# Which linguistic cues make people fall for fake news?
A comparison of cognitive and affective processing

Bernhard Lutz<EMAIL_ADDRESS>University of
FreiburgRempartstr. 16FreiburgGermany ,  Marc Adam<EMAIL_ADDRESS>University of NewcastleCallaghan University DriveNewcastleAustralia ,  Stefan
Feuerriegel  LMU MunichGeschwister-Scholl-Platz 1MunichGermany
<EMAIL_ADDRESS>,  Nicolas Pröllochs<EMAIL_ADDRESS>University of GiessenLicher Straße 74GiessenGermany and  Dirk Neumann
<EMAIL_ADDRESS>University of FreiburgRempartstr.
16FreiburgGermany

###### Abstract.

Fake news on social media has large, negative implications for society.
However, little is known about what linguistic cues make people fall for fake
news and, hence, how to design effective countermeasures for social media. In
this study, we seek to understand which linguistic cues make people fall for
fake news. Linguistic cues (e.g., adverbs, personal pronouns, positive emotion
words, negative emotion words) are important characteristics of any text and
also affect how people process real vs. fake news. Specifically, we compare
the role of linguistic cues across both cognitive processing (related to
careful thinking) and affective processing (related to unconscious automatic
evaluations). To this end, we performed a within-subject experiment where we
collected neurophysiological measurements of 42 subjects while these read a
sample of 40 real and fake news articles. During our experiment, we measured
cognitive processing through eye fixations, and affective processing _in situ_
through heart rate variability. We find that users engage more in cognitive
processing for longer fake news articles, while affective processing is more
pronounced for fake news written in analytic words. To the best of our
knowledge, this is the first work studying the role of linguistic cues in fake
news processing. Altogether, our findings have important implications for
designing online platforms that encourage users to engage in careful thinking
and thus prevent them from falling for fake news.

fake news; affective computing; neurophysiological measurements; regression
analysis; social media

††ccs: Human-centered computing Social media††ccs: Applied computing
Psychology††ccs: Human-centered computing Empirical studies in collaborative
and social computing

##  1\. Introduction

Fake news presents a major threat to social media integrity (Pröllochs and
Feuerriegel, 2023; Starbird et al., 2019), with negative implications for the
opinions of individuals and society at large (Aral and Eckles, 2019; Bär et
al., 2023; Lazer et al., 2018). As examples, fake news has spread widely
during the 2020 US Presidential Election (Flamino et al., 2023; Pennycook and
Rand, 2021a), the COVID-19 pandemic (Cinelli et al., 2020; Solovev and
Pröllochs, 2022), and the 2022 Russian invasion of Ukraine (Geissler et al.,
2022). According to global surveys, $86\text{\,}\mathrm{\char 37\relax}$ of
Internet users have at least once fallen for fake news (Centre for
International Governance Innovation, 2019). To curb the spread, an important
prerequisite is to understand why users fall for fake news.

Prior research has aimed to understand how and why people engage with fake
news. For example, research modeled the dynamics behind fake news sharing
(Pröllochs and Feuerriegel, 2023; Vosoughi et al., 2018; Solovev and
Pröllochs, 2022; Pröllochs et al., 021b, 021a; Drolsbach and Pröllochs, 2023;
Friggeri et al., 2014). Other works look at the individual characteristics of
fake news recipients and why some people are more likely to believe in fake
news. Here, research has looked at different dimensions, such as political
orientation (Traberg and van der Linden, 2022), religious animosity (Bronstein
et al., 2019), and trust in science (Scheufele and Krause, 2019). Even others
look at behavioral aspects such as the extent to which users engage in
analytical thinking (Arechar et al., 2022; Pennycook and Rand, 2021b, 2019),
as more extensive reasoning should make it easier to debunk false statements.
A related stream focuses on how different forms of presentation (e.g.,
presenting the source of news) affect beliefs (e.g., Spezzano et al., 2021;
Gao et al., 2018; Kirchner and Reuter, 2020; Lu et al., 2022). In contrast to
that, we focus on information processing, which is the precursor to belief
formation, and thus allows us to examine how users process fake news and why
they fall for it.

In this study, we analyze why users fall for fake news based on different
linguistic cues. Linguistic cues have been studied extensively in the context
of deception detection in online communication (e.g., Conroy et al., 2015; Ho
et al., 2015; Siering et al., 2016; Zhou et al., 2004), providing evidence
that deceptive content is qualitatively different from truthful content as
deceivers are lacking precise knowledge and details. For instance, deceivers
were shown to write messages with lower complexity (Zhou et al., 2004), but
with more words conveying emotions (Ho et al., 2016). To this end, we posit
that linguistic cues trigger different types of information processing, which,
in turn, explains why users fall for fake news.

Here, we compare the role of linguistic cues across both (i) cognitive
processing and (ii) affective processing of fake news. Cognitive processing
refers to careful thinking that users engage in, which is crucial to correctly
assess news as real or fake (e.g., Pennycook and Rand, 2020; Moravec et al.,
2022; Arechar et al., 2022). Affective processing refers to an unconscious
automatic evaluation. Specifically, it is a “neural activity representing the
most basic decision-making quality that guides human behavior“ (Walla, 2018,
p. 148). As such, affective processing can guide human behavior involuntarily
and even without users noticing. Of note, affective processing occurs _in
situ_ , that is, at the moment when users process the content, because of
which affective processes should be measured _in situ_ , that is, through
neurophysiological measurements such as heart rate variability (Appelhans and
Luecken, 2006; Lane et al., 2009). Importantly, prior research has previously
shown that the processing of fake news is governed by _both_ cognitive
processes and affective processes (Lutz et al., 2020a, 2023), yet it remains
unclear how these processes are triggered. Identifying the driving factors of
cognitive and affective processing would hence allow for a deeper
understanding of why users fall for fake news and may enable the development
of more effective mitigation strategies. Our research question is thus:

> > Research Question: _What is the role of linguistic cues (e.g., adverbs,
> personal pronouns, positive emotion words, negative emotion words) in users’
> (i) cognitive processing and (ii) affective processing of fake news?_

To study the above research question, we conducted a within-subject experiment
with $N=42$ subjects that were presented with a sample of 40 news articles of
different veracity (i.e., 20 real and 20 fake news articles). During our
experiment, we collected neurophysiological measurements. Specifically, we
measured (i) cognitive processing through eye fixations and (ii) affective
processing through heart rate variability. We then perform a regression
analysis, where we aim to explain cognitive and affective information
processing based on different linguistic cues. For this purpose, we compare a
comprehensive panel of linguistic cues (e.g., adverbs, personal pronouns,
positive emotion words, negative emotion words). For better readability, we
highlight linguistic cues in small caps. To the best of our knowledge, this is
the first work studying the role of linguistic cues in fake news through a
neurophysiological lens.

Our findings reveal that users engage more in cognitive processing of fake
news articles when they are longer and written with lower complexity. In
contrast, we find that users experience affective processing when fake news
articles are written with more words linked to analytic thinking. A possible
reason for the latter is that analytic words may make a fake news article
appear more truthful and thus trigger an affective state of psychological
discomfort. Our findings can assist online platforms in implementing
countermeasures against fake news. Specifically, algorithmic curation at
social media platforms is desirable that incentivizes cognitive processing,
which, in turn, suggests that platforms should particularly prefer elaborate
social media posts over short posts and posts with few analytical thinking
words. This would promote cognitive thinking among users and make them more
resilient against fake news. On the other hand, analytical words are
especially dangerous as they may trigger affective processing, because of
which users can fall for fake news inadvertently.

Our main contributions are as follows:

  1. (1)

We study how linguistic cues in online news articles are linked to cognitive
and affective processing of fake news. To the best of our knowledge, this is
the first work studying the role of linguistic cues in fake news processing.

  2. (2)

We provide results from a within-subject experiment where users process a
sample of 20 real and 20 fake news during which we collected
neurophysiological measurements.

  3. (3)

We discuss important implications of our work for designing online platforms
so that they encourage users to engage in careful thinking and thus prevent
them from falling for fake news.

##  2\. Background

We first provide a background on fake news (Section 2.1) and human information
processing (Section 2.2). We then review works at the intersection, that is,
information processing of real vs. fake news (Section 2.3). To understand
differences in the information processing of real vs. fake news, we then
motivate our choice of linguistic cues as important determinants (Section
2.4).

###  2.1. Fake News

Fake news has been defined as news articles that are verifiably false and,
could mislead readers (Lazer et al., 2018). There are several forms of fake
news, including, fabricated content about terrorism or natural disasters,
alleged claims about products, or false statements discrediting political
rivals. As such, fake news provides a considerable threat to how users form
opinions and thus to the functioning of our society (Lazer et al., 2018).

Several works have aimed to understand fake news along different dimensions
capturing human judgment and decision-making. The considered dimensions
include, among others, how exposure to fake news influences existing beliefs
(e.g., Lazer, 2019; Pennycook et al., 2018), which motivational and socio-
demographic factors make users fall for fake news (e.g., Pennycook and Rand,
2020; Pennycook et al., 2018), and how fake news propagates (e.g., Solovev and
Pröllochs, 2022; Drolsbach and Pröllochs, 2023; Pröllochs and Feuerriegel,
2023; Hopp et al., 2020; Vosoughi et al., 2018). For detailed literature
reviews about research on fake news, we refer to Aghajari et al. (2023) and
Pennycook and Rand (2021b). Our work extends current research on fake news by
disentangling the linguistic cues that guide human information processing, as
detailed in the following.

###  2.2. Information Processing

Information processing starts after a piece of information is translated into
signals that can be processed by the brain (Walla, 2018). One then
distinguishes between (1) cognitive processing and (2) affective processing.
Cognitive processing leads to an understanding of _what_ something is, while
affective processing is evaluative, leading to a decision on _how_ something
is (LeDoux, 1989). According to Walla (2018), affective processing forms the
basis of any human behavior. Therefore, affective processing also has a
salient influence on how humans respond to fake news. Importantly, cognitive
and affective processing occur in different brain regions. Cognitive
processing occurs in the cortical brain regions, while affective processing
occurs in the subcortical brain regions (Walla, 2018). With language being a
cortical brain function, self-reports cannot fully reflect on the processes
that occur deep inside the brain but instead must be measured _in situ_
through neurophysiological measurements (vom Brocke et al., 2020; Walla,
2018).

To generate a better understanding of human interaction with technology,
research has increasingly made use of neurophysiological data (vom Brocke et
al., 2020). Neurophysiological data from the human body has been repeatedly
shown to help explain the variance in human behavior (Zhang, 2013). Employing
measures from neuroscience in particular allows researchers to gain a deeper
understanding of the underlying cognitive and affective processes in human-
computer interaction (Dimoka et al., 2011). Specifically, neurophysiological
measurements address limitations of self-reports as they can capture these
processes _in situ_ , i.e., directly at the moment they occur.

Cognitive processing: Cognitive processing can be measured with eye tracking
(Rayner, 1998). When reading, the eyes quickly move from one position to
another. Between these movements, the eyes are relatively stable, which is
referred to as a fixation (Rayner, 1978). Fixations denote short pauses of
200–250ms where the textual information is processed (Rayner, 1998). The
number of eye fixations hence provides a measure of cognitive processing (Just
and Carpenter, 1980; Loftus, 1972; Rayner, 1978). Hence, we later measure
cognitive processing through eye fixations.

Affective processing: Affective processing can be measured in terms of heart
rate variability (HRV) (Appelhans and Luecken, 2006; Thayer et al., 2012). The
neuropsychological relationship between affective processing and HRV is as
follows. The human body continuously processes external stimuli in order to
accommodate the entire system to environmental demands. Thereby, the human
body prepares a regulated response to these external stimuli through the
autonomic nervous system (ANS). The ANS is regulated by the hypothalamus,
which is located in the subcortical brain regions (Uylings et al., 2000) and
it is divided into two subsystems, namely, the inhibitory parasympathetic
nervous system (“rest and digest”) and an excitatory sympathetic nervous
system (“flight or fight”) (Appelhans and Luecken, 2006). These branches often
interact antagonistically to produce varying degrees of physiological arousal.
During psychological discomfort, the balance between these branches (i.e.,
sympathovagal balance) is shifted to a state where the sympathetic branch
becomes dominant. This is accompanied by lower HRV (Pumprla et al., 2002).
Hence, we later measure affective processing through HRV.

###  2.3. Information Processing of Real and Fake News

Research on information processing of fake news is still in its infancy.
Recent work has shown that the processing of fake news is governed by both
cognitive processes and affective processes (Lutz et al., 2020a, 2023).
However, it remains unclear how these processes are triggered. Below, we
provide an overview of prior research on the role of cognition and affect in
the context of fake news.

Cognitive processing: Cognitive processing is considered a necessary
requirement for correctly identifying fake news (Moravec et al., 2019;
Pennycook et al., 2020; Pennycook and Rand, 2020). If users engage in
cognitive processing by thinking critically about the content, they are less
likely to fall for fake news. This effect is even independent of personal
attitudes and partisanship (Pennycook and Rand, 2020). Therefore, researchers
try to identify ways to make users engage in cognitive processing when reading
fake news. However, the crux of identifying ways of making users think
critically about the content is that users generally tend to minimize
cognitive effort (Taylor and Fiske, 1978). The tendency to spend cognitive
effort further depends on users’ mindset. Users on social media are generally
in a hedonic mindset, where they are seeking pleasure and entertainment,
rather than spending cognitive effort into separating real from fake news
(Moravec et al., 2019).

Affective processing: As discussed above, affective processing is typically
contrasted with cognitive processing as it occurs automatically and
unconsciously (vom Brocke et al., 2020; Walla, 2018). It might thus provide a
key determinant of why users perceive online news articles as real or fake
and, eventually, explain why users fall for fake news. The results of recent
studies suggest that affective processing can indeed occur when users are
presented with fake news (Lutz et al., 2020a, 2023), yet it remains unclear
why affective processing occurs. Here, we seek to explain the processing of
fake news through linguistic cues.

###  2.4. Linguistic Cues

Linguistic cues (e.g., adverbs, personal pronouns, positive emotion words,
negative emotion words) provide an important tool to infer textual features
and thus characterize _how_ texts are written. Crucially, linguistic cues
distinguish truthful from deceptive content such as fake news (Gravanis et
al., 2019). Authors of fake news, for obvious reasons, do not have factual
details about the fake story they are about to tell. Hence, they can provide
less precise information about their fake story (Newman et al., 2003; Zhou et
al., 2004).

The role of linguistic cues has been studied in the context of human behavior,
yet outside of fake news. Here, we give several examples to motivate our focus
on linguistic cues as determinants of how users process fake news. For
instance, in the context of online news consumption, positive emotion words
(e.g., _love, nice, sweet_) and negative emotion words (e.g., _hurt, ugly,
nasty_) increase the consumption of the corresponding online news (Robertson
et al., 2023). In general, emotion words may elicit cognitive processes such
as attention (e.g., Kissler et al., 2007; Smith and Petty, 1996). For
instance, messages in discussion forums with more emotion words receive more
feedback than messages with fewer emotion words (Huffaker, 2010). Emotion
words are also fixated more often than other words during reading (Scott et
al., 2012). In contrast, longer articles appear more persuasive as they
provide more arguments in favor of the advocated opinion (Schwenk, 1986;
Tversky and Kahneman, 1974). Likewise, analytic cues (e.g., _think, know, but,
else_) may thus have a deterrent effect on users’ willingness to engage in
cognitive processing. Motivated by this, we later analyze a wide,
comprehensive array of different linguistic cues.

###  2.5. Research Gap

Prior research has shown that the processing of fake news is governed by both
cognition and affect, yet it has remained unclear how these processes are
triggered. Here, we perform a within-subject experiment with _in-situ_
neurophysiological measurements to understand how linguistic cues in fake news
are linked to cognitive and affective processing. To the best of our
knowledge, this is the first work studying the role of linguistic cues in real
and fake news through a neurophysiological lens.

##  3\. Method

The objective of this study is to understand how linguistic cues in real and
fake news are linked to the recipients’ cognitive and affective processing.
The overall research framework is shown in Figure 1. We first collect a sample
of 40 real and fake news articles. Second, we calculate a comprehensive set of
linguistic cues. Third, we perform a within-subject experiment with
neurophysiological measurements.111Collected data from this experiment was
also used for the evaluation of another study (Lutz et al., 2020a, b, 2023).
Each subject was shown our sample of real and fake news in random order.
During this, we measure (1) the number of eye fixations via eye tracking as a
proxy for cognitive processing, and (2) heart rate variability via
electrocardiography as a proxy for affective processing. Finally, we use the
experimental data to estimate mixed-effects regression models to obtain the
marginal effects of linguistic cues on cognitive and affective processing.

Figure 1. Research framework.

###  3.1. Materials

Our sample of 20 real and 20 fake news articles originates from the fact-
checking website “Politifact.” Politifact is a common choice in related
studies for retrieving fake news articles and fact-checks (Allcott and
Gentzkow, 2017; Lazer et al., 2018; Vosoughi et al., 2018). Politifact builds
upon trained journalists to investigate political statements and online news
articles to assign a veracity label that ranges from “true” for real news to
“pants on fire” for fake news (Graves, 2016). Following Vosoughi et al.
(2018), we validated the Politifact labels against other fact-checking
websites, namely, snopes.com, factcheck.org, truthorfiction.com, hoax-
slayer.com, and urbanlegends.about.com. We found a perfect pairwise agreement
(i.e., Fleiss kappa of 1.0) for articles that are also listed on other sites.
Details on our news sample, including their veracity labels and URLs to the
fact-checking websites are provided in Appendix A of the Supplementary
Materials.

In line with previous research (e.g., Bago et al., 2022; Pennycook and Rand,
2019; Pennycook et al., 2020), the experimental objective is to differentiate
between real news articles published by mainstream media and fake news
articles disseminated through alternative media outlets. This choice is
natural as mainstream media is generally considered unlikely to intentionally
create fake news (Tsfati et al., 2020). Although it remains uncertain whether
the alternative media outlets intentionally publish fake news, existing
studies suggest that these outlets exhibit a discernible disregard for truth
(Grinberg et al., 2019), aligning with the definition of fake news proposed by
Lazer et al. (2018).

Note that we do not manipulate the cues manually in our news as this would
change the underlying content and would no longer reflect actual news articles
as they appear “in the wild.” Hence, a strength of our experimental design is
to avoid such interventions but, instead, ensure that we use real-world news
articles.

###  3.2. Calculation and Selection of Linguistic Cues

We follow prior research (e.g., Ho et al., 2016; Newman et al., 2003) in
calculating linguistic cues with “Linguistic Inquiry and Word Count” (LIWC)
(Pennebaker et al., 2015). This software is based on a dictionary that
contains more than 4,500 words, each of which is classified into one or more
dimensions of linguistic cues. A linguistic cue is then calculated as the
fraction of words linked to a particular dimension (e.g., positive emotion
words, we-references, certainty) over all words of a text. For better
readability, we highlight linguistic cues in small caps.

Given that LIWC contains more than 90 linguistic cues, we focus on an
informative subset, which allows us to make meaningful inferences.
Specifically, we focus on those cues for our evaluation, for which the average
fraction of matched words over all news articles is at least 1%. In addition,
we include the summary variables tone, analytic thinking (Pennebaker et al.,
2014), and authentic (Newman et al., 2003) as these are calculated based on
multiple individual cues. Furthermore, we include the length (in words) and
complexity measured as Gunning-Fog readability score (Gunning, 1968).
Following this approach, we obtain a total of 33 linguistic cues as shown in
Table 1. The table provides the corresponding LIWC dimension and several
examples according to Pennebaker et al. (2015).

Table 1. Descriptions of linguistic cues (sorted as in the LIWC manual (Pennebaker et al., 2015)).  Linguistic cue | LIWC dimension |  Example words
---|---|---
Length | WC |  –
Complexity | Gunning-Fog indexa |  –
Personal pronouns | ppron |  _I, them, her_
3rd-person singular pronouns | shehe |  _she, her, him_
3rd-person plural pronouns | they |  _they, their, they’d_
Impersonal pronouns | ipron |  _it, it’s, those_
Auxiliary verbs | auxverb |  _am, will, have_
Adverbs | adverb |  _very, really_
Comparisons | compare |  _greater, best, after_
Interrogatives | interrog |  _how, when, what_
Numbers | number |  _second, thousand_
Quantifiers | quant |  _few, many, much_
Affective processes | affect |  _happy, cried_
Positive emotion words | posemo |  _love, nice, sweet_
Negative emotion words | negemo |  _hurt, ugly, nasty_
Male references | male |  _boy, his, dad_
Insight | insight |  _think, know_
Causation | cause |  _because, effect_
Tentative | tentat |  _maybe, perhaps_
Differentiation | differ |  _hasn’t, but, else_
Perception | percept |  _look, heard, feeling_
See | see |  _view, saw, seen_
Biological processes | bio |  _eat, blood, pain_
Health | health |  _clinic, flu, pill_
Affiliation | affiliation |  _ally, friend, social_
Achievement | achieve |  _win, success, better_
Power | power |  _superior, bully_
Motion | motion |  _arrive, car, go_
Work | work |  _job, majors, xerox_
Money | money |  _audit, cash, owe_
Analytic thinking | Analytic |  –
Authenticity | Authentic |  –
Tone | Tone |  –
Note: Examples are taken from the LIWC 2015 manual (Pennebaker et al., 2015).
a Gunning-Fox readability index is calculated using the
“quanteda” package in R.

LengthComplexityPersonal pronouns3-rd person singular3rd-person
pluralImpersonal pronounsAuxiliary
verbsAdverbsComparisonsInterrogativesNumbersQuantifiersAffective
processesPositive emotionsNegative emotionsMale
referencesInsights$0$$5$$10$$15$$20$Mean

CausationsTentativeDifferentiationsPerceptionsSeeBiological
processesHealthAffiliationAchievementsPowerMotionWorkMoneyAnalyticAuthenticTone$0$$5$$10$$15$$20$MeanReal
NewsFake News

Figure 2. Barplot showing the computed values for the linguistic cues as
means. The error bars denote the standard deviation.

###  3.3. Theoretical Justification

The selected linguistic cues are associated with different psychological
indicators (Tausczik and Pennebaker, 2010). For instance, deceivers tend to
write their messages with lower complexity as it requires cognitive effort to
produce deceptive content. Similarly, the usage of tentative language (_maybe,
perhaps, guess_) signals uncertainty in the sense that a story is not yet
fully established. Conversely, the usage of differentiation words signals that
the author can precisely distinguish one aspect of the story from another
(Newman et al., 2003). Deceivers also tend to use more negative emotion words,
as well as less third person and first person singular pronouns (Bond and Lee,
2005; Newman et al., 2003; Zhou et al., 2004). From an author’s perspective,
personal pronouns can be used to guide the reader’s attention. Gunsch et al.
(2000) found that positive political ads often refer to the own candidate or
party using more self-references (_I, we_), whereas negative political ads
(e.g., about an opponent) tend to use more 3rd-person pronouns (_he, she,
they_). Attention can also reflect how the author is processing the described
event. Kowalski (2000) found that students used more first-person singular and
fewer 3rd-person pronouns when describing an event when they were being teased
compared to describing an event were they were teasing someone else. Finally,
the usage of emotion words tells how authors are experiencing the world.
Positive emotion words (_nice, sweet, cute_) are more frequently used to
describe positive events, while negative emotion words (_ugly, brutal,
horrible_) are more frequently used to describe negative events (Kahn et al.,
2007). The usage of emotion words may hence reflect how the author copes with
an event and how much the event will affect the future. Note that these
explanations only explain the psychological underpinnings of a subset of our
cues selection. For more comprehensive explanations, we refer the reader to
Tausczik and Pennebaker (2010).

###  3.4. Summary Statistics

Figure 2 shows the computed values for all considered cues for real news (in
blue) and fake news (in red). We observe that the linguistic cues of real and
fake news in our sample have a similar frequency. Detailed numeric summary
statistics are provided in Appendix B of the Supplementary Materials.

We provide an illustrative example of one real and one fake news article in
Figure 3. We highlighted the top-5 most frequent linguistic cues (personal
pronouns, impersonal pronouns, auxillary verbs, affective words, and work-
related words) in different colors. The example also shows that the LIWC 2015
dictionaries used to calculate linguistic cues are not mutually exclusive.
Instead, different linguistic cues often share several words. For instance,
the word _benefits_ occurs in the list of affective words, positive emotion
words, and work-related terms.

Therefore, we also consider the correlations of linguistic cues across all
news articles. We find that several linguistic cues exhibit high positive
correlations. The top-3 correlations across all news articles occur between
positive emotion words and tone ($r=0.800,p<0.001$), see and perception
($r=0.796,p<0.001$), and biological processes and health ($r=0.774,p<0.001$).
Correlation plots are shown in Appendix C of the Supplementary Materials. We
later account for the correlations of linguistic cues by including only a
single linguistic cue in each regression model. By estimating one regression
model for each cue, the coefficients are thus not affected by
multicollinearity issues.

__Is There a Right to Social Security?
You worked hard your whole life and paid thousands of dollars in Social
Security taxes. Now it’s time to retire. You’re llegally entitled to Social
Security benefits, right? Wrong. There is no legal right to Social Security,
and that is one of the considerations that may decide the coming debate over
Social Security reform. [...] Ephram Nestor was a Bulgarian immigrant who came
to the United States in 1918 and paid Social Security taxes from 1936, the
year the system began operating, until he retired in 1955. A year after he
retired, Nestor was deported for having been a member of the Communist Party
in the 1930s. In 1954 Congress had passed a law saying that any person
deported from the United States should lose his Social Security benefits.
Accordingly, Nestor’s $55.60 per month Social Security checks were stopped.
[...]__
Source: https://www.cato.org/publications/commentary/is-there-right-social-
security

(a) Real news.

__Obamacare will question your sex life
Are you sexually active? If so, with one partner, multiple partners or same-
sex partners?" Be ready to answer those questions and more the next time you
go to the doctor, whether it’s the dermatologist or the cardiologist and no
matter if the questions are unrelated to why you’re seeking medical help. And
you can thank the Obama health law. "This is nasty business," says New York
cardiologist Dr. Adam Budzikowski. He called the sex questions "insensitive,
stupid and very intrusive." He couldn’t think of an occasion when a
cardiologist would need such information - but he knows he’ll be pushed to ask
for it. [...] Doctors and hospitals who don’t comply with the federal
government’s electronic-health-records requirements forgo incentive payments
now; starting in 2015, they’ll face financial penalties from Medicare and
Medicaid. [...]__
Source: https://nypost.com/2013/09/15/obamacare-will-question-your-sex-life/

(b) Fake news.

Personal pronounsImpersonal pronounsAuxillary verbsAffective wordsWork-related
terms

Figure 3. Illustrative examples of real and fake news articles. Linguistic
cues are highlighted in different colors.

###  3.5. Procedure

The procedure of our experiment was as follows. After signing the consent
form, each subject took a comfortable sitting position in our laboratory and
was asked to turn off all electronic devices. Subsequently, the electrodes of
the electrocardiography (ECG) device were placed on the subject’s arms. The
calibration and tests of the measurement devices concluded the preparation.
The subject was then presented with a dummy news article in order to become
familiar with the experimental setting. Afterwards, we scheduled a break of
five minutes, such that the subject’s HRV normalizes.

The subject was then presented with the aforementioned sample of 40 news
articles in random order. The news articles were displayed in a generic format
on a 22-inch monitor with 1920x1080 resolution; the distance to the monitor
was approx. $60\text{\,}\mathrm{c}\mathrm{m}$. Each news article was presented
in two steps. In the first step, only the headline was shown, and, based on
this, the subject assessed the veracity solely based on the headline (from
0=strongly real to 6=strongly fake). In the second step, the subject read the
news body and categorized the article as either real or fake. This two-step
approach was chosen as users generally see the headline first and only
subsequently the news body. Subjects earned €0.50 (approx. USD 0.60) for each
correct news categorization; however, they did not receive feedback on whether
their answers were correct. Instead, the total amount was only displayed at
the end of the experiment. During the experimental procedure, the subject had
to keep their arms in a fixed position in order to ensure correct ECG
measurements. For the same reason, all decisions needed to be announced to the
supervisor, who then entered the answers. For all measurements, the
experimental platform “Brownie” (Hariharan et al., 2017) was used.

After the experiment, the subject filled out a survey and provided the
following information: age, gender, cultural background as European or non-
European, disposition to trust based on six questions on 7-point Likert scales
from 0=low to 6=high (Gefen and Straub, 2004), news consumption from 0=low to
6=high on a 7-point Likert scale, and political attitude towards social and
economic issues on 7-point Likert scales from 0=strongly liberal to 6=strongly
conservative (Pennycook et al., 2018). In our analysis, we later account for
subject heterogeneity by using a random-effect model, but we use the socio-
demographic data as part of our robustness checks.

###  3.6. Participants

The required number of participants was determined using “G*Power” (Faul et
al., 2009). Assuming an effect size of $d=0.10$ (due to neurophysiological
measurements), $alpha=0.05$, and $power=0.80$, the power analysis suggested a
minimum number of 36 participants. The participants were recruited during
lectures and exercises, as well as, via student mailing lists. Ethics approval
was reviewed and granted by the ethics committee at the University of
Freiburg.

A total of N $=42$ subjects participated in our study. We had to discard two
subjects and an additional two observations from the remaining subjects, where
the sensor measurements failed. The final dataset thus consisted of 1598
observations from 40 subjects. All subjects were college students in a first-
or second-degree program, 26 male and 14 female with a mean age of
$26.0\text{\,}\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{s}$ (SD = 3.33).

###  3.7. Measures

During the whole experiment, we measured (1) cognitive processing as the
number of eye fixations with the eye tracker “Tobi Pro X3-120” and (2)
affective processing as heart rate variability from the ECG measurement device
“BioSignals Plux Solo.” Both represent later our dependent variables:

  1. (1)

_Cognitive processing ( $\hat{=}$eye fixations)_. While reading, the number of
eye fixations presents a measure for cognitive processing (Rayner, 1978,
1998). Our eye tracker measured the relative coordinates $(x,y)$ for both eyes
on the screen between $0$ and $1$. We removed all observations, where at least
one data point was not available, which can happen when users blink or when
their gaze is outside the screen. Subsequently, we calculated the number of
eye fixations with the package “saccades” in R. The package computes the
fixations with the velocity-based algorithm from (Engbert and Kliegl, 2003).
However, note that an increased number of eye fixations does not necessarily
imply a more accurate veracity assessment. Instead, eye fixations measure
cognitive effort, which may also increase when users are unsure about whether
an article is real or fake.

  2. (2)

_Affective processing ( $\hat{=}$heart rate variability)_. Affective states of
discomfort are reflected in lower heart rate variability (Appelhans and
Luecken, 2006). This is because lower HRV reflects dominance of the
sympathetic nervous system, which prepares the human body for a “flight or
fight” response. We calculated HRV as follows. First, we performed an
automated detection of the Q, R, and S waves to identify the heartbeats in the
ECG signal (Astor et al., 2013). The heartbeats are denoted by the most
characteristic R-peaks. The detection was subsequently validated and corrected
during manual inspection with the software “ECGEditor.” Second, we used the
tool “cmetx” (Allen et al., 2007) to calculate HRV as standard deviation in
milliseconds of normal-to-normal inter-beat intervals (SDNN) as this is
considered the most common HRV metric (Pumprla et al., 2002). For instance,
the average SDNN of resting females between 20 and 26 years is 49.51
milliseconds. During situations of discomfort, SDNN can decrease to 32.70
milliseconds (Pereira et al., 2017).

##  4\. Empirical Analysis

Our final dataset consists of 1,598 observations from 40 subjects and 20 real
and 20 fake news articles (recall that we had to delete two observations
during preprocessing). Overall, the subjects identified the veracity correctly
in $73.47\text{\,}\mathrm{\char 37\relax}$ of all observations. This suggests
that separating real from fake news in our sample of news articles was
challenging, even for subjects with higher education.

###  4.1. Regression Model

The objective of this study is to explain how linguistic cues are linked to
cognitive and affective processing. To this end, we estimate linear mixed-
effects models with two dependent variables, namely, subjects’ eye fixations
(cognitive processing) and HRV (affective processing). More eye fixations
indicate that subjects engage more in cognitive processing, while lower HRV
indicates that users experience an affective state of discomfort.

The explanatory variables of interest are the linguistic cues. We hence
estimate regression models that use linguistic cues to explain eye fixations
or HRV. To avoid biased coefficients due to multicollinearity issues, we
estimate separate regression models for each cue, each including one of the 33
cues from Table 1. In each model, we also include an interaction term Cue
$\times$ Veracity, which allows us to quantify whether the marginal effects of
linguistic on subjects’ cognitive/affective processing differs across fake
news vs. real news. The dummy variable Veracity equals 1 for fake news, and 0
for real news.

We include the following control variables in each model. First, we include
the veracity dummy variable as real and fake news articles may directly be
linked to cognitive and affective processing. Second, we control for the
sequence number (0–39) that specifies when the news article was shown during
the experiment. Including the sequence number into our model allows us to
account for effects regarding habituation and fatigue. Third, we control for
the veracity assessment of an article that was formed based on the headline
(from 0=strongly real to 6=strongly fake). This is based on the idea that
users typically see the headline before seeing the body. Accordingly, the
assessment towards the headline may influence how the body is processed.
Fourth, we control for the time in seconds that subjects required to read the
news body. Given that we measure cognitive processing as the number of eye
fixations, a longer reading time is likely to yield a greater number of
fixations. Fifth, we control for the length and complexity of a news article
as longer or harder to read texts may directly influence how information is
processed. Complexity is measured as the Gunning-Fog index (Gunning, 1968),
which indicates the number of years of formal education required to comprehend
the text. Accordingly, greater complexity scores indicate less readable texts.
We standardize all explanatory variables in order to assess marginal effects.
Accordingly, the coefficients of the linguistic cues denote how the dependent
variable changes if the corresponding variable is increased by one standard
deviation.

Finally, we control for subject heterogeneity by including a subject-specific
random intercept. The reason is that humans may have general differences in
how much they engage in cognitive processing, resulting in additional variance
in the number of eye fixations that cannot be explained by other variables.
Furthermore, heart rate is regulated differently across different individuals,
and some individuals are, in general, more or less likely to experience an
affective state of discomfort. Different from fixed effects, including a
random effect still allows us to analyze the between-group variation given by
how subjects’ cognitive and affective processing varies between real and fake
news (Townsend et al., 2013).

Taken together, the resulting linear mixed-effect regression models explaining
eye fixations (cognitive processing) and HRV (affective processing) for a
particular linguistic cue are given as

eye fixations
$\displaystyle=\alpha+\beta_{1}\,\text{Veracity}+\beta_{2}\,\text{Sequence
number}+\beta_{3}\,\text{Headline belief}$
$\displaystyle+\beta_{4}\,\text{Reading
time}+\beta_{5}\,\text{Length}+\beta_{6}\,\text{Complexity}+\beta_{7}\,\text{Length}\times\text{Veracity}$
(1)
$\displaystyle+\beta_{8}\,\text{Complexity}\times\text{Veracity}+\gamma_{1}\,Cue+\gamma_{2}\,\text{Cue}\times\text{Veracity}+\delta_{U}+\varepsilon,$
HRV $\displaystyle=\alpha+\beta_{1}\,\text{Veracity}+\beta_{2}\,\text{Sequence
number}+\beta_{3}\,\text{Headline belief}$
$\displaystyle+\beta_{4}\,\text{Reading
time}+\beta_{5}\,\text{Length}+\beta_{6}\,\text{Complexity}+\beta_{7}\,\text{Length}\times\text{Veracity}$
(2)
$\displaystyle+\beta_{8}\,\text{Complexity}\times\text{Veracity}+\gamma_{1}\,Cue+\gamma_{2}\,\text{Cue}\times\text{Veracity}+\delta_{U}+\varepsilon,$

with intercept $\alpha$, subject-specific random intercept $\delta_{U}$, and
error term $\varepsilon$. Hence, we are primarily interested in the
coefficients $\gamma_{1}$ and $\gamma_{2}$. The marginal effect of linguistic
cues for the processing of real news is given as $\gamma_{1}$, while the
marginal effect for this cue for the processing of fake news is given as
$\gamma_{1}+\gamma_{2}$.

Importantly, we followed best practices in regression modeling to ensure the
validity of our estimates. Among other checks, we calculated the variance
inflation factors for all variables to check for possible multicollinearity
(and ensured that all values are below the critical threshold of 4). All
models are estimated using the “lmer” function of the “lme4” package in R
4.1.2.

###  4.2. Empirical Results

####  4.2.1. Linguistic Cues and Cognitive Processing

We first analyze how linguistic cues are linked to cognitive processing.
Cognitive processing is measured as the number of eye fixations, i.e., a
higher number of eye fixations indicates a greater cognitive effort. We use
the mixed-effects model from Equation 1 to calculate the marginal effects (ME)
of linguistic cues on the number of eye fixations. Here, marginal effects
measure the impact that one standard deviation change in one explanatory
variable (i.e., a linguistic cue) has on the outcome variable (i.e., eye
fixations) while all other variables are held constant. Figure 4 shows the
marginal effects222The numerical results of the marginal effects are provided
in Appendix E of the Supplementary Materials. of all linguistic cues for real
news (blue) and fake news (red). The error bars denote the 95% confidence
intervals. The marginal effect of an explanatory variable is statistically
significant if the 95% confidence interval (given by its lower and upper
boundaries denoted by the error bars) does not include zero. For instance, the
marginal effects of personal pronouns on cognitive processing for real and
fake news are not statistically significant, since both confidence intervals
touch the zero line. However, if the lower and upper boundaries are both above
or below zero, the marginal effect of the corresponding variable is
significantly positive or negative, respectively.

Our results show that a total of 17 linguistic cues are statistically
significantly linked to cognitive processing. For real news, we find that
users engage more in cognitive processing if an article is written with
greater complexity, more comparisons, more words linked to affective
processes, positive emotion words, negative emotion words, male references,
words linked to biological processes, and words reflecting analytic thinking.
For instance, an increase of one standard deviation in the complexity of a
real news article (SD=2.591) leads to 6.62 more eye fixations when reading and
assessing the veracity of the article. In contrast, users engage less in
cognitive processing of real news if an article is written with more 3rd-
person plural pronouns, adverbs, interrogatives, tentative statements,
differentiation, words linked to perceptual processes, seeing, or work-related
terms. For instance, if the usage of adverbs increases by one standard
deviation (SD=1.634), then users perform on average 7.42 fewer eye fixations.

By contrast, for fake news, we find that users engage more in cognitive
processing when an article is written with more power words or work-related
terms. Conversely, cognitive processing of fake news is less pronounced if an
article is written with lower complexity. In addition, we find weak evidence
that cognitive processing of fake news is less pronounced when an article is
written with a higher number of adverbs. If the usage of adverbs increases by
one standard deviation in a fake news article (SD=1.634), then users perform
3.37 fewer eye fixations.

LengthComplexityPersonal pronouns3-rd person singular3rd-person
pluralImpersonal pronounsAuxiliary
verbsAdverbsComparisonsInterrogativesNumbersQuantifiersAffective
processesPositive emotionsNegative emotionsMale
referencesInsights$-20$$-10$$0$$10$$20$ Marginal effect on cognitive
processing

CausationsTentativeDifferentiationsPerceptionsSeeBiological
processesHealthAffiliationAchievementsPowerMotionWorkMoneyAnalyticAuthenticTone$-20$$-10$$0$$10$$20$
Marginal effect on cognitive processing Real NewsFake News

Figure 4. Marginal effects of linguistic cues on cognitive processing (number
of eye fixations). A larger number of eye fixations indicates that cognitive
processing is more pronounced. Marginal effects are calculated based on the
mixed-effects regression model in Equation 1. Control variables and subject-
specific random effects are included. The error bars denote the 95% confidence
intervals.

We make the following observations regarding the control variables in our
model (see Appendix D of the Supplementary Materials for detailed results).
First, users engage less in cognitive processing if the news article is
labeled as fake according to official fact-checking. Specifically, users
perform 7.967 fewer eye fixations when reading a fake news article as compared
to a real news article. Second, we find that the reading time has a strong
positive influence on cognitive processing, which is expected given that an
increased reading time also allows for more eye fixations.

####  4.2.2. Linguistic Cues and Affective Processing

Next, we analyze the link between linguistic cues and affective processing.
Affective processing is measured as heart rate variability in milliseconds
(HRV). Lower HRV indicates that users experience an affective state of
discomfort. We use the mixed-effects model from Equation 2 to calculate the
marginal effects (the numerical values are provided in Appendix E of the
Supplementary Materials) of linguistic cues on the the HRV. Figure 5 shows the
marginal effects of linguistic cues for real and fake news along with the 95%
confidence intervals.

We find that a total of eight linguistic cues are statistically significantly
linked to affective processing. Interestingly, we only observe significant
estimates for affective processing of fake news, but none for real news.
Specifically, users are less likely to experience an affective state of
discomfort reflected by higher HRV if a fake news article is written with more
personal pronouns, 3rd-person plural pronouns, impersonal pronouns, insights,
differentiation, words linked to biological processes, or health. In contrast,
users are more likely to experience an affective state of discomfort when a
fake news article is written with more terms linked to analytic thinking.
Specifically, an increase of one standard deviation in the linguistic cues
analytic thinking lowers users’ heart rate variability by 2.64 milliseconds.

LengthComplexityPersonal pronouns3-rd person singular3rd-person
pluralImpersonal pronounsAuxiliary
verbsAdverbsComparisonsInterrogativesNumbersQuantifiersAffective
processesPositive emotionsNegative emotionsMale
referencesInsights$-4$$-2$$0$$2$$4$ Marginal effect on affective processing

CausationsTentativeDifferentiationsPerceptionsSeeBiological
processesHealthAffiliationAchievementsPowerMotionWorkMoneyAnalyticAuthenticTone$-4$$-2$$0$$2$$4$
Marginal effect on affective processing Real NewsFake News

Figure 5. Marginal effects of linguistic cues on affective processing (heart
rate variability). Lower heart rate variability indicates that users
experience an affective state of discomfort. Marginal effects are calculated
based on the mixed-effects regression model in Equation 2. Control variables
and subject-specific random effects are included. The error bars denote the
95% confidence intervals.

Regarding the control variables in our analysis (see Appendix D of the
Supplementary Materials for detailed results), we find that affective
processing does not depend on whether the article is labeled as fake according
to fact-checking. The coefficient of the the veracity dummy is not
statistically significant. In addition, we observe that affective processing
is less pronounced when the article has a higher sequence number (marginal
effect: ME $=$ 0.290, $p<0.001$), indicating potential habituation effects
over time.

###  4.3. Analysis of Socio-Demographics

As an exploratory analysis, we consider the socio-demographic variables from
the survey, including age, gender, cultural background (European or non-
European), disposition to trust, news consumption, and political attitude.
Recall that we accounted for subject heterogeneity by using subject-random
effects as our focus was on within-subject effects. However, including socio-
demographic variables allows us to analyze whether cognitive and affective
processing depends on age, gender, or political attitude (e.g., Laserna et
al., 2014; Riedl et al., 2010; Pennycook et al., 2018). We find that older,
European, and more liberal subjects engage more in cognitive processing. The
same holds true for subjects with a higher disposition to trust and lower news
consumption. On the other hand, younger, European, and more liberal subjects
are more likely to experience an affective state of discomfort. The regression
results using socio-demographics are provided in Appendix F of the
Supplementary Materials.

##  5\. Discussion

###  5.1. Implications for Research

We performed an experiment with neurophysiological measurements to understand
if linguistic cues can help to explain how users process real and fake news.
Identifying the driving factors of cognitive and affective processing allows
for a deeper understanding of why users fall for fake news. While cognitive
processing is considered crucial for correctly identifying fake news,
affective processing involves automatic and unconscious evaluations. Hence,
linguistic cues that trigger affective processing may make users may fall for
fake news inadvertently and even without noticing. To the best of our
knowledge, this is the first work studying the role of linguistic cues in fake
news through a neurophysiological lens.

Table 2. Summary of findings. Marginal effects of linguistic cues on cognitive and affective processing. Linguistic cue |  | Cognitive Processing
---
(eye fixations)
| Affective Processing
---
(heart rate variability)
| Real News | Fake News | Real News | Fake News
Length |  |  $\boldsymbol{\oplus}$ |  |
Complexity |  $\boldsymbol{\oplus}$ |  $\boldsymbol{\ominus}$ |  |
Personal pronouns |  |  |  |  $\boldsymbol{\oplus}$
3rd-person plural pronouns |  $\boldsymbol{\oplus}$ |  |  |  $\boldsymbol{\oplus}$
Impersonal pronouns |  |  |  |  $\boldsymbol{\oplus}$
Adverbs |  $\boldsymbol{\ominus}$ |  |  |
Comparisons |  $\boldsymbol{\oplus}$ |  |  |
Interrogatives |  $\boldsymbol{\ominus}$ |  |  |
Affective processes |  $\boldsymbol{\oplus}$ |  |  |
Positive emotion words |  $\boldsymbol{\oplus}$ |  |  |
Negative emotion words |  $\boldsymbol{\oplus}$ |  |  |
Male references |  $\boldsymbol{\oplus}$ |  |  |
Insights |  |  |  |  $\boldsymbol{\oplus}$
Tentative |  $\boldsymbol{\ominus}$ |  |  |
Differentiation |  $\boldsymbol{\ominus}$ |  |  |  $\boldsymbol{\oplus}$
Perceptions |  $\boldsymbol{\ominus}$ |  |  |
See |  $\boldsymbol{\ominus}$ |  |  |
Biological processes |  $\boldsymbol{\oplus}$ |  |  |  $\boldsymbol{\oplus}$
Health |  |  |  |  $\boldsymbol{\oplus}$
Power |  $\boldsymbol{\oplus}$ |  $\boldsymbol{\oplus}$ |  |
Work |  $\boldsymbol{\ominus}$ |  $\boldsymbol{\oplus}$ |  |
Analytic thinking |  $\boldsymbol{\oplus}$ |  |  |  $\boldsymbol{\ominus}$
Note that an affective state of discomfort is accompanied by _lower_ heart
rate variability.

Our findings are summarized in Table 2. We found that users engage more in
cognitive processing of fake news articles when they have greater length.
However, this finding does not apply to real news. One possible explanation is
that fake news articles may provide more (fake) justifications to make a fake
story appear more truthful, whereas real news articles may provide further
factual details besides the actual information. The actual information in real
news articles may also be described in a concise way, such that users require
less cognitive effort to comprehend the content. The marginal effect of
complexity has opposite signs for real and fake news. When the complexity of
an article increases, users engage less in cognitive processing of real news,
but more in the cognitive processing of fake news. In the literature on
deception detection, documents with higher complexity are considered more
truthful (e.g., Newman et al., 2003; Zhou et al., 2004). Processing news
articles with greater complexity also demands more cognitive resources.
However, the subjects in our experiment did not engage more in cognitive
processing when fake news articles are written with greater complexity. A lack
of cognitive processing is an important factor of why users fall for fake news
(Moravec et al., 2019; Pennycook and Rand, 2020, 2019). Therefore, fake news
articles written with greater complexity may be more successful in deceiving
the public. Furthermore, we find that cognitive processing of fake news
articles is more pronounced when an article is written with more power words
or work-related terms. Accordingly, news articles with more power words or
work-related terms may be less likely to deceive their readers.

Recent research has shown that, in addition to cognition, the processing of
fake news is also governed by affect (Lutz et al., 2020a, 2023). The presence
of affect can make users fall for fake news automatically – and without even
noticing which renders the role of affect highly concerning (Lutz et al.,
2020a, 2023). However, it has remained unclear whether and how affective
processes are triggered. Here, our study extends the literature by
demonstrating that users experience an affective state of discomfort when fake
news articles are written with more words reflecting analytic thinking. Words
linked to analytic thinking can make an article appear more truthful as they
clearly distinguish the content from what happened and what did not happen.
There appear two possible explanations for why users experience an affective
state of discomfort if the content of a fake news article contradicts a user’s
worldview. Alternatively, users may experience discomfort in processing fake
news when the article describes unpleasant events. We built upon
neurophysiological measurements to study users’ cognitive and affective
processing, which is motivated by the fact that self-reported data often
deviates from the underlying processes (Walla, 2018). The reason is that
humans cannot fully reflect on the affective processes occurring deep inside
the brain (vom Brocke et al., 2020; Walla, 2018). In contrast,
neurophysiological measurements can capture affective responses _in situ_ ,
i.e., directly at the moment when users process news. Thereby, we obtain
complementary results that self-reports alone cannot capture (Tams et al.,
2014). To this end, our research question can _only_ be answered by using
neurophysiological measurements.

###  5.2. Implications for Practice

Social media platforms need to be aware that real and fake news articles can
differently trigger cognitive and affective processing in the user, subject to
different linguistic cues of an article. Since linguistic cues can be
calculated efficiently and reliably using existing tools from text mining, it
is straightforward for social media platforms to gauge how different articles
will be processed by the users. Based on that, platforms may prioritize
content that elicits cognitive processing (as opposed to affective processing)
in their ranking algorithms. Adapting the ranking and filtering algorithms of
social media platforms in this way may present a cost-efficient and scalable
countermeasure to reduce the likelihood that users fall for fake news.
Notwithstanding these opportunities of social media platforms, regulators
could also mandate that social media platforms account for cognitive-
processing-inducing cues.

In particular, social media platforms, users, and politicians need to be
cautious of fake news written with greater complexity and more words linked to
analytic thinking. Fake news articles with greater complexity may appear
truthful, while requiring more cognitive effort to be processed. Such fake
news articles may thus increase users’ propensity to fall for fake news. In
addition, fake news articles can be subject to automatic and unconscious
evaluations, if they are written with words linked to analytic thinking. This
means that users may fall for such fake news articles even without realizing
it. Social media platforms should therefore be alerted when news articles of
unclear veracity are characterized by greater complexity and more analytic
words. Accordingly, they should consider designing their platforms in a way
that prevents users from being exposed to these articles.

Many commercial large language models (LLMs) use a professional language
characterized by analytic cues, and it is thus likely that our findings also
apply to why users are prone to fall for hallucinations of LLMs. Furthermore,
LLMs could even be employed by malicious actors to compose fake news
(Feuerriegel et al., 2023) in a way that the above-mentioned psychological
dynamics are strategically targeted to elicit certain beliefs, which poses a
serious concern that could be addressed by better media literacy training and
better algorithmic curation on social media platforms (as discussed above).

###  5.3. Limitations and Future Research

Our study suffers from the general limitations of laboratory studies. Our
experiment was conducted in the context of a neutral laboratory that differs
from how subjects are used to reading online news articles in their everyday
life. In addition, our subjects were undergraduate students, which may not be
representative of the whole society (Koriat et al., 1980). Furthermore, the
reward for positive identification of real and fake news could have induced a
utilitarian mindset, while users on social media can generally be assumed to
be in a hedonic mindset (Moravec et al., 2019). Consequently, the presence of
affective information processing could be even more pronounced in real-world
scenarios. To address this issue, conducting field experiments utilizing
wearable devices for measuring HRV presents a potential avenue for future
research. However, the reliability and accuracy of measurements obtained from
wearable devices remain subjects of debate (Stangl and Riedl, 2022). Moreover,
the adoption of such an approach raises privacy concerns, as it would
necessitate the recording of social media interactions. As such, it is common
practice for related studies on fake news to use lab settings (Moravec et al.,
2019, 2022).

###  5.4. Conclusion

Understanding how real and fake news is processed is important for curbing the
spread of fake news. Cognitive processing is considered a necessary
requirement to prevent users from falling for fake news, while affective
processing occurs automatically and unconsciously, thereby increasing the
likelihood of less elaborated decisions. Linguistic cues are an important tool
in deception detection and they can easily be measured using established text
analysis software. However, little is known about how linguistic cues
influence users’ cognitive and affective processing. We performed a lab
experiment with eye-tracking and heart rate measurements to explain cognitive
and affective processing of real and fake news based on linguistic cues. Our
results suggest that cognitive processing of fake news is less pronounced if
an article is written with lower complexity or more adverbs. In addition, we
find that affective processing of fake news is more pronounced when an article
is written with greater usage of terms reflecting analytic thinking. Social
media platforms aiming to curb the spread of fake news should hence be
cautious when news articles are written with low complexity, a high number of
adverbs, or high number of terms associated with analytic thinking. To the
best of our knowledge, this is the first study employing neurophysiological
measurements to explain human information processing according to linguistic
cues.

## Acknowledgement

We appreciate funding from the German Academic Exchange Service (grant number
57317901).

## References

  * (1)
  * Aghajari et al. (2023) Zhila Aghajari, Eric P. S. Baumer, and Dominic DiFranzo. 2023\.  Reviewing interventions to address misinformation: The need to expand our vision beyond an individualistic focus.  (2023).
  * Allcott and Gentzkow (2017) Hunt Allcott and Matthew Gentzkow. 2017.  Social media and fake news in the 2016 election.  _Journal of Economic Perspectives_ 31, 2 (2017), 211–236.
  * Allen et al. (2007) John J. B. Allen, Andrea S. Chambers, and David N. Towers. 2007.  The many metrics of cardiac chronotropy: A pragmatic primer and a brief comparison of metrics.  _Biological Psychology_ 74, 2 (2007), 243–262.
  * Appelhans and Luecken (2006) Bradley M. Appelhans and Linda J. Luecken. 2006.  Heart rate variability as an index of regulated emotional responding.  _Review of General Psychology_ 10, 3 (2006), 229–240.
  * Aral and Eckles (2019) Sinan Aral and Dean Eckles. 2019.  Protecting elections from social media manipulation.  _Science_ 365, 6456 (2019), 858–861.
  * Arechar et al. (2022) Antonio Alonso Arechar, Jennifer Nancy Lee Allen, Rocky Cole, Ziv Epstein, Kiran Garimella, Andrew Gully, Jackson G Lu, Robert M Ross, Michael Stagnaro, Jerry Zhang, et al. 2022\.  Understanding and reducing online misinformation across 16 countries on six continents.  _PsyArXiv_ (2022).
  * Astor et al. (2013) Philipp J. Astor, Marc T. P. Adam, Petar Jerčić, Kristina Schaaff, and Christof Weinhardt. 2013.  Integrating biosignals into information systems: A NeuroIS tool for improving emotion regulation.  _Journal of Management Information Systems_ 30, 3 (2013), 247–278.
  * Bago et al. (2022) Bence Bago, Leah R Rosenzweig, Adam J Berinsky, and David G Rand. 2022.  Emotion may predict susceptibility to fake news but emotion regulation does not seem to help.  _Cognition and Emotion_ 36, 6 (2022), 1166–1180.
  * Bär et al. (2023) Dominik Bär, Nicolas Pröllochs, and Stefan Feuerriegel. 2023\.  New threats to society from free-speech social media platforms.  _Commun. ACM_ 66, 10 (2023), 37–40.
  * Bond and Lee (2005) Gary D Bond and Adrienne Y Lee. 2005.  Language of lies in prison: Linguistic classification of prisoners’ truthful and deceptive natural language.  _Applied Cognitive Psychology_ 19, 3 (2005), 313–329.
  * Bronstein et al. (2019) Michael V Bronstein, Gordon Pennycook, Adam Bear, David G Rand, and Tyrone D Cannon. 2019\.  Belief in fake news is associated with delusionality, dogmatism, religious fundamentalism, and reduced analytic thinking.  _Journal of Applied Research in Memory and Cognition_ 8, 1 (2019), 108–117.
  * Centre for International Governance Innovation (2019) Centre for International Governance Innovation. 2019.  CIGI-Ipsos global survey on internet security and trust.  Available at https://www.cigionline.org/cigi-ipsos-global-survey-internet-security-and-trust/.
  * Cinelli et al. (2020) Matteo Cinelli, Walter Quattrociocchi, Alessandro Galeazzi, Carlo Michele Valensise, Emanuele Brugnoli, Ana Lucia Schmidt, Paola Zola, Fabiana Zollo, and Antonio Scala. 2020\.  The COVID-19 social media infodemic.  _Scientific Reports_ 10, 1 (2020), 1–10.
  * Conroy et al. (2015) Niall J. Conroy, Victoria L. Rubin, and Yimin Chen. 2015\.  Automatic deception detection: Methods for finding fake news.  _Proceedings of the Association for Information Science and Technology_ 52, 1 (2015).
  * Dimoka et al. (2011) Angelika Dimoka, Paul A. Pavlou, and Fred D. Davis. 2011\.  NeuroIS: The potential of cognitive neuroscience for information systems research.  _Information Systems Research_ 22, 4 (2011), 687–702.
  * Drolsbach and Pröllochs (2023) Chiara Patricia Drolsbach and Nicolas Pröllochs. 2023.  Diffusion of community fact-checked misinformation on Twitter. In _CSCW_.
  * Engbert and Kliegl (2003) Ralf Engbert and Reinhold Kliegl. 2003.  Microsaccades uncover the orientation of covert attention.  _Vision Research_ 43, 9 (2003), 1035–1045.
  * Faul et al. (2009) Franz Faul, Edgar Erdfelder, Axel Buchner, and Albert-Georg Lang. 2009.  Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses.  _Behavior Research Methods_ 41, 4 (2009), 1149–1160.
  * Feuerriegel et al. (2023) Stefan Feuerriegel, Renée DiResta, Josh A. Goldstein, Srijan Kumar, Philipp Lorenz-Spreen, Michael Tomz, and Nicolas Pröllochs. 2023\.  Research can help to tackle AI-generated disinformation.  _Nature Human Behaviou_ (2023).
  * Flamino et al. (2023) James Flamino, Alessandro Galeazzi, Stuart Feldman, Michael W Macy, Brendan Cross, Zhenkun Zhou, Matteo Serafino, Alexandre Bovet, Hernán A Makse, and Boleslaw K Szymanski. 2023.  Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections.  _Nature Human Behaviour_ 7 (2023), 904–916.
  * Friggeri et al. (2014) Adrien Friggeri, Lada A Adamic, Dean Eckles, and Justin Cheng. 2014.  Rumor cascades. In _ICWSM_.
  * Gao et al. (2018) Mingkun Gao, Ziang Xiao, Karrie Karahalios, and Wai-Tat Fu. 2018\.  To label or not to label: The effect of stance and credibility labels on readers’ selection and perception of news articles. In _CSCW_.
  * Gefen and Straub (2004) David Gefen and Detmar W. Straub. 2004.  Consumer trust in B2C e-commerce and the importance of social presence: Experiments in e-products and e-services.  _Omega_ 32, 6 (2004), 407–424.
  * Geissler et al. (2022) Dominique Geissler, Dominik Bär, Nicolas Pröllochs, and Stefan Feuerriegel. 2022.  Russian propaganda on social media during the 2022 invasion of Ukraine.  _EPJ Data Science_ 12, 1 (2022), 35.
  * Gravanis et al. (2019) Georgios Gravanis, Athena Vakali, Konstantinos Diamantaras, and Panagiotis Karadais. 2019.  Behind the cues: A benchmarking study for fake news detection.  _Expert Systems with Applications_ 128 (2019), 201–213.
  * Graves (2016) Lucas Graves. 2016\.  Boundaries not drawn: Mapping the institutional roots of the global fact-checking movement.  _Journalism Studies_ 19, 5 (2016), 613–631.
  * Grinberg et al. (2019) Nir Grinberg, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, and David Lazer. 2019\.  Fake news on Twitter during the 2016 US presidential election.  _Science_ 363, 6425 (2019), 374–378.
  * Gunning (1968) R. Gunning. 1968\.  _The Technique of Clear Writing_.  McGraw-Hill, New York.
  * Gunsch et al. (2000) Mark A Gunsch, Sheila Brownlow, Sarah E Haynes, and Zachary Mabe. 2000.  Differential forms linguistic content of various of political advertising.  _Journal of Broadcasting & Electronic Media_ 44, 1 (2000), 27–42.
  * Hariharan et al. (2017) Anuja Hariharan, Marc Thomas Philipp Adam, Ewa Lux, Jella Pfeiffer, Verena Dorner, Marius B. Müller, and Christof Weinhardt. 2017\.  Brownie: A platform for conducting NeuroIS experiments.  _Journal of the Association for Information Systems_ 18, 4 (2017), 264–296.
  * Ho et al. (2015) Shuyuan Mary Ho, Hengyi Fu, Shashanka S. Timmarajus, Cheryl Booth, Jung Hoon Baeg, and Muye Liu. 2015\.  Insider threat. In _SIGMIS Conference on Computers and People Research_.
  * Ho et al. (2016) Shuyuan Mary Ho, Jeffrey T. Hancock, Cheryl Booth, and Xiuwen Liu. 2016.  Computer-mediated deception: Strategies revealed by language-action cues in spontaneous communication.  _Journal of Management Information Systems_ 33, 2 (2016), 393–420.
  * Hopp et al. (2020) Toby Hopp, Patrick Ferrucci, and Chris J Vargo. 2020\.  Why do people share ideologically extreme, false, and misleading content on social media? A self-report and trace data–based analysis of countermedia content dissemination on Facebook and Twitter.  _Human Communication Research_ 46, 4 (2020), 357–384.
  * Huffaker (2010) David Huffaker. 2010\.  Dimensions of leadership and social influence in online communities.  _Human Communication Research_ 36, 4 (2010), 593–617.
  * Just and Carpenter (1980) Marcel A. Just and Patricia A. Carpenter. 1980.  A theory of reading: From eye fixations to comprehension.  _Psychological Review_ 87, 4 (1980), 329.
  * Kahn et al. (2007) Jeffrey H Kahn, Renee M Tobin, Audra E Massey, and Jennifer A Anderson. 2007.  Measuring emotional expression with the Linguistic Inquiry and Word Count.  _The American Journal of Psychology_ 120, 2 (2007), 263–286.
  * Kirchner and Reuter (2020) Jan Kirchner and Christian Reuter. 2020.  Countering fake news: A comparison of possible solutions regarding user acceptance and effectiveness. In _CSCW_.
  * Kissler et al. (2007) Johanna Kissler, Cornelia Herbert, Peter Peyk, and Markus Junghofer. 2007.  Buzzwords: Early cortical responses to emotional words during reading.  _Psychological Science_ 18, 6 (2007), 475–480.
  * Koriat et al. (1980) Asher Koriat, Sarah Lichtenstein, and Baruch Fischhoff. 1980\.  Reasons for confidence.  _Journal of Experimental Psychology: Human Learning & Memory_ 6, 2 (1980), 107–118.
  * Kowalski (2000) Robin M Kowalski. 2000\.  “i was only kidding!”: Victims’ and perpetrators’ perceptions of teasing.  _Personality and Social Psychology Bulletin_ 26, 2 (2000), 231–241.
  * Lane et al. (2009) Richard D. Lane, Kateri McRae, Eric M. Reiman, Kewei Chen, Geoffrey L. Ahern, and Julian F. Thayer. 2009.  Neural correlates of heart rate variability during emotion.  _NeuroImage_ 44, 1 (2009), 213–222.
  * Laserna et al. (2014) Charlyn M. Laserna, Yi-Tai Seih, and James W. Pennebaker. 2014\.  Um . . . who like says you know: Filler word use as a function of age, gender, and personality.  _Journal of Language and Social Psychology_ 33, 3 (2014), 328–338.
  * Lazer (2019) David Lazer. 2019\.  Studying human attention on the internet.  _Proceedings of the National Academy of Sciences of the United States of America_ 117, 1 (2019), 21–22.
  * Lazer et al. (2018) David M. J. Lazer, Matthew A. Baum, Yochai Benkler, Adam J. Berinsky, Kelly M. Greenhill, Filippo Menczer, Miriam J. Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, Michael Schudson, Steven A. Sloman, Cass R. Sunstein, Emily A. Thorson, and Duncan J. Watts. 2018\.  The science of fake news.  _Science_ 359, 6380 (2018), 1094–1096.
  * LeDoux (1989) Joseph E. LeDoux. 1989\.  Cognitive-emotional interactions in the brain.  _Cognition & Emotion_ 3, 4 (1989), 267–289.
  * Loftus (1972) Geoffrey R. Loftus. 1972\.  Eye fixations and recognition memory for pictures.  _Cognitive Psychology_ 3, 4 (1972), 525–551.
  * Lu et al. (2022) Zhuoran Lu, Patrick Li, Weilong Wang, and Ming Yin. 2022\.  The effects of ai-based credibility indicators on the detection and spread of misinformation under social influence. In _CSCW_.
  * Lutz et al. (2020a) Bernhard Lutz, Marc T. P. Adam, Stefan Feuerriegel, Nicolas Pröllochs, and Dirk Neumann. 2020a.  Affective information processing of fake news: Evidence from NeuroIS.  In _NeuroIS Retreat 2019_ , Fred D. Davis, René Riedl, and Jan vom Brocke (Eds.). Springer, Heidelberg, 121–128.
  * Lutz et al. (2020b) Bernhard Lutz, Marc T. P. Adam, Stefan Feuerriegel, Nicolas Pröllochs, and Dirk Neumann. 2020b.  Identifying linguistic cues of fake news associated with cognitive and affective processing: Evidence from neurois. In _NeuroIS Retreat 2020_. Springer, 16–23.
  * Lutz et al. (2023) Bernhard Lutz, Marc T. P. Adam, Stefan Feuerriegel, Nicolas Pröllochs, and Dirk Neumann. 2023\.  Affective information processing of fake news: Evidence from NeuroIS.  _European Journal of Information Systems_ Forthcoming (2023).
  * Moravec et al. (2022) Patricia L. Moravec, Antino Kim, Alan R. Dennis, and Randall K. Minas. 2022.  Do you really know if it’s true? How asking users to rate stories affects belief in fake news on social media.  _Information Systems Research_ 33, 3 (2022), 887–907.
  * Moravec et al. (2019) Patricia L. Moravec, Randall Minas, and Alan R. Dennis. 2019\.  Fake news on social media: People believe what they want to believe when it makes no sense at all.  _MIS Quarterly_ 43, 4 (2019), 1343–1360.
  * Newman et al. (2003) Matthew L. Newman, James W. Pennebaker, Diane S. Berry, and Jane M. Richards. 2003.  Lying words: Predicting deception from linguistic styles.  _Personality and Social Psychology Bulletin_ 29, 5 (2003), 665–675.
  * Pennebaker et al. (2015) James W. Pennebaker, Ryan L. Boyd, Kayla Jordan, and Kate Blackburn. 2015.  _The Development and Psychometric Properties of LIWC2015_.  LIWC.net, Austin.
  * Pennebaker et al. (2014) James W. Pennebaker, Cindy K. Chung, Joey Frazee, Gary M. Lavergne, and David I. Beaver. 2014\.  When small words foretell academic success: The case of college admissions essays.  _PLOS ONE_ 9, 12 (2014), e115844.
  * Pennycook et al. (2020) Gordon Pennycook, Adam Bear, Evan T. Collins, and David G. Rand. 2020.  The implied truth effect: Attaching warnings to a subset of fake news stories increases perceived accuracy of stories without warnings.  _Management Science_ 66, 11 (2020), 4921–5484.
  * Pennycook et al. (2018) Gordon Pennycook, Tyrone D. Cannon, and David G. Rand. 2018\.  Prior exposure increases perceived accuracy of fake news.  _Journal of Experimental Psychology: General_ 147, 12 (2018), 1865–1880.
  * Pennycook and Rand (2019) Gordon Pennycook and David G. Rand. 2019.  Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning.  _Cognition_ 188 (2019), 39–50.
  * Pennycook and Rand (2020) Gordon Pennycook and David G. Rand. 2020.  Who falls for fake news? The roles of analytic thinking, motivated reasoning, political ideology, and bullshit receptivity.  _Journal of Personality_ 88, 2 (2020), 185–200.
  * Pennycook and Rand (2021a) Gordon Pennycook and David G Rand. 2021a.  Examining false beliefs about voter fraud in the wake of the 2020 Presidential Election.  _The Harvard Kennedy School Misinformation Review_ 2, 1 (2021).
  * Pennycook and Rand (2021b) Gordon Pennycook and David G. Rand. 2021b.  The psychology of fake news.  _Trends in Cognitive Sciences_ 25, 5 (2021), 388–402.
  * Pereira et al. (2017) Tania Pereira, Pedro R Almeida, Joao PS Cunha, and Ana Aguiar. 2017.  Heart rate variability metrics for fine-grained stress level assessment.  _Computer Methods and Programs in Biomedicine_ 148 (2017), 71–80.
  * Pröllochs et al. (021a) Nicolas Pröllochs, Dominik Bär, and Stefan Feuerriegel. 2021a.  Emotions explain differences in the diffusion of true vs. false social media rumors.  _Scientific Reports_ 11, 22721 (2021a).
  * Pröllochs et al. (021b) Nicolas Pröllochs, Dominik Bär, and Stefan Feuerriegel. 2021b.  Emotions in online rumor diffusion.  _EPJ Data Science_ 10, 1 (2021b).
  * Pröllochs and Feuerriegel (2023) Nicolas Pröllochs and Stefan Feuerriegel. 2023.  Mechanisms of true and false rumor sharing in social media: Collective intelligence or herd behavior?. In _CSCW_.
  * Pumprla et al. (2002) Jiri Pumprla, Kinga Howorka, David Groves, Michael Chester, and James Nolan. 2002.  Functional assessment of heart rate variability: Physiological basis and practical applications.  _International Journal of Cardiology_ 84, 1 (2002), 1–14.
  * Rayner (1978) Keith Rayner. 1978\.  Eye movements in reading and information processing.  _Psychological Bulletin_ 85, 3 (1978), 618–660.
  * Rayner (1998) Keith Rayner. 1998\.  Eye movements in reading and information processing: 20 years of research.  _Psychological Bulletin_ 124, 3 (1998), 372–422.
  * Riedl et al. (2010) René Riedl, Marco Hubert, and Peter Kenning. 2010\.  Are there neural gender differences in online trust? An fMRI study on the perceived trustworthiness of ebay offers.  _MIS Quarterly_ 34, 2 (2010), 397–428.
  * Robertson et al. (2023) Claire Robertson, Nicolas Pröllochs, Kaoru Schwarzenegger, Phillip Parnamets, Jay J. Van Bavel, and Stefan Feuerriegel. 2023.  Negativity drives online news consumption.  _Nature Human Behaviour_ 7 (2023), 812––822.
  * Scheufele and Krause (2019) Dietram A Scheufele and Nicole M Krause. 2019.  Science audiences, misinformation, and fake news.  _Proceedings of the National Academy of Sciences_ 116, 16 (2019), 7662–7669.
  * Schwenk (1986) Charles H. Schwenk. 1986\.  Information, cognitive biases, and commitment to a course of action.  _Academy of Management Review_ 11, 2 (1986), 298–310.
  * Scott et al. (2012) Graham G. Scott, Patrick J. O’Donnell, and Sara C. Sereno. 2012.  Emotion words affect eye fixations during reading.  _Journal of Experimental Psychology: Learning, Memory, and Cognition_ 38, 3 (2012), 783–792.
  * Siering et al. (2016) Michael Siering, Jascha-Alexander Koch, and Amit V. Deokar. 2016.  Detecting fraudulent behavior on crowdfunding platforms: The role of linguistic and content-based cues in static and dynamic contexts.  _Journal of Management Information Systems_ 33, 2 (2016), 421–455.
  * Smith and Petty (1996) Stephen M. Smith and Richard E. Petty. 1996.  Message framing and persuasion: A message processing analysis.  _Personality and Social Psychology Bulletin_ 22, 3 (1996), 257–268.
  * Solovev and Pröllochs (2022) Kirill Solovev and Nicolas Pröllochs. 2022.  Moral emotions shape the virality of COVID-19 misinformation on social media. In _WWW_.
  * Spezzano et al. (2021) Francesca Spezzano, Anu Shrestha, Jerry Alan Fails, and Brian W Stone. 2021.  That’s fake news! reliability of news when provided title, image, source bias & full article. In _CSCW_.
  * Stangl and Riedl (2022) Fabian J Stangl and René Riedl. 2022.  Measurement of heart rate and heart rate variability: A review of neurois research with a focus on applied methods. In _NeuroIS Retreat 2022_ , Fred D Davis, René Riedl, Jan vom Brocke, Pierre-Majorique Léger, Adriane B. Randolph, and Gernot R. (Eds.) Müller-Putz (Eds.). 264–280.
  * Starbird et al. (2019) Kate Starbird, Ahmer Arif, and Tom Wilson. 2019.  Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations.  _CSCW_ (2019).
  * Tams et al. (2014) Stefan Tams, Kevin Hill, Ana Ortiz de Guinea, Jason Thatcher, and Varun Grover. 2014.  NeuroIS-Alternative or complement to existing methods? illustrating the holistic effects of neuroscience and self-reported data in the context of technostress research.  _Journal of the Association for Information Systems_ 15, 10 (2014), 723\.
  * Tausczik and Pennebaker (2010) Yla R Tausczik and James W Pennebaker. 2010.  The psychological meaning of words: LIWC and computerized text analysis methods.  _Journal of Language and Social Psychology_ 29, 1 (2010), 24–54.
  * Taylor and Fiske (1978) Shelley E. Taylor and Susan T. Fiske. 1978.  Salience, attention, and attribution: Top of the head phenomena.  In _Advances in Experimental Social Psychology_. Vol. 11. Elsevier, Amsterdam, 249–288.
  * Thayer et al. (2012) Julian F. Thayer, Fredrik Ahs, Mats Fredrikson, John J. Sollers, and Tor D. Wager. 2012.  A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health.  _Neuroscience and Biobehavioral Reviews_ 36, 2 (2012), 747–756.
  * Townsend et al. (2013) Zac Townsend, Jack Buckley, Masataka Harada, and Marc A Scott. 2013.  The choice between fixed and random effects.  _The SAGE Handbook of Multilevel Modeling_ (2013), 73–88.
  * Traberg and van der Linden (2022) Cecilie Steenbuch Traberg and Sander van der Linden. 2022.  Birds of a feather are persuaded together: Perceived source credibility mediates the effect of political bias on misinformation susceptibility.  _Personality and Individual Differences_ 185 (2022), 111269.
  * Tsfati et al. (2020) Yariv Tsfati, Hajo G Boomgaarden, Jesper Strömbäck, Rens Vliegenthart, Alyt Damstra, and Elina Lindgren. 2020.  Causes and consequences of mainstream media dissemination of fake news: Literature review and synthesis.  _Annals of the International Communication Association_ 44, 2 (2020), 157–173.
  * Tversky and Kahneman (1974) Amos Tversky and Daniel Kahneman. 1974.  Judgment under uncertainty: Heuristics and biases.  _Science_ 185, 4157 (1974), 1124–1131.
  * Uylings et al. (2000) H. Uylings, C. van Eden, J. de Bruin, M. Feenstra, and C. Pennartz. 2000.  The integration of stress by the hypothalamus, amygdala and prefrontal cortex: Balance between the autonomic nervous system and the neuroendocrine system.  _Progress in Brain Research_ 126 (2000), 117–132.
  * vom Brocke et al. (2020) Jan vom Brocke, A. Hevner, P. M. Léger, P. Walla, and R. Riedl. 2020.  Advancing a NeuroIS research agenda with four areas of societal contributions.  _European Journal of Information Systems_ 29, 1 (2020), 9–24.
  * Vosoughi et al. (2018) Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018.  The spread of true and false news online.  _Science_ 359, 6380 (2018), 1146–1151.
  * Walla (2018) Peter Walla. 2018\.  Affective processing guides behavior and emotions communicate feelings: Towards a guideline for the NeuroIS community.  In _Information Systems and Neuroscience_. Springer, Heidelberg, 141–150.
  * Zhang (2013) Ping Zhang. 2013\.  The affective response model: A theoretical framework of affective concepts and their relationships in the ict context.  _MIS Quarterly_ 37, 1 (2013), 247–274.
  * Zhou et al. (2004) Lina Zhou, Judee K. Burgoon, Jay F. Nunamaker, and Doug Twitchell. 2004.  Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communications.  _Group Decision and Negotiation_ 13, 1 (2004), 81–106.