You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
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You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
8
Misinformation and Its Correction
Chloe Wittenberg and Adam J. Berinsky

Fake news is big news. From the diffusion of rumors and conspiracies in the
United States to the spread of disinformation by Russian troll farms,
misinformation is a hot topic among academics and journalists alike. How
can we understand and correct such misinformation? A logical starting point
is to ﬁght ﬁction with fact. Indeed, many proposed solutions to the problem of
misinformation assume that the proper remedy is merely to provide more
information. In this view, if citizens were only better informed,
misinformation would lose its power. However, ample research suggests that
the answer is not so simple. Misinformation may continue to endure postcorrection for several reasons. First, corrections are rarely able to fully
eliminate reliance on misinformation in later judgments. Even when people
recall hearing a retraction, the original misinformation may still inﬂuence
their attitudes and beliefs (what is known as the continued inﬂuence effect).
Worse yet, people may come to believe in misinformation even more strongly
post-correction. In particular, retractions that run counter to individuals’ prior
attitudes may bolster beliefs in the original misinformation (what are known as
worldview backﬁre effects). These worldview backﬁre effects have their roots in
directionally motivated reasoning; individuals process misinformation and
corrections through the lens of their preexisting beliefs and partisan
attachments, so they may actively dispute corrections that contradict their
broader worldviews.
Although political misinformation is not a new phenomenon, the topic has
received renewed attention in recent years, in conjunction with sweeping
changes in the contemporary media environment. As the Internet and,
particularly, social media become an increasingly common source for political
information (Shearer and Matsa 2018), citizens receive more and more of their
news in an uncontrolled and minimally regulated setting where misinformation
may easily spread (Vosoughi, Roy, and Aral 2018). Validating these concerns,
numerous studies of “fake news” spotlight social media platforms, including
both Facebook and Twitter, as the primary incubators of misinformation
163

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Chloe Wittenberg & Adam J. Berinsky

during the 2016 US presidential election (e.g., Allcott and Gentzkow 2017;
Guess, Nyhan, and Reiﬂer 2020). However, even if the sources of
misinformation have fundamentally changed, best practices for correcting
misinformation have not. While many of the pieces cited in this chapter do
not focus explicitly on the Internet or social media, these works can still inform
scholarly understanding of how to correct misinformation on these platforms.
The cognitive processes we highlight are likely to translate to the digital realm
and are thus crucial to understand when developing prescriptions for social
media–based misinformation. Nevertheless, we also spotlight a number of
recent studies that examine methods for correcting misinformation in the
context of social media.

previous review pieces
Several excellent review articles have already greatly enriched our knowledge of
misinformation and its correction. Each is a valuable resource for deeper
reading on this subject. In the interest of not rehashing existing work, we have
made a conscious choice to showcase topics not already covered in these
reviews. However, for the beneﬁt of the reader, we summarize the primary
takeaways from each piece and preview how we build on the groundwork they
laid. First, Lewandowsky et al. (2012) provide a comprehensive summary of the
literature on misinformation and its correction. In particular, they delve into the
psychological roots of the continued inﬂuence effect and backﬁre effects and
recommend appropriate interventions for practitioners seeking to mitigate
these effects. However, since the article’s publication in 2012, the ﬁeld has
evolved in notable ways – especially regarding the existence and magnitude of
different types of backﬁre effects. Swire and Ecker (2018) thus provide an
updated summary of the literature and offer several new strategies for
effectively correcting misinformation. We pick up where these two articles
leave off; we discuss newer research on both the continued inﬂuence effect
and backﬁre effects and suggest ways for future work to continue to ﬂesh out
these topics in even greater detail.
Second, Flynn, Nyhan, and Reiﬂer (2017) offer a more recent review of the
misinformation literature, with a speciﬁc focus on the relationship between
directionally motivated reasoning and political misperceptions. In their view,
individuals’ preexisting beliefs strongly affect their responses to corrections,
such that individuals with different partisan or ideological leanings may
respond to the same political facts in profoundly different ways. Importantly,
the authors document a number of individual and contextual moderators of
directionally motivated reasoning that predispose certain subsets of the
population to be more vulnerable to worldview backﬁre effects. However,
motivated reasoning is not the sole reason why misinformation persists over
time. As studies of the continued inﬂuence effect demonstrate, individuals may
continue to hold misinformed beliefs post-correction, even in the absence of

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Misinformation and Its Correction

165

strong prior attitudes. As such, we take a closer look at the full range of
psychological mechanisms that may impede attempts to correct
misinformation.
Finally, Tucker et al. (2018) focus on the interplay of social media and
political polarization in enabling the spread of misinformation, with
particular emphasis on the speciﬁc actors who manufacture misinformation.
However, though they present a wide-ranging and thorough analysis of the
contemporary research on the production of digital misinformation, these
authors devote substantially less attention to best practices for correction. As
a complement to this work, we discuss recent research on strategies to correct
misinformation appearing on social media platforms, including Facebook and
Twitter.

organization of the chapter
In this chapter, we synthesize recent work on misinformation and its correction.
Knowledge of this subject is still rapidly developing, and many questions remain
unanswered and unresolved.1 Here, we pay particular attention to one of these
important questions: Why does misinformation persist even after it has been
corrected? To this end, we ﬁrst provide a deﬁnition of misinformation and
specify the core criteria that help to discriminate between the many related
concepts in this area. Second, we discuss two key perspectives on the
perseverance of misinformation post-correction: backﬁre effects and the
continued inﬂuence effect. Third, we outline a number of individual and
contextual moderators that might make certain individuals or groups
especially susceptible to misinformation. Finally, we conclude with a series of
recommendations for future research.

defining misinformation: mapping key criteria
To understand how best to tackle the problem of misinformation, it is essential
to ﬁrst deﬁne what this term means. However, scholarly notions of what
constitutes misinformation often differ signiﬁcantly across works and across
disciplines. These deﬁnitions are highly variable, ranging from simple
statements about the misleading nature of misinformation to commentaries
on the motivation for the spread of misinformation. Some scholars broadly
characterize misinformation as false information; for example, Fetzer (2004)
deﬁnes it as “false, mistaken, or misleading information” (p. 231), and Berinsky
(2017) deﬁnes it as “information that is factually unsubstantiated” (p. 242).
Other scholars take a more restricted view, contrasting the term with other
concepts, such as disinformation. For instance, Wardle (2018) argues that
1

Indeed, though we attempt to provide a comprehensive review of the literature on misinformation
correction, the ﬁeld is moving so fast that this review may soon be out of date.

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Chloe Wittenberg & Adam J. Berinsky

misinformation is “information that is false, but not intended to cause harm”
(p. 5), whereas disinformation is “false information that is deliberately created
or disseminated with the express purpose to cause harm” (p. 4). Finally, a third
approach emphasizes the temporal nature of misinformation processing,
arguing that misinformation’s primary feature is that it is ﬁrst presented as
true but later revealed to be false. Ecker et al. (2015) state that misinformation is
“information that is initially presented as factual but subsequently corrected”
(p. 102). Similarly, Lewandowsky et al. (2012) deﬁne misinformation as “any
piece of information that is initially processed as valid but that is subsequently
retracted or corrected” (pp. 124–125). In this sense, information only becomes
misinformation when it is ﬁrst believed and later corrected, separating
misinformation from other false information that goes unrebutted.
Compounding the problem is the fact that the term “misinformation” is
often confounded with other similar concepts. For instance, as noted in the
previous paragraph, some authors attempt to draw a line between
misinformation and disinformation, or “information that is false and
deliberately created to harm a person, social group, organization, or country”
(Wardle and Derakhshan 2017, p. 20). Other scholars speak of misperceptions,
or “cases in which people’s beliefs about factual matters are not supported by
clear evidence and expert opinion” (Nyhan and Reiﬂer 2010, p. 305). Still
others make reference to conspiracy theories, which offer unconventional
explanations of the causes of events in terms of the “signiﬁcant causal agency
of a relatively small group of persons – the conspirators – acting in secret”
(Keeley 1999, p. 116; see also Oliver and Wood 2014). Similar to, though
broader in scope than, conspiracy theories are political rumors, which are
“unveriﬁed stories or information statements people share with one another”
(Weeks and Garrett 2014, p. 402). Finally, since the 2016 US presidential
election, there has been much talk of fake news, which shares many
similarities with disinformation but differs in its presentation. In particular,
recent work deﬁnes fake news as “fabricated information that mimics news
media content in form but not in organizational process or intent” (Lazer et al.
2018, p. 1094).
The multitude of deﬁnitions of misinformation speaks to the need for clarity
on what exactly we, as a scholarly community, mean when we talk about
misinformation. In an attempt to provide such structure, we compiled a wide
variety of deﬁnitions of misinformation and related terms. Looking for common
threads, we identiﬁed four overarching criteria for differentiating types of
misinformation.2 First, we found that different deﬁnitions of misinformation
place more or less emphasis on the truth value of the information – that is,
2

We are certainly not the ﬁrst to propose such a typology (see Born and Edgington 2017; Tucker et
al. 2018; Wardle 2018). However, we take a more comprehensive view than many of these
previous works in that we seek to integrate a larger number of related concepts into a common
theoretical framework.

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Misinformation and Its Correction

167

whether the information has been proven to be untrue or whether it is merely
unsubstantiated. Second, we noted that deﬁnitions of misinformation vary in
their area of focus, particularly whether they emphasize the effects of false
information versus false beliefs. Third, we found that scholars distinguish
forms of misinformation based on their format, including whether or not the
presentation of the information is designed to resemble traditional news
sources. Finally, we noted differences in the perceived intentions of the actors
who spread misinformation, in terms of their level of awareness that the
information was false.

four key criteria
First, truth value: All forms of misinformation, at least to some degree, rest on
shaky factual foundations. That is, all misinformation is in some way
inaccurate. In some cases, misinformation is characterized by a lack of
conclusive evidence to support a particular position, whereas, in others, it
involves statements that run counter to mainstream consensus or expert
opinion. However, the extent to which information is untrue varies across
forms of misinformation; some subtypes may be deﬁnitively false (e.g.,
disinformation or fake news), whereas others may be merely misleading or
unveriﬁed (e.g., political rumors).
Second, the area of focus: It is important to separate the presence of false
information (misinformation) from the endorsement of false beliefs
(misperceptions). This distinction is valuable because, as Thorson (2015)
highlights, misperceptions are not exclusively caused by misinformation. Even
if individuals only encounter true information, they may still arrive at
inaccurate beliefs for other reasons, such as cognitive biases or
misinterpretation of available facts. In this sense, the appropriate tools for
correction may depend heavily on whether false beliefs are the clear product
of misinformation or if they instead originate via other channels.
Third, format: Different types of misinformation may be presented in
different ways. In some cases, misinformation may be embedded within
otherwise accurate reports, whereas, in other cases, it may exist as standalone
content. This is especially relevant to the study of fake news, or fabricated
articles that imitate the appearance of traditional news stories (Allcott and
Gentzkow 2017; Lazer et al. 2018). Fake news is a form of disinformation, as
it is spread despite being known to be false, but it may be distinguished from
other types of disinformation by its unique format – namely, its emulation of
legitimate media outlets (Pennycook and Rand 2018). In addition to fake news,
recent work also looks beyond textual forms of misinformation to other types
of media, including manipulated images and videos (Kasra, Shen, and O’Brien
2016; Schwarz, Newman, and Leach 2016; Shen et al. 2019).
Finally, intentionality: Does the person transmitting misinformation
sincerely believe it to be true or are they aware that it is false? By most

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Chloe Wittenberg & Adam J. Berinsky

accounts, this is the primary means of distinguishing between misinformation
and disinformation (for a review, see Wardle 2018). On the one hand,
misinformation may circulate without any intent to deceive. For instance, in
the wake of breaking news events, people increasingly turn to the Internet, and
especially social media, for real-time updates. As new information is released in
a piecemeal fashion, individuals may inadvertently propagate information that
later turns out to be false (Nyhan and Reiﬂer 2015a; Zubiaga et al. 2016). On
the other hand, disinformation is false or inaccurate information that is
deliberately distributed despite its inaccuracy (Stahl 2006; Born and
Edgington 2017). People may choose to share ﬁctitious stories, even when
they recognize that these stories are untrue. Why might people knowingly
promulgate false information? One answer relates to the disseminators’
motivations; although misinformation is typically not designed to advance a
particular agenda, disinformation is often spread in service of concrete goals.
For instance, fake news is often designed to go viral on social media (Pennycook
and Rand 2018; Tandoc, Lim, and Ling 2018), enabling rapid transmission of
highly partisan content and offering a reliable stream of advertising revenue
(Tucker et al. 2018). In practice, however, determining a person or group’s
intentions is extremely difﬁcult. It is hard to uncover people’s “ground truth”
beliefs about the veracity of a piece of information, and it is even harder to
ascertain their underlying motivations. That said, recognizing the range of
motivations for spreading misinformation is valuable, even if these
motivations are hard to disentangle in the wild.
For the purposes of this chapter, we consider “misinformation” an umbrella
term under which many associated concepts are subsumed. Moving forward,
we recommend misinformation as the default term to use, unless explicitly
referring to one of these more speciﬁc constructs. Given the difﬁculty of
proving the motivations underlying the spread of false information, we adopt
an intent-agnostic approach; we make no assumptions about what compels
individuals or groups to broadcast misinformation. Instead, we take the view
that misinformation – in all of its forms – may have a considerable, harmful
impact on people’s beliefs and behavior. As such, in the discussion that follows,
we cite examples of corrective strategies targeted at all different types of
misinformation.

responses to corrections: continued influence
and backfire effects
Detailing types of information is not a mere technical exercise. A wellfunctioning democratic society does not necessarily need to be guided by fully
informed citizens, but an environment rife with misinformation can easily derail
democracy. An uninformed citizenry is arguably far less pernicious than a
misinformed citizenry (Kuklinski et al. 2000); as Hochschild and Einstein

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Misinformation and Its Correction

169

(2015) write, “people’s unwillingness or inability to use relevant facts in their
political choices may be frustrating, but people’s willingness to use mistaken
factual claims in their voting and public engagement is actually dangerous to a
democratic polity” (p. 14). When the public holds misinformed beliefs, this can
not only affect their individual attitudes and behaviors but also shape largescale policy outcomes (e.g., health care reform, see Nyhan 2010; Berinsky
2017). Correcting misinformation is therefore a worthy goal; but how can it
best be accomplished?
Previous research suggests that not all corrections are effective in reducing
individuals’ reliance on misinformation. There are two pathways through
which misinformation might continue to shape attitudes and behaviors postcorrection: the continued inﬂuence effect and backﬁre effects. Engrained in the
former is the notion that corrections are somewhat, but not entirely, effective at
dispelling misinformation. More concerning, however, are the latter, in which
corrections not only fail to reduce but actually strengthen beliefs in the original
misinformation. Neither of these phenomena offers a particularly sanguine take
on the ability to curtail the spread of misinformation. However, each offers its
own unique predictions about the most promising avenues for corrections. We
begin by reviewing the extant literature on backﬁre effects and then turn to the
continued inﬂuence effect.

backfire effects
Providing factual corrections of misinformation may, under certain
circumstances, only make things worse. Speciﬁcally, retractions that challenge
people’s worldviews may entrench beliefs in the original misinformation. This
phenomenon is known as a backﬁre effect or, more precisely, a worldview
backﬁre effect.3 Nyhan and Reiﬂer (2010) sounded the ﬁrst alarm bells about
the possibility of these worldview backﬁre effects. Across a series of studies,
they found that, when certain subjects were presented with factual corrections
that contradicted their political beliefs, they responded by becoming more,
rather than less, wedded to their previous misperceptions. Since their highly
inﬂuential piece was published, concerns about worldview backﬁre effects have
taken hold both in popular media and in academic circles. This widespread
interest has spawned an entire line of work dedicated to elucidating the
psychological mechanisms that drive these effects.
Worldview backﬁre effects can be understood as a product of directionally
motivated reasoning (for a comprehensive review, see Flynn et al. 2017).
According to theories of motivated reasoning, individuals are motivated to
process information in ways that align with their ultimate goals (Kunda
1990). In particular, individuals must balance several competing impulses,
3

Other terms for worldview backﬁre effects include “boomerang effects” (Hart and Nisbet 2012;
Garrett, Nisbet, and Lynch 2013; Zhou 2016) or “backlash” (Guess and Coppock 2018).

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Chloe Wittenberg & Adam J. Berinsky

including directional goals (to attain a desired outcome) and accuracy goals (to
reach the correct conclusion). Worldview backﬁre effects transpire when
directional motivations take precedence over accuracy goals – a frequent
occurrence in the realm of politics (Lodge and Taber 2013).
Two complementary processes are at the heart of these effects. First,
conﬁrmation bias: Individuals tend to seek out and interpret new information in
ways that validate their preexisting views. Along these lines, individuals also tend
to perceive congenial information as more credible or persuasive than opposing
evidence (Guess and Coppock 2018; Khanna and Sood 2018). Second,
disconﬁrmation bias: When exposed to ideologically dissonant information,
individuals will call to mind opposing arguments (counterarguing).4 In
combination, these two processes can cultivate worldview backﬁre effects; when
individuals are confronted with a correction that contradicts their past beliefs, they
will act to both discount the correction and bolster their prior views.
Several studies have investigated the potential for worldview backﬁre effects
in the context of misinformation. Although Nyhan and Reiﬂer issued the
earliest warnings about this phenomenon, it has since been reproduced across
other settings. First, worldview backﬁre effects have been tied to message
presentation, with individuals most resistant to message framing that
contradicts their broader worldviews (Zhou 2016). Second, worldview
backﬁre effects have been linked to source cues. For instance, several studies
ﬁnd that Republicans are averse to corrections from Democratic elites (Berinsky
2017) or nonpartisan fact-checking sources (Holman and Lay 2019). Finally,
worldview backﬁre effects extend to the behavioral realm; across multiple
studies, exposure to pro-vaccine corrections decreased future vaccination
intentions among those already hesitant to get vaccinated (Skurnik, Yoon,
and Schwarz 2007; Nyhan et al. 2014; Nyhan and Reiﬂer 2015b; but see
Haglin 2017).
Empirical studies have also taught us about the mechanisms that undergird
worldview backﬁre effects. Consistent with a motivated reasoning
perspective, worldview backﬁre effects appear rooted in counterarguing. In
one experiment, Schaffner and Roche (2017) examine differences in survey
response times following the release of the October 2012 jobs report, which
announced a sharp decrease in the unemployment rate under the Obama
administration. They ﬁnd that those Republicans who took longer to
provide estimates of the unemployment rate after the report’s release were
less accurate in their responses, suggesting that worldview backﬁre effects may
arise out of deliberate, effortful processes. However, more work beyond this
initial study is certainly needed to isolate the mechanisms that underlie
worldview backﬁre effects.
4

Counterarguing typically involves generating arguments to dispute a correction. Inverting this
process, Chan et al. (2017) also ﬁnd that corrections are generally less effective when people are
asked to record arguments in favor of the original misinformation.

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Misinformation and Its Correction

171

avoiding worldview backfire effects
In light of mounting concerns about the potential for worldview backﬁre effects,
scholars have explored several tactics for correcting misinformation while
circumventing these effects. Although many routes to correction are possible,
all designed to counteract directionally motivated reasoning, we summarize
here two main subcategories of these corrections focused on source credibility
and worldview afﬁrmation.
First, the source of misinformation – as well as its correction – may have a
profound impact on responses to corrections. When evaluating the accuracy
of a claim, individuals rely heavily on source cues (Schwarz et al. 2016), which
signal a source’s expertise or trustworthiness. In terms of expertise, if sources
are depicted as authorities on a given subject, they are likely to be deemed
more credible (Vraga and Bode 2017). In fact, expert consensus is considered a
key “gateway belief” that can override directional impulses. For example,
communicating the broad scientiﬁc agreement about climate change reduces
partisan differences in climate change attitudes (van der Linden et al. 2015;
van der Linden et al. 2017; Druckman and McGrath 2019; but see Kahan,
Jenkins-Smith, and Braman 2011). However, the trustworthiness of a source
seems to matter even more than expertise when countering misinformation
(McGinnies and Ward 1980; Guillory and Geraci 2013). People are more
likely to view sources as trustworthy if they share similar traits. As a result,
corrections that are attributed to an in-group member (e.g., a leader of one’s
preferred party) may be more effective than those credited to an out-group
member (e.g., an opposing partisan, see Swire, Berinsky et al. 2017).
Furthermore, though corrections are most frequently issued by elites,
individuals are also receptive to corrections from members of their social
circles (Margolin, Hannak, and Weber 2018; Vraga and Bode 2018), who
may not be experts but may still be deemed trustworthy. Finally,
trustworthiness is a function of one’s perceived stake in an issue. Recent
research on “unlikely sources” (Berinsky 2017; Benegal and Scruggs 2018;
Wintersieck, Fridkin, and Kenney 2018; Holman and Lay 2019) indicates that
corrections are most persuasive when they come from sources who stand to
beneﬁt from the spread of misinformation (e.g., a Democratic politician or
left-leaning publication correcting a fellow Democrat).
Second, where possible, corrections should be tailored to their target
audience: the subset of people for whom these corrections would feel most
threatening. Framing corrections to be consonant with, rather than
antagonistic to, this group’s values and worldviews may thus be a successful
corrective strategy (Kahan et al. 2010; Feinberg and Willer 2015; for reviews,
see Lewandowsky et al. 2012; Swire and Ecker 2018). In a similar vein, some
scholars propose using self-afﬁrmation exercises (Cohen, Aronson, and Steele
2000; Cohen et al. 2007) to subdue directional motivations (Trevors et al. 2016;
Carnahan et al. 2018); if people feel validated in their global self-worth,

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Chloe Wittenberg & Adam J. Berinsky

corrections that impugn their political views may provoke a less defensive
response (but see Nyhan and Reiﬂer 2019).

backlash against worldview backfire effects
The previous discussion presumes that worldview backﬁre effects are not only
dangerous but prevalent. Recently, however, there has been backlash against
the very notion of worldview backﬁre effects, with some suggesting they are
extremely rare in practice. Wood and Porter (2019) attempt to detect worldview
backﬁre effects across a wide array of divisive issues and ﬁnd little evidence to
support their existence, even when using language identical to Nyhan and
Reiﬂer (2010). When examining several highly polarized issues, including gun
control and capital punishment, Guess and Coppock (2018) likewise fail to
uncover any worldview backﬁre effects in response to counter-attitudinal
information. Instead, individuals seem to accommodate novel information
into their later assessments of issues, even if that information runs counter to
their beliefs (see also Porter, Wood, and Kirby 2018). However, even if
corrections largely improve belief accuracy, these messages seem to have little
impact on individuals’ subsequent attitudes, evaluations of politicians, or policy
preferences (Swire, Berinsky et al. 2017; Aird et al. 2018; Nyhan et al. 2019;
Porter, Wood, and Bahador 2019; Barrera et al. 2020).
What accounts for the discrepancies in results across studies? The answer
may be both theoretical and methodological. First, there is a large body of work
on the theoretic side. Some scholars have suggested that worldview backﬁre
effects are more likely when corrections necessitate attitude change versus only
pertain to a single, speciﬁc event (Ecker et al. 2014; Ecker and Ang 2019). Why
are people better able to absorb ideologically dissonant corrections for one-off
events? To answer this question, Ecker and Ang (2019) draw on stereotype
subtyping theory (Richards and Hewstone 2001). According to this theory,
individuals possess stereotypes about the customary behavior of different
groups (e.g., members of a political party). Subtyping is a common response
when group members act in ways that ﬂout a well-established stereotype; rather
than forming a new stereotype, individuals instead label contrary cases as
exceptions to the broader rule. If misinformation pertains to a single, isolated
event, individuals may thus be able to internalize disconﬁrming corrections
without altering their deep-seated worldviews. It is much harder, however, to
dismiss general patterns of behavior as anomalous. As such, people are more
likely to resist corrections that, on acceptance, would require a large-scale shift
in their core beliefs.
Redlawsk, Civettini, and Emmerson (2010) provide a somewhat different
perspective on the boundaries of worldview backﬁre effects. They posit an
“affective tipping point” at which individuals cease to engage in motivated
reasoning and instead revise their beliefs to be more accurate – in other
words, the point at which individuals pivot from directional to accuracy

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Misinformation and Its Correction

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goals. As people encounter more and more disconﬁrming information, they may
reach a critical threshold at which they are no longer motivated to defend their
previous views. In this view, worldview backﬁre effects will occur until enough
contradictory evidence accumulates. After this point, individuals will begin to
rationally update their beliefs in response to corrections rather than double
down on their previously misinformed views. This theoretical account generates
somewhat divergent predictions from Ecker and Ang. For both sets of authors,
general cases of misinformation should provoke heightened discomfort.
However, if enough contrary evidence comes to light, Redlawsk and
colleagues anticipate a diminished likelihood of worldview backﬁre effects,
whereas Ecker and Ang seem to predict the exact opposite. Further work may
be needed to adjudicate between these two explanations. In particular, efforts to
pinpoint the precise location of this tipping point may prove fruitful.
Methodological differences may play a role as well. Worldview backﬁre effects
may not be immediately apparent post-correction. Instead, they may only emerge
after some time has elapsed (Peter and Koch 2016; Pluviano, Watt, and Della Sala
2017). However, most research on worldview backﬁre effects just measures the
effect of corrections after a short distraction task. In contrast, studies that do
incorporate lengthy time delays (e.g., Berinsky 2017; Swire, Berinsky et al. 2017)
ﬁnd that the beneﬁts of corrections quickly dissipate. Worldview backﬁre effects
may therefore only be visible after a delay. Studies of these effects should
therefore aim to measure responses at multiple points in time. In addition,
worldview backﬁre effects are more probable for high-salience issues where
individuals have strong prior attitudes (Flynn et al. 2017). Nevertheless, the
deep-rooted nature of these issues may limit the range of effect sizes that a
single experimental manipulation can elicit. As a result, worldview backﬁre
effects may be especially hard to detect for highly polarized issues – the very
issues where we would expect the most pervasive effects.
On a related note, it is essential to come to some consensus regarding what,
exactly, we consider a “backﬁre effect.” In particular, worldview backﬁre
effects may be an artifact of the baseline against which they are measured.
Scholars generally deﬁne worldview backﬁre effects as cases where the
presentation of both misinformation and its correction is worse than
presenting misinformation uncorrected. Yet when considering the deleterious
effects of misinformation in society, a more expansive deﬁnition may be
appropriate. Worldview backﬁre effects are commonly measured
experimentally by comparing respondents who were exposed to
misinformation to respondents who were exposed to both misinformation
and corrections. However, when examining information that has already
spread through society – beliefs about President Obama’s citizenship, for
example – a better baseline might be people’s beliefs if they had not been
reexposed to misinformation as part of an experiment. If providing a
correction to misinformation is worse than providing no information at all,
strategies for mitigating misinformation may require substantial adjustment.

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continued influence effect
The ubiquity of worldview backﬁre effects remains an open question. However,
even if these effects are overblown, valid concerns about the unintended
consequences of corrections remain. In particular, the format in which
corrections are delivered may bolster beliefs in misinformation, even in the
absence of worldview backﬁre effects. A near-universal ﬁnding in the
misinformation literature is that, even after its correction, misinformation
continues to inﬂuence people’s attitudes and beliefs (for a review, see Walter
and Tukachinsky 2019). This is known as the continued inﬂuence effect (Wilkes
and Leatherbarrow 1988; Johnson and Seifert 1994). Importantly, people may
correctly recall a retraction yet still use outdated misinformation when
reasoning about an event. From this perspective, corrections can partially
reduce misperceptions but cannot fully eliminate reliance on misinformation
in later judgments.
Why does misinformation linger post-correction? Scholars suggest two
potential reasons for the continued inﬂuence effect. First, according to the
mental model theory, individuals construct models of external events in their
heads, which they continuously update as new information becomes
available (Johnson and Seifert 1994; Swire and Ecker 2018). However,
retractions often threaten the internal coherence of these models (Gordon
et al. 2017; Swire and Ecker 2018). As a result, even if individuals explicitly
recall corrections, they may nevertheless continue to invoke misinformation
until a plausible alternative takes its place. Numerous studies ﬁnd that
corrections are more effective when they contain alternative causal
accounts rather than just negate the original misinformation (Johnson and
Seifert 1994; Ecker, Lewandowsky, and Tang 2010; Nyhan and Reiﬂer
2015a; but see Ecker et al. 2015).
Secondly, the continued inﬂuence effect can be understood through dualprocess theory. Dual-process theory distinguishes between two types of
memory retrieval: automatic and strategic. Automatic processing is fast
and unconscious, whereas strategic processing is deliberate and effortful. In
addition, automatic processing is relatively acontextual, distilling
information down only to its most essential properties, whereas strategic
processing is required to retrieve speciﬁc details about a piece of information
(Ecker et al. 2011). As a result, individuals may be able to remember a piece
of misinformation but not recall relevant features, such as its source or
perceived accuracy (Swire and Ecker 2018). In this view, the continued
inﬂuence effect constitutes a form of retrieval failure; misinformation is
automatically retrieved, but its retraction is not. This emphasis on
automatic versus strategic processing is also consistent with an online
processing model of misinformation (Lodge and Taber 2013; Thorson
2016). According to this model, initial misinformation is encoded with a
stronger affective charge than its correction, meaning that misinformation

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will continue to dominate subsequent evaluations until individuals engage in
the strategic processing necessary to explicitly recall a correction.
Most direct studies of the continued inﬂuence effect use variants of the same
research design, based on the “warehouse ﬁre” script (Wilkes and
Leatherbarrow 1988; Johnson and Seifert 1994). In this scenario, the cause of
a ﬁre is initially attributed to volatile chemicals stored in a closet, but the closet
is later revealed to have been empty. Subsequent studies have adapted this
narrative to other contexts, such as police reports or political misconduct, but
all follow a similar format in which information about a breaking news event is
relayed over a series of short messages. Experimenters randomly assign some
subjects to read a critical piece of misinformation (e.g., the presence of
ﬂammable materials) as well as its retraction (e.g., the empty closet).
However, this communication technique is arguably ill-suited to the study of
political misinformation. First, many of these studies present misinformation
and corrections as coming from the same source. However, in the realm of
politics, the sources most likely to issue corrections may be the ones least likely
to spread the misinformation in the ﬁrst place. Second, the sequencing of
messages may not accurately mimic how individuals encounter information in
the real world, where the temporal distance may be either much shorter
(instantaneous, if people see a correction before or concurrently with the
original misinformation) or much longer (if corrections are issued at a later
date). Finally, these studies usually rely on ﬁctional scenarios that do not
implicate social identities or prior attitudes (but see Ecker et al. 2014), both of
which may increase the likelihood of the continued inﬂuence effect.
Accordingly, recent work has sought to investigate the presence of the
continued inﬂuence effect in the political domain. Most notably, Thorson
(2016) introduces the concept of “belief echoes,” a version of the continued
inﬂuence effect focused on attitudes rather than causal inferences. According to
her theory, misinformation may continue to inﬂuence political attitudes
through two separate processes. First, automatic belief echoes develop as a
byproduct of online processing. Even when individuals accept corrections as
true, misinformation may still be automatically activated, thereby continuing to
affect attitudes outside of conscious awareness. Deliberative belief echoes, on
the other hand, occur when individuals assume that the existence of one piece of
negative information – even if it is known to be false – increases the likelihood
that other relevant negative information is true (a “where there’s smoke, there’s
ﬁre” philosophy). Together, these automatic and deliberative belief echoes may
contribute to the perpetuation of misinformation post-correction.

familiarity backfire effects
The continued inﬂuence effect suggests that corrections are somewhat, though
not entirely, effective in reducing belief in misinformation. In fact, contrary to
worldview backﬁre effects, the continued inﬂuence effect does not require the

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existence of strong prior attitudes. However, backﬁre effects might occur even
in the absence of worldview threat. In particular, corrections that repeat
misinformation may amplify its inﬂuence, constituting an alternate form of
backlash known as familiarity backﬁre effects. Of note, within the political
science literature, the term “backﬁre effect” almost exclusively refers to
worldview backﬁre effects. However, familiarity backﬁre effects are a much
more common area of focus within the psychology literature. To avoid
confusion, we treat these concepts as separate phenomena.
Familiarity backﬁre effects involve cases in which retractions increase, rather
than reduce, reliance on misinformation by making misinformation feel more
familiar. These effects are primarily studied in the context of repetition. In
particular, familiarity backﬁre effects are considered the product of the
illusory truth effect, wherein “repeated statements are easier to process, and
subsequently perceived to be more truthful, than new statements” (Fazio et al.
2015, p. 993). The illusory truth effect operates through a series of
complementary psychological mechanisms. First, repeating information
strengthens its encoding in memory, enabling easier retrieval later on (for
reviews, see Lewandowsky et al. 2012; Peter and Koch 2016). Second, the
difﬁculty with which information is processed inﬂuences its perceived
authenticity. This is tied to the metacognitive experience of “processing
ﬂuency” (Schwarz, et al. 2007); information that is easier to process feels
more familiar, and familiarity is a key criterion by which individuals judge
accuracy (Alter and Oppenheimer 2009). Accordingly, if individuals have
repeated contact with a piece of misinformation, they may perceive it as more
credible than if they encounter it only once, regardless of its content.
The illusory truth effect is of particular concern in regard to misinformation
correction, given the standard format of corrections. In particular, as part of the
debunking process, most corrections directly reference the original
misinformation. For instance, the commonly employed “myths vs. facts”
strategy involves repeating misinformation (the “myth”) while simultaneously
discrediting it (the “fact”). As such, repeated exposure to misinformation – even
during its correction – may activate the familiarity heuristic and therefore
enhance the perceived accuracy of misinformation. Indeed, familiarity
backﬁre effects have been detected across numerous studies of this speciﬁc
correction style (Schwarz et al. 2007; Peter and Koch 2016; but see Cameron
et al. 2013).
Familiarity backﬁre effects may be especially prominent after a time delay.
Though individuals are typically able to differentiate fact from ﬁction
immediately after viewing a correction, they may soon forget the details of the
correction and retain only the gist of the original misinformation. For example,
Skurnik et al. (2007) ﬁnd that subjects were able to distinguish between myths
and facts about the ﬂu vaccine right after reading an informational ﬂyer but,
after only a short break, were signiﬁcantly more likely to mistake myths for facts
than the reverse. In addition, in a study of healthcare reform, Berinsky (2017)

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Misinformation and Its Correction

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notes that the effectiveness of corrections faded rapidly over time, with subjects
exposed to corrections no more likely than those in a control group to reject a
rumor about “death panels” after just a week. Even if corrections are initially
able to reduce misperceptions, their beneﬁts may be short-lived.
Familiarity backﬁre effects are also likely to be relatively universal, as the
illusory truth effect is largely robust across individuals and situations. Even
when they have prior knowledge about a subject, individuals tend to rate
repeated statements as truer than new statements (Fazio et al. 2015). In
addition, the illusory truth effect is only modestly associated with
dispositional skepticism (DiFonzo et al. 2016) and is uncorrelated with
several psychological traits, such as analytical thinking and need for closure,
that are otherwise connected to the processing of misinformation (De
keersmaecker et al. 2020). Finally, the illusory truth effect appears
independent of motivated reasoning; across both politically consistent and
discordant statements, repeated exposure corresponds to higher accuracy
ratings (Pennycook, Cannon, and Rand 2018).
Not all scholars, though, have found evidence of familiarity backﬁre effects.
Although most scholars acknowledge that familiarity affects the processing of
corrections, some dispute the negative relationship between repetition of
misinformation and belief accuracy (e.g., Swire, Ecker, and Lewandowsky
2017; Pennycook et al. 2018). In fact, Ecker, Hogan, and Lewandowsky
(2017) ﬁnd that retractions that include reminders of the original
misinformation are more effective than retractions without this repetition.5
They attribute these results to the beneﬁts of coactivating misinformation and
corrections (see also Swire and Ecker 2018). When misinformation and its
correction are summoned simultaneously, individuals are better able to detect
discrepancies between the original misinformation and the factual evidence.
This “conﬂict detection” expedites the knowledge revision process, leading to
more efﬁcient belief updating. In light of these contradictory ﬁndings, it remains
unclear how concerned we should be about familiarity backﬁre effects when
correcting misinformation. However, we discuss a number of strategies in the
following section to minimize the risk of these effects, regardless of their
prevalence.

avoiding familiarity backfire effects
What strategies exist to correct misinformation while evading familiarity
backﬁre effects? The most obvious solution is to focus on the correction
5

As they note, however, their experimental design includes only a short distraction task (30
minutes) separating the presentation of misinformation and its correction from measurement of
their dependent variables. Although this time interval is consistent with previous studies (e.g.,
Skurnik, Yoon, and Schwarz 2007), it is possible that their results would be different after a
longer delay.

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Chloe Wittenberg & Adam J. Berinsky

without alluding to the original misinformation. However, several of the pieces
cited in this chapter suggest that avoiding repetition is not a magic bullet; at
times, providing details about a piece of misinformation can aid in the
correction process. Moreover, even if avoiding repetition is the goal, this may
not always be possible. In many cases, misinformation is published by one
source and corrected by another. Rather than just afﬁrm the facts, corrections
may need to invoke the original misinformation in order to provide proper
contextualization. Instead of avoiding repetition of misinformation, it may thus
be more valuable to focus on reiterating corrections, as a means of increasing
the familiarity of accurate information (Ecker et al. 2011).
Furthermore, processing ﬂuency is not solely a function of repetition
(Schwarz et al. 2007). On the whole, information that is easier to process will
be perceived as more familiar (and therefore more valid). Consequently,
corrections may be more successful when they are less cognitively taxing. For
example, visual corrections may be easier to digest than long-form factchecking articles (Alter and Oppenheimer 2009; Schwarz et al. 2016).
Previous studies using photographs (Garrett, Nisbet, and Lynch 2013),
infographics (Nyhan and Reiﬂer 2019), and videos (Young et al. 2018) largely
corroborate this hypothesis (but see Nyhan et al. 2014). Similarly, corrections
that employ simple words or grammatical structures may be more decipherable
than linguistically complex corrections (Alter and Oppenheimer 2009). The
readability of corrections is thus another important consideration for future
research to explore. Finally, it may be optimal for corrections to combine
multiple approaches. For instance, corrections that pair pithy images (e.g.,
PolitiFact’s Truth-O-Meter) with accompanying descriptive text may be
especially effective (Amazeen et al. 2016).

victims of misinformation: moderators
of misinformation and its correction
Overall, misinformation appears both pervasive and difﬁcult to correct once it
spreads. However, not all misinformation is created equal, nor are all
individuals equally susceptible to its inﬂuence. Thus, it is important to
examine which groups are most likely to be affected by misinformation in
society. In the sections that follow, we outline several factors – both
individual and contextual – that may affect the persistence of misinformation
among certain groups, by making individuals either more likely to believe
misinformation or more resistant to its correction.

individual factors
We ﬁrst discuss several individual-level moderators of receptiveness to
misinformation and responsiveness to corrections. These factors may be

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Misinformation and Its Correction

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bifurcated into two strands: those that are explicitly political in nature and
those that reﬂect more fundamental personal or psychological orientations.
Political Factors
Two main political factors contribute to the nature and severity of
misinformation effects: political sophistication and ideology. One of the most
frequently studied moderators of correction effectiveness is political
sophistication, which includes aspects of political knowledge, engagement,
and education (for a review, see Flynn et al. 2017). At ﬁrst glance, more
politically sophisticated individuals should be less susceptible to
misinformation than less-informed citizens, as they can draw on their superior
knowledge to discern fact from ﬁction. Along these lines, Berinsky (2012) ﬁnds
that more politically engaged individuals are, on the whole, more likely than
others to reject political rumors. However, he also ﬁnds that politically
sophisticated Republicans are more likely to accept rumors about Democrats,
suggesting that political knowledge does not entirely inoculate individuals
against misinformation.
In fact, belief in misinformation may actually be more prevalent within this
more educated and engaged group. Recent research ﬁnds that individuals who
are more politically active and engaged are more likely to share misinformation
via social media, thereby contributing to the spread of misinformation to other
members of the public (Valenzuela et al. 2019). Moreover, politically
sophisticated individuals may be more resistant to corrections. In general,
politically sophisticated individuals tend to evince the strongest directional
motivations (Lodge and Taber 2013), corresponding to greater endorsement
of misinformation that reinforces their prior beliefs (Nyhan, Reiﬂer, and Ubel
2013; Miller, Saunders, and Farhart 2016; Jardina and Traugott 2019). Nyhan
and Reiﬂer (2010) propose two mechanisms by which this might be the case: a
biased information search and biased information processing. First, politically
sophisticated individuals may be more likely to selectively consume
ideologically consistent media (conﬁrmation bias), thereby ﬁltering out the
sources most likely to publish attitude-incongruent corrections. Second, when
encountering attitude-incongruent corrections, politically sophisticated
individuals may be best equipped to counterargue against these corrections
(disconﬁrmation bias).
The most politically sophisticated individuals seem the least amenable to
corrections when misinformation supports their preexisting beliefs. As a result,
corrections may fail to reduce and may even enhance belief in misinformation
among this small but consequential group. From this perspective, political
sophistication is a crucial determinant of responses to misinformation and its
correction. Highly sophisticated partisans have both the motivation and the
expertise to discount corrections that run counter to their predispositions.
Furthermore, less engaged citizens are unlikely to be exposed to corrections in

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the ﬁrst place. When considering solutions to the spread of misinformation, the
standard prescription is merely to provide more information. However, this
heightened susceptibility to misinformation among the most informed citizens
exposes the limits to this approach; when individuals are knowledgeable about
and involved in politics, this engagement may ironically engender the strongest
opposition to corrections. Thus, a more informed populace may not be a
panacea if corrections continue to heighten directional motivations.
Political Ideology and Partisanship
An active debate in the misinformation literature concerns potential
asymmetries in responses to misinformation based on political ideology
and partisan identiﬁcation (for a review, see Swire, Berinsky et al. 2017).
Speciﬁcally, some scholars claim that conservatives and Republicans are
especially vulnerable to misinformation. In a widely cited article, Jost et
al. (2003) catalog a laundry list of predictors of conservatism (e.g., closemindedness, intolerance of ambiguity), many of which could engender
openness to misinformation and resistance to corrections. In a later
piece, Jost et al. (2018) highlight several other factors associated with
conservatism, including an emphasis on in-group consensus and
homogeneous social networks, that may give rise to “echo chambers” in
which misinformation can easily spread (see also Nam, Jost, and Van
Bavel 2013; Ecker and Ang 2019). Taken together, these pieces paint a
picture of conservatives as resistant to change, averse to uncertainty, and
drawn to one-sided information environments – all of which might
predispose those on the right to favor misinformation, relative to their
moderate or liberal counterparts.
These theoretical expectations have some empirical backing. Recent research
ﬁnds that, during the 2016 election, Republicans were more likely than
Democrats to read and share fake news (Grinberg et al. 2019; Guess, Nagler,
and Tucker 2019; Guess et al. 2020). Furthermore, ideology and partisanship
are associated with differences in responses to corrections. For example, Nyhan
and Reiﬂer (2010) report evidence of ideological asymmetry in responses to
corrections. Although they ﬁnd that, regardless of partisan leaning, corrections
were generally less effective when they were attitude-incongruent, worldview
backﬁre effects were visible for Republicans but not Democrats (see also Ecker
and Ang 2019).6 These individual-level differences may be exacerbated by
system-wide differences in conservative versus liberal media. Although
misinformation originates in both liberal and conservative circles, the insular
nature of the conservative media ecosystem may be more conducive to the
spread of misinformation (Faris et al. 2017; see also Barberá, Chapter 3, this
6

This observed asymmetry, however, cannot be deﬁnitively ascribed to individual-level differences
across ideological groups. For instance, there may be qualitative differences between conservative- and liberal-leaning misinformation that make the former stickier. In particular, Nyhan and

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Misinformation and Its Correction

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volume), and conservative media sources are more likely than liberal sites to
dismiss or otherwise derogate nonpartisan fact-checkers (Iannucci and Adair
2017). Finally, these system-wide differences also extend to individual
behavior. In an analysis of tweets about the 2012 presidential election, Shin
and Thorson (2017) ﬁnd that Republicans retweeted or replied much less
frequently to fact-checking sites than Democrats – and their replies tended to
be more acrimonious. Similarly, across both Facebook and Twitter, Amazeen,
Vargo, and Hopp (2018) ﬁnd that liberal-leaning individuals tend to be more
likely than others to share fact-checking information.
However, this emphasis on the psychological proﬁles of political
conservatives is not without controversy. Kahan and colleagues contend that
motivated reasoning is not a uniquely right-wing phenomenon. Instead, all
individuals are motivated to express and maintain beliefs similar to those of
other members of their identity groups (the “cultural cognition thesis,” e.g.,
Kahan et al. 2011; Kahan 2013). In line with this perspective, several recent
works suggest that liberals are not, in fact, immune to the effects of
misinformation (Aird et al. 2018; Guess et al. 2019). Across numerous ﬁelds,
ranging from science to politics, both conservatives and liberals evince similar
levels of motivated reasoning (Nisbet, Cooper, and Garrett 2015; Meirick and
Bessarabova 2016; Frimer, Skitka, and Motyl 2017; Swire, Ecker et al. 2017;
Ditto et al. 2019). While conservatives may disproportionately display the
motivational tendencies associated with belief in misinformation, these
proclivities do not necessarily translate to behavioral differences.
While political knowledge has been ﬁrmly established as a key moderator of
misinformation effects, via its relationship to directionally motivated reasoning,
the jury is still out regarding the role of political ideology and partisanship.
Although conservatives and Republicans may, under certain conditions, be
more sensitive to misinformation than others, this divide may be overstated.
Are observed cases of ideological asymmetry a function of deeply rooted
psychological traits, or do they instead reﬂect systematic differences in
conservative versus liberal media environments (or in the misinformation
itself)? Future work should continue to grapple with this tricky distinction.
Personal and Psychological Factors
Misinformation, however, is not contained to the political sphere. More basic
personal and psychological factors may predispose certain individuals to
champion misinformation and disavow corrections across domains. We
Reiﬂer’s (2010) experiments rely on actual examples of misinformation (stem cell research and
weapons of mass destruction in Iraq). While this approach has the beneﬁt of greater external
validity, these cases may diverge in notable ways beyond their ideological slant (e.g., issue salience
or importance). Studies that focus on fabricated misinformation, rather than real-world rumors,
may thus be better suited to identifying potential partisan or ideological asymmetries.

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highlight four of these potential moderators, namely age, analytical thinking,
need for closure, and psychological reactance.7 Scholars highlight age as a key
demographic variable inﬂuencing both exposure and responses to
misinformation. Several recent studies ﬁnd that older adults are more likely
than others to share fake news stories on social media (Grinberg et al. 2019;
Guess et al. 2019). However, other work ﬁnds that old age is also associated
with greater sharing of fact-checks on social media (Amazeen et al. 2018),
suggesting that older cohorts may engage differently with political content on
social media, relative to their younger counterparts.
Scholars have also identiﬁed analytical thinking, or a person’s capacity to
override gut feelings and intuitions, as another determinant of their responses to
misinformation. In this sense, individuals who are more prone to careful,
deliberate processing of information (or “cognitive reﬂection”) seem to be less
susceptible to misinformation. Analytical thinking is associated with reduced
belief in conspiracy theories (Swami et al. 2014) and increased accuracy in
judging fake news headlines (Pennycook and Rand 2018; Bronstein et al.
2019; Pennycook and Rand 2020). Furthermore, highly analytical individuals
are more willing than others to adjust their attitudes post-correction, even after
controlling for a host of other variables (De keersmaecker and Roets 2017; see
also Tappin, Pennycook, and Rand 2018). While most studies conceptualize
analytical thinking as a dispositional trait, recent work suggests that
interventions designed to encourage greater deliberation may also prove an
effective tool for correcting misinformation (Bago, Rand, and Pennycook
2020).
Need for closure may also shape an individual’s susceptibility to
misinformation. Need for closure refers to “the expedient desire for any ﬁrm
belief on a given topic, as opposed to confusion and uncertainty” (Jost et al.
2003, p. 348, italics in original). This motivation fosters two main behavioral
inclinations: the propensity to seize on readily available information and the
tendency to cling to previous information (Jost et al. 2003; Meirick and
Bessarabova 2016; De keersmaecker et al. 2020). Consequently, individuals
with a high need for closure may be more trusting of initial misinformation,
which provides closure through explaining the causes of events, and more
resistant to corrections, which may sow feelings of confusion and uncertainty
(Rapp and Salovich 2018). Need for closure, however, is primarily used as a
control variable in studies of misinformation and is rarely the main construct of
interest. Indeed, the few studies connecting a need for closure to misinformation
focus solely on the endorsement, rather than correction, of misinformation (e.
g., Leman and Cinnirella 2013; Moulding et al. 2016; Marchlewska, Cichocka,
and Kossowska 2018). Nevertheless, need for closure may also moderate the
7

Of course, these factors may be correlated with political sophistication and ideology and may
therefore be at the root of some of the empirical regularities cited in the “Political Factors”
section.

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Misinformation and Its Correction

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effectiveness of corrections. For instance, individuals with a high need for
closure may be especially vulnerable to the continued inﬂuence effect; if these
individuals are less acceptant of gaps in their mental models of an event, they
may be more likely to retain misinformation in the absence of plausible
alternative explanations. Moving forward, future research should continue to
probe the extent to which a high need for closure predisposes certain individuals
to disregard corrections.
Finally, high levels of psychological reactance may trigger backﬁre effects by
stimulating counterarguing. Psychological reactance occurs when individuals
perceive a threat to their intellectual or behavioral freedoms, such as when they
feel strong pressure to adopt a certain attitude or belief (Sensenig and Brehm
1968). In short, many people do not like being told what or how to think. As a
result, they may actively defy corrections that seem overly authoritative (Garrett
et al. 2013; Weeks and Garrett 2014). Misperceptions may thus be even more
difﬁcult to remedy for individuals who eschew conformity. Indeed, across
countries, anti-vaccination attitudes are signiﬁcantly and positively correlated
with psychological reactance (Hornsey, Harris, and Fielding 2018). Moreover,
several studies document a link between psychological reactance and resistance
to climate change messaging (Nisbet et al. 2015; Ma, Dixon, and Hmielowski
2019). A deeper focus on psychological reactance may therefore help reconcile
previously perplexing ﬁndings in the misinformation literature. Some accounts
of the continued inﬂuence effect posit that individuals continue to endorse
misinformation because they do not believe corrections to be true (Guillory
and Geraci 2013). This tendency may be heightened among those with a
contrarian streak. In addition, several scholars caution against providing too
many corrections (“overkill” backﬁre effects, see Cook and Lewandowsky
2011; Lewandowsky et al. 2012; Ecker et al. 2019). The purported perils of
overcorrection may have their roots in psychological reactance (Shu and
Carlson 2014); inundating people with a surfeit of corrections may provoke
feelings of reactance, particularly among those already liable to reject consensus
views.

contextual factors
Along with individual-level moderators of misinformation effects,
contextual factors may play an important role in guiding responses to
misinformation and its correction. These variables include the content of
misinformation as well as the environments in which misinformation is
consumed and corrected.

content-based factors
The actual substance of misinformation – including its subject matter and
tone – is an important determinant of its correctability. First, corrections may

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be differentially effective across issue areas. For example, in a meta-analysis
of studies of misinformation correction, Walter and Murphy (2018) ﬁnd that
corrections are more effective for health-focused misinformation than for
political and scientiﬁc misinformation. Second, misinformation may vary in
its affective content. Negatively valenced misinformation tends to be more
durable than positive or neutral misinformation (Forgas, Laham, and Vargas
2005; Guillory and Geraci 2016; but see Mirandola and Toffalini 2016).
Moreover, the emotions that misinformation arouses may also inﬂuence its
persistence (Vosoughi et al. 2018). In particular, Weeks (2015) ﬁnds that
feelings of anger tend to encourage directionally motivated processing of
corrections, whereas feelings of anxiety tend to reduce partisan differences in
responses to corrections. However, misinformation does not seem to inspire
these emotions in equal measure. Text analysis of comments on Facebook
posts containing misinformation ﬁnds that responses to misinformation are
more frequently characterized by anger as opposed to anxiety (Barfar 2019).

environmental factors
How people encounter misinformation may also inﬂuence both their contact
with and their responses to corrections. Although misinformation is an ageold problem, the topic has garnered attention in recent years due to concerns
about how the Internet – and especially social media – might extend its reach.
Many producers of misinformation use social media sites as their main means
of disseminating misinformation (Tucker et al. 2018). Reﬂecting this fact,
several recent studies emphasize the role of social networking sites,
including Facebook and Twitter, in amplifying exposure to fake news
content (Allcott and Gentzkow 2017; Allcott, Gentzkow, and Yu 2019;
Guess, Nyhan, and Reiﬂer 2020). However, some work suggests that
exposure to fake news on social media is limited to only a small subset of the
population (Grinberg et al. 2019; Guess et al. 2019), and others ﬁnd that social
media use is only weakly associated with the endorsement of false information
(Garrett 2019).
Even if misinformation may propagate easily via social media, these platforms
may be essential to combating its spread. After all, social media can spread
corrections in addition to misinformation (Vraga 2019). Much work focuses on
efforts by social media sites to prevent the spread of misinformation or other
harmful rhetoric in the ﬁrst place (for reviews, see Guess and Lyons, Chapter 2,
and Siegel, Chapter 4, this volume). However, social media platforms can also
play an active role in correcting misinformation after the fact. To this end,
scholars have studied the effectiveness of two types of social media–based
corrections: algorithmic and social corrections. Some social media sites have
built-in functionalities that can be deployed to combat misinformation. For
example, Bode and Vraga (2015, 2018) focus on Facebook’s “related stories”
feature, which recommends relevant articles underneath shared links, and ﬁnd

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185

that fact-checking articles publicized through this system may be effective in
increasing belief accuracy – especially on issues where individuals do not
possess strong prior attitudes. Another proposed form of algorithmic correction
relies on “crowdsourced” data on the trustworthiness of different news outlets to
decrease the likelihood that individuals will encounter posts from unreliable
sources (Pennycook and Rand 2019). In addition to these algorithmic
corrections, other social media users (e.g., Facebook friends or Twitter
followers) can intervene to provide corrections (Vraga and Bode 2018). These
social corrections may be especially effective, as individuals are more likely to
accept corrections from people they already know (Friggeri et al. 2014; Margolin
et al. 2018).
However, some scholars caution about the potential for social media to
undermine the correction of misinformation. The “social” nature of social
media may increase levels of exposure to misinformation, as individuals are
more likely to read news that has been shared or endorsed by members of
their social networks (Messing and Westwood 2014; Anspach 2017). The
nature of the social media environment may also inhibit corrections of
misinformation; Jun, Meng, and Johar (2017) warn that people are less
likely to fact-check statements in social settings – a form of “virtual
bystander effect.” Furthermore, even if corrections circulate on social
media, individuals may be more attentive to user comments on these posts
than to the actual fact-checking messages themselves. If these comments
distort or otherwise misrepresent corrections, individuals may not become
better informed, despite their exposure to fact-checking information
(Anspach and Carlson 2018).
Finally, and most importantly, corrective efforts on social media may
have unintended consequences. Given the difﬁculties of correcting
misinformation postexposure, many scholars recommend preemptive
interventions designed to induce skepticism prior to misinformation
exposure (Ecker et al. 2010; Peter and Koch 2016; Cook, Lewandowsky,
and Ecker 2017). Speciﬁcally, some scholars recommend training
individuals to detect and resist misinformation by highlighting the
techniques commonly deployed by creators of misinformation
(Roozenbeek and van der Linden 2019a, 2019b). Social networking sites
have adopted similar models. For example, after the 2016 US presidential
election, Facebook rolled out a new system to ﬂag potentially inaccurate
stories as disputed or false. Warning labels of this sort may be effective in
reducing the sharing of ﬂagged stories (Mena 2019). However, false stories
that go undetected by this system may be viewed as more accurate than they
would have were the system never put in place (Pennycook et al. 2020).
Similarly, general warnings about the potentially misleading nature of social
media posts may decrease beliefs in the accuracy of true headlines (Clayton
et al. 2019), suggesting that corrections issued on social media might
inadvertently erode trust in credible media content.

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exposure to fact checks
Only a small subset of the population will likely encounter both
misinformation and corrections. On the misinformation side, while some
types of misinformation are widespread (e.g., the birther movement), many
remain fringe beliefs. Despite rampant fears about “fake news,” fake news
sites during the 2016 and 2018 elections received the bulk of their trafﬁc
from a very small set of highly partisan consumers (Grinberg et al. 2019;
Guess et al. 2019, 2020). On the corrections side, a limited number of people
view a limited number of corrections. Relatively few people ever visit
professional fact-checking sites, such as PolitiFact or Factcheck.org,
without external prompting; the public appreciates fact-checking in theory
but shows little interest in practice (Nyhan and Reiﬂer 2015c). These low
levels of engagement are exacerbated by patterns of selective exposure to and
sharing of fact-checking messages on social media platforms (Shin and
Thorson 2017; Zollo et al. 2017; Hameleers and van der Meer 2020), as
partisans tend to seek out and share fact checks that reinforce their prior
attitudes. If highly engaged members of the public cherry-pick favorable factchecking messages to share with others, those exposed to these messages may
observe only a narrow, unrepresentative slice of the available set of
corrections.
Finally, even if individuals do take the initiative to visit fact-checking sites,
these sites frequently choose to cover markedly different topics. In fact, even
when their coverage does overlap, fact-checking organizations often reach
diametrically opposed conclusions about the factual basis for a given piece of
information (Marietta, Barker, and Bowser 2015). These potential
discrepancies are consequential, as several studies of fact-checking messages
ﬁnd that the content of these messages (e.g., afﬁrming or refuting information)
matters more than their source (e.g., Fox News, MSNBC, or PolitiFact) in
increasing belief accuracy (Wintersieck 2017; Wintersieck et al. 2018).

conclusion
Within both popular media and academia, concerns abound regarding the
prevalence and persistence of misinformation. In an age where
misinformation can diffuse rapidly via the Internet and social media, it is
more imperative than ever to think creatively about how best to debunk
misinformation. Although misinformation may take many forms – ranging
from political rumors to disinformation – each of these forms presents a
potential threat to democracy by distorting attitudes, behavior, and public
policy. Deﬁnitional concerns should therefore take a backseat to mitigating
the harmful effects of misinformation. Given the potential dangers of
misinformation, devising effective strategies for correction is crucial, yet
previous prescriptions have often come up short.

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In this review, we have discussed two phenomena that may contribute to the
durability of misinformation post-correction: the continued inﬂuence effect and
backﬁre effects. Though scholars have found evidence that each of these
processes undermines the effectiveness of corrections, recent works have cast
doubt on their pervasiveness. In light of these ﬁndings, several areas merit
further research. First, although worldview backﬁre effects may be less
widespread than originally thought, the existence of these effects remains an
open question. Efforts to isolate the conditions, both theoretical and
methodological, under which worldview backﬁre effects are most likely to
occur may help to resolve this ongoing debate. Similarly, though scholars
frequently discourage the repetition of misinformation within corrections,
more recent studies have cast doubt on the prevalence of familiarity backﬁre
effects. Given that traditional methods of correction often cite the original
misinformation, understanding whether and how this repetition might
undercut their effectiveness is important. In particular, clarifying the
conditions under which repetition is a beneﬁt versus a hindrance may yield
practical recommendations for improving the success of fact-checking sites.
Finally, misinformation does not affect all individuals equally, nor is all
misinformation equally persuasive. Continuing to identify these places of
heterogeneity may enable more active targeting of corrections to those
subgroups where misinformation is most likely to take root.

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</reference>

<statements>
1. Need for closure generally increases people’s willingness to accept misinformation—especially simple, salient explanations for uncertain events—and makes them more resistant to later corrections, but the effect is modest and strongly context‑dependent.
2. Because misinformation often offers simple, causal stories that quickly “explain” complex events, it is especially attractive to people who want rapid, definite answers.
3. Individuals high in need for closure tend to trust the first coherent narrative they encounter about an event and to cling to that narrative even when later corrections are presented.
4. This pattern contributes to the “continued influence effect,” where people continue to rely on debunked misinformation in reasoning because corrections create gaps in their mental model that feel uncomfortable for someone seeking closure.
5. In misinformation research more broadly, need for closure is often included as a control variable and typically shows modest associations with acceptance of false claims, compared with stronger predictors like prior attitudes or institutional trust.
6. For high‑closure audiences, corrections are more effective when they replace misinformation with a clear, coherent alternative explanation rather than simply negating the false claim and leaving explanatory gaps.
7. Designing debunks that quickly re‑establish a sense of order—by offering complete causal stories, highlighting how and why the misinformation arose, and explicitly resolving uncertainties—can reduce the incentive to “seize and freeze” on the original false narrative.
8. Overall, the role of need for closure is to bias *how* people process and stick with information: it nudges them toward accepting the first plausible story that promises certainty and, once adopted, to resist revising that story—whether it is true or misinformation.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.