data, audience research
2	 Social Media + Society
many “technopanics” (Marwick, 2008) that have emerged
with the rise of digital media. These panics repeat themselves
cyclically (Orben, 2020), and are fueled by a wide range of
actors such as the mass media, policymakers, and experts
(Cohen, 1972, p. 8).Awell-known example is the broadcast of
Orson Welles’ radio drama The War of the Worlds, in 1938,
which was quickly followed by alarmist headlines from the
newspaper industry claiming that Americans suffered from
mass hysteria. Some years later, a psychologist Hadley Cantril
gave academic credence to this idea by estimating that a million
Americans truly believed in the Martian invasion (Cantril
et al., 1940). Yet, his claim turned out to be empirically
unfounded. As Brad Schwartz (2015, p. 184) explains “With
the crude statistical tool of the day, there was simply no way of
accurately judging how many people heard War of the Worlds,
much less how many of them were frightened by it. But all the
evidence—the size of the Mercury’s audience, the relatively
small number of protest letters, and the lack of reported damage
caused by the panic—suggest that few people listened and
even fewer believed. Anything else is just guesswork.”
The “Myth of the War of the Worlds Panic” illustrates how
academic research can fuel, and legitimize, misconceptions
about misinformation. Much research has documented the
role played by media and political discourse in building an
alarmist narrative on misinformation (Anderson, 2021;
Carlson, 2020; Jungherr & Schroeder, 2021; Mitchelstein
et al., 2020). Here, we offer a critical review of academic
discourse on misinformation, which exploded after the 2016
US presidential election with the publication of more than
2,000 scientific articles (Allen et al., 2020). We explain why
overgeneralizations of scientific results and poor considerations
of methodological limitations can lead to misconceptions
about misinformation in the public sphere. Drawing on
research questioning the use of big data in social sciences
(Boyd & Crawford, 2012; Kitchin, 2014; Tufekci, 2014) and
reception studies (Livingstone, 2019), we identify six common
misconceptions about misinformation (i.e., statements
often found in press articles and scientific studies but not
supported by empirical findings). These misconceptions can
be divided into two groups: one concerning misconceptions
about the prevalence and circulation of misinformation, and
one concerning misconceptions about the impact and reception
of misinformation. Table 1 offers an overview of the
misconceptions tackled in this article. Given that definitions
of misinformation vary from one study to another, we use the
term in its broadest sense, that is, as an umbrella term encompassing
all forms of false or misleading information regardless
of the intent behind it.
Misconceptions About the Prevalence
and Circulation of Misinformation
Misinformation Is Just a Social Media Problem
The internet drastically reduces the cost of accessing, producing,
and sharing information. Social media reduces friction
Table 1.  Frequently Encountered Misconceptions About Misinformation in Column Two, and Misinterpretation of Research Results
Associated With These Misconceptions in Column Three.
Misconceptions about
misinformation
Misinterpretations of research results
Prevalence and
circulation
1. Misinformation is just a
social media problem
Scientists focus on social media because it is methodologically convenient. Yet,
misinformation in legacy media and offline networks is understudied. Social media
make visible and quantifiable social phenomena that were previously difficult to
observe
2. The internet is rife with
misinformation
Big numbers are the rule on the internet. The misinformation problem should
be evaluated at the scale of the information ecosystem, e.g., by including news
consumption and news avoidance in the equation
3. Falsehoods spread faster
than the truth
How (mis)information is defined influences the perceived scale of the problem
and the solutions to fight it. Misinformation should not be framed only in terms
of accuracy (true vs. false), it could also be framed in terms of harmfulness or
ideological slant
Impact and
reception
4. People believe
everything they see on
the internet
Prevalence should not be conflated with impact or acceptance. Sharing or liking
is not believing. Digital traces do not always mean what we expect them to, and
often, to fully understand them, fine-grained analyses are needed
5. A large number of
people are misinformed
Surveys overestimate people’s misperceptions or beliefs in conspiracy theories,
and poorly measure rare behaviors. This is due to poor survey practices, but
also the instrumental use that some participants make of these surveys (e.g.,
expressive responding)
6. Misinformation has a
strong influence on
people’s behavior
People mostly consume misinformation they are predisposed to accept.
Acceptance should not be conflated with attitude or behavioral change. People do
not change their minds easily, let alone their behavior
Altay et al.	 3
even more: the cost of connecting with physically distant
minds is lower than ever. The media landscape is no longer
controlled by traditional gatekeepers, and misinformation,
just like any other content, is easier to publish. Today’s wide
access to rich digital traces makes contemporary large-scale
issues like misinformation easier to study, which can give the
impression that misinformation was less prevalent in the past
(compared to reliable information). Alarmist headlines about
the effect of social media and new technologies are widespread,
such as “How technology disrupted the truth” (Viner,
2016) or “You thought fake news was bad? Deep fakes are
where truth goes to die” (Schwartz, 2018). Yet, we need to be
careful when comparing the big data sets that we have today
with the much poorer and smaller data sets of the past. We
should also resist the temptation of idealizing the past. There
was no golden age in which people only believed and communicated
true information (Nyhan, 2020). Conspiracy theories
spread long before social media (Allcott & Gentzkow,
2017). Misinformation, such as false rumors, is a universal
feature of human societies, not a modern exception (Acerbi,
2020). Before the advent of the internet, in the 70s, rumors
about “sex thieves” arose from interactions between strangers
in markets and spread throughout Africa (Bonhomme, 2016).
In 1969, rumors about women being abducted and sold as sex
slaves proliferated in the French city of Orleans (Morin,
1969). One cannot assume that misinformation is more
common today simply because it is more available and
measured.
Social scientists focus on social media—Twitter in particular—because
it is methodologically convenient (Tufekci,
2014). But active social media users are not representative of
the general population (Mellon & Prosser, 2017), and most
U.S. social media users (70%) say they rarely, or never, post
about political or social issues (and only 9% say they often
do so; Mcclain, 2021). Traditional media may play an important
role in spreading misinformation, but because of the current
focus on social media, little is known about
misinformation in widely used sources of information such
as television or radio. According to Google Scholar, in May
2022, only seven papers had both “fake news” and “television”
in their title, compared to 578 with “fake news” and
“social media” (the results are similar with misinformation, 6
vs 389). Television is a gateway to misinformation from
elites, most notably from politicians (Benkler et al., 2018;
Tsfati et al., 2020). For instance, Trump’s tweets in his final
year of office were on cable news channels for a total of 32 h
(Wardle, 2021). More broadly, the detrimental consequences
of mainstream partisan media, where politicians routinely
spread misinformation, are well documented (e.g.,
Broockman & Kalla, 2022). If political elites or partisan
media did not wait for social media to spread misinformation,
now they can leverage social media features (e.g.,
hashtags) to frame topics of discussion in online public
debate and to impose their partisan agenda based (sometimes)
on outright lies.
These limitations invite more nuanced views on the role
of social media in the misinformation problem and suggest
that more work is needed on misinformation in legacy media
and offline networks.
The Internet is Rife With Misinformation
During the 2016 US presidential campaign, the top 20 fake
news stories on Facebook accumulated nearly nine million
shares, reactions, and comments, between August 1 and
November 8 (BuzzFeed, 2016). But what do nine million
interactions mean on Facebook? If the 1.5 billion Facebook
users in 2016 had commented, reacted, or shared just one
piece of content per week, the nine million engagements
with the top fake news stories would represent only 0.042%
of all their actions during the period studied (Watts &
Rothschild, 2017).
According to one estimate, in the US, between 2019 and
2020, traffic to untrustworthy websites increased by 70%,
whereas traffic to trustworthy websites increased by 47%
(Majid, 2021), which led to the following headline: “Covid-
19 and the rise of misinformation and misunderstanding”
(Majid, 2021). Yet, traffic to trustworthy websites is one
order of magnitude higher than the traffic to untrustworthy
websites. In March 2020, untrustworthy websites received
30 million additional views compared to March 2019,
whereas traffic to trustworthy websites increased by two billion
(Majid, 2021).
These two examples highlight the need to zoom out of the
big numbers surrounding misinformation and to put them in
perspective with people’s information practices. The average
US internet user spends less than 10 min a day consuming
news online (Allen et al., 2020) and the average French internet
user spends less than 5 min (Cordonier & Brest, 2021).
News consumption represents only 14% of Americans’ media
diet and 3% of the time French people spend on the internet
(Cordonier & Brest, 2021). Where is misinformation in all
that? It represents 0.15% of the American media diet and
0.16% of the French (Cordonier & Brest, 2021). In addition,
misinformation consumption is heavily skewed: 61% of the
French participants did not consult any unreliable sources during
the 30 days of the study (Cordonier & Brest, 2021), and a
small minority of people account for most of the misinformation
consumed and shared online (Grinberg et al., 2019).
Just as the internet is not rife with news, the internet is not
rife with misinformation. When people want to inform themselves,
they primarily access established news websites
(Fletcher et al., 2018; Guess et al., 2020), or, more commonly,
turn on the TV (Allen et al., 2020). Misinformation
receives little online attention compared to reliable news,
and, in turn, reliable news receives little online attention
compared to everything else that people do (Cordonier &
Brest, 2021). People do not use the internet primarily to be
informed, but rather to connect with friends, shop, work, and
more generally for entertainment (Cordonier & Brest, 2021).
4	 Social Media + Society
Documenting the large volume of interactions generated by
misinformation is important, but it should not be taken as
proof of its predominance. Big numbers are the rule on the
internet, and to be properly understood, they must be contextualized
within the entire information ecosystem.
That being said, it is not always possible for researchers to
consider the big picture since access to social media data is
restricted and often partial. As a result, many research questions
remain unexplored and conclusions are sometimes
uncertain, even when private companies provide access to
huge amounts of data. For instance, despite its unprecedented
size, the URLs dataset released by Facebook, via Social
Science One’s program, only includes URLs that have been
publicly shared at least 100 times. This threshold of 100 public
shares overestimates by a factor of four the prevalence of
misinformation on Facebook (Allen et al., 2021). Despite our
limited access to social media data, methodological improvements
are possible. For example, even with partial data
access, it is possible to conduct cross-platform studies, which
are needed to obtain accurate estimates of misinformation
prevalence and to quantify the diversity of people’s news
diets (Cointet et al., 2021).
Falsehoods Spread Faster than the Truth
The idea that falsehoods spread faster than the truth was
amplified and legitimized by an influential article published
in Science which found, among others, that “it took the truth
about six times as long as falsehood to reach 1,500 people”
(Vosoughi et al., 2018, p. 3). The study was quickly covered
in prominent news outlets and led to alarmist headlines such
as “Want something to go viral? Make it fake news” (Fox,
2018). Yet, Vosoughi and his colleagues did not examine the
spread of true and false news online but of “contested news”
that fact-checkers classified as either true or false, leaving
aside a large number of uncontested news that is extremely
viral. For instance, the royal wedding between Prince Harry
and Meghan Markle attracted 29 million viewers in the
United States and generated at least 6.9 million interactions
on Facebook (Berger, 2018). Similarly, the picture of Lionel
Messi holding the 2022 world cup trophy generated 75 million
likes on Instagram (and more than 2 million comments)—beating
the record of likes on Instagram previously
held by the photo of an egg with 59 million likes. The authors
themselves publicly explained this sampling bias.1
Yet, journalists
and scientists quickly overgeneralized, and nuances
were lost. Subsequent studies found conflicting results, with
science-based evidence and fact-checking being more
retweeted than false information during the COVID-19 pandemic
(Pulido et al., 2020) and news from hyperpartisan sites
not disseminating more quickly than mainstream news on
Australian Twitter (Bruns & Keller, 2020). Moreover, a
cross-platform study found no difference in the spreading
pattern of news from reliable and questionable sources
(Cinelli et al., 2020). This is not to say that the Vosoughi
et al. findings on fact-checked news do not hold, but rather
that how information and misinformation are defined greatly
influences the conclusions we draw.
Misinterpretations of that article raise the following questions:
What categories should we use? Do dichotomous measures
of veracity make sense? How should we define
misinformation? These past few years, important efforts
have been made to refine the blurred concept of “fake news”
(for a review, see Tandoc et al., 2018). In practice, though,
researchers use the ready-made categories constructed by
fact-checkers, putting under the same heading content that
differs in terms of harmfulness or facticity.
How (mis)information is defined influences the perceived
scale of the problem and the solutions to fight it (Rogers,
2020). For instance, in 2016, a widely circulated BuzzFeed
article showed that fake news outperformed true news on
Facebook in terms of engagement before the U.S. presidential
election (Silverman, 2016). However, when excluding
hyperpartisan news (such as Fox News) from the fake news
category, reliable news largely outperformed fake news
(Rogers, 2020). Politically biased information that is not
false could have harmful effects (Longhi, 2021), but does it
belong in the misinformation category? As Rogers (2021)
explains, “stricter definitions of misinformation (imposter
sites, pseudo-science, conspiracy, extremism only) lessen the
scale of the problem, while roomier ones (adding “hyperpartisan”
and “junk” sites serving clickbait) increase it, albeit
rarely to the point where it outperforms non-problematic (or
more colloquially, mainstream) media” (p. 2). Of course, it
would be impossible to find a unanimous typology that
would put every piece of news in the “right” category, but it
is important to be mindful of the limitations of the categories
we use.
In the first part of this article, we have highlighted three
misconceptions about the prevalence and circulation of
misinformation. We encourage future research to broaden
the scope of misinformation studies by (1) putting misinformation
consumption into perspective with broader people’s
information practices, as well as (2) all their online
activities, and (3) moving away from blatantly false information
to study subtler forms of information disorders.
(See Figure 1. From the innermost circle outward: factchecked
false news; broader forms of misinformation,
including, e.g., partisan interpretations; all online information;
all information, including offline.)
Misconceptions About the Impact and
Reception of Misinformation
In the second part of this article, we examine misconceptions
about the impact and reception of misinformation. It is not
because the numbers generated by misinformation are small
compared to reliable information or non-news content that we
can dismiss them. Small numbers can make a difference at a
societal level, but to accurately assess their impact and how
Altay et al.	 5
they translate into attitudes and behaviors, we need to understand
what they mean. Until now, studies on misinformation
have been dominated by experimental approaches, self-report
surveys, and big data methods reducing the reception of misinformation
to mere stimulus and response mechanisms, while
failing to capture audience agency and contexts. Drawing on
an active audience approach, the following sections explain
why certain findings from quantitative studies can lead to misconceptions
about the impact and reception of misinformation
(see Figure 2).
People Believe Everything They See on the
Internet
Media effects are increasingly measured via big data methods
(Athique, 2018, p. 59) neglecting the agency of people
and paying little attention to communicative context
(Livingstone, 2019). As danah Boyd and Kate Crawford
(2012, p. 666) put it, “why people do things, write things, or
make things can be lost in the sheer volume of numbers.”
For instance, it is tempting to conflate prevalence with
impact, but the diffusion of inaccurate information should
be distinguished from its reception. People are more likely
to share true and false news they perceive as more accurate
(e.g., Altay et al., 2021), but there is also a well-documented
disconnect between sharing intentions and accuracy
judgments (Altay et al., 2021). Sharing and liking are not
believing. People interact with misinformation for a variety
of reasons: to socialize, to express skepticism, outrage or
anger, to signal group membership, or simply to have a good
laugh (Acerbi, 2019; Berriche & Altay, 2020; Metzger et al.,
2021; Osmundsen et al., 2021). Engagement metrics are an
imperfect proxy of information reception and are not sufficient
to estimate the impact of misinformation. As Wagner
and Boczkowski (2019, p. 881) note, “studies that measure
exposure to fake news seem sometimes to easily infer acceptance
of content due to mere consumption of it, therefore
perhaps exaggerating potential negative effects [. . .] future
studies should methodologically account for critical or
ironic news sharing, as well as other forms of negotiation
and resignification of false content.”
With the datafication of society, scientists’ attention
shifted from empirical audiences to the digital traces that
they leave. However, we should not forget the people who
left these traces in the digital record, nor neglect the rich
theoretical framework produced by decades of reception
studies based on in situ observations of people consuming
media content (Lull, 1988; Morley, 1980). People are not
passive receptacles of information. They are active, interpretative,
and domesticate technologies in complex and unexpected
ways (Livingstone, 2019). People are more skeptical
than gullible when browsing online (Fletcher & Nielsen,
Figure 1.  Contextualizing misinformation prevalence and circulation.
6	 Social Media + Society
2019). Trust in the media is low, but trust in information
encountered on social media is even lower. And people
deploy a variety of strategies to detect and counter misinformation,
such as checking different sources or turning to factchecks
(Wagner & Boczkowski, 2019). People often
disqualified as “irrational” or “gullible” in public discourse,
such as “conspiracy theorists” or “anti-vaxxers,” deploy
sophisticated verification strategies to “fact-check” the news
in their own way (Tripodi, 2018) and produce “objectivist
counter-expertise” (Ylä-Anttila, 2018) by doing their “own
research” (Marwick & Partin, 2022). For instance, a recent
in-depth analysis of around 15,000 comments, based on mixmethods,
showed how “anti-vaxxers” cite scientific studies
on Facebook groups to support their positions and challenge
the objectivity of mainstream media (Berriche, 2021).
Given people’s skepticism toward information encountered
online and the low prevalence of misinformation in
their media diet, interventions aimed at reducing the acceptance
of misinformation are bound to have smaller effects
than interventions increasing trust in reliable sources of
information (Acerbi et al., 2022). More broadly, enhancing
trust in reliable sources should be a priority over fostering
distrust in unreliable sources (Altay, 2022).
A Large Number of People are Misinformed
Headlines about the ubiquity of misbeliefs are rampant in the
media and are most often based on surveys. But how well do
surveys measure misbeliefs? Luskin and colleagues (2018)
analyzed the design of 180 media surveys with closed-ended
questions measuring belief in misinformation. They found
that more than 90% of these surveys lacked an explicit “Don’t
know” or “Not sure” option and used formulations encouraging
guessing such as “As far as you know . . .,” or “Would you
say that . . .” Often, participants answer these questions by
guessing the correct answer and report holding beliefs that
they did not hold before the survey (Graham, 2021). Not providing,
or not encouraging “Don’t know” answers is known
to increase guessing even more (Luskin & Bullock, 2011).
Guessing would not be a major issue if it only added noise to
the data. To find out, Luskin and colleagues (2018) tested the
impact of not providing “Don’t know” answers and encouraging
guessing on the prevalence of misbeliefs. They found that
it overestimates the proportion of incorrect answers by nine
percentage points (25 to 16), and, when considering only people
who report being confident in holding a misperception, it
overestimates incorrect answers by 20 percentage points (25
Figure 2.  Contextualizing misinformation effect and reception.
Altay et al.	 7
to 5). In short, survey items measuring misinformation overestimate
the extent to which people are misinformed, eclipsing
the share of those who are simply uninformed.
In the same vein, conspiratorial beliefs are notoriously
difficult to measure and surveys tend to exaggerate their
prevalence (Clifford et al., 2019). For instance, participants
in survey experiments display a preference for positive
response options (yes vs no, or agree vs disagree) which
inflates agreement with statements, including conspiracy
theories, by up to 50% (Hill & Roberts, 2021; Krosnick,
2018). Moreover, the absence of “Don’t know” options,
together with the impossibility to express one’s preference
for conventional explanations in comparison to conspiratorial
explanations, greatly overestimate the prevalence of con-
spiratorialbeliefs(Cliffordet al.,2019).Thesemethodological
problems contributed to unsupported alarmist narratives
about the prevalence of conspiracy theories, such as Qanon
going mainstream (Uscinski et al., 2022a).
Moreover, the misperceptions that surveys measure are
skewed toward politically controversial and polarizing
misperceptions, which are not representative of the misperceptions
that people actually hold (Nyhan, 2020). This could
contribute to fueling affective polarization by emphasizing
differences between groups instead of similarities and inflate
the prevalence of misbeliefs. When misperceptions become
group markers, participants use them to signal group membership—whether
they truly believe the misperceptions or
not (Bullock et al., 2013). Responses to factual questions in
survey experiments are known to be vulnerable to “partisan
cheerleading” (Bullock et al., 2013; Prior et al., 2015), in
which, instead of stating their true beliefs, participants give
politically congenial responses. Quite famously, a large share
of Americans believed that Donald Trump’s inauguration in
2017 was more crowded than Barack Obama’s in 2009,
despite being presented with visual evidence to the contrary.
Partisanship does not directly influence people’s perceptions:
misperceptions about the size of the crowds were
largely driven by expressive responding and guessing.
Respondents who supported President Trump “intentionally
provide misinformation” to reaffirm their partisan identity
(Schaffner & Luks, 2018, p. 136). The extent to which
expressive responding contributes to the overestimation of
other political misbeliefs is debated (Nyhan, 2020), but it is
probably significant.
Solutions have been proposed to overcome these flaws
and measure misbeliefs more accurately, such as including
confidence-in-knowledge measures (Graham, 2020) and
considering only participants who firmly and confidently say
they believe misinformation items as misinformed (Luskin
et al., 2018). Yet, even when people report confidently holding
misbeliefs, these misbeliefs are highly unstable across
time, much more so than beliefs (Graham, 2021). For
instance, the responses of people saying they are 100% certain
that climate change is not occurring have the same measurement
properties as responses of people saying they are
100% certain the continents are not moving or that the sun
goes around the Earth (Graham, 2021). A participant’s
response at time T does not predict their answer at time T + 1.
In other words, flipping a coin would give a similar response
pattern.
So far, we have seen that even well-designed surveys
overestimate the prevalence of misbeliefs. A further issue is
that surveys unreliably measure exposure to misinformation
and the occurrence of rare events such as fake news exposure.
People report being exposed to a substantial amount of
misinformation and recall having been exposed to particular
fake headlines (Allcott & Gentzkow, 2017). To estimate the
reliability of these measures, Allcott and Gentzkow (2017)
showed participants the 14 most popular fake news during
the American election campaign, together with 14 made-up
“placebo fake news.” 15% of participants declared having
been exposed to one of the 14 “real fake news,” but 14% also
declared having been exposed to one of the 14 “fake news
placebos.”
During the pandemic, many people supposedly engaged
in extremely dangerous hygiene practices to fight COVID-
19 because of misinformation encountered on social media,
such as drinking diluted bleach (Islam et al., 2020). This led
to headlines such as “COVID-19 disinformation killed thousands
of people, according to a study” (Paris Match Belgique,
2020). Yet, the study is silent regarding causality, and cannot
be taken as evidence that misinformation had a causal impact
on people’s behavior (France info, 2020). For instance, 39%
of Americans reported having engaged in at least one cleaning
practice not recommended by the CDC, 4% ofAmericans
reported drinking or gargling a household disinfectant, while
another 4% reported drinking or gargling diluted bleach
(Gharpure et al., 2020). These percentages should not be
taken at face value. A replication of the survey found that
these worrying responses are entirely attributable to problematic
respondents who also reported “recently having had
a fatal heart attack” or “eating concrete for its iron content”
at a rate similar to that of ingesting household cleaners
(Litman et al., 2020; reminiscent of the “lizardman’s constant”
by Alexander, 2013). The authors conclude that “Once
inattentive, mischievous, and careless respondents are taken
out of the analytic sample we find no evidence that people
ingest cleansers to prevent Covid-19 infection” (Alexander,
2013, p. 1). This is not to say that COVID-19 misinformation
had no harmful effects (such as creating confusion or eroding
trust in reliable information), but rather that surveys using
self-reported measures of rare and dangerous behaviors
should be interpreted with caution.
Misinformation Has a Strong Influence on
People’s Behavior
Sometimes, people believe what they see on the internet and
engagement metrics do translate into belief. Yet, even when
misinformation is believed, it does not necessarily mean that
8	 Social Media + Society
it changed anyone’s mind or behavior. First, people largely
consume politically congenial misinformation (Guess et al.,
2019, 2021). That is, they consume misinformation they
already agree with, or are predisposed to accept. Congenial
misinformation “preaches to the choir” and is unlikely to
have drastic effects beyond reinforcing previously held
beliefs. Second, even when misinformation changes people’s
minds and leads to the formation of new (mis)beliefs, it is not
clear if these (mis)beliefs ever translate into behaviors.
Attitudes are only weak predictors of behaviors. This problem
is well known in public policies as the value-action gap
(Kollmuss & Agyeman, 2002). Most notoriously, people
report being increasingly concerned about the environment
without adjusting their behaviors accordingly (Landry et al.,
2018).
Common misbeliefs, such as conspiracy theories, are
likely to be cognitively held in such a way that limits their
influence on behaviors (Mercier, 2020; Mercier & Altay,
2022). For instance, the behavioral consequences that follow
from common misbeliefs are often at odds with what we
would expect from people actually believing them. As
Jonathan Kay (2011, p. 185) noted, “one of the great ironies
of the Truth movement is that its activists typically hold their
meetings in large, unsecured locations such as college auditoriums—even
as they insist that government agents will
stop at nothing to protect their conspiracy for world domination
from discovery.” Often, these misbeliefs are likely to be
post hoc rationalizations of pre-existing attitudes, such as
distrust of institutions.
In the real world, it is difficult to measure how much
attitude change misinformation causes, and it is a daunting
task to assess its impact on people’s behavior. Surveys
relying on correlational data tell us little about causation.
For example, belief in conspiracy theories is associated
with many costly behaviors, such as COVID-19 vaccine
refusal (Uscinski et al., 2022b). Does this mean that vaccine
hesitancy is caused by conspiracy theories? No, it
could be that both vaccine hesitancy and belief in conspiracy
theories are caused by other factors, such as low trust
in institutions (Mercier & Altay, 2022; Uscinski et al.,
2022b). A few ingenious studies allowed some causal
inferences to be drawn. For instance, Kim and Kim (2019)
used a longitudinal survey to capture people’s beliefs and
behaviors both before and after the diffusion of the “Obama
is a Muslim” rumor. They found that after the rumor
spread, more people were likely to believe that Obama was
a Muslim. Yet, this effect was “driven almost entirely by
those predisposed to dislike Obama” (p. 307), and the diffusion
of the rumor had no measurable effect on people’s
intention to vote for Obama. This should not come as a
surprise, considering that even political campaigns and
political advertising only have weak and indirect effects on
voters (Kalla & Broockman, 2018). As David Karpf (2019)
writes “Generating social media interactions is easy; mobilizing
activists and persuading voters is hard.”
The idea that exposure to misinformation (or information)
has a strong and direct influence on people’s attitudes
and behaviors comes from a misleading analogy of social
influence according to which ideas infect human minds like
viruses infect human bodies. Americans did not vote for
Trump in 2016 because they were brainwashed. There is
no such thing as “brainwashing” (Mercier, 2020).
Information is not passed from brain to brain like a virus is
passed from body to body. When humans communicate,
they constantly reinterpret the messages they receive, and
modify the ones they send (Claidière et al., 2014). The same
tweet will create very different mental representations in
each brain that reads it, and the public representations people
leave behind them, in the form of digital traces, are only
an imperfect proxy of their private mental representations.
The virus metaphor, all too popular during the COVID-19
pandemic—think of the “infodemic” epithet—is misleading
(Simon & Camargo, 2021). It is reminiscent of outdated
models of communication (e.g., “hypodermic needle
model”) assuming that audiences were passive and easily
swayed by pretty much everything they heard or read
(Lasswell, 1927). As Anderson (2021) notes “we might see
the role of Facebook and other social media platforms as
returning us to a pre-Katz and Lazarsfeld era, with fears that
Facebook is “radicalizing the world” and that Russian bots
are injecting disinformation directly in the bloodstream of
the polity.” These premises are at odds with what we know
about human psychology and clash with decades of data
from communication studies.
Conclusion
We identified six misconceptions about the prevalence and
impact of misinformation and examined the conceptual and
methodological challenges they raise. First, social media
makes the perfect villain; but before blaming it for the misinformation
problem, more work needs to be done on legacy
media and offline networks. Second, the misinformation
problem should be evaluated at the scale of the information
ecosystem, for example, by including news consumption and
news avoidance in the equation. Third, we should be mindful
of the categories that we use, like “fake” versus “true” news
since they influence our results and their practical implications.
Fourth, more qualitative and quantitative reception
studies are needed to understand people’s informational
practices. Digital traces do not always mean what we expect
them to, and often, to fully understand them, fine-grained
analyses are needed. Fifth, the quantity of misinformed people
is likely to be overestimated. Surveys measuring misbeliefs
should include “Don’t know” or “Not sure” options and
avoid wording that encourages guessing. Sixth, we should
resist monocausal explanations and blaming misinformation
for complex socioeconomic problems. People mostly consume
information they are predisposed to accept; this acceptance
should not be conflated with attitude or behavioral
Altay et al.	 9
change. More broadly, conclusions drawn from engagement
metrics, online experiments, or surveys need to be taken with
a grain of salt, as they tend to overestimate the prevalence of
misbeliefs, and tell us little about the causal influence of misinformation
and its reception. In the lines below, we detail
practical avenues for future research to overcome some of
these challenges.
We need to move away from fake news and blatantly
false information as their prevalence and effects are likely
minimal. Instead, we should investigate subtler forms of
misinformation that produce biased perceptions of reality.
For instance, partisan media use true information in misleading
ways by selectively reporting on some issues but
not others and framing them in a politically congruent manner
(Broockman & Kalla, 2022).
We need to better interpret data collected via computational
methods or self-report surveys. So far, misinformation
studies have been dominated by big data methods or experimental
approaches. This research could benefit from being
completed by active audience research (Livingstone, 2019).
For instance, instead of focusing on media effects where participants
are often implicitly depicted as passive, it would be
more fruitful to study the different ways people use the information
they consume online. Similarly, digital ethnographies
are needed to understand the reception of misinformation in
our complex digital ecosystem.
We need to rely on more complex models of influence.
Metaphors like “contagion” and “infodemic” provide an
overly simplified—and incorrect—view of how influence
works (Simon & Camargo, 2021). People do not change their
minds when exposed to “fake news” on social media. Social
media does not turn people into conspiracy theorists.
Individual predispositions, such as conspiracy mentality,
mediate the relationship between social media use and belief
in conspiracy theories (Uscinski et al., 2022b). These mediating
variables, which explain for instance why some people
but not others visit untrustworthy websites, should be investigated
further.
We need to ask better research questions. The misconceptions
outlined above (mis)guided some research agenda by
focusing excessively on why people “fall for fake news” or on
how to make people more skeptical. Yet, most people do not
fall for fake news and are (overly) skeptical of news on social
media. This excessive focus on biases, common in psychology,
left some pressing questions unanswered. For example,
we know very little about the cognitive and social tools that
people use to verify information online. Future studies should
move from a perspective centered on the identification of isolated
failures of individual cognition to an approach attentive
to audience critical skills. Our digital environment empowered
us with resources such as fact-checking websites, search
engines and Wikipedia, making it easier than ever before to
verify content encountered online and offline. Yet, research
on the uses (and misuses) of these new information search
and verification tools is drastically lacking.
The study of misconceptions does not entail a focus on
misinformation. People commonly hold false beliefs and
engage in detrimental behaviors because they do not trust
institutions and choose not to inform themselves. Instead of
focusing on the small number of people who consume news
from unreliable sources, it would be more fruitful to focus on
the large share of people who are overly skeptical of reliable
sources and rarely consume any news (Allen et al., 2020;
Cordonier & Brest, 2021). Despite legitimate concerns about
misinformation, people are more likely to be uninformed
than misinformed (Li & Wagner, 2020), even during the
COVID-19 pandemic (Cushion et al., 2020).
To appropriately understand and fight misinformation, it
is crucial to have these conceptual and methodological blind
spots in mind. Just like misinformation, misinformation on
misinformation could have deleterious effects (Altay et al.,
2020; Jungherr & Schroeder, 2021; Miró-Llinares &
Aguerri, 2021; Van Duyn & Collier, 2019), such as diverting
society’s attention and resources from deeper socioeconomic
issues or fueling people’s mistrust of the media even
more (Altay & Acerbi, 2022). Misinformation is mostly a
symptom of deeper sociopolitical problems rather than a
cause of these problems (Ramaciotti Morales et al., 2022).
Fighting the symptoms can help, but it should not divert us
from the real causes, nor overshadow the need to fight for
access to accurate, transparent, and quality information.
Acknowledgements
We would like to thank Dominique Cardon, Héloïse Théro, and
Shaden Shabayek for proofreading our manuscript and for their precious
feedbacks and corrections.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article:
Project De Facto Observatory of Information x Connecting Europe
Facility-2020-FR-IA-0291 Reboot Foundation x.
ORCID iD
Manon Berriche https://orcid.org/0000-0003-1381-8330
Note
1.	 For instance, see Sinan Aral’s tweet: https://twitter.com/
sinanaral/status/974276513741844480.
References
Acerbi, A. (2019). Cognitive attraction and online misinformation.
Palgrave Communications, 5(1), 15.
Acerbi, A. (2020). Cultural evolution in the digital age. Oxford
University Press.
10	 Social Media + Society
Acerbi, A., Altay, S., & Mercier, H. (2022). Research note:
Fighting misinformation or fighting for information? Harvard
Kennedy School (HKS) Misinformation Review. https://doi.
org/10.37016/mr-2020-87
Alexander, S. (2013). Lizardman’s constant is 4%. Slate Star
Codex.
Allcott, H., & Gentzkow, M. (2017). Social media and fake news
in the 2016 election. Journal of Economic Perspectives, 31(2),
211–236. https://doi.org/10.1257/jep.31.2.211
Allen, J., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J.
(2020). Evaluating the fake news problem at the scale of the
information ecosystem. Science Advances, 6(14), eaay3539.
https://doi.org/10.1126/sciadv.aay3539
Allen, J., Mobius, M., Rothschild, D. M., & Watts, D. J. (2021).
Research note: Examining potential bias in large-scale censored
data. Harvard Kennedy School Misinformation Review.
https://doi.org/10.37016/mr-2020-74
Altay, S. (2022). How effective are interventions against misinformation?
https://doi.org/10.31234/osf.io/sm3vk
Altay, S., & Acerbi, A. (2022). Misinformation is a threat because
(other) people are gullible. https://doi.org/10.31234/osf.io/n4qrj
Altay, S., de Araujo, E., & Mercier, H. (2021). “If this account is
true, it is most enormously wonderful”: Interestingness-if-true
and the sharing of true and false news. Digital Journalism, 10,
373–394. https://doi.org/10.1080/21670811.2021.1941163
Altay, S., Hacquin, A.-S., & Mercier, H. (2020). Why do so few people
share fake news? It hurts their reputation. New Media & Society,
24, 1303–1324. https://doi.org/10.1177/1461444820969893
Altay, S., Nielsen, R. K., & Fletcher, R. (2022). Quantifying the
“infodemic”: People turned to trustworthy news outlets during
the 2020 coronavirus pandemic. Journal of Quantitative
Description: Digital Media, 2. https://reutersinstitute.politics.
ox.ac.uk/quantifying-infodemic-people-turned-trustworthy-
news-outlets-during-2020-coronavirus-pandemic
Anderson, C. W. (2021). Fake news is not a virus: On platforms and
their effects. Communication Theory, 31(1), 42–61. https://doi.
org/10.1093/ct/qtaa008
Athique, A. (2018). The dynamics and potentials of big data for
audience research. Media, Culture & Society, 40(1), 59–74.
https://doi.org/10.1177/0163443717693681
Auxier, B. (2020). 64% of Americans say social media have a
mostly negative effect on the way things are going in the U.S.
today. Pew Research Center. https://www.pewresearch.org/
fact-tank/2020/10/15/64-of-americans-say-social-media-have-
a-mostly-negative-effect-on-the-way-things-are-going-in-the-
u-s-today/
Benkler, Y., Faris, R., & Roberts, H. (2018). Network propaganda:
Manipulation, disinformation, and radicalization in American
politics. Oxford University Press.
Bennett, W. L., & Livingston, S. (Eds.). (2020). The disinformation
age: Politics, technology, and disruptive communication
in the United States. Cambridge University Press. https://doi.
org/10.1017/9781108914628
Berger, S. (2018). Here’s how many Americans watched Prince
Harry and Meghan Markle’s royal wedding. CNBC. https://
www.cnbc.com/2018/05/21/ratings-for-prince-harry-and-
meghan-markle-royal-wedding.html
Berriche, M. (2021). In search of sources. Politiques de
Communication, 1, 115–154. https://www.cairn.info/revue-
politiques-de-communication-2021-1-page-115.htm
Berriche, M., & Altay, S. (2020). Internet users engage more with
phatic posts than with health misinformation on Facebook.
Palgrave Communications, 6, 71. https://doi.org/10.1057/
s41599-020-0452-1
Bonhomme, J. (2016). The Sex Thieves. The Anthropology of a
Rumor. Média Diffusion.
Boyd, D., & Crawford, K. (2012). Critical questions for big data:
Provocations for a cultural, technological, and scholarly phenomenon.
Information, Communication & Society, 15(5), 662–679.
Broockman, D., & Kalla, J. (2022). The manifold effects of partisan
media on viewers’ beliefs and attitudes: A field experiment
with Fox News viewers. OSF Preprints, 1. https://osf.io/jrw26/
Bruns, A., & Keller, T. (2020). News diffusion on Twitter: Comparing
the dissemination careers for mainstream and marginal news.
Social Media & Society. https://snurb.info/node/2601
Bullock, J. G., Gerber, A. S., Hill, S. J., & Huber, G. A. (2013).
Partisan bias in factual beliefs about politics. National Bureau
of Economic Research. http://www.nber.org/papers/w19080
BuzzFeed. (2016). Here are 50 of the biggest fake news hits on
Facebook from 2016. https://www.buzzfeednews.com/article/
craigsilverman/top-fake-news-of-2016
Cantril, H., Gaudet, H., & Herzog, H. (1940). The invasion from
Mars. Princeton University Press.
Carlson, M. (2020). Fake news as an informational moral panic:
The symbolic deviancy of social media during the 2016 US
presidential election. Information, Communication & Society,
23(3), 374–388.
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M.,
Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A.
(2020). The COVID-19 social media infodemic. Scientific
Reports, 10(1), 1–10.
Claidière, N., Scott-Phillips, T. C., & Sperber, D. (2014). How
Darwinian is cultural evolution? Philosophical Transactions
of the Royal Society B: Biological Sciences, 369(1642),
20130368. http://rstb.royalsocietypublishing.org/content/369/
1642/20130368.short
Clifford, S., Kim, Y., & Sullivan, B. W. (2019). An improved question
format for measuring conspiracy beliefs. Public Opinion
Quarterly, 83(4), 690–722.
Cohen, S. (1972). Folk devils and moral panics. Routledge. https://
doi.org/10.4324/9780203828250
Cointet, J.-P., Cardon, D., Mogoutov, A., Ooghe-Tabanou, B.,
Plique, G., & Morales, P. (2021). Uncovering the structure of
the French media ecosystem. http://arxiv.org/abs/2107.12073
Cordonier, L., & Brest, A. (2021). How do the French inform themselves
on the Internet? Analysis of online information and
disinformation behaviors. Fondation Descartes. https://hal.
archives-ouvertes.fr/hal-03167734/document
Cushion, S., Soo, N., Kyriakidou, M., & Morani, M. (2020).
Research suggests UK public can spot fake news about
COVID-19, but don’t realise the UK’s death toll is far higher
than in many other countries. LSE COVID- Blog. https://blogs.
lse.ac.uk/covid19/2020/04/28/research-suggests-uk-public-
can-spot-fake-news-about-covid-19-but-dont-realise-the-uks-
death-toll-is-far-higher-than-in-many-other-countries/
Fletcher, R., Cornia, A., Graves, L., & Nielsen, R. K. (2018).
Measuring the reach of “fake news” and online disinformation
in Europe. Reuters Institute Factsheet. https://reutersinstitute.
politics.ox.ac.uk/our-research/measuring-reach-fake-news-
and-online-disinformation-europe
Altay et al.	 11
Fletcher, R., & Nielsen, R. K. (2019). Generalised scepticism:
How people navigate news on social media. Information,
Communication & Society, 22(12), 1751–1769.
Fox, M. (2018). Fake News: Lies spread faster on social media
than truth does. NBC News. https://www.nbcnews.com/health
/health-news/fake-news-lies-spread-faster-social-media-truth-
does-n854896
France info. (2020). Désintox. Non, la désinformation n’a pas fait
800 morts. France Info. https://www.francetvinfo.fr/sante/
maladie/coronavirus/desintox-non-la-desinformation-n-a-
pas-fait-800-morts_4109117.html
Gharpure, R., Hunter, C. M., Schnall, A. H., Barrett, C. E., Kirby,
A. E., Kunz, J., Berling, K., Mercante, J. W., Murphy, J. L.,
& Garcia-Williams, A. G. (2020). Knowledge and practices
regarding safe household cleaning and disinfection for
COVID-19 prevention—United States, May 2020. Morbidity
and Mortality Weekly Report, 69(23), 705–709.
Graham, M. H. (2020). Self-awareness of political knowledge.
Political Behavior, 42(1), 305–326.
Graham, M. H. (2021). Measuring misperceptions? American
Political Science Review.
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., &
Lazer, D. (2019). Fake news on twitter during the 2016 US
Presidential election. Science, 363(6425), 374–378. https://doi.
org/10.1126/science.aau2706
Guess, A., Aslett, K., Tucker, J., Bonneau, R., & Nagler, J. (2021).
Cracking open the news feed: Exploring what us Facebook
users see and share with large-scale platform data. Journal
of Quantitative Description: Digital Media, 1. https://doi.
org/10.51685/jqd.2021.006
Guess, A., Lockett, D., Lyons, B., Montgomery, J. M., Nyhan, B.,
& Reifler, J. (2020). “Fake news” may have limited effects
beyond increasing beliefs in false claims. Harvard Kennedy
School Misinformation Review, 1(1). https://doi.org/10.37016/
mr-2020-004
Guess, A., Nagler, J., & Tucker, J. (2019). Less than you think:
Prevalence and predictors of fake news dissemination on
Facebook. Science Advances, 5(1), eaau4586. https://doi.
org/10.1126/sciadv.aau4586ex
Hill, S. J., & Roberts, M. E. (2021). Acquiescence bias inflates estimates
of conspiratorial beliefs and political misperceptions.
http://www.margaretroberts.net/wp-content/uploads/2021/10/
hillroberts_acqbiaspoliticalbeliefs.pdf
Islam, M. S., Sarkar, T., Khan, S. H., Kamal, A.-H. M., Hasan, S.
M., Kabir, A., Yeasmin, D., Islam, M. A., Chowdhury, K. I. A.,
& Anwar, K. S. (2020). COVID-19–related infodemic and its
impact on public health: A global social media analysis. The
American Journal of Tropical Medicine and Hygiene, 103(4),
1621.
Jungherr, A., & Schroeder, R. (2021). Disinformation and the
structural transformations of the public arena: Addressing the
actual challenges to democracy. Social Media+ Society, 7(1),
2056305121988928.
Kalla, J. L., & Broockman, D. E. (2018). The minimal persuasive
effects of campaign contact in general elections: Evidence
from 49 field experiments. American Political Science Review,
112(1), 148–166. https://doi.org/10.1017/S0003055417000363
Karpf, D. (2019). On digital disinformation and democratic myths.
Mediawell (Social Science Research Council).
Kay, J. (2011). Among the Truthers: A journey through America’s
growing conspiracist underground. Harper Collins.
Kim, J. W., & Kim, E. (2019). Identifying the effect of political
rumor diffusion using variations in survey timing. Quarterly
Journal of Political Science, 14(3), 293–311. http://dx.doi.
org/10.1561/100.00017138
Kitchin, R. (2014). Big Data, new epistemologies and paradigm
shifts. Big Data & Society, 1(1), 2053951714528481. https://
doi.org/10.1177/2053951714528481
Kollmuss, A., & Agyeman, J. (2002). Mind the gap: Why do people
act environmentally and what are the barriers to pro-environmental
behavior? Environmental Education Research, 8(3),
239–260.
Krosnick, J. A. (ed) (2018). Questionnaire design. In The Palgrave
handbook of survey research (pp. 439-455). Palgrave Macmillan.
Landry, N., Gifford, R., Milfont, T. L., Weeks, A., & Arnocky,
S. (2018). Learned helplessness moderates the relationship
between environmental concern and behavior. Journal of
Environmental Psychology, 55, 18–22.
Lasswell, H. D. (1927). The theory of political propaganda. The
American Political Science Review, 21(3), 627–631. https://
doi.org/10.2307/1945515
Li, J., & Wagner, M. W. (2020). The value of not knowing: Partisan
cue-taking and belief updating of the uninformed, the ambiguous,
and the misinformed. Journal of Communication, 70(5),
646–669.
Litman, L., Rosen, Z., Ronsezweig, C., Weinberger, S. L., Moss,
A. J., & Robinson, J. (2020). Did people really drink bleach
to prevent COVID-19? A tale of problematic respondents and
a guide for measuring rare events in survey data. Medrxiv.
https://doi.org/10.1101/2020.12.11.20246694
Livingstone, S. (2019). Audiences in an age of datafication:
Critical questions for media research. https://jour-
nals.sagepub.com/doi/full/10.1177/1527476418811118?
casa_token=cRGn8e2DIJ0AAAAA%3A1ym-F7rXnWx-
w5MsEwNZxjdKQi94zcMjqHc_dI0W9E8qoC4sQbfsnIv-
LMVuqojtxjlbv3K_b956A
Longhi, J. (2021). Mapping information and identifying disinformation
based on digital humanities methods: From accuracy to
plasticity. Digital Scholarship in the Humanities, 36, 980–998.
https://doi.org/10.1093/llc/fqab005
Lull, J. (1988). World families watch television. SAGE.
Luskin, R. C., & Bullock, J. G. (2011). “Don’t know” means “don’t
know”: DK responses and the public’s level of political knowledge.
The Journal of Politics, 73(2), 547–557.
Luskin, R. C., Sood, G., Park, Y. M., & Blank, J. (2018).
Misinformation about misinformation? Of headlines and survey
design. Unpublished Manuscript. http://www.gsood.com/
research/papers/misinformation_misinformation.pdf
Majid, A. (2021). Covid-19 and the rise of misinformation and misunderstanding.
PressGazette. https://www.pressgazette.co.uk/
covid-19-rise-in-news-misinformation-data/
Marwick, A. E., & Partin, W. C. (2022). Constructing alternative
facts: Populist expertise and the QAnon conspiracy. New
Media & Society. Advance online publication. https://doi.
org/10.1177/14614448221090201
Marwick, A. E. (2008). To catch a predator? The MySpace
moral panic. First Monday, 13(6). https://doi.org/10.5210/
fm.v13i6.2152
12	 Social Media + Society
Mcclain, C. (2021). 70% of U.S. social media users never or rarely
post or share about political, social issues. https://www.pewre-
search.org/fact-tank/2021/05/04/70-of-u-s-social-media-users-
never-or-rarely-post-or-share-about-political-social-issues/
Mellon, J., & Prosser, C. (2017). Twitter and Facebook are
not representative of the general population: Political
attitudes and demographics of British social media users.
Research & Politics, 4(3), 2053168017720008. https://doi.
org/10.1177/2053168017720008
Mercier, H. (2020). Not born yesterday: The science of who we
trust and what we believe. Princeton University Press.
Mercier, H., & Altay, S. (2022). Do cultural misbeliefs cause
costly behavior? In Musolino, J., Hemmer, P. & Sommer,
J. (Eds.), The science of beliefs. https://www.cambridge.
org/core/books/abs/cognitive-science-of-belief/do-cultural-
misbeliefs-cause-costly-behavior/C09911219EEEF54
A3CCBB939EC238D3D
Metzger, M. J., Flanagin, A. J., Mena, P., Jiang, S., & Wilson, C.
(2021). From dark to light: The many shades of sharing misinformation
online. Media and Communication, 9, 3409.
Miró-Llinares, F., & Aguerri, J. C. (2021). Misinformation about
fake news: A systematic critical review of empirical studies
on the phenomenon and its status as a “threat.” European
Journal of Criminology. Advance online publication. https://
doi.org/10.1177/1477370821994059
Mitchell, A., Gottfried, J., Stocking, G., Walker, M., & Fedeli, S.
(2019). Many Americans say made-up news is a critical problem
that needs to be fixed. Pew Research Center’s Journalism
Project. https://www.journalism.org/2019/06/05/many-amer-
icans-say-made-up-news-is-a-critical-problem-that-needs-to-
be-fixed/
Mitchelstein, E., Matassi, M., & Boczkowski, P. J. (2020). Minimal
effects, maximum panic: Social media and democracy in Latin
America. Social Media + Society, 6(4), 2056305120984452.
https://doi.org/10.1177/2056305120984452
Morin, E. (1969). The rumor of Orleans. Le Seuil.
Morley, D. (1980). The nationwide audience. British Film Institute.
Newman, N., Fletcher, R., Schulz, A., Andı, S., & Nielsen, R. K.
(2020). Reuters institute digital news report 2020. https://
reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-06/
DNR_2020_FINAL.pdf
Nyhan, B. (2020). Facts and myths about misperceptions. Journal
of Economic Perspectives, 34(3), 220–236.
Orben, A. (2020). The Sisyphean cycle of technology panics.
Perspectives on Psychological Science, 15(5), 1143–1157.
https://doi.org/10.1177/1745691620919372
Osmundsen, M., Bor, A., Vahlstrup, P. B., Bechmann, A., &
Petersen, M. B. (2021). Partisan polarization is the primary
psychological motivation behind political fake news sharing on
Twitter. American Political Science Review, 115, 999–1015.
https://doi.org/10.1017/S0003055421000290
Paris Match Belgique. (2020). Misinformation about coronavirus
responsible for thousands of deaths, study finds.
Prior, M., Sood, G., & Khanna, K. (2015). You cannot be serious:
The impact of accuracy incentives on partisan bias in reports of
economic perceptions. Quarterly Journal of Political Science,
10(4), 489–518.
Pulido, C. M., Villarejo-Carballido, B., Redondo-Sama, G., &
Gómez, A. (2020). COVID-19 infodemic: More retweets for
science-based information on coronavirus than for false information.
International Sociology, 35(4), 377–392.
Ramaciotti Morales, P., Berriche, M., & Cointet, J.-P. (2022). The
geometry of misinformation: Embedding Twitter networks of
users who spread fake news in geometrical opinion spaces.
https://hal.archives-ouvertes.fr/hal-03725556
Rogers, R. (2020). The scale of Facebook’s problem depends
upon how “fake news” is classified. Harvard Kennedy School
Misinformation Review, 1, 1–15.
Rogers, R. (2021). Marginalizing the mainstream: How social media
privilege political information. Frontiers in Big Data. Advance
online publication. https://doi.org/10.3389/fdata.2021.689036
Schaffner, B. F., & Luks, S. (2018). Misinformation or expressive
responding? What an inauguration crowd can tell us about the
source of political misinformation in surveys. Public Opinion
Quarterly, 82(1), 135–147.
Schwartz, A. B. (2015). Broadcast hysteria: Orson Welles’s War of
the Worlds and the art of fake news. Macmillan.
Schwartz, O. (2018, November 12). You thought fake news was
bad? Deep fakes are where truth goes to die. The Guardian.
https://www.theguardian.com/technology/2018/nov/12/deep-
fakes-fake-news-truth
Silverman, C. (2016, November 16). This analysis shows how viral
fake election news stories outperformed real news on Facebook.
Buzzfeed News. https://www.buzzfeednews.com/article/craig-
silverman/viral-fake-election-news-outperformed-real-news-
on-facebook
Simon, F. M., & Camargo, C. (2021). Autopsy of a metaphor:
The origins, use, and blind spots of the “infodemic..” New
Media & Society. Advance online publication. https://doi.
org/10.1177/14614448211031908
Tandoc Jr, E. C., Lim, Z. W., & Ling, R. (2018). Defining “fake
news”: A typology of scholarly definitions. Digital Journalism,
6(2), 137–153.
Tripodi, F. (2018). Searching for alternative facts (p. 64). Data &
Society.
Tsfati, Y., Boomgaarden, H., Strömbäck, J., Vliegenthart, R.,
Damstra, A., & Lindgren, E. (2020). Causes and consequences
of mainstream media dissemination of fake news:
Literature review and synthesis. Annals of the International
Communication Association, 1–17.
Tufekci, Z. (2014). Big questions for social media big data:
Representativeness, validity and other methodological pitfalls.
Eighth International AAAI Conference on Weblogs and Social
Media, 10. https://arxiv.org/abs/1403.7400
Uscinski, J., Enders, A., Klofstad, C., Seelig, M., Drochon, H.,
Premaratne, K., & Murthi, M. (2022a). Have beliefs in conspiracy
theories increased over time? PLoS One, 17(7), e0270429.
Uscinski, J., Enders, A. M., Klofstad, C., & Stoler, J. (2022b). Cause
and effect: On the antecedents and consequences of conspiracy
theory beliefs. Current Opinion in Psychology, 47, 101364.
Van Duyn, E., & Collier, J. (2019). Priming and fake news: The
effects of elite discourse on evaluations of news media. Mass
Communication and Society, 22(1), 29–48.
Viner, K. (2016). How technology disrupted the truth. The
Guardian. https://www.theguardian.com/media/2016/jul/12/
how-technology-disrupted-the-truth
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and
false news online. Science, 359(6380), 1146–1151.
Wagner, M. C., & Boczkowski, P. J. (2019). The reception of fake
news: The interpretations and practices that shape the consumption
of perceived misinformation. Digital Journalism,
7(7), 870–885.
Altay et al.	 13
Wardle, C. (2021). Broadcast news’ role in amplifying Trump’s
tweets about election integrity. First Draft. https://firstdraft-
news.org:443/long-form-article/cable-news-trumps-tweets/
Watts, D. J., & Rothschild, D. M. (2017). Don’t blame the election
on fake news. Blame it on the media. Columbia Journalism
Review, 5. https://www.cjr.org/analysis/fake-news-media-elec-
tion-trump.php
World Risk Poll. (2020). “Fake news” is the number one worry
for internet users worldwide. https://wrp.lrfoundation.org.uk/
wp-content/uploads/2020/08/World-Risk-Poll-Press-Release-
Cyber-risk-061020-1.pdf
Ylä-Anttila, T. (2018). Populist knowledge: “Post-truth” repertoires
of contesting epistemic authorities. European Journal of
Cultural and Political Sociology, 5(4), 356–388. https://doi.org
/10.1080/23254823.2017.1414620
Author Biographies
Sacha Altay is a postdoctoral fellow at the Reuters Institute for the
Study of Journalism. He holds a PhD in cognitive science and his
work mostly focuses on (mis)information and (mis)trust.
Manon Berriche is a PhD student in sociology at Sciences Po medialab.
Her research focuses on the reception of (mis)information in
the digital age and aims to confront media discourse on “fake news”
with the information practices of laypeople by using both quantitative
and qualitative methods.
Alberto Acerbi is a researcher in the field of cultural evolution. His
research utilizes evolutionary and quantitative approaches to shed
light on contemporary cultural phenomena. More recently, he
became particularly interested in using cultural evolution theory to
study specifically the effect of online digital media.