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journal of cognition
Basol, M., et al. 2020 Good News about Bad News: Gamified Inoculation
Boosts Confidence and Cognitive Immunity Against Fake News.
Journal
of Cognition,
3(1): 2, pp. 1–9. DOI: https://doi.org/10.5334/joc.91
RESEARCH ARTICLE
Good News about Bad News: Gamified Inoculation
Boosts Confidence and Cognitive Immunity Against
Fake News
Melisa Basol, Jon Roozenbeek and Sander van der Linden
Department of Psychology, University of Cambridge, UK
Corresponding author: Sander van der Linden (sander.vanderlinden@psychol.cam.ac.uk)
Recent research has explored the possibility of building attitudinal resistance against
online misinformation through psychological inoculation. The inoculation metaphor relies on
a medical analogy: by pre-emptively exposing people to weakened doses of misinformation
cognitive immunity can be conferred. A recent example is the
Bad News
game, an online fake
news game in which players learn about six common misinformation techniques. We present
a replication and extension into the effectiveness of
Bad News
as an anti-misinformation
intervention. We address three shortcomings identified in the original study: the lack of a
control group, the relatively low number of test items, and the absence of attitudinal certainty
measurements. Using a 2 (treatment vs. control) × 2 (pre vs. post) mixed design (N = 196) we
measure participants’ ability to spot misinformation techniques in 18 fake headlines before
and after playing
Bad News
. We find that playing
Bad News
significantly improves people’s
ability to spot misinformation techniques compared to a gamified control group, and crucially,
also increases people’s level of confidence in their own judgments. Importantly, this confidence
boost only occurred for those who updated their reliability assessments in the correct direc-
tion. This study offers further evidence for the effectiveness of psychological inoculation
against not only specific instances of fake news, but the very strategies used in its production.
Implications are discussed for inoculation theory and cognitive science research on fake news.
Keywords: Judgment; Decision making: Reasoning
Introduction
The prevalence and propagation of online misinformation is a threat to science, society, and democracy
(Lazer et al., 2018; Lewandowsky et al., 2017; van der Linden, Maibach, et al., 2017). Recent research has
shown that increased exposure to false and misleading information can have serious consequences rang-
ing from societal misconceptions around climate change and vaccinations (Schmid & Betsch, 2019; van
der Linden, Leiserowitz, et al., 2017) to physical danger and death (Arun, 2019). Although much research
continues to debate the effectiveness of debunking and fact-checking (Chan et al., 2017; Nyhan & Reifler,
2019), a large body of research in cognitive psychology emphasises the continued influence of misinforma-
tion: falsehoods are difficult to correct once they have manifested themselves in memory (Lewandowsky
et al., 2012) and repeated exposure increases the perceived accuracy of fake news (Pennycook et al., 2018).
Consequently, some scholars have started to explore the possibility of “prebunking”, i.e. preventative
strategies against the spread of misinformation (Roozenbeek & van der Linden, 2018, 2019). Because the
spread of fake news in online networks bears close resemblance to the manner in which a virus replicates
(Kucharski, 2016), one promising avenue has been the revival of inoculation theory.
Cognitive inoculation is based on the biological analogy of vaccine immunisation (McGuire & Papageorgis,
1961; McGuire, 1964). It posits that the process of injecting a weakened dose of a virus to activate antibody
production (to help confer resistance against future infection) can similarly be applied to the context of
information processing. In other words, by warning and exposing people to severely weakened doses of
attitudinal challenges, cognitive resistance or “mental antibodies” are generated against future persuasion
Basol et al: Good News about Bad NewsArt. 2, page 2 of 9
attempts (Compton & Pfau, 2005), partly by fortifying the structure of associative memory networks (Pfau
et al., 2005). Although meta-analyses have shown that inoculation messages are effective (Banas & Rains,
2010), early inoculation research was mostly restricted to “cultural truisms”, i.e. beliefs so commonly shared
across the social milieu that the notion of persuasive attacks against them appeared unlikely (McGuire,
1964). In the real-world, however, people will often hold very different prior beliefs about a particular
issue. Accordingly, McGuire’s restrictive use of the metaphor has been criticized (Pryor & Steinfatt, 1978)
and ultimately led to a rethinking of the medical analogy (Wood, 2007). In fact, more recent studies have
demonstrated the efficacy of inoculation even when participants have differing prior attitudes, for exam-
ple in the context of disinformation campaigns about climate change (Cook et al., 2017; van der Linden,
Leiserowitz, et al., 2017). Accordingly, the consensus view is that “the analogy is more instructive than restric-
tive” (Compton, 2013, p. 233). Of course, from a theoretical point of view, we cannot speak of purely prophy-
lactic inoculation in the context of most real-world settings but just as medicine has advanced to distinguish
between prophylactic and therapeutic vaccines, therapeutic inoculation approaches can still confer protec-
tive benefits even among those already “afflicted” by boosting immune responses in the desired direction
(Compton, 2019). Yet, it remains unclear whether the same theoretical mechanisms that facilitate prophylac-
tic inoculation (e.g. confidence in defending one’s beliefs) also boost the efficacy of therapeutic inoculation.
Moreover, current inoculation research suffers from two primary limitations; 1) scholarship has pre-
dominantly focused on conferring attitudinal resistance against specific issues and 2) preemptive refu-
tation has traditionally been done in a passive rather active manner (Banas & Rains, 2010). These two
issues substantially limit both the scalability and generalisability of the “vaccine” metaphor (Bonetto et
al., 2018; Roozenbeek & van der Linden, 2019). Accordingly, recent research has focused on the possibility
of a “broad-spectrum vaccine” against misinformation (Roozenbeek & van der Linden, 2018, 2019). The
broad-spectrum approach requires two theoretical innovations; 1) shifting focus away from pre-emptively
exposing participants to weakened examples of specific instances of (mis)information to pre-emptively
exposing participants to weakened examples of the techniques that underlie the production of most mis-
information and 2) revisiting McGuire’s original prediction (McGuire & Papageorgis, 1961) that active
inoculation (letting participants generate their own “antibodies”) would be more effective in conferring
resistance to persuasion than when participants are provided with a defensive pre-treatment in a passive
manner. In a novel paradigm pioneered by Roozenbeek and van der Linden (2019), participants enter a
simulated social media environment (Twitter) where they are gradually exposed to weakened “doses” of
misinformation strategies and actively encouraged to generate their own content. The intervention is a
free social impact game called Bad News (www.getbadnews.com; Figure 1A), developed in collaboration
with the Dutch media platform DROG (DROG, 2018), in which players learn about six common misinfor-
mation techniques (impersonating people online, using emotional language, group polarisation, spread-
ing conspiracy theories, discrediting opponents, and trolling, Figure 1B).
The purpose of the game is to produce and disseminate disinformation in a controlled environment whilst
gaining an online following and maintaining credibility. Players start out as an anonymous netizen and
eventually rise to manage their own fake news empire. The theoretical motivation for the inclusion of these
six strategies are explained in detail in Roozenbeek and van der Linden (2019) and cover many common
disinformation scenarios including false amplification and echo chambers. Moreover, although the game
scenarios themselves are fictional they are modelled after real-world events. In short, the gamified inocula-
tion treatment incorporates an active and experiential component to resistance-building.
The initial study by Roozenbeek and van der Linden (2019) relied on a self-selected online sample of
approximately 15,000 participants in a pre-post (within) gameplay design. Although the study provided
Figure 1: Landing screen Bad News (Panel A) and simulated twitter engine (Panel B).
Basol et al: Good News about Bad News Art. 2, page 3 of 9
preliminary evidence that the game increases people’s ability to detect and resist a whole range of misinfor-
mation (in the form of deceptive Twitter posts), the study suffered from a number of important theoretical
and methodological limitations. For example, although the original study did include various “real news”
control items, it lacked a proper randomized control group. This is important because there could be a secu-
lar trend so that people downgrade their reliability ratings of the fake tweets (pre-post) regardless of what
intervention they are assigned to. Second, because the testing happened within the game environment,
the original study only included a limited number of fake news items (one survey item per misinformation
technique). Third, on a theoretical level, the study only looked at reliability judgments and thus could not
determine how confident or certain people actually were in their beliefs. This is important, because atti-
tude certainty (a dimension of attitude strength) is generally regarded as the conviction that held attitudes
are correct (Tormala & Petty, 2004) and functions as a critical mechanism in resisting persuasion attempts
(Compton & Pfau, 2005). Accordingly, this study addresses three key shortcomings in the original research
by 1) including a randomized control group, 2) adding a larger battery of items, and 3) evaluating whether
the intervention also boosts confidence in reliability judgments.
Participants and procedure
This study employed a 2 (Bad News. vs. Control) * 2 (pre-post) mixed design to test the efficacy of active
(gamified) inoculation in conferring attitudinal resistance to misinformation. The independent vari-
able consisted of either the treatment condition in which participants played the Bad News game or a
control condition in which participants were assigned to play Tetris (to control for gamification; Tetris
specifically was chosen because it is in the public domain and requires little prior explanation before
playing).
Following Roozenbeek and van der Linden (2019), the dependent variable consisted of an assessment
of the reliability of 18 misinformation headlines in the form of Twitter posts (please see Supplementary
Figure S5). As the Bad News game covers six misinformation techniques, three items per technique were
included.1 These Twitter posts were created to be realistic, but not real, both to avoid memory confounds
(participants may have seen “real” fake news headlines before) and to able to experimentally isolate the mis-
information techniques. Taking into account the average inoculation effect reported in previous research
(Roozenbeek & van der Linden, 2019), an a priori power analysis was conducted with G* power using
α = 0.05, f = 0.26 (d = 0.52) and power of 0.90 with two experimental conditions. The minimal sample size
required for detecting the main effect was approximately 158. A total of 197 participants were recruited
through the online crowdsourcing platform, Prolific Academic, which has been reported to produce higher
data quality than MTurk (Peer et al., 2017). Consenting participants (58% male, modal age bracket = 18–24,
20% higher educated, 61% liberal, 80% white2) completed the survey, were debriefed, and paid £2.08 in
compensation. This study was approved by the Cambridge Psychology Research Ethics Committee.
A plug-in was created so that the game could be embedded in Qualtrics and pre-post testing could take
place outside of the game environment to further enhance ecological validity. Upon giving informed con-
sent, participants were randomly presented with 18 fictitious Twitter posts (Figure S5) and on a standard
7-point scale, reported on how reliable they received each post to be and how confident they were in their
judgements. Subsequently, participants were randomly assigned to a condition. In the inoculation condi-
tion participants (n = 96) were asked to play the “Bad News” game for about 15 minutes. Participants were
assigned a password for completion which they could only receive after completing the final level (badge).
Participants (n = 102) in the control condition played Tetris for 15 minutes in the same manner. After treat-
ment exposure, all participants were asked to complete the same set of outcome measures.
Outcome Measures
Perceived reliability
To assess participants’ perceived reliability, a single-item measure was presented alongside 18 (6*3) fake
Twitter posts (example item polarization; “New study shows that right-wing people lie more often than left-
wing people”, see Figure S5). Participants reported the perceived reliability of each post on a 7-point Likert-
scale from not reliable at all (1), neutral (4) to very reliable (7). Following Roozenbeek and van der Linden
(2019), to form a general fake news scale of perceived reliability, all 18 fake news items were averaged. An
initial reliability analysis suggested good internal consistency (M = 3.17, SD = 0.85, α = 0.84) of the 18-item
1 In the original study by Roozenbeek and van der Linden (2019), only six items were included. We included the original items plus
two new ones for each badge using the same approach.
2 Socio-demographics (except for ideology) were answered by 52% (n = 104) of the 197 participants.
Basol et al: Good News about Bad NewsArt. 2, page 4 of 9
fake news scale. A subsequent exploratory principal component analysis (PCA) was also run on the fake news
items. According to the Kaiser criterion, results indicated that the items clearly loaded on a single dimen-
sion with an eigenvalue of 3.15, accounting for 53% of the variance (please see Scree plot, Supplementary
Figure S6). Thus, for ease of interpretation and to limit multiple testing, all 18 items were collapsed and
treated as one overall measure of fake news judgments. Nonetheless, descriptive statistics for badge-level
results are also presented in Supplementary Table 1.
Attitudinal certainty
Similarly, a single-item measure was presented alongside each of the news items, asking participants to
indicate how confident they are in their reliability assessment on a 7-point Likert scale, ranging from not
at all confident (1) to neutral (4) to very confident (7). Scale reliability analysis on the averaged 18 attitude
certainty items (6*3) indicated high internal validity (M = 5.23, SD = 0.84, α = .89). Similarly, PCA results
indicated that the items loaded on a single dimension with an eigenvalue of 3.88, accounting for 65% of
variance (Supplementary Figure S7, for badge-level results see Table S2).
Political ideology
Political ideology was measured on a standard self-placement scale, ranging from 1 = very conservative,
4 = moderate, to 7 = very liberal. Although often more diverse than Mturk (Peer et al., 2017), the Prolific
sample (M = 4.69, SD = 1.42) was fairly liberal with 21% conservatives, 18% moderates, and 61% identifying
as liberal.
Results
A One-way ANOVA was conducted to compare the effect of treatment condition (inoculation, control) on the
difference in pre-and-post reliability scores of the fake news items. Results demonstrate a significant main
effect of treatment condition on aggregated reliability judgements: F(1, 196) = 17.54, MSE = 0.36, p < .001,
η2 = .082).3 Specifically, compared to the control condition, the shift in post-pre difference scores was sig-
nificantly more negative in the inoculation condition (M = –0.09 vs M = –0.45, Mdiff = –0.36, 95% CI [–0.19,
–0.52], d = –0.60, Figure 2). A separate two-way ANOVA revealed no main effect F(2, 179) = 2.80, p = 0.06
nor interaction F(2, 179) = 0.96, p = 0.38 with political ideology.4 In short, compared to their assessments
on the pre-test, individuals demonstrated a larger decrease in perceived reliability of fake news items when
in the inoculation group versus the control condition. Similar patterns were observed at the badge level in
the game (please see Supplementary Table 1) although there was some heterogeneity across badges with
average effect-sizes ranging from d = 0.14 (polarization) to d = 0.58 (discrediting).
3 A linear regression with post-test as the dependent variable, condition as a dummy, and pre-test as a covariate gives the same
result. There was no significant difference at pre-test between the conditions (Minoculation = 3.14 vs. Mcontrol = 3.32, Mdiff = –0.185 95%
CI [–0.42 0.05], p = 0.12, see Supplementary Table S1 and Figs S1–2).
4 Though conservatives (M = 3.56) were significantly more susceptible than liberals (M = 3.05) on the pre-test, t(147) = 3.22, d = 0.61,
p < 0.01, consistent with Roozenbeek and van der Linden (2019).
Figure 2: Median difference (post-pre) in reliability assessments of fake news items across treatment
conditions with jitter (Panel A) and density plots of the data distributions (Panel B).
Basol et al: Good News about Bad News Art. 2, page 5 of 9
Furthermore, a one-way ANOVA also demonstrated a significant main effect of treatment condition on
(post-pre) confidence scores (Figure 3), F(1, 196) = 13.49, MSE = 0.27, p < .001, η2 = .06. Mean difference
comparisons across conditions indicate a significantly higher (positive) difference score in the inoculation
group compared to the control condition (M = 0.22 vs. M = –0.06, Mdiff = 0.27, 95% CI [0.13, 0.42], d = 0.52).5
This suggests that compared to their assessments prior to treatment exposure, individuals demonstrated a
larger increase in confidence in the inoculation versus the control condition. Once again a two-way ANOVA
revealed no main effect F(2, 179) = 1.22, p = 0.30 nor interaction F(2, 179) = 0.14, p = 0.87 with political
ideology. At the badge level (Supplementary Table 2), effect-sizes for increased confidence ranged from
d = 0.23 (discrediting) to emotion (d = 0.49). Importantly, the increase in confidence only occurred for those
(71%) who broadly updated their reliability judgments in the right direction6 (Minoculation = 0.29 vs. Mcontrol =
–0.02 Mdif f = 0.31, 95%[0.13, 0.49], t(126) = 3.37, p < 0.01). In contrast, no gain in confidence was found
among those who either did not change or updated their judgments in the wrong direction (Minoculation = 0.03
vs. Mcontrol = –0.11, Mdiff = 0.14 95%[–0.11, 0.39], t(68) = 1.13, p = 0.26).
Discussion and conclusion
This study successfully demonstrated the efficacy of a “broad-spectrum” inoculation against misinforma-
tion in the form of an online fake news game. Using a randomized design, multiple items, and measures
of attitudinal certainty, we expand on the initial study by Roozenbeek and van der Linden (2019). Overall,
we find clear evidence in support of the intervention. Whereas Roozenbeek and van der Linden (2019)
reported an average effect-size of d = 0.52 for aggregated reliability judgments using a self-selected within-
subject design, we find very similar effect-sizes in a randomized controlled design (d = 0.60). The range in
effect-sizes observed on the badge level (d = 0.14 to d = 0.58) are also similar to what Roozenbeek and van
der Linden (2019) reported (d = 0.16 to d = 0.36), and can be considered sizeable in the context of resist-
ance to persuasion research (Banas & Rains, 2010; Walter & Murphy, 2018). In fact, Funder and Ozer (2019)
recommend describing these effects as medium to large and practically meaningful, especially considering
the refutational-different rather than refutational-same approach adopted here, i.e. in the game, partici-
pants were trained on different misleading headlines than they were tested on pre-and-post. Moreover, the
fictitious nature of the items help rule out potential memory confounds and the lack of variation on the
measures (pre-post) in the control group should decrease concerns about potential demand characteristics.
Importantly, consistent with Roozenbeek and van der Linden (2019), none of the main effects revealed
an interaction with political ideology, suggesting that the intervention works as a “broad-spectrum” vaccine
across the political spectrum. However, it is interesting that in both studies, the smallest effect is observed
5 A linear regression with post-test as the dependent variable, condition as a dummy, and pre-test as a covariate gives the same result.
There was no significant difference in confidence judgments at pre-test between conditions (Minoculation = 5.25 vs. Mcontrol = 5.27,
Mdiff = 0.02 95% CI [–0.24 0.20], p = 0.88, please see Supplementary Table S2 and Figures S3–4).
6 Meaning that fake headlines were deemed less reliable on the post-test compared to the pre-test (i.e. Mdiff < 0).
Figure 3: Median change scores (post-pre) of confidence in reliability judgments across treatment condi-
tions with jitter (Panel A) and density plots of the data distributions (Panel B).
Basol et al: Good News about Bad NewsArt. 2, page 6 of 9
for the polarization badge. One potential explanation for the lower effect on polarization is confirmation
bias: in the game, decisions can still be branched in an ideologically congenial manner. Given the worldview
backfire effect (Lewandowsky et al., 2012), future research should evaluate to what extent inoculation is
effective for ideologically congruent versus non-congruent fake news. Nonetheless, these results comple-
ment prior findings which suggest that susceptibility to fake news is the result of lack of thinking rather
than only partisan motivated reasoning (Pennycook & Rand, 2019).
Lastly, the current study also significantly advances our understanding of the theoretical mechanisms on
which the intervention acts. For example, while inoculated individuals improved in their reliability assess-
ments of the fake news items, the average confidence they expressed in their judgements also increased sig-
nificantly and substantially. Importantly, the intervention only significantly increased confidence amongst
those who updated their judgments in the right direction (i.e. correctly judging manipulative items to be
less reliable). These findings are supported by previous literature demonstrating the certainty-bolstering
effects of inoculation treatments (Tormala & Petty, 2004) and may suggest that confidence plays a key role
in both prophylactic and therapeutic inoculation approaches. Yet, more research is required to identify
whether an increase in confidence pertains to the fake items themselves or rather the ability to refute mis-
information in general. For example, Tormala and Petty (2004) have argued that these mechanisms are likely
to be intertwined as individuals might be confident in their ability to refute counterarguments because they
perceive their attitudes to be valid and therefore, are both more willing and likely to defend their beliefs.
This study did suffer from a number of necessary limitations. First, we controlled for modality (given that
both Bad News and Tetris are games), but lacked a condition that is cognitively comparable to the inoculation
condition. It will be important for future research to evaluate to what extent “active” gamified inoculation
is superior to “passive” approaches—including traditional fact-checking and other critical thinking interven-
tions—especially in terms of eliciting a) motivation, b) the ability to help people discern reliable from fake
news, and c) the rate at which the inoculation effect decays over time. Second, although we improved on
the initial design by having participants evaluate simulated twitter posts (pre and post) outside of the game
environment, we were not able to determine if playing the Bad News game led to increased ability to detect
real news or changes in online behaviour (e.g. if players shared less fake news on social media than people
who did not play the game). Third, the fact that a small minority of individuals appear to engage in contrary
updating is worth noting and a finding future work may want to investigate further (e.g. in terms of prior
motivations). Fourth, we did not examine the duration of the inoculation effect over time but we encourage
future research to do so given that inoculation treatments are known to decay over time (Banas & Rains,
2010). Lastly, our Prolific sample was likely not representative of the U.K. population.
In conclusion, this study addressed the main shortcomings identified by Roozenbeek and van der Linden
(2019) in their original evaluation of the Bad News game: the lack of a control group, a relatively small
number of items to measure effectiveness, and the absence of attitudinal certainty measurements. We con-
clude that, compared to a control group, the generalized inoculation intervention not only successfully
conferred resistance to online manipulation, but also boosted confidence in the ability to resist fake news
and misinformation.
Data Accessibility Statement
The raw dataset necessary to reproduce the analyses reported in this paper can be retrieved from https://
figshare.com/s/818c1a38da814b0bdf20.
Additional Files
The additional files for this article can be found as follows:
• Supplementary Table 1. Average reliability (pre-post) judgments overall and for each fake news
badge by experimental condition. DOI: https://doi.org/10.5334/joc.91.s1
• Supplementary Table 2. Average confidence (pre-post) judgments overall and for each fake news
badge by experimental condition. DOI: https://doi.org/10.5334/joc.91.s2
• Supplementary Figure 1. Mean reliability judgments by condition (pre-test). DOI: https://doi.
org/10.5334/joc.91.s3
• Supplementary Figure 2. Mean reliability judgments by condition (post-test). DOI: https://doi.
org/10.5334/joc.91.s4
• Supplementary Figure 3. Mean confidence judgments by condition (pre-test). DOI: https://doi.
org/10.5334/joc.91.s5
Basol et al: Good News about Bad News Art. 2, page 7 of 9
• Supplementary Figure 4. Mean confidence judgments by condition (post-test). DOI: https://doi.
org/10.5334/joc.91.s6
• Supplementary Figure 5. All 18 fake news items participants viewed pre-post by badge. DOI:
https://doi.org/10.5334/joc.91.s7
• Supplementary Figure 6. Scree plot for reliability judgments following PCA. DOI: https://doi.
org/10.5334/joc.91.s8
• Supplementary Figure 7. Scree plot for confidence judgments following PCA. DOI: https://doi.
org/10.5334/joc.91.s9
Ethics and Consent
Ethics for this study was approved by the Cambridge Psychology Research Ethics Committee
(PRE.2018.085).
Acknowledgements
We would like to thank Ruurd Oosterwoud, DROG and Gusmanson Design for their efforts in helping to
create the Bad News game.
Funding Information
The authors thank the University of Cambridge and the Bill and Melinda Gates Foundation for funding
this research.
Competing Interests
The authors have no competing interests to declare.
Author Contributions
M.B. and J.R. designed the study, developed the items and measures, and carried out the study. M.B. con-
ducted the data analysis and wrote the majority of the paper. J.R. developed the content of the Bad News
game and wrote part of the paper. S.v.d.L. wrote part of the paper, conducted data analysis, co-developed the
Bad News game, and supervised the development of the survey items and study design.
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How to cite this article: Basol, M., Roozenbeek, J., and van der Linden, S. 2020 Good News about Bad News:
Gamified Inoculation Boosts Confidence and Cognitive Immunity Against Fake News.
Journal of Cognition,
3(1):
2, pp. 1–9. DOI: https://doi.org/10.5334/joc.91
Submitted: 05 August 2019 Accepted: 02 December 2019 Published: 10 January 2020
Copyright: © 2020 The Author(s). This is an open-access article distributed under the terms of the Creative
Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/
licenses/by/4.0/.
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