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UC San Diego
UC San Diego Previously Published Works
Title
WARNING This Contains Misinformation: The Effect of Cognitive
Factors, Beliefs, and Personality on Misinformation Warning Tag
Attitudes

Permalink
https://escholarship.org/uc/item/4h12v5s8

Journal
Proceedings of the ACM on Human-Computer Interaction, 9(7)

ISSN
2573-0142

Authors
Kaufman, Robert
Broukhim, Aaron
Haupt, Michael

Publication Date
2025-10-18

DOI
10.1145/3757521

Copyright Information
This work is made available under the terms of a Creative Commons
Attribution License, available at
https://creativecommons.org/licenses/by/4.0/
Peer reviewed

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WARNING This Contains Misinformation: The Effect of
Cognitive Factors, Beliefs, and Personality on Misinformation
Warning Tag Attitudes
ROBERT KAUFMAN, University of California, San Diego, USA
AARON BROUKHIM, University of California, San Diego, USA
MICHAEL HAUPT, University of California, San Diego, USA
Social media platforms enhance the propagation of online misinformation by providing large user bases with
a quick means to share content. One way to disrupt the rapid dissemination of misinformation at scale is
through warning tags, which label content as potentially false or misleading. However, past warning tag
mitigation studies yield mixed results for diverse audiences. We hypothesize that personalizing warning tags
to the individual characteristics of their diverse users may enhance mitigation effectiveness. To reach the goal
of personalization, we need to understand how people differ and how those differences predict a person’s
attitudes and behaviors toward tags and tagged content. In this study, we leverage Amazon Mechanical Turk (n
= 132) and undergraduate students (n = 112) to provide this foundational understanding. With all participants
combined, we find attitudes towards warning tags and self-described behaviors are significantly influenced by
factors such as Need for Cognitive Closure (NFCC), Political orientation, and Trust in Medical Scientists when
controlled for covariates such as age and recruiting platform. Analyses of each sample further show that tag
attitudes were influenced by Trust in Religious Leaders, and Big Five Inventory (BFI) traits for Openness and
Conscientiousness. We synthesize these results into design insights and a future research agenda for more
effective and personalized warning tags and misinformation mitigation strategies more generally.
CCS Concepts: • Human-centered computing → User models; Collaborative and social computing.
Additional Key Words and Phrases: Misinformation, Social Media, Bias, Individual Differences
ACM Reference Format:
Robert Kaufman, Aaron Broukhim, and Michael Haupt. 2025. WARNING This Contains Misinformation: The
Effect of Cognitive Factors, Beliefs, and Personality on Misinformation Warning Tag Attitudes. Proc. ACM
Hum.-Comput. Interact. 9, 7, Article CSCW340 (November 2025), 32 pages. https://doi.org/10.1145/3757521

1

Introduction

Misinformation, or “fake news”, is a long-standing and pervasive issue with many adverse implications. Affecting a wide range of domains, misinformation has been tied to lower vaccine
usage during the COVID-19 pandemic [11], climate change denial [113], and election interference
[32] among other harmful effects. Social media platforms uniquely contribute to the spread of
misinformation due to their large user bases, under-moderation, and the network effects of virality
[7, 29, 106]. Since the rise of social media – where 62% of people get their news [30] – the spread
of misinformation has dramatically increased [114]. The World Health Organization (WHO) has
gone so far as to declare an “infodemic” characterized by the intentional spreading of inaccurate
information to undermine public health efforts [80].
Authors’ Contact Information: Robert Kaufman, rokaufma@ucsd.edu, University of California, San Diego, La Jolla, California,
USA; Aaron Broukhim, aabroukh@ucsd.edu, University of California, San Diego, La Jolla, California, USA; Michael Haupt,
mhaupt@ucsd.edu, University of California, San Diego, La Jolla, California, USA.

This work is licensed under a Creative Commons Attribution 4.0 International License.
© 2025 Copyright held by the owner/author(s).
ACM 2573-0142/2025/11-ARTCSCW340
https://doi.org/10.1145/3757521
Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

CSCW340:2

Robert Kaufman, Aaron Broukhim, and Michael Haupt

As the problem of misinformation continues to grow, it is critical to develop effective mitigation methods that can work at scale. Several approaches have been taken to prevent false belief
formation from misinformation as well as curb misinformation spread, each approach depending
on the ubiquitousness of the case. Early algorithmic intervention – often using artificial intelligence or crowdsourcing – can be highly effective at moderating content visibility and preventing
misinformation from spreading in the first place [29, 56, 60, 97, 98]. There are far fewer options
for misinformation that has already become pervasive on social platforms – i.e. “gone viral”. For
these cases, tagging potentially misleading content with misinformation warning labels may be
effective [17], but warnings must be convincing and trustworthy to the viewer in order to be taken
seriously. This is a tall order, as different people perceive and act on information in different ways
[37, 56, 84, 95]. If done correctly, warning tags present a potentially transparent [104], prudent
[101], and effective [72] means of intervention that prevents the spread of misinformation.
In this paper, we focus on warning tags for misinformation on social media, as these are a
front-line defense to prevent the proliferation of fake news at the point of human interaction and
information uptake. We define “warning tags” as communicative actions that are spatially and
temporally attached to a piece of information, news story, or source. The purpose of the tag is
to alert the viewer that the content they are about to view is potentially unreliable, false, and/or
encouraging them to exercise caution before accepting or sharing the information [17]. This is a
common method for misinformation prevention on social media, and has been used on popular
social media platforms such as X (formerly Twitter) and Facebook [75, 83]. Figure 1 shows an
example from X shown to participants in the present study. Despite the wide adoption of tags
across social media platforms, past studies show mixed results regarding the effectiveness of tags in
combating misinformation belief and spread. Differences in efficacy are often attributed to political
orientation and cognitive ability [67, 84, 95]; while these factors may predict tag effectiveness in
some cases, we posit that additional characteristics may provide further insight into what types of
tags work best, and for whom.

Fig. 1. Warning tag example shown to participants (from [18]). News articles were presented with the prompt
above.

An intuitive method to mitigate group differences in tag efficacy is to tailor communications to
the diverse needs of the audience. In practice, few studies have explored this possibility [101], and
no major social media platform – to our knowledge – has publicly implemented personalized tags in
practice. A lack of personalization limits the effectiveness of the labels due to individual differences
in perception and information-related behaviors [56, 67, 84, 95]. Thus, one-size-fits-all approaches
to prevent misinformation uptake and spread will fail if they don’t harmonize with the specific
needs and motivations of a particular individual. In the case of misinformation, tags warning
of potential falsehoods need to be tailored to address the perceptions of the specific recipient,
while online interventions designed to convince someone away from spreading falsehoods need
to be met at the motivating source. We posit that the current one-size-fits-all implementation
Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

CSCW340:3

of misinformation tags can benefit from modifications that account for personal and contextual
flexibility. Personalization and information tailoring have been topics of discussion in many other
domains where people need to make sense of AI-based decisions [41, 51, 103, 115], ranging from
knowledge systems [69], healthcare [53, 57], image classification [81, 108], and autonomous driving
[52, 54, 55]. The need for tailored messaging is also discussed in user persona work that accounts
for multiple factors such as psychological traits, situational circumstances, and demographics to
produce nuanced depictions of public response towards health issues [3, 40, 42, 73].
In a similar vein, effective interventions for misinformation, especially those that are visible to
users, need to account for the diversity of user reactions. However, there is little known about factors
that influence user attitudes towards tags, limiting efforts to develop customized interventions.
Recent work showing that warning flags can lower perceived accuracy of false news headlines
demonstrates the need for user-centric models in warning tag systems [8]. As reported in Barman
et al. [8], traits such as neuroticism and tendency for reflective thinking showed higher trust for
warning flags with explanatory text, while political conservatism was associated with distrust
towards flagging systems. However, this work does not account for factors such as the sender of
the post, displays of previous engagement (eg., number of likes/shares shown to users), and may
not generalize to misinformation topics other than COVID-19. Other work outlining typologies
of misinformation such as satire, parody, misleading advertising, and fabricated news sources
[6, 112, 118] further illustrate the need for general assessments of attitudes on tag systems. The
current study fills this knowledge gap by examining attitudes about warning tag systems as a
main outcome, which can guide the development of personalized tag systems across contexts. In
psychology, an attitude is defined as “a psychological tendency that is expressed by evaluating a
particular entity with some degree of favor or disfavor” [24]. The present study assesses attitudes
of tag warning systems, which can subsequently influence social media engagement behaviors. In
order to guide the design of personalized tags, we also examine how user characteristics relevant
to misinformation engagement such as political beliefs and attitudes towards institutions, thinking
style, and personality traits influence favorability of tag warning systems.
The findings presented in this paper follows up on previously published work by Kaufman et al.
[56], where the results showed that personal factors such as individual traits and beliefs can be used
to predict misinformation assessment accuracy in tweets. In the present study, we use datasets from
two diverse sample populations (undergraduates and Amazon Mechanical Turk workers) to predict
attitudes towards misinformation warning tags from similar traits. We measure political beliefs,
institutional trust, cognitive thinking styles, and personality traits with the purpose of highlighting
how individual differences across different populations (SONA and MTurk) impact attitudes toward
misinformation warning tags. The purpose of this work is to establish a foundation for personalized
warning tags and provide a deeper understanding of factors that need to be accounted for to
effectively mitigate misinformation uptake and propagation at the individual level.
Our results indicate that the dispositional trait Need For Cognitive Closure (NFCC), Trust in
Medical Scientists, and political orientation were the strongest predictors for favorable attitudes
towards tags among our overall sample. Trust in Religious Leaders and recruiting platform also
showed statistically significant effects on tag attitudes. When examining differences between
recruiting platforms, Big Five Inventory (BFI) personality trait Openness, political orientation, and
Trust in Medical Scientists were the most influential predictors among our undergraduate sample.
Among a more general population sample collected via Amazon Mechanical Turk (MTurk), we found
that NFCC showed the strongest association with positive warning tag attitudes followed by BFI
Conscientiousness, political orientation, Trust in Medical Scientists, and Trust in Religious Leaders.
Concurrent work by Barman et al. [8] also investigate the role of age, gender, education level,
political ideology, CRT, and BFI on perceived accuracy, sharing behavior, and trust in flagged content
Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

CSCW340:4

Robert Kaufman, Aaron Broukhim, and Michael Haupt

for COVID-related misinformation. Notably, this work does not test NFCC, Trust in Elected Officials,
Trust in News Media, Trust in Religious Leaders, and Trust in Medical Scientists. Additionally,
previous work does not measure other relevant outcomes for tag engagement such as whether the
user would ignore the intervention, still read the post, research the content themselves, and how
the intervention influences user perception of the post author and platform. We expand on this
prior work by adding further granularity to relevant outcome measures for tag systems.
In sum, the study presented here contributes:
(1) An analysis that quantifies and ranks the effect sizes of individual differences on attitudes for
warning tag systems.
(2) A comparison of two widely-used human-computer interaction research populations: Amazon
Mechanical Turk (MTurk) and undergraduate study participants (SONA) in the context of
misinformation warning tags.
(3) Insights and design implications for personalized and user-centric warnings tags that can
more effectively mitigate the spread of misinformation among diverse populations.
Taken in full, this paper provides insight into how personal factors impact a person’s attitude
towards misinformation warning labels, an important and necessary first step towards developing
tailored interventions that are effective not just for some, but for all.
2
2.1

Related Work
Individual Differences in Misinformation Vulnerability and Propagation

Several studies have examined misinformation vulnerability and spreading behavior. In this section,
we focus on who is most vulnerable and likely to spread misinformation and use this as a basis to
inform our study on individual differences in attitudes towards tag interventions. We list out our
hypotheses based on the prior work.
Increasing reliance on social media for news contributes to the rapid dissemination of misinformation on social media [30, 114]. Since 2016, foreign agents have leveraged social media to
attempt to influence the United States presidential election in favor of their preferred candidate
[5], damaging voter agency and potentially swaying election outcomes against the interest of
the electorate. Following this election, in which the term “fake news” became pervasive in popular culture, the COVID-19 pandemic showed the influence of misinformation via debate around
vaccine efficacy. A study by Loomba et al. [71] showed a negative correlation between exposure
to misinformation and intent to vaccinate against the virus, with potentially detrimental health
outcomes. Importantly, this study was also one of the first to highlight individual differences in
misinformation susceptibility and behavioral intention to vaccinate. They found that people of
different races, religious backgrounds, and genders demonstrated different levels of susceptibility
to misinformation.
In the United States, prior work has consistently highlighted political differences in misinformation vulnerability [68] and sharing behavior [38, 39]. Past studies show that people who identify
as politically conservative share more misinformation and may be less accurate at identifying
misinformation than those who identify as liberal [33, 56, 92]. The divide is not absolute, however:
both liberals and conservatives are more likely to share misinformation that positively reflects their
own ideological groups [88] and seek out information that confirms their previously held beliefs
[23]. Partisanship also plays a role in attitudes toward misinformation interventions: Saltz et al.
[100] found heavily-leaning liberals supported a variety of online misinformation interventions,
while conservatives opposed interventions outright. Due to previously observed political differences
in preferences for misinformation interventions, the present study seeks to build on this past work
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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

CSCW340:5

by examining the impact of political orientation on attitudes towards tag mitigation solutions when
controlled for effects from other personal traits. In the present study, we hypothesize:
• H1: Political conservatism will be negatively associated with favorable attitudes
towards misinformation tags.
There is a mounting archive of evidence suggesting that the informational sources a person trusts
may impact their susceptibility to misinformation, including interpersonal connections, institutions,
and news outlets. Intuitively, trusting experts – particularly medical experts – has been shown
to decrease misinformation susceptibility, while trust in family, friends, and less reliable sources
increases susceptibility [13, 56, 76]. Surprisingly, individuals with high trust in science may be more
susceptible to misinformation that contains pseudo-scientific content [78]. Divergent from trust in
science, Trust in Religious Leaders has been associated with belief in misinformation [87]. It’s worth
noting that Jasinskaja-Lahti and Jetten [46] highlights a distinction between individuals who selfcategorize as religious and individuals who endorse religious worldviews, stating that endorsement
of religious worldviews may be a better predictor of susceptibility to conspiracy theories. Prior
work by Kaufman et al. [56] found crowdworkers with less Trust in Religious Leaders to be more
effective at labeling misinformation. Trust characteristics may underlie political differences in
misinformation vulnerability, sharing behavior, and attitudes [1]. It is plausible that trust in other
institutional authorities such as elected officials and media outlets may also influence warning
tag attitudes, as suggested by previous work showing that posts from political figures and media
affiliates were often shared in COVID-related misinformation discourse [36, 38, 39]. Therefore,
we seek to understand how trust in informational sources including news media, elected officials,
medical scientists and religious leaders may impact the viability of warning tags for misinformation
mitigation. Based on the literature, we propose the following hypotheses:
• H2: Trust in medical scientists, elected officials, and news media will be positively
associated with favorable attitudes towards misinformation tags.
• H3: Trust in religious leaders will be negatively associated with favorable attitudes
towards misinformation tags.
Prior studies have shown that cognitive, information assessment, and personality traits may
impact misinformation vulnerability and spreading behavior. Kaufman et al. [56] found that respondents with high Cognitive Reflection Test (CRT) scores, Conscientiousness, and Trust in Medical
Scientists were more aligned with experts in an information assessment task while respondents
with high Need for Cognitive Closure (NFCC) and those who lean politically conservative were
less aligned with experts.
Often measured by the Cognitive Reflection Test (CRT), tendency for reflective thinking has been
found to be correlated with misinformation sharing and intervention attitudes. CRT performance is
associated with the ability to override incorrect analytical intuitions through reflection. Pehlivanoglu
et al. [82] found positive correlations between CRT score and an individual’s ability to identify
misinformation. Another study showed individuals with lower CRT scores were more willing to
overclaim knowledge and had a general tendency to be more receptive to “pseudo-profound bullshit”
[86]. CRT has also been shown to be positively associated with trust in medical scientists [65] and
negatively associated with misinformation engagement [37] and susceptibility [85]. Since CRT is a
relevant factor for evaluating information veracity and online sharing behaviors, the present study
tests whether CRT is also a significant predictor for warning tag attitudes. We hypothesize:
• H4: CRT will be positively associated with favorable attitudes towards misinformation tags.

Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

CSCW340:6

Robert Kaufman, Aaron Broukhim, and Michael Haupt

Other information assessment traits, such as a person’s Need for Cognitive Closure (NFCC), have
been shown to impact vulnerability to misinformation [9, 89]. Need for Cognitive Closure (NFCC)
is a measure of one’s desire for order, predictability, and discomfort with ambiguity [119]. Prior
work has shown NFCC to be negatively correlated with misinformation identification accuracy
[56]. Other studies have shown that individuals with high NFCC may be more likely to spread
misinformation on social media networks, with the effect attributed to avoidance behavior when
asked to provide evidence for beliefs [9]. The effect of NFCC in other information evaluation
domains has been contested [21]. Within the context of warning tags, the presence of a tag can
introduce uncertainty to users by explicitly questioning the veracity of the information presented
in the post. As warning tags may impact the level of ambiguity for online information assessment,
we seek to examine the influence of NFCC on warning tag attitudes. We hypothesize:
• H5: NFCC will be positively associated with favorable attitudes towards misinformation tags.
Prior work has established connections between personality and misinformation vulnerability
[56, 63] and spreading behavior [4, 37, 43, 66]. The Big Five Inventory (BFI) Conscientiousness
scale, for example, measures an individual’s tendency to be orderly, cautious, and self-disciplined.
Conscientiousness has been associated with better news discernment [16], and individuals low in
Conscientiousness may be more likely to share fake news [66]; although, later research refutes
these claims [70]. BFI Openness measures a person’s curiosity, receptivity to new experiences,
and imagination. Some reports find that individuals high in Openness may be more vulnerable
to misinformation [2]. However, there is also conflicting evidence observed with this BFI trait:
prior work demonstrates a negative association between Openness and belief in myths [111]
and a positive association with news discernment [16]. BFI Agreeableness also shows conflicting
evidence, as it has been associated with users who validate news prior to sharing [102] and with
misinformation susceptibility [15]. Misinformation studies testing BFI traits do not often report
effects from BFI Extraversion, however, there is work showing a negative association between
this trait and news discernment ability [14]. BFI Neuroticism is similarly underreported in the
misinformation literature, although recent work shows a positive association between Neuroticism
and trust in misinformation warning tags [8]. While these traits have been used to examine
misinformation susceptibility and sharing behaviors, there is little work assessing how these traits
influence attitudes towards misinformation warning tags. In the present work, we assess the
influence of personality traits on tag attitudes and self-report behaviors, as these dimensions may
impact how information is evaluated and perceptions of interventions. We use the BFI scale to
measure personality, as it is often used to evaluate the effects of individual traits on attitudes and
acceptance of social networking technologies [58, 99, 110]. We hypothesize:
• H6: BFI Conscientiousness, Openness, and Neuroticism will be positively associated
with favorable attitudes towards misinformation tags.
• H7: BFI Agreeableness and Extraversion will be negatively associated with favorable
attitudes towards misinformation tags.
Despite all of these studies assessing the impact of individual factors on misinformation vulnerability
and spread, very little work has focused on the impact of personal factors on misinformation
mitigation solutions like warning tags [31] specifically. In this work, we quantify and rank the
relative importance of personal factors that may influence attitudes towards misinformation tags.
No prior work, to our knowledge, directly compares the impact of individual differences at the
level of cognitive style, beliefs, and personality traits on general attitudes towards tag warning
systems. We seek to fill these knowledge gaps.

Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

2.2

CSCW340:7

Misinformation Warning Tags as Interventions

Due to the ubiquitous nature of misinformation spread online, deployment of mitigation solutions
that can work at-scale is vital. Prior studies have found the efficacy of tags and other intervention
methods to prevent belief in misinformation is complex and multi-faceted.
Attempts to curb the sharing of misinformation often involve moderation. Determining what
content to moderate is a difficult problem within itself: algorithms that can work swiftly at scale
typically come at the cost of nuance [29]. Recruiting a human to handle moderation edge cases
can help [64], but this approach assumes underlying algorithms can identify edge cases accurately.
Further, human-identified misinformation introduces the partiality of a human into the moderation
process [35]. Some users may be skeptical of warning tag accuracy depending on if the correction
comes from a community member or an algorithm [47] and many believe tags from humans are more
biased than tags from algorithms [116]. As a result, AI-based tagging of potential misinformation
and AI-generated labeling of online posts is seen as a potential soft-moderation middle ground that
can help curb misinformation spread and vulnerability without completely removing the agency of
the reader. Deployment efforts have been made by a number of social media platforms, including
Facebook and X (formerly Twitter) [19].
While the effectiveness of warning labels for misinformation has generally seen positive results,
there is an ongoing debate on their efficacy. As a whole, misinformation tags have been shown to
have a modest, positive effect on reducing belief in misinformation [17, 22, 60, 83]. Initial reports
of a “backfire” or “boomerang” effect that claim misinformation corrections might actually increase
misperceptions [77] have since been refuted [120], however, additional work has found that topic
domain may play a role in correction effectiveness [93]. For example, a meta-analysis by pui
Sally Chan and Albarracín [93] found correction attempts similar to misinformation tag labels are
more successful in scientific domains outside of health.
We argue that one of the reasons for prior mixed results on the efficacy of tags is due to the complex nature of misinformation vulnerability, spreading behavior, and attitudes towards mitigation
efforts. Misinformation vulnerability and spreading behavior is affected by a person’s psychological faculty [59], frequency of experience with the information [84], personal beliefs [47], and
socioeconomic status [117]. Intervention-centered factors include the phrasing of the intervention
[17, 25] and prevalence of alternative information during intervention [50]. Though not a complete
list, these findings demonstrate the complexity of evaluating the efficacy of misinformation tags.
Figure 2 shows an illustration on how users who differ based on thinking style, political beliefs and
attitudes, and personality traits could have different perceptions of misinformation tags, which
can subsequently influence engagement with social media posts. To some users, the presence of a
warning tag may have no impact on sharing behaviors while for others, the display of a warning
tag may prevent engagement. While factors such as thinking styles and personality traits may show
consistent effects on tag attitudes across misinformation topics, political beliefs and institutional
trust may vary more often based on whether the topic is aligned with relevant group identities. In
the present study, we hope to illuminate individual factors which may impact tag efficacy.
Given the wide range of individual factors which may impact a person’s attitudes towards tags,
it is unsurprising that prior research on warning label attitudes has resulted in mixed findings. As
noted, Saltz et al. [100] found conservatives oppose labeling interventions while liberals supported
labeling. Additionally, both conservatives and liberals attributed perceived labeling errors to bias
in human judgments against their beliefs, rather than accidental algorithmic or human mistakes.
Other works support the notion that who provides the warning label matters. Jia et al. [47] found
warning labels provided by an algorithm, community, or third-party fact-checker were trusted
by Democrats regardless of post ideology, while only algorithmic labels impacted Republican’s

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CSCW340:8

Robert Kaufman, Aaron Broukhim, and Michael Haupt

Fig. 2. Framework for how dispositional traits of users influence perceptions of misinformation warning tags
and engagement behaviors.

belief of false conservative news. All intervention methods were effective for Republicans when
the content was liberal-leaning.
Though it is clear that there are strong individual differences in the efficacy of tags, little work
has been conducted on the personalization of warning tags. Bhuiyan et al. [10] investigated the
impact of attention cue interventions (i.e., “nudges”) on users exposed to general – not medical –
true and false news headlines with consideration of political affiliation, media cynicism, and media
skepticism characteristics. However, they do not examine personality traits such as those from
BFI. The present study includes such characteristics, as well as provides a more nuanced attitude
scale for evaluating how users may interact with warning tags. In a related study, Saltz et al. [101]
identifies significant variance in individual preferences for warning tags. When asked about their
attitudes towards warning tags, a plethora of concerns arose for users. Some users felt patronized
by the social media platforms, thought the platforms were being too political, found the labels
insufficient, or preferred that the posts be removed altogether. A recent attempt at personalizing
moderation in the context of misinformation presents posts in a more culturally aligned way to the
user’s identifying political party Pretus et al. [91]. This study found that users are more receptive
to posts that align with their political party, especially in polarizing contexts, providing “initial
evidence that identity-based interventions may be more effective than identity-neutral alternatives
for addressing partisan misinformation”. The implication is that personalization may be an effective
direction to increase the efficacy of mitigation efforts like tags. In order to get there, we need to
know what individual differences matter.
Though prior work largely attributes discrepancies in warning tag efficacy to political affiliation,
there is increasing evidence that other characteristics better account for an individual’s misinformation susceptibility and sharing behaviors [10, 56, 66, 90, 109]. While this work finds that political
orientation is associated with attitudes towards warning tags, factors such as CRT, NFCC, BFI
Conscientiousness, Trust in Medical Scientists, and Trust in Religious Leaders were also influential.
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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

CSCW340:9

Such results are important to design and deploy misinformation warning labels that can meet the
needs of diverse groups.
3

Method

Participants from Amazon Mechanical Turk (MTurk) and SONA – an undergraduate population –
filled out survey scales to determine their political affiliation, trust in institutions, BFI scores, CRT,
and NFCC. The resulting scores were then analyzed with respect to the attitude scale found in Tables
5 and 7. Multivariate regression analysis was used to determine which individual characteristics
best predict respondent attitudes towards warning tag labels.
3.1

Data Collection

The first set of respondents were recruited from SONA (n = 112), a pool of undergraduate students
from a large university in the United States. The mean age of the SONA respondents was 21.6, of
which 71.4% identified as female. For the MTurk sample (n = 132), the mean age was 37.3 years
old and 42.4% identified as female. All respondents were anonymous. Respondents who failed
an attention check and/or finished under the 10th percentile of completion time (<4.5 minutes,
median completion time = 24 minutes) were excluded from analysis. Compensation varied between
sources: MTurk workers were compensated financially at standard survey-taking rates, while SONA
respondents were given course credits. Additional variables were included in the SONA sample to
further examine effects from personality factors (i.e., openness, agreeableness, neuroticism, and
extroversion). See Appendix Table 4 for detailed descriptions of the samples used in the present
analysis.
3.2

Variable Descriptions

To measure attitudes and self-described behaviors towards misinformation warning tags – the
main outcomes of this study – a tag attitude scale was adapted from existing scales used to assess
attitudes towards explainable AI explanations (these provide justifications for AI-decisions, similar
to algorithmically-generated tags) [105]. The tag scale consisted of 9 individual, 5-point likert scale
items ranging from strongly disagree to strongly agree. Participants first saw the statement “When
I see a social media post tagged as potential misinformation...” followed by each item. An image of
a warning tag within a newsfeed was also shown as an example to respondents (See Figure 1). An
aggregate (overall) score was calculated by averaging across items. The full scale can be viewed
in Table 7. A higher scale score indicates more favorable attitudes towards misinformation tags.
The first item “I usually ignore the tag” was reversed coded since it expresses negative sentiment
towards tags. Tag attitude scale items showed acceptable consistency within the merged SONA
and MTurk samples (Cronbach’s alpha = 0.659) and was not normally distributed (Shapiro-Wilk p <
0.01). Within the SONA sample, tag attitude items were more consistent (Cronbach’s alpha = 0.752)
and normally distributed (Shapiro-Wilk = 0.176) but showed less consistency (Cronbach’s alpha =
0.546) and not normally distributed (Shapiro-Wilk p < 0.01) for the MTurk sample.
To assess individual differences, the following scales were used:
• Political orientation – One question asking participants to identify their political beliefs, with
1 = Strong Democrat/Liberal to 6 = Strong Republican/Conservative.
• Trust in Institutions – One 4-item scale adapted from the 2019 Pew Research Center’s American
Trends Panel survey [28] asking respondents “How much confidence, if any, do you have in
each of the following to act in the best interests of the public?”. The institutions asked about
were elected officials, news media, medical scientists, and religious leaders with response
options ranging from 1 = No confidence at all to 4 = A great deal.
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CSCW340:10

Robert Kaufman, Aaron Broukhim, and Michael Haupt

• Big Five Inventory – One 41-item scale from the Big Five Inventory (BFI) was used to evaluate
participants across the personality dimension of Extroversion, Neuroticism, Agreeableness,
Openness, and Conscientiousness [48, 49, 94]. Only the subscale for Conscientiousness was
given to participants in the MTurk samples due to length constraints.
• Cognitive Reflection Test – The Cognitive Reflection Test (CRT) was employed to assess
cognitive reflection using three questions. Each question initially suggests an “intuitive”
answer, but arriving at the correct solution requires reflective thought. For instance, Question
1 states: “A bat and a ball together cost $1.10. The bat costs $1.00 more than the ball. How
much does the ball cost?” The intuitive answer is 10 cents, but the correct answer is 5 cents.
A higher number of correct answers indicates a higher CRT [27].
• Need for Cognitive Closure – A shortened 15-item scale [96], adapted from the original 42-item
scale [119], was utilized to assess NFCC.
Bivariate correlations were run between all trait variables and each individual tag item. Tag
items were not normally distributed, therefore the Kendall’s Tau correlation coefficient was used.
Correlations were also run between tested trait variables and the composite tag scale. Since the
composite tag scale was normally distributed, Pearson’s correlation was used with traits that were
also normally distributed, while Kendall’s Tau was used for traits with non-normal distributions.
Multiple regression was also run between tested traits and the composite tag scale as the dependent
variable. In order to account for potential multicollinearity of independent variables, a Shapley
Value regression was run to show the variance contributed by each tested trait. A Shapley Value
regression assesses the relative importance of predictors by computing all possible combinations of
independent variables within a regression model and recording how much the R-sq changes with
the addition or subtraction of each variable [12]. Shapley coefficients are standardized as they reflect
the proportion of model variance (R-sq) attributed to each independent variable. See the following
for further description and related applications of Shapley Value regressions: [20, 37, 44, 55, 56,
79, 107]. Regression model results were used to evaluate the strongest predictors of tag attitudes
across thinking styles (CRT, NFCC), political beliefs and attitudes (political orientation, trust in
institutions), and personality traits (BFI Extroversion, Neuroticism, Agreeableness, Openness, and
Conscientiousness).
4

Results

First, we present bivariate correlations of tested traits (BFI, NFCC, etc.) and tag attitudes (aggregate
score and individual items). Bivariate correlations were used to assess effects between the tested
predictors and the tag attitudes independent of covariates. Since researchers or developers may not
always have access to all available trait measures from users, we believe these results can reveal
effective proxy measures. Further, the bivariate correlations provide further granularity to our
results by showing how the predictor variables are associated with individual items from the tag
attitude scale.
We then present multiple regression results, which include Shapley value coefficients to break
down the top factors predicting warning tag attitudes. Regression modeling and Shapley coefficients
were used to assess and rank the strength of tested predictors when controlled for each other.
The strongest predictors are those that remain statistically significant when controlled for other
factors in a multiple regression model and explain the largest proportion of the variance for tag
attitudes based on Shapley coefficients. Effects from contextual differences of recruiting platforms
(undergraduates from SONA pool vs. workers on Amazon Mechanical Turk) and respondent age
were also tested as predictor variables. If recruiting platform shows a statistically significant effect
when controlled for all tested user characteristics, this would suggest that contextual factors
associated with the platforms further influence warning tag attitudes.
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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

4.1

CSCW340:11

Predicting Tag Attitudes

Table 1 shows bivariate correlations for the merged MTurk and SONA samples. See appendix Tables
5 and 7 for bivariate correlations for each sample separately. NFCC is positively correlated with the
aggregated tag attitude scale (r = 0.267, p < 0.01) and shows significant correlations with almost
every tag item (p < 0.01). Trust in medical scientists is also positively correlated with the tag attitude
scale (r = 0.178, p < 0.01) and significantly correlated with 6 of the 9 tag items (p < 0.05). Most
items show a positive correlation with Trust in Medical Scientists, except for the item “I usually
ignore the tag.” Political orientation is the last variable to show a significant correlation with the
aggregated scale (r = -0.152, p < 0.01). These results show that political conservatism was positively
correlated with agreeing with the item “I usually ignore the tag” (r = 0.179, p < 0.01) while political
liberalism was positively correlated with agreeing with items such as “I am less likely to read the
post” (r = -0.152, p < 0.01) and “I approach the post content with suspicion or hesitancy” (r = -0.157,
p < 0.01). Despite not showing significant correlations with the aggregated tag scale, all tested
traits showed at least two statistically significant correlations with individual items. For the item
“I usually ignore the tag,” CRT and BFI Conscientiousness were negatively correlated (r = -0.111,
p < 0.05; r = -0.214, p < 0.01) while trust in elected officials and religious leaders were positively
correlated (r = 0.154, p < 0.01; r = 0.203, p < 0.01). Those with greater Trust in Elected Officials,
News Media, and Medical Scientists were more likely to agree with the item “I evaluate the post
content based on who the author is” (r = 0.156, p < 0.01; r = 0.116, p < 0.05; r = 0.161, p < 0.01). BFI
Conscientiousness was negatively correlated with agreeing with the item “I trust that the post is,
in fact, misinformation” (r = -0.100, p < 0.05) while age and Trust in Elected Officials, News Media,
and Religious Leaders were positively correlated (r = 0.124, p < 0.05 ; r = 0.175, p < 0.01; r = 0.136,
p < 0.05; r = 0.110, p < 0.05). When examining BFI traits only measured in the SONA sample (see
Appendix Table 5), positive correlations were shown between the composite tag attitude scale and
BFI traits for Openness (r = 0.40, p < 0.01), Conscientiousness (r = 0.19, p < 0.05), and Agreeableness
(r = 0.29, p < 0.01).
Multiple regression model results with Shapley coefficients for both SONA and Mturk samples
are shown in Table 2. Predictor variables for dispositional traits were selected based on being
measured in both samples. The effect of recruitment platform is also tested (“Platform: SONA”),
which shows the difference of tag attitude effects associated with the SONA respondents compared
to MTurk. Respondent age is included as well to control for age-related effects. The current model
is shown to be a solid predictor of attitude variance (R2 of 0.23, adjusted R2 of 0.20) and shows that
NFCC is positively associated with favorable tag attitudes and is highly statistically significant (𝛽 =
0.21, p < 0.001) when controlled for all other variables. NFCC is also the strongest predictor in the
model by explaining over a third of the total R2 (36.3%). Additionally, Trust in Medical Scientists was
positively associated with favorable tag attitudes (𝛽 = 0.15, p < 0.001) and is the second strongest
predictor, contributing to 22.5% of variance. Both political conservatism (𝛽 = -0.07, p < 0.01) and
Trust in Religious Leaders (𝛽 = -0.10, p < 0.05) were negatively associated with the aggregated
attitude scale when controlled for tested predictors. However, political conservatism explained
over twice the variance of tag attitudes compared to Trust in Religious Leaders (16.9% vs. 6.5%).
Recruiting platform also showed a statistically significant effect, where respondents recruited on
SONA were less likely to have favorable tag attitudes compared to MTurk workers (𝛽 = -0.22,
p < 0.05). This effect explains up to 6.4% of tag attitude variance when controlled for all tested
predictors.

Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

0.029
0.267**
-0.013
-0.152**
0.086
0.074
0.178**
-0.028
0.033

“I am
less
likely
to read
the
post”

-0.001
0.186**
-0.053
-0.152**
0.049
0.061
0.011
-0.089
-0.036

“I
usually
ignore
the tag"

-0.111*
0.250**
-0.214**
0.179**
0.154**
0.066
-0.139*
0.203**
0.029
-0.022
0.171**
-0.015
-0.100
-0.046
-0.012
0.199**
-0.099
-0.082

“I am
less
likely
to
engage
with
the post
through
sharing,
replying, or
liking”

0.060
0.156**
-0.002
0.037
0.156**
0.116*
0.161**
0.102
0.089

“I evaluate
the
post
content
based
on who
the
author
is”

0.026
0.077
-0.026
-0.018
0.103
0.094
0.180**
0.090
0.072

“I am
more
likely
to research
the
post’s
topic in
order
to evaluate
the
content
myself”
0.102
0.186**
-0.099*
-0.157**
-0.023
0.003
0.221**
-0.052
-0.068

“I
approach
the post
content
with
suspicion
or
hesitancy”

Attitude responses prefaced with “When I see a social media post tagged as potential misinformation..."
Significance codes: *p < 0.05, **p < 0.01

CRT
NFCC
Conscientious
Politics
Trust Elected
Trust News Media
Trust Med Sci
Trust Religious
Age

Overall

-0.020
0.296**
-0.118*
-0.081
0.185**
0.096
-0.042
-0.001
0.176**

“I view
the
author
of the
post in
a negative
way"

-0.081
0.169**
-0.100*
-0.112*
0.175**
0.136**
0.035
0.110*
0.124**

“I trust
that the
post is,
in fact,
misinformation"

0.005
0.163**
0.067
-0.140**
0.025
0.081
0.222**
-0.013
-0.034

“I appreciate the
social
media
platform’s
attempt
to warn
me about
potential
misinformation”

CSCW340:12
Robert Kaufman, Aaron Broukhim, and Michael Haupt

Table 1. Bivariate correlations between tested variables and tag attitude items – SONA and MTurk combined
(n=244)

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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

CSCW340:13

Table 2. Multiple regression – Tag attitude aggregate as dependent variable (MTurk & SONA)

4.2

𝛽 coef

Shapley
𝑅2

std err

tvalue

pvalue

Politics
CRT
NFCC
Conscientiousness
Trust Elected
Trust News Media
Trust Med Sci
Trust Religious
Age
Platform: SONA

-0.07
0.01
0.21
-0.02
0.06
0.04
0.15
-0.10
0.00
-0.22

16.9%
0.5%
36.3%
0.2%
4.4%
4.9%
22.5%
6.5%
1.3%
6.4%

0.02
0.03
0.04
0.06
0.05
0.04
0.04
0.04
0.00
0.10

-3.32
0.53
5.04
-0.29
1.20
0.99
-2.45
-2.45
-1.23
-2.06

<0.01
0.59
<0.001
0.77
0.23
0.33
0.02
0.02
0.22
0.04

𝑅2
Adj 𝑅 2

0.23
0.20

SONA and MTurk Comparison

Both SONA and MTurk (Appendix 5 and 7) showed correlations between Conscientiousness and
the aggregate tag attitude score, however, the effects were in different directions. For SONA, higher
conscientiousness was correlated with more favorable tag attitudes while among MTurk workers
conscientiousness was negatively correlated. The samples did not share any other correlations with
respect to the aggregate tag attitude – though they did with certain individual attitude measures.
While NFCC showed no significant overall effects among SONA participants, among MTurkers
NFCC was positively correlated with the aggregate tag attitude scale (r = 0.447, p < 0.01) as well as all
individual items. Significant effects found in the SONA sample but not in the MTurk sample include
Politics, Trust in Medical Scientists, and Trust in Religious Leaders. BFI Openness, Agreeableness,
Extraversion, and Neuroticism were not tested in the MTurk sample.
Comparing multiple regression results (Appendix 6 and 8), both found Politics and Trust in
Medical Scientists to be significant predictors of tag attitudes when controlling for the other tested
variables. The SONA sample also found Openness to be significant, which was not available in the
MTurk sample. Further, the MTurk sample found NFCC, Conscientiousness, and Trust in Religious
Leaders to be statistically significant while the SONA sample did not. It is possible that the SONA
regression model may show fewer significant effects than MTurk due to having a greater number
of examined covariates.
4.3

Summary of Results

In summary, the multiple regression model and Shapley coefficients show that NFCC was the
strongest predictor of tag attitudes when controlled for all tested variables, followed by Trust in
Medical Scientists and political orientation. Trust in Religious Leaders and recruiting platform also
showed significant effects on tag attitudes, although the effects were weaker. Bivariate correlations
show that every tested factor had a statistically significant effect with individual tag attitude items,
which has implications for future warning tag design. When examining the SONA sample only,
the regression shows that Politics, Openness, and Trust in Medical Scientists were significant (p
< 0.05) when controlling for other variables. Within the MTurk sample, NFCC was the strongest
predictor by a large margin when controlled for all other variables, followed by Politics, Trust in
Medical Scientists, and Trust in Religious Leaders. Differences between the two distinct samples
Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

5

Personality H6: BFI Conscientiousness will be positively associated
with favorable tag attitudes
H7: BFI Openness and Neuroticism will be positively associated with favorable tag attitudes
H8: BFI Agreeableness and Extraversion will be negatively associated with favorable tag attitudes

H4: CRT is positively associated with favorable tag atti- H4: Not Supported
tudes
H5: Full Support
H5: NFCC is positively associated with favorable tag attitudes

Thinking
Styles

H6: Partial Support – Only showed
significant effect in MTurk model
and was negative
H7: Partial Support – Only BFI
Openness shows expected effect
H8: Not Supported

H1: Full Support
H2: Partial Support – Only Trust in
Medical Scientists showed expected
effects
H3: Full Support

H1: Political conservatism is negatively associated with
favorable tag attitudes
H2: Trust in Medical Scientists, Elected Officials, and
News Media will be positively associated with favorable
tag attitudes
H3: Trust in Religious Leaders is negatively associated
with favorable tag attitudes

Political
Beliefs
and
Attitudes

Present Findings*

Hypotheses

Traits

CSCW340:14
Robert Kaufman, Aaron Broukhim, and Michael Haupt

tested in this study further emphasize the importance of individual differences in understanding
the impact of misinformation mitigation strategies. Hypotheses and support for these hypotheses
are summarized in Table 3.
Table 3. Tested variables and hypotheses for attitudes on misinformation tags.

*Note: Support for hypotheses are based on results from the multiple regression models.

Discussion

The present study supports the premise that cognitive factors, beliefs, and personality traits impact
how a person views and interacts with misinformation warning tags. Specifically, our results

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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

CSCW340:15

indicate that several individual factors – including Politics, NFCC, Openness, Conscientiousness,
Agreeableness, Trust in Medical Scientists, and Trust in Religious Leaders – predict a person’s
attitudes towards misinformation warning tags. When controlled for effects from covariates and
multicollinearity, NFCC, political orientation, and Trust in Medical Scientists were the strongest
predictors for tag attitude variance among our merged sample. These results may explain discrepancies in past literature on the effectiveness of tags as a mitigation strategy, as certain tags may
work for some people but not others. Our results highlight the premise that designing tailored
misinformation interventions as opposed to taking one-size-fits-all strategies may benefit designers
seeking to curb misinformation for diverse groups.
Predictably, we find differences in characteristics between MTurk and SONA samples. Differences
seen between the samples strengthen the case that different populations may have different needs
for warning tags, and thus may require personalization. The SONA population presents a younger,
female-majority cohort of undergraduate students motivated by course extra credit, while MTurk
presents a more moderate, older and balanced gender population motivated by financial gain.
Improved predictions of a user’s attitude towards warning labels can be leveraged by future
designers as a means to personalize interventions that work best for specific user groups. In the
remainder of this paper, we will discuss the implications of our results and provide recommendations
for designing more effective misinformation warning tags.
5.1

Individual Factors Predict Warning Tag Attitudes

Political orientation is one indicator of warning tag attitudes, but does not show the whole
picture. We echo prior results showing political orientation plays a large role in attitudes towards
warning tags, confirming H1 [8, 100]. Indeed, Politics was the strongest predictor of tag attitudes for
the SONA dataset. For MTurk, however, Politics was a weaker predictor of tag attitudes compared
to other significant effects. In all samples, liberal-leaning respondents were more likely to have
a positive attitude towards warning tags, while conservative-leaning respondents were likely to
have negative attitudes.
Importantly, while Politics may be an effective indicator of tag attitudes in some cases, our
findings indicate that other factors such as Openness and NFCC are also strong predictors of a
respondent’s attitude towards warning tags. The inclusion of these other factors can provide a
more comprehensive understanding of how different groups may think about and behave toward
warning tags. In a more general sense, these results imply that the research community should look
beyond well-studied factors like political orientation and examine other personal characteristics
– including a person’s cognitive and psychological profile and trust judgments – to deepen our
understanding of how they may interact with misinformation mitigation strategies like tags.
The sources of information a person trusts may be an important predictor of their attitude
towards tags. Those who had greater trust in medical scientists were more likely to have positive
attitudes about warning tags for both the merged and SONA-only samples. This confirms part of H2:
when examining individual tag attitudes, respondents who had higher trust in medical scientists
report being more likely to perceive a post as misinformation and less likely to engage with a post
when tagged as misinformation. As prior work shows that those who use factual news sources
have higher trust in medical scientists [65], the positive associations with tag attitudes may reflect
a higher respect for institutional guidance and public health recommendations. More generally, it
may also reflect a person’s preference for expert judgment and evidence-based decision-making.
Trust in the other tested institutions, News Media and Elected Officials, showed no significant
effects on the aggregated tag attitude score.
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CSCW340:16

Robert Kaufman, Aaron Broukhim, and Michael Haupt

Trust in Religious Leaders was negatively associated with favorable tag attitudes among the
merged samples, supporting H3. One explanation for this finding may be due to differences in the
criteria by which credibility of information is assessed: people who have higher trust in religious
leaders may prioritize spiritual authorities over scientific authorities and distrust secular institutions
like social media platforms. Though prior work has found that religious beliefs do not indicate
conspiracy theory belief [46], differences in credibility assessment and trust in institutions has
important implications on misinformation intervention design.
Information assessment traits may be strong predictors of tag attitudes. CRT did not show a
significant effect on the aggregate attitudes score when controlled for our tested covariates, leading
us to reject H4. When examining bivariate correlations, CRT was only shown to have a negative
effect with the individual item “I usually ignore the tag”. While prior work shows that respondents
higher in CRT tend to be more capable of distinguishing real from fake news potentially due to
their propensity to research the topics themselves [82] [8], our findings suggest that CRT is not a
major predictor for general attitudes towards tag warning systems. The discrepancy between our
results and previous work may be further explained by differences in outcome measures, as CRT
may be a relevant predictor for evaluating the veracity of news headlines but less effective when
examining other measures of propagation behaviors on social media [37].
We also found that respondents who have a desire for certainty and predictability (i.e. high
NFCC) are more likely to have positive attitudes towards warning tags, supporting H5. Intuitively,
this makes sense: warning tags have the potential to bring clarity on whether a post’s information
can be relied upon. Interestingly, we did not find significant effects between NFCC and tag attitudes
for the SONA sample. This distinction between the SONA and MTurk sample warrants further
investigation, though we suspect it may be due to the overall higher tolerance for uncertainty
observed by the university students as compared to our more general MTurk population (see
Appendix Table 4 for mean comparisons).
Personality matters: BFI Conscientiousness was negatively associated with tag attitudes
among Mturk workers, and Openness was positively associated for SONA participants
when controlling for tested covariates. These findings partially support H6, as Conscientiousness
showed a significant association with tag attitudes in the MTurk-only regression, but was in the
opposite direction than expected. This effect could stem from a desire for self-sufficiency or a
preference to avoid outside judgments if they believe they can handle the task themselves and
believe those providing interventions may have goals counter to their own. If the less favorable
views of tags among Conscientious individuals is driven by stronger confidence in information
assessment abilities, then the present results may still be consistent with previous work showing
associations between Conscientiousness with better news discernment and lower tendency to share
misinformation [16, 66].
We did find mixed results, however, when comparing samples. Conscientiousness was negatively
correlated with aggregate tag attitudes in the MTurk sample, but showed a positive correlation for
SONA respondents. It is also worth noting that the effect from Conscientiousness from MTurkers
remained significant when controlled for the influence from the other tested variables while it no
longer showed an effect in the SONA regression model. This may indicate that for MTurk workers,
Conscientiousness is a more influential trait for misinformation-related outcome measures. This
is further suggested in related work, which showed that Conscientiousness was the strongest
predictor for misinformation detection accuracy among MTurkers [56]. The difference in the
direction of correlation effects may also be attributed to contextual differences when recruiting
respondents from MTurk compared to SONA. For MTurk workers, Conscientiousness may be a
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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

CSCW340:17

more influential factor because being orderly and cautious would be more useful attributes when
completing other tasks on the platform, which typically involve an attention to detail to receive
approval for the work (e.g., correctly labeling images to be used as training data). In contrast,
undergraduates enrolled in a university course may place less importance on being Conscientious,
as they are not receiving monetary compensation for completing tasks requiring attention to
detail. Undergraduates may also have more favorable views of social media companies in general,
which could result in more favorable views of tagging interventions. Results in Table 5 support
this possibility, where Conscientious was positively correlated with the statement “I appreciate
the social media platform’s attempt to warn me about potential misinformation” among SONA
respondents.
Our findings partially support H7, as Openness is positively associated with favorable tag attitudes
in the SONA-only regression. However, there are no effects observed with Neuroticism. This is
consistent with previous work showing that Openness is associated with better news discernment
and skepticism towards myths [8, 16, 111]. We believe openness in this case may be reflective of how
willing a person is to accept an outside judgment of the credibility of information. It is important to
keep in mind that Openness was not tested in the MTurk sample, and thus comparisons cannot be
drawn between samples. Our findings are inconsistent with [8], as we do not observe a significant
effect between Neuroticism and tag attitudes. Similar to the effects previously described for CRT,
differences in outcome measures may explain discrepancies between the current findings and prior
work. In other words, Neuroticism may be an effective predictor for evaluating truthfulness of news
headlines but may not be a reliable factor that influences general attitudes towards tag warning
systems.
Lastly, there were no significant effects observed in the regression analysis for both Agreeableness
and Extraversion on tag attitudes. Therefore, we reject H8. While we did not observe significant
effects from Agreeableness when controlled for our tested covariates, our results show significant
effects with the aggregate attitude score and individual items when using bivariate correlations.
These findings suggest that respondents who are more Agreeable – i.e. they have a greater care
for social harmony and cohesion – may have more positive attitudes towards some aspects of
warning label interventions. This intuitively makes sense as well: misinformation warning tags
are, in a general sense, pro-social, and may be seen as a way to promote truth, show care for
one’s community, and reduce the negative impacts of spreading false information. This aligns with
a previous work claiming users high in agreeableness are more likely to validate news prior to
sharing said news [102] but diverges with work finding no correlation between agreeableness and
sharing flagged content [8]. Since Agreeableness did not show significant effects in the multiple
regression model, this implies that it is less important to consider than other characteristics.
5.2

Design Implications

In this study, we found that certain traits can predict people’s attitudes and self-described behaviors
towards misinformation warning tag interventions. These results have implications for the design
of misinformation mitigation solutions like warning tags. Most importantly, our results highlight
the need for personalized and culturally-relevant warning tag interventions, as one-size-fits-all
approaches to prevent misinformation uptake and spread may fail if they don’t harmonize with the
specific needs and motivations of a particular individual. These include structuring messages for
audiences with different psychological, cognitive, and information-assessment traits as well as using
an understanding of credibility signaling to help people who trust different authority sources heed
similar warnings. Concretely, this could be realized in the real world by offering curated interaction
experiences on social media sites like Facebook or X to people with different trait profiles, such
as showing certain information for some groups but not for others. For developers interested in
Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

CSCW340:18

Robert Kaufman, Aaron Broukhim, and Michael Haupt

tagging interventions, labels can be further tailored to trait profiles by adapting text wording,
graphics used (e.g., red exclamation mark), and whether to cite authorities or official institutions
(e.g., World Health Organization). Taken together, our findings provide actionable direction for
future warning tag design and misinformation mitigation solution design more generally.
Designing For Cognitive and Informational Assessment Traits. Our results imply that cognitive and information assessment traits like Need for Cognitive Closure (NFCC) may meaningfully
impact a person’s attitude and behavior towards misinformation warning tags. The general implication is that these traits should be accounted for when designing warning tags and other
misinformation mitigation solutions. The results of our study can provide some direction for designers, however, further research is needed on how exactly to design for people with high and low
NFCC. As we may not always be able to detect a person’s cognitive and informational assessment
traits, it may be best to design for the anti-tag attitude cases.
People high in NFCC prefer clear, unambiguous information, and thus we saw in our study that
these individuals (particularly in our MTurk sample) had more favorable attitudes towards warning
tags, and thus warning tags may be appropriate and effective for this population. A more challenging
design problem is designing for people low in NFCC. For these people, messaging regarding the
importance of certainty and clarity on the truthfulness of information and the implications of
spreading false information may be helpful. In this way, even people who tend to be less concerned
about ambiguity may engage in more information discernment behaviors.
Our results suggest that people high in CRT are less likely to ignore the warning (MTurk)
and are more likely to research the topic themselves (SONA). Warning tag interventions may
capitalize on the user’s interest in self determination by presenting relevant research articles
or news from reputable sources. For misinformation in the health domain specifically, harder
moderation approaches with detailed explanation on why the determination was made may be
appropriate for these users. Users lower in CRT may warrant behavioral intervention or harder
("hands-off") moderation approaches. Since these users tend to ignore warning tags and are less
likely to conduct their own research, prompts requesting behavior (for example, "Are you sure you
want to share this potentially false post?") may be helpful. Severe cases of misinformation may need
to be completely removed for these users.
Designing For Personality. Our results indicate that tailoring misinformation warning tags and
other mitigation solutions based on a person’s personality may be an effective approach. Prior
work has shown connections between personality and misinformation vulnerability, and our study
shows consistent results [4, 43, 56, 63, 66]. Recent work in social media research has shown that
personality can be accurately detected from a person’s social media behavior [26] – we suggest that
similar approaches may be used as a means to inform the tailoring of personality-based warning
tags.
People high in Openness may benefit from warning tags as a means to show them external
perspectives. To design for people low in Openness, messaging emphasizing the importance of
exploring new, diverse, or more objective perspectives to their own may be necessary for warning
tag acceptance. Considering people low in Openness are less interested in conducting their own
research, providing justification for misinformation as part of the label may provide a prudent safe
guard for these more susceptible users. Other priming interventions, such as "accuracy prompts"
which asks users to rate a neutral headline to encourage users to scrutinize future articles [87],
may also be pertinent.
Similar to Openness, people high in Agreeableness may benefit from warning tags inherently.
For people high in Agreeableness, emphasis on the pro-social nature of warnings may increase their
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effectiveness. For people low in Agreeableness, prompts to increase empathy, orient the person to
understand the impact of misinformation on vulnerable communities, and emphasize compassion
may be helpful to increase warning tag efficacy.
People high in Conscientiousness may benefit from warning tags emphasizing the importance
of sharing verified information, whereas those low in Conscientiousness may be less organized, so
simple messaging and clear visuals that require less detail orientation may be helpful. Those high
in Conscientiousness and more trusting of social media platforms (such as from our undergraduate
sample) will likely benefit from warning tag interventions as they are. Users similar to those in
the MTurk population that are also high in Conscientiousness trust warning tags less and may
require interventions that increase trust for these particular users. Providing explanations about
the underlying decision making process (algorithmic, community, 3rd party, etc.), could increase
user trust with platforms as well. We posit 3rd party moderators may be particularly effective at
increasing platform trust with these users.
Designing For Trust And Credibility Based On Identity And Belief. Our study results suggest
that the informational sources one trusts as well as their belief systems impact a person’s attitudes
towards misinformation warning tags.
In the case of political orientation, self-identified liberals were generally more positive about
warning tags than conservatives. Understanding why there may be differences based on political
ideology may reflect differences in media consumption habits, including trust in mainstream
media and the perceived bias of misinformation-identifying algorithms, as well as the bias of the
misinformation itself (i.e. if the misinformation supports republican or democratic-led claims).
Designing warning tags based on partisan divides is a challenge when one wants to remain objective
in the discernment of trust based on demonstrable fact, however, some general principles may apply.
There is evidence to suggest that republicans and democrats may differ in their value judgments
[34], and thus tailored tags that appeal to each group’s value systems may be a good place to start.
For example, liberals may respond better to appeals to institutional authority, collective well-being,
or scientific argument, whereas conservatives may respond better to appeals to individual freedom
and personal responsibility. In both cases, endorsement by authority figures from their respective
parties may help increase the effectiveness of warning tags.
In a similar vein, we find that the specific institutions a person trusts impacts their warning
tag attitudes. Our study found people high in Trust in Medical Scientists had more favorable tag
attitudes, while people with high Trust in Religious Leaders had less favorable tag attitudes. These
results imply the importance of mentioning authority figures that are credible to users as well as
using messaging that aligns with a person’s reasoning priorities. For example, warning tags for
people high in Trust in Medical Scientists may benefit from logical arguments based in scientific
reasoning, while people low in Trust in Medical Scientists but high in trust in a different institution
– such as Religious Leaders – may benefit from appeals to specific spiritual values, culturallyspecific messaging, and moral responsibility. Previous work showing that medical authority is often
used to propagate misinformation (e.g., stating a false health treatment is endorsed by a medical
professional) [36, 39] should also be taken into consideration when designing tag interventions,
as this increases the difficulty for algorithms to detect misinformation. In these scenarios, tag
labels that hedge or use less certain language (e.g., this may contain misinformation) could be
more effective by mitigating effects from potentially labeling accurate scientific information as
misinformation. For people high in Trust in Religious Leaders specifically, however, it is possible
that warning tags may not be the best intervention. These users may be more likely to ignore tags
and may even have more positive views of authors of potential misinformation. More generally,
interventions may also attempt to improve trust by catering to a person’s preference of moderation
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Robert Kaufman, Aaron Broukhim, and Michael Haupt

group (community, third party, algorithmic) to best determine what should be removed, as long as
it remains objective.
Ethical Implications of Tailored Warning Tags. Our work has significant ethical implications.
Prior research demonstrates that social media behavior, such as Facebook likes, can reveal psychological characteristics, including Big Five personality traits [61]. Tag warning systems can leverage
this insight by classifying users and tailoring interventions based on their online behaviors. While
this approach presents a promising opportunity to personalize misinformation warnings at scale, it
raises important ethical concerns, particularly regarding user consent and agency. Personalized
interventions could be implemented without the explicit consent of users, relying on assumptions
about their preferences and vulnerabilities. Although these interventions aim to assist users in
identifying misinformation, it is crucial to preserve user autonomy and privacy. Users should have
the ability to opt out of personalized interventions or biasing algorithms, ensuring they retain
control over their online experience. Future work should explore the default settings for platforms
employing such interventions and consider the implications of involving users in decisions about
their desired social media experience. Furthermore, it is essential to address the risk of misusing personalization data, especially for vulnerable populations. Careful safeguards must be implemented
to ensure equitable access to tagging information and to mitigate algorithmic biases that could
disproportionately affect marginalized groups. Lastly, it is worth noting that those who might
benefit most from these interventions – such as individuals with a high distrust of institutions –
may also be the least likely to adopt them. Addressing this challenge remains an open question. We
argue that, above all, users must have the agency to decide what level and type of personalization
is appropriate for them, striking a balance between effective misinformation mitigation and respect
for individual autonomy.
Broader Implications. Up until this point, we have primarily focused on individual differences with
regards to warning tag interventions for online misinformation specifically. We expect that many
of the implications of this study – particularly the need for personalization and culturally-relevant
design – to generalize to other types of misinformation interventions or even to other domains
where algorithmically-determined decisions need to be trusted or understood by a particular user,
such as explainable AI. In particular, we expect that many of the design implications detailed
above, including how to design for cognitive and informational traits, personality, and beliefs
about trust and credibility, may generalize to other contexts. The concept of personalization is not
new – personalization and tailoring has been commonplace in public health and advertising for
decades [62], and has become a major topic in human-computer (particularly human-AI) interaction
[41, 57, 81, 103, 115]. Our results support this direction of work and may be used by practitioners
in a variety of HCI domains as a means to better understand and design for diverse populations.
6

Limitations and Future Work

This study was not without limitations. This study was conducted in the United States, as such
the results may not generalize to other populations. The SONA results may be more applicable
to younger/liberal leaning populations, while MTurk results may can be applied to more general
populations. MTurk workers were motivated via financial incentives while SONA participants were
motivated by class credits, resulting in potentially different values when taking the survey, and
though unlikely, it is possible compensation may have biased results. When assessing the validity
of our tags attitude scale, our results show a lower Cronbach’s alpha value (0.546) for the MTurk
sample. While the individual items in our scale still reflect specific types of reactions to warning
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tags (e.g., ignore it) that can inform interventions, further refinement may be warranted to measure
general attitudes towards tag warning systems more consistently.
There is always the risk of dishonest answers from respondents who partook in this study, though
the study was voluntary and anonymous to limit this risk. Since data is self-reported, we do not
know how actual behavior in real world settings may differ from the reported attitudes in this study.
Mosleh at al. [74] find self-reported willingness to share data translates to actual sharing behavior
in the context of sharing news. We make the assumption that such indications hold for other
interaction behavior in our survey but we encourage future work to investigate if self-reported
actions such as ignoring tags, likelihood to read a post, post author evaluation in the presence
of a warning tag, suspicion or hesitancy in the presence of a warning tag, and attitudes towards
a platform in the presence of warning tags are consistent with these self-reported responses. In
addition, self-reported political affiliation may have variation stemming from misperception of
what it means to identify with a party [121]. This may in part explain some of the variance seen in
the earlier results.
These limitations motivate potential future research and design studies. A user study testing the
effectiveness of tag interventions "in the wild" to better assess real world behavior towards different
types of tailored warning tags would be a natural next step. Future work should also consider
adapting simulated social media newsfeeds [45] to further assess the effects from interventions and
user characteristics on behavioral outcomes. In fact, the use of simulated newsfeeds and survey scales
can reveal greater nuance in user reactions to tag warning systems by accounting for both behaviors
and attitudes. Our results show differences between MTurk and SONA recruiting platforms when
controlled for age and political orientation, implying that contextual differences (e.g., incentive)
and other characteristics not analyzed in the current study (e.g., education level, income) may
also influence tag attitudes. Further examination is needed to further explain attitude differences
between recruiting platforms. As our results are based only on US samples, future research should
consider sampling from other countries to assess cultural differences in warning tag attitudes;
warning tags may then be personalized accordingly to these additional user populations. Beyond
cultural differences, future work may investigate additional populations or characteristics to better
understand how attitudes may differ.

7

Conclusion

In this study, we investigated how cognitive factors, beliefs, and personality impact attitudes
towards misinformation warning tags. This is an important area of study, as online misinformation
is rampant and warning tags may provide an effective mitigation solution. Using two distinct
sample groups, we find that NFCC, Trust in Medical Scientists, and political liberalism are the
strongest predictors of favorable attitudes towards tag warning systems while Trust in Religious
Leaders is negative associated. When examining effects within each sample, we find evidence
that Openness, Agreeableness, and CRT can also influence attitudes towards warning tags. These
characteristics can be used to predict user attitudes towards warning labels and be leveraged to
personalize misinformation warning tags, which can potentially increase their efficacy for diverse
populations. Results are synthesized to provide direction for designing online misinformation
interventions based on a person’s cognitive traits, personality, and beliefs about trust and credibility.
The implications of this study may generalize to other domains where algorithmically-determined
decisions are deployed at-scale, including other human-computer interaction domains.

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Robert Kaufman, Aaron Broukhim, and Michael Haupt

Acknowledgments
The authors would like to acknowledge Steven Dow, Chloe Lee, and Joseline Chang for supporting
the data collection process. A special thanks to the Discovery Way Foundation for their generous
usage of lab space.
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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes

CSCW340:27

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CSCW340:28

A
A.1

Robert Kaufman, Aaron Broukhim, and Michael Haupt

Additional Tables
Mean Comparison of MTurk and SONA

Based on Table 4, the MTurk sample is older and more conservative compared to SONA. MTurkers
also show a statistically significant difference with higher Trust in News Outlets, Trust in Religious
Leaders, Trust in Elected Officials, and NFCC compared to SONA respondents. MTurkers also have
slightly more favorable attitudes towards warning tags on average than SONA.
Table 4. Mean Comparisons in Factor Measures by Platform

Age
Political Conservativeness
Trust News
Trust Medical
Trust Religious
Trust Elected
NFCC
CRT
Conscientious
Extraversion
Openness
Neuroticism
Agreeableness
Aggregate Tag Score

MTurk

SONA

Diff

Scale Range

37.25
3.39
3.09
3.07
2.69
2.85
4.29
0.98
3.34
NA
NA
NA
NA
3.60

21.63
2.54
2.04
3.13
1.65
1.83
3.97
1.15
3.44
3.08
3.74
3.12
3.46
3.46

15.62
0.85
1.05
-0.06
1.04
1.02
0.32
-0.17
-0.10
NA
NA
NA
NA
-0.1

Open
(1 to 6)
(1 to 4)
(1 to 4)
(1 to 4)
(1 to 4)
(1 to 6)
(0 to 3)
(1 to 5)
(1 to 5)
(1 to 5)
(1 to 5)
(1 to 5)
(1 to 5)

Note: Emboldened characteristics are significantly different
at p < 0.05 in the two-sided test of equality for column means.

A.2

Bivariate Correlations for SONA Sample Only

Bivariate correlations were run on the SONA sample between tag attitudes items and individual
traits as shown in Table 5. Positive correlations were shown between the composite tag attitude
scale and BFI traits for Openness (r = 0.40, p < 0.01), Conscientiousness (r = 0.19, p < 0.05), and
Agreeableness (r = 0.29, p < 0.01). For demographic variables, the tag attitude scale was positively
correlated with stronger identification with being a Democrat (0.36, p < 0.01) and higher Trust in
Medical Scientists (r = 0.27, p < 0.01). However, Trust in Religious Leaders was negatively correlated
with tag attitudes (r = -0.20, p <.01). All variables that showed significant correlations with the
composite attitude scale also showed correlations with multiple individual tag items. While CRT
shows no significant correlation with the tag attitude scale in the SONA sample, it was positively
associated with the tag item “I am more likely to research the post’s topic in order to evaluate the
content myself” (r = 0.18, p < 0.05).
A.3

Multiple Regression Results for SONA Sample Only

Multiple regression model results with Shapley coefficients for the SONA sample are shown in
Table 6. The current model is shown to be a strong predictor of tag attitude variance (𝑅 2 of 0.38
and adjusted 𝑅 2 of 0.30). Political orientation is the most influential variable explaining 39.01%
of variance for tag attitudes as shown from the Shapley results. Further, political liberalism was
Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

0.070
0.050
0.400**
0.190*
0.290**
0.110
0.070
-0.360**
-0.020
-0.030
0.270**
-0.200**
-0.050

0.020
0.001
-0.243**
-0.199**
-0.125
-0.111
-0.013
0.285**
0.139
0.053
-0.102
0.127
0.067

“I
usually
ignore
the tag"

-0.036
0.096
0.091
0.049
0.139
-0.003
0.088
-0.194*
0.110
0.130
0.125
-0.144
-0.051

“I am
less
likely
to read
the
post”

-0.074
0.0270
0.244**
0.100
0.173*
0.130
-0.105
-0.230**
-0.024
0.019
0.265**
-0.114
-0.028

“I am
less
likely
to
engage
with
the post
through
sharing,
replying, or
liking”
0.130
-0.025
0.207**
0.086
0.194**
0.110
0.032
-0.005
0.068
-0.088
0.129
-0.015
-0.092

“I evaluate
the
post
content
based
on who
the
author
is”

“I approach
the
post
content
with
suspicion
or
hesitancy”

0.111
0.063
0.310**
0.245**
0.268**
0.006
-0.019
-0.254**
-0.140
-0.030
0.339**
-0.188*
-0.114

“I am
more
likely
to research
the
post’s
topic in
order
to evaluate
the
content
myself"
0.180*
-0.030
0.280**
-0.007
0.061
0.021
0.039
-0.125
-0.001
0.072
0.252**
-0.094
-0.043

Attitude responses prefaced with “When I see a social media post tagged as potential misinformation..."
Significance codes: *p < 0.05, **p < 0.01

CRT
NFCC
Openness
Conscientious
Agreeableness
Extroversion
Neuroticism
Politics
Trust Elected
Trust News Media
Trust Med Sci
Trust Religious
Age

Overall

0.010
0.050
0.078
-0.020
0.066
-0.081
0.067
-0.300**
0.160
-0.043
0.043
-0.190*
0.104

“I view
the
author
of the
post in
a negative
way”

0.000
-0.060
0.130
0.060
0.080
0.097
0.009
-0.332**
-0.007
-0.025
0.040
-0.061
-0.006

“I trust
that the
post is,
in fact,
misinformation"

0.065
0.0280
0.359**
0.228**
0.256**
0.101
-0.040
-0.415**
-0.111
-0.004
0.251**
-0.162
-0.082

“I appreciate the
social
media
platform’s
attempt
to warn
me about
potential
misinformation”

WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality
on Misinformation Warning Tag Attitudes
CSCW340:29

Table 5. Bivariate correlations between tested variables and tag attitude items (SONA)

Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

CSCW340:30

Robert Kaufman, Aaron Broukhim, and Michael Haupt

Table 6. Multiple regression – Tag attitude aggregate as dependent variable (SONA)
𝛽 coef

Shapley
𝑅2

std err

tvalue

pvalue

Politics
CRT
NFCC
Openness
Conscientiousness
Agreeableness
Extraversion
Neuroticism
Trust Elected
Trust News Media
Trust Med Sci
Trust Religious
Age

-0.28
-0.01
0.09
0.21
0.15
0.08
-0.03
0.09
0.07
-0.01
0.15
-0.07
0.00

39.01%
0.30%
1.95%
19.18%
5.61%
7.95%
0.91%
2.72%
0.86%
0.34%
13.54%
7.21%
0.42%

0.07
0.04
0.09
0.10
0.09
0.11
0.10
0.08
0.08
0.07
0.07
0.07
0.01

-4.05
-0.34
1.08
2.00
1.55
0.77
-0.32
1.21
0.94
-0.11
2.24
-1.00
0.02

0.00
0.73
0.28
0.05
0.12
0.44
0.75
0.23
0.35
0.91
0.03
0.32
0.98

𝑅2
Adj 𝑅 2

0.38
0.30

positively associated with tag attitudes when controlling for all other tested variables (𝛽 = -0.28, p
< 0.001). BFI Openness was the second most influential variable contributing up to 19.18% of tag
attitude variance, and was shown to be positively associated when controlled for all other factors (𝛽
= 0.21, p < 0.05). Trust in Medical Scientists was the third most influential variable contributing to
13.54% of tag attitude variance. This model also shows that Trust in Medical Scientists is positively
correlated with tag attitudes when controlled for all other traits and demographic factors (𝛽 = 0.15,
p < 0.05). No other variables in the model showed a significant effect with aggregate tag attitude.
A.4

Bivariate Correlations for MTurk Sample Only

Table 7 shows bivariate correlations for the MTurk sample. NFCC was positively correlated with
the tag attitude scale (r = 0.447, p < 0.01). Further, Conscientiousness within the MTurk sample
was negatively correlated with the tag attitude scale (r = -0.161, p < 0.05). All remaining variables
except for Trust in Elected Officials showed no statistically significant effects with the composite
tag attitude scale. While CRT was not statistically significant with respect to aggregate tag attitudes,
those higher in CRT were less likely to agree with the individual statement "I usually ignore the tag"
(r = -0.230, p < 0.01 ). Political orientation did not show a significant correlation with the aggregate
attitude scale, however, being liberal was correlated with agreeing with the item "I am less likely
to read the post” (r = -0.165, p < 0.05) when seeing a tag for potential misinformation. For trust
in new media, only “I appreciate the social media platform’s attempt to warn me about potential
misinformation" showed a significant effect (r = 0.182, p < 0.05). Trust in Medical Scientists had
three significant correlations with "I usually ignore the tag", "I evaluate the post content based
on who the author is", and "I appreciate the social media platform’s attempt to warn me about
potential misinformation" despite the aggregate not being significant. Trust in Religious Leaders
had significant correlations with "I usually ignore the tag" and "I am more likely to research the
post’s topic in order to evaluate the content myself". Lastly, the response "I usually ignore the tag"
was negatively correlated with age (r = -0.154, p < 0.05).
Proc. ACM Hum.-Comput. Interact., Vol. 9, No. 7, Article CSCW340. Publication date: November 2025.

-0.005
0.447**
-0.161*
-0.079
0.084
0.075
0.109
0.015
-0.077

"I am
less
likely
to read
the
post”

0.031
0.218**
-0.125
-0.165*
-0.005
0.020
-0.061
-0.066
-0.114

"I
usually
ignore
the tag"

-0.230**
0.399**
-0.214**
0.122
0.133
-0.017
-0.160*
0.212**
-0.154*
0.013
0.320**
-0.127
-0.028
-0.053
0.009
0.146
-0.057
-0.108

“I am
less
likely
to
engage
with
t

[The evaluation harness truncated this reference: showing the first 120000 of 122950 characters.]
</reference>

<statements>
1. High NFCC positively predicted favorable attitudes toward unambiguous warning flags, as explicit labels immediately reduce interpretive ambiguity.
2. A related facet of this dynamic involves receptivity to warning systems and prebunking frameworks on social media platforms.
3. While individuals high in NFCC actively resist nuanced retractions that create ambiguity, they show elevated approval and adherence toward definitive, authoritative warning tags.
4. Finally, platform warning mechanisms should leverage high NFCC individuals' preference for structure by presenting clear, definitive, and authoritative guidance that resolves ambiguity without inducing analytical fatigue.
</statements>

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