Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=upcp20
Political Communication
ISSN: 1058-4609 (Print) 1091-7675 (Online) Journal homepage: https://www.tandfonline.com/loi/upcp20
Mistrust, Disinforming News, and Vote Choice: A
Panel Survey on the Origins and Consequences
of Believing Disinformation in the 2017 German
Parliamentary Election
Fabian Zimmermann & Matthias Kohring
To cite this article: Fabian Zimmermann & Matthias Kohring (2020): Mistrust, Disinforming
News, and Vote Choice: A Panel Survey on the Origins and Consequences of Believing
Disinformation in the 2017 German Parliamentary Election, Political Communication, DOI:
10.1080/10584609.2019.1686095
To link to this article: https://doi.org/10.1080/10584609.2019.1686095
© 2019 The Author(s). Published with
license by Taylor & Francis Group, LLC.
View supplementary material
Published online: 01 Jan 2020.
Submit your article to this journal
View related articles
View Crossmark data
Mistrust, Disinforming News, and Vote Choice: A
Panel Survey on the Origins and Consequences of
Believing Disinformation in the 2017 German
Parliamentary Election
FABIAN ZIMMERMANN and MATTHIAS KOHRING
In this paper, we address the question of whether disinforming news spread online possesses
the power to change the prevailing political circumstances during an election campaign. We
highlight factors for believing disinformation that until now have received little attention,
namely trust in news media and trust in politics. A panel survey in the context of the 2017
German parliamentary election (N = 989) shows that believing disinforming news had
a specific impact on vote choice by alienating voters from the main governing party (i.e., the
CDU/CSU), and driving them into the arms of right-wing populists (i.e., the AfD).
Furthermore, we demonstrate that the less one trusts in news media and politics, the more
one believes in online disinformation. Hence, we provide empirical evidence for Bennett and
Livingston’s notion of a disinformation order, which forms in opposition to the established
information system to disrupt democracy.
Keywords online disinformation, institutional mistrust, voting behavior, panel data,
structural equation modeling
So-called “fake news”is not a novel phenomenon, but what certainly is new is its
environment of dissemination. Digital and, especially, social media facilitate the wide-
spread distribution of false assertions with a relatively professional layout at minimal
cost. Such disinformation campaigns try to undermine the voters’ability to make their
decisions based on accurate beliefs about the political system. This involves a danger for
Fabian Zimmermann is a research associate and doctoral student at the Department of Media
and Communication Studies, University of Mannheim, Germany. His research addresses political
disinformation, media trust and distrust, as well as mediatization of society. Matthias Kohring is a
professor of media and communication studies at the University of Mannheim, Germany. His
research addresses public communication, journalism theory, trust in news media, and science
communication.
Address correspondence to Fabian Zimmermann, Department of Media and
Communication Studies, University of Mannheim, B 6, 30-32, Mannheim 68159, Germany.
E-mail: fabian.zimmermann@uni-mannheim.de
Color versions of one or more of the figures in the article can be found online at
www.tandfonline.com/UPCP.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-
NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which
permits non-commercial re-use, distribution, and reproduction in any medium, provided the original
work is properly cited, and is not altered, transformed, or built upon in any way.
Political Communication, 00:1–23, 2019
© 2019 The Author(s). Published with license by Taylor & Francis Group, LLC.
ISSN: 1058-4609 print / 1091-7675 online
DOI: https://doi.org/10.1080/10584609.2019.1686095
1
the quality and legitimacy of the democratic process, as a well-informed electorate is
essential for the collective autonomy of democracies.
In contrast, a study concerning “fake news”on social media in the 2016 US presidential
election calls its impact on the outcome into question due to its limited reach (Allcott &
Gentzkow, 2017). However, the authors did not empirically test this assumption of minimal
effects. Therefore, we strive for empirical clarification on this matter considering the question of
whether disinformation spread online possesses the power to change the prevailing political
circumstances during an election campaign. Moreover, we broaden the understanding of the
origins of such disinformation effects in a digital environment: Based on theories drawing on
conceptions of social trust, we identify the lack of institutional trust in established news media
and politics as a crucial reason why people believe fabricated news to be true.
To address the aforementioned questions, we will present a longitudinal study in the context
of the German federal election in 2017. Germany is an appropriate research location as it is
particularly affected by the European refugee situation, a major anchoring point for online
disinformation. Additionally, its multiparty system, which has been stable for a long time, is
recently in a state of flux. A new right-wing populist party (i.e., the AfD) has entered the political
arena and been elected into parliament for the first time in 2017—a development that could have
been fostered by political disinformation disseminated online.
Disinforming News and the Disinformation Order
The term “fake news”has been repeatedly misused by politicians such as Donald Trump as
a label to discredit traditional news media (Egelhofer & Lecheler, 2019), which impairs its
scientific value. Therefore, we prefer the term disinforming news (or disnews) to explicitly
indicate it to be a specific “species of disinformation”(Gelfert, 2018, p.103; see also Marwick &
Lewis, 2017, p. 44; Wardle & Derakhshan, 2017, p. 20). We define it to be untruthful and
empirically false news pretending to be true (see Allcott & Gentzkow, 2017,p.213).Asitis
knowingly false, disinforming news is clearly different from inadvertent misinformation (e.g.,
honest mistakes) (Weedon, Nuland, & Stamos, 2017,p.5).Itisdistinguished from other forms
of disinformation (e.g., conspiracy theories) by its distinct news character: By applying news
values such as unexpectedness and negativity as well as news formats, disnews purports to hold
journalistic credibility (Lazer et al., 2018;Levy,2017; Tandoc, Lim, & Ling, 2018a, p. 143). Our
understanding of disinforming news does not only cover propagandistic disinformation made
up to manipulate (political) attitudes and behavior. It also takes clickbait disnews into account,
which employs inaccurate information to generate advertising revenues (Allcott & Gentzkow,
2017,p.217).
From a societal perspective, considering disnews as isolated falsehoods is insuffi-
cient. In contrast, we agree with Bennett and Livingston (2018, p. 124) in framing the
problem as the ongoing systematic division and disruption of the democratic public
spheres, aiming at destabilizing democratic institutions and processes (e.g., elections).
In a similar vein, Lewandowsky, Ecker, and Cook (2017) suggest disinformation should
be embedded into a broader societal context, which they refer to as a post-truth world. In
general, this term connotes that previously familiar mechanisms of knowledge production
with certain responsible actors and institutions (e.g., science, politics, and legacy media)
on the one hand, and corresponding publics on the other, are fundamentally challenged
(Gibson, 2018; Harsin, 2015).
In a post-truth era, a portion of society no longer adheres to the conventional
principles of factual reasoning. Instead, these people seek to adopt a different form of
2 Fabian Zimmermann and Matthias Kohring
viewing the world (Lewandowsky et al., 2017). The originators of disinformation take
advantage of this development by creating “alternative information systems that block the
mainstream press and provide followers with emotionally satisfying beliefs around which
they can organize”(Bennett & Livingston, 2018, p. 132). Accordingly, we are not simply
confronted with single pieces of disinformation but with a comprehensive disinformation
order. In most countries, this network of disinformation builds on right-wing sentiments
and narratives. In Germany, they are primarily focused on attacking and vilifying
(Muslim) immigrants, as the refugee situation has been on top of the national news
agenda for a long time (Humprecht, 2019).
Institutional Mistrust as an Origin for Believing Disinforming News
Mere exposure to (dis)information does not necessarily translate into believing it, which is a
plausible precondition for a direct electoral effect of truth claims. Therefore, our study firstly
concentrates on the (institutional) reasons to perceive disnews as true. According to the theory of
motivated reasoning, truth judgments are generally driven by two possibly conflicting motiva-
tions: The accuracy goal of trying to arrive at a preferably correct conclusion, and the directional
goal of preferring a previously desired outcome. Interestingly, there is evidence that individuals
are more likely to engage in the latter (Kunda, 1990).
People evaluate (political) statements in the light of their predispositions so that
factual beliefs align with their (political) stances (Bartels, 2002). Repeated studies have
confirmed this partisan, or confirmation, bias in truth judgments (Reedy, Wells, & Gastil,
2014; Swire, Berinsky, Lewandowsky, & Ecker, 2017). For example, people tend to
believe conspiracy theories that correspond to their political attitudes (Swami, 2012;
Uscinski, Klofstad, & Atkinson, 2016). Furthermore, selective exposure to partisan
(news) media and its content can evoke misperceptions in line with the user’s views
(Meirick & Bessarabova, 2016). This holds especially true in online environments, where
audiences have a larger choice of attitude-consistent messages (Winter, Metzger, &
Flanagin, 2016). Taken together, political ideology is one of the most important predictors
of the perceived truthfulness of online disinforming news (Allcott & Gentzkow, 2017).
Another bias influencing truth judgments, and thereby producing misperceptions, is
the so-called truth effect (Hasher, Goldstein, & Toppino, 1977). This implies that people
ascribe more substance to assertions that they have heard, read, or seen repeatedly, and
has been repeatedly proven in cognitive psychology as well as in political communication
(Dechêne, Stahl, Hansen, & Wänke, 2010; Ernst, Kühne, & Wirth, 2017; Koch &
Zerback, 2013). Based on this, the mere exposure to disnews could affect its believability.
Indeed, DiFonzo, Beckstead, Stupak, and Walders (2016) found that the repeated pre-
sentation of uncertain rumors had an effect on participants' validity judgments. Likewise,
the exposure to false news stories seems to enhance familiarity and perceptions of their
accuracy, even when controlling for correction and political ideology (Pennycook,
Cannon, & Rand, 2017; Polage, 2012).
(News) Media literacy is also a crucial factor in promoting accurate truth judgments.
Especially in the online context, media literacy facilitates authentication of true and
rejection of false news (Tandoc et al., 2018b). In line with this, Craft, Ashley, and
Maksl (2017) demonstrate that greater knowledge about news media leads to fewer
endorsements of conspiracy theories, even when these match political views. Likewise,
findings by Kahne and Bowyer (2017) indicate that media literacy helps to assess the
veracity of simulated online posts.
Mistrust, Disinforming News, and Vote Choice 3
In the following, we want to highlight reasons for believing disnews to which until
now sufficient attention has not been paid. Following Bennett and Livingston (2018,
p. 127), the breakdown of trust in democratic institutions is the central origin that paves
the way for the disinformation order. Alternative realities in a post-truth world “emerge
from, and take advantage of …a loss of faith in institutions that anchor truth claims”
(Gibson, 2018, p. 3180).
In general, the importance of institutional trust is rooted in the differentiation of
expert systems in modern society (Giddens, 1990), for example science or law. Whereas
we can hardly abstain from these systems, we are confronted with the continuous risk that
our expectations toward them could not be met, or even be disappointed. Trust is a social
mechanism to deal with this risk (Luhmann, 1979). Thus, to trust means that an actor
delegates responsibility for a certain action to another actor, though they know that this
actor potentially could not meet their expectation (Barber, 1983; Simmel, 2004, pp.
173–181).
We can also consider the news media and politics to be such expert systems. Trust in
news media refers to the expectation that the news media supplies its publics with
specific information, which serves to orientate users in a complex and otherwise unma-
nageable society (Kohring & Matthes, 2007). The need for orientation has thus been
shown to be an important driver of people’s media use for political information, and
corresponding media effects (Matthes, 2005; Weaver, 1980). The established news media
are capable of satisfying a user’s need for orientation, but only if they are trusted to
convey useful political information. In constrast, mistrust toward mainstream media
prompts a switch to nonmainstream or alternative media (Tsfati & Peri, 2006). Online
media especially seems to be the perfect venue for those mistrustful publics in search of
alternative information about the political system (Tsfati, 2010; Marwick & Lewis, 2017,
pp. 40–41). At the same time, the digital media environment is the ideal breeding ground
for disinformation. As a counter-public against the established information system, the
disinformation order offers 'facts' that skeptics believe to have been missing from the
mainstream media in the first place. People not trusting the news media should be
inclined to believe these 'alternative facts' because they dissent from the mainstream
media coverage, thereby satisfying the need for counter-orientation.
H1: The less the trust in traditional news media, the higher the perceived believability of
disinforming news spread online during the German election campaign.
Furthermore, political mistrust should play an important role in believing disinfor-
mation. In general, trust in politics refers to “the probability …that the political system
(or some part of it) will produce preferred outcomes even if left untended”(Gamson,
1968, p. 54; Easton, 1975, p. 447). It is deemed to serve as an indispensable resource for
the political system (Easton, 1975, pp. 447–448; Hetherington, 2005). In turn, mistrustful
citizens cast doubt on the political system’s capability to make the right decisions. They
might even expect the government and the mainstream parties to worsen the problems
that society is facing (Citrin & Stoker, 2018;Cook & Gronke, 2005). The disinformation
order tries to nurture such feelings of mistrust by drawing an extremely negative picture
of established democratic officials and institutions, and by fabricating stories about
political malfunctions. According to the theory of motivated reasoning, people not
trusting the political system should tend to believe disinforming news, as it confirms
their mistrust toward the political system.
4 Fabian Zimmermann and Matthias Kohring
H2: The less the trust in the political system, the higher the perceived believability of
disinforming news spread online during the German election campaign.
Electoral Consequences of Disinforming News
There is reasonable doubt about a comprehensive disnews influence on the election result
given that the average amount of disinformation exposure appears to be rather low in
users’overall media diet (Allcott & Gentzkow, 2017; Grinberg, Joseph, Friedland, Swire-
Thompson, & Lazar, 2019). Nevertheless, this aggregation clouds the fact that exposure
to disinformation is extremely concentrated and attributable to specific parts of the
population, such as elderly and conservative people (Grinberg et al., 2019; Guess,
Nyhan, & Reifler, 2018). As there are indeed fractions of the population that are highly
exposed to disnews, among these this can act as a gateway for the disruptive influence of
online disinformation. Hence, to address its direct influence on vote choice, one has to
focus on the individual rather than aggregate level. Moreover, we do not assume mere
exposure, but rather believing disinformation to make the difference regarding people’s
vote decisions.
In fact, studies show that distorted beliefs about a political issue can influence
people’s vote on a ballot question concerning that issue even when controlling for
preexisting views and political sophistication (Reedy et al., 2014; Wells, Reedy, Gastil,
& Lee, 2009). Likewise, there is evidence that voting to leave the European Union during
the British referendum (i.e., “Brexit”) was fostered by the endorsement of Islamophobic
conspiracy theories (Swami, Barron, Weis, & Furnham, 2018). The same pattern applies
to presidential elections. Barrera, Guriev, Henry, and Zhuravskaya (2018) demonstrate
that exposure to misleading statements regarding the European refugee situation signifi-
cantly increased voting intentions for the extreme right-wing candidate Le Pen.
Additionally, people believing false rumors about particular candidates in the 2008
U.S. presidential election were less likely to vote for those candidates (Weeks &
Garrett, 2014).
Since previous studies employed cross-sectional research designs, the issue of
causality in this relationship remains unclear. In addition, most of these investigations
deal with other forms of inaccurate information (e.g., misinformation, conspiracy the-
ories, and rumors) instead of disinforming news. We will however build on these
unambiguous findings, as we are concerned with a closely related phenomenon. Hence,
we suppose that believing disnews should also affect the outcome of parliamentary
elections based on proportional representation such as in Germany. Here, a causal impact
at the individual level is indicated by changing the probability of electing a given party.
However, there is a question as to the direction in which individual vote choice will shift
in reaction to political disinformation. This obviously depends on the ideological orienta-
tion of disinformation, as the framing of news has been shown to affect individual vote
decision in consistency with a frame’s leaning (Van Spanje & de Vreese, 2014). As
mentioned before, disnews articles in online media are overwhelmingly xenophobic in
Germany. This negative framing with regard to immigrants (e.g., as criminal foreigners)
prompts negative attitudes toward immigration and its consequences and raises the
salience of immigration as a problem, which is not appropriately addressed by the
political system (Barrera et al., 2018; Igartua & Cheng, 2009).
Mistrust, Disinforming News, and Vote Choice 5
There are three possibilities for people to deal with such political disaffection at the
ballot box: First, voters could nonetheless stay loyal to the established political system
and elect one of the mainstream parties. Second, citizens could voice their dissatisfaction
by casting their votes for a right-wing populist or extremist party. And third, they could
exit the party system entirely through abstention from the vote (Hirschman, 1970;
Hooghe, Marien, & Pauwels, 2011). With no system of compulsory voting and a new
populist party on the rise, there was both a viable exit and voice option in the 2017
German elections. Hence, opting for loyalty does not seem a reasonable electoral con-
sequence of believing disinformation. It should rather stimulate people to turn away from
the political parties representing the established political system (i.e., CDU/CSU, SPD,
FDP, Green Party, and Left party), which are declared incapable to solve the refugee
situation.
H3a: Higher perceived believability of disinforming news spread online during the
German election campaign decreases the likelihood of voting for a mainstream political
party.
At the same time, disinformation should promote voice in terms of supporting the
“Alternative for Germany”(AfD), which was the most promising right-wing party in
Germany according to the polls. As its campaign mainly focused on criticizing (political)
elites as well as Islam, the AfD seemed to be the perfect incarnation of political protest in
the German context.
H3b: Higher perceived believability of disinforming news spread online during the
German election campaign increases the likelihood of voting for the right-wing party
AfD.
Lastly, not participating in the election at all (exit) could be another possible outcome
of believing online disinformation. However, taking the rather disruptive and remonstra-
tive character of the disinformation order into account, it is debatable whether it induces
abstention.
RQ1: Does higher perceived believability of disinforming news spread online during the
German election campaign increase the likelihood of abstaining from the vote?
Method
Participants and Design
Our study addressed the institutional antecedents and electoral consequences of disnews
based on data from a three-wave panel survey. We conducted the survey during the
campaign of the German parliamentary election in fall 2017. The data were collected
around two months prior to the election (Wave 1: July 31–August 8 2017), shortly after
the television debate between the two candidates for the chancellorship (Wave 2:
September 4–12 2017), and right after the election day (Wave 3: September 25–28 2017).
The fieldwork was performed by the German research company “respondi AG.”
A quota sample was drawn from the “respondi”online access panel.
1
Representative quotas
6 Fabian Zimmermann and Matthias Kohring
(regarding the electorate) for gender, age, education, and federal state were implemented in
sampling. Initially, a total of 2,301 people were approached. After removing careless
responders and lurkers identified by a quality fail question, the response time, and the
amount of missing values, we obtained an adjusted sample of 1,664 respondents in the first
wave (American Association for Public Opinion Research [AAPOR] RR1: 72.3%). A total
of 1,267 of them also took part in Wave 2 (recontact rate AAPOR RR1: 76.1%), and 989
participants eventually completed the questionnaire in Wave 3 (recontact rate AAPOR
RR1: 78.1%). Our analyses are based only on those participants who participated in all
three waves (N = 989).
On average, there were no significant differences between this final sample and the
representative sample in Wave 1 concerning gender (female: 48.4%, male: 51.6%),
education (low: 34.1%, medium: 34.7%, high: 31.2%), political ideology, and the degree
of believing disinforming news. Solely, respondents who were part of all three waves
were slightly older (M
age
= 48.18, SD = 13.49). However, because these differences were
very small, we assume that our findings are not biased through panel attrition.
Measures
Disnews Exposure and Placebo News Recall. To acquire a stock of disnews articles that
circulated online during the election campaign, we applied an approach introduced by
Allcott and Gentzkow (2017, pp. 219–220). That is, we repeatedly looked through the
major German-language fact-checking websites (e.g., “correctiv.org,”“faktenfinder.
tagesschau.de,”“mimikama.at”) and gathered a variety of recent stories covering political
issues that were designated as deliberately and verifiably false. As expected, nearly all
disnews stories in Germany contained right-wing implications such as skepticism toward
the European Union (e.g., “The European Union is going to abolish cash money starting
in 2018.”), attacking politicians (e.g., “The father of the candidate for chancellorship
Martin Schultz was a captain of the SS and commander of the concentration camp
Mauthausen.”) and above all the exclusion of migrants and refugees (e.g., “Refugees
from Arabia cause hepatitis A epidemic across Europe.”).
2
We were careful to represent
this spectrum of narratives as precisely as possible in our selection. We made also sure to
pick those disnews that fact-checkers reported as having triggered significant online
resonance for each wave (e.g., disnews for Wave 2 were released between Wave 1 and
Wave 2). Finally, we compressed the stories’message into meaningful headlines. We also
fabricated some placebo disnews, which conveyed similar right-wing narratives but never
circulated online. These placebo news items were meant to control for a possible inflation
of the disnews scores in the upcoming analysis (Allcott & Gentzkow, 2017, p. 220). We
also mixed in some true news headlines on the same topics in order to distract the
participants, thereby preventing bias caused by only showing false assertions.
At each wave, we confronted our respondents with these headlines (T
1
: six disnews,
four placebos; T
2
: six disnews, five placebos; T
3
: seven disnews, five placebos). We
asked whether they have already encountered a statement, as well as how they assessed
its truthfulness. Responses to the former question were combined into two analog sum
scores across all three waves and dichotomized, with 0 = “no disnews exposure”and 1 =
“disnews exposure”, as well as 0 = “no placebo news recall”and 1 = “placebo news
recall”(disnews exposure: 57.1%; placebo news recall: 47.7%).
Mistrust, Disinforming News, and Vote Choice 7
Disinforming and Placebo News Believability. Perceived believability of the disnews and
placebo messages was measured by a 5-point scale ranging from 0 “certainly false”to 4
“certainly true”. We calculated a composite score from the disnews beliefs for each point in
time by adding up all the single values and dividing by their number. The variable thus
mirrors the average believability of disnews articles in a respective time period. Per wave, the
items showed a fairly satisfying internal consistency (T
1
:M=1.94,SD=.73,Cronbach’sα=
.66; T
2
:M=1.44,SD=.76,Cronbach’sα=.73;T
3
: M = 1.47, SD = .72, Cronbach’sα=.76).
In contrast, all placebo news items were condensed to a single composite score across all
waves (M = 1.77, SD = .58, Cronbach’sα=.78).
Trust in Traditional News Media. We applied a scale introduced by Kohring and Matthes
(2007; see also Prochazka & Schweiger, 2019) to measure trust in traditional news media
at T
1
,T
2
, and T
3
. On a seven-point scale (1 = “not correct at all,”7=“fully correct”),
respondents rated if they considered several statements about the news coverage on
politics as correct. Originally, the scale consists of the four subscales “trust in selectivity
of topics,”“trust in selectivity of facts,”“trust in accuracy of depictions,”and “trust in
journalistic assessment.”In order to keep our statistical model as parsimonious as
possible, we employed a short version by only taking the highest loading item of each
subscale into account. Consequently, four (reflective) indicators (e.g., “The information in
the reporting would be verifiable if examined.”) formed a latent factor “trust in traditional
news media”per wave (T
1
: AVE = .66, Jöreskog’s Rho = .89; T
2
: AVE = .70, Jöreskog’s
Rho = .90; T
3
: AVE = .69, Jöreskog’s Rho = .90).
Trust in Politics. The scale “trust in politics”was gathered at T
1
,T
2
,andT
3
by four
items. The indicators mirror the phases of the policy cycle, namely agenda setting,
policy formulation, policy adoption, and policy implementation. Respondents were
asked to what extent they would agree with four statements (e.g., “In general, one can
rely on politics to make the right decisions.”) on a seven-point scale (1 = “not correct
at all,”7=“fully correct”). The last item (i.e., “Frequently, political decisions are not
implemented properly afterward.”) had to be removed due to its poor loading (see
results). The latent factors derived from the remaining indicators performed well in
terms of its average extracted variance and reliability in each wave (T
1
: AVE = .66,
Jöreskog’sRho=.85;T
2
: AVE = .67, Jöreskog’s Rho = .86; T
3
: AVE = .67,
Jöreskog’s Rho = .86).
Voting Intention and Vote Choice. To assess change over time, we acquired data about
our respondent’s voting intention at T
1
as well as their actual vote choice at T
3.
The
values for voting intention were dichotomized into five dummy variables for the CDU/
CSU, SPD, the mainstream opposition parties (which comprised the Green Party, the
liberal FDP, and the Left party), the AfD, and the undecided.
3
Vote choice at T
3
was
recoded into a nominal variable with five categories, namely CDU/CSU, SPD, main-
stream opposition parties, AfD, and abstention.
Controls. Based on our literature review, we added several potential confounders to
our questionnaire at T
1
in addition to our focal variables. Besides the demographics
gender, age,andeducation, “political ideology”was measured on a 10-point scale
ranging from 1 “left-leaning”to 10 “right-leaning”(M = 5.06, SD = 2.00). To account
for a potential truth effect of disnews spread online, we questioned our respondents
8 Fabian Zimmermann and Matthias Kohring
about their “social media news use,”employing a 5-point scale (1 = “never,”5=
“very often”) by Choi (2016), comprising three items. These were combined to
a composite score showing high levels of internal consistency (M = 2.37, SD =
1.21, Cronbach’sα= .93). Additionally, we asked for “traditional news media use”
“on TV,”“on the radio,”“in printed newspapers or magazines,”and “on websites of
established news media outlets”(M = 4.36, SD = 1.76, Cronbach’sα= .59). Finally,
the composite score “news media literacy”was gathered by adding up four items
which captured a respondent’s ability to assess and handle the relevance, amount,
substance, and rationale of news on a 7-point scale (M = 3.64, SD = 1.16, Cronbach’s
α= .69).
Results
Preliminary Analysis
In order to study their impact during the German election, we first had to test whether
exposure to online disnews articles has occurred. On average, the respondents have
encountered 2.19 (SD = 3.23; 95% CI: 1.99 to 2.39) of the 19 disinforming news headlines
that we presented to them over all three waves. That equals 11.5%, which sounds few at
first glance. Given that the participants also came across only 15.9% of our true news
headlines, the amount of false news exposure nevertheless seems quite substantial.
However, the amount of disnews exposure was only slightly though significantly higher
than the falsely reported average placebo news recall (10.9%). As this difference was
statistically significant (ΔM [988] = .006, p= .03), there still should have been some
meaningful exposure to disinformation during the campaign period.
4
Besides, even though the frequency distribution is very right-skewed and zero-
inflated, it shows a “long tail”of exposure to disinforming news (see Figure 1, left
panel). That is, most people saw no false stories at all (42.9%), but some came across
a large amount during the election campaign. Eighteen percent of our participants
reported exposure to five or more disnews articles. Hence, although exposure may be
low on average,anindividual’s exposure may still be high. Unlike exposure, disinforma-
tion beliefs across all waves were approximately normally distributed (M = 1.61, SD =
.62; 95% CI: 1.57 to 1.65), meaning that most people neither strongly believed nor
Figure 1. Frequency distribution of the disnews variables.
Distribution of “disnews exposure”(left panel) and “disnews believability”(right panel) across all
three waves in the full sample (N = 989).
Mistrust, Disinforming News, and Vote Choice 9
strongly disbelieved the false messages which had circulated online (see Figure 1, right
panel).
Measurement Model
To test our main assumptions, we performed structural equation modeling (SEM) using
the software Mplus (Muthén & Muthén, 2015). We were guided by the procedure Cole
and Maxwell (2003, pp. 570–572) suggested when using SEM to test mediational
processes in longitudinal designs. Hence, we conducted a confirmatory factor analysis
(CFA) employing maximum likelihood estimation with robust standard errors (MLR) to
assess the adequacy of our measures first. The latent constructs were modeled to cause
their respective indicators (reflective measures). The measurement model comprised our
focal variables “trust in traditional news media”and “trust in politics”at all three points
in time. All six latent exogenous variables in the model were correlated.
The initial model provided a poor fit to the data.
5
We tried to enhance our model by
a) removing an indicator of the “political trust”factors due to poor loadings, b) adding
a residual correlation between the first two items of “trust in traditional news media,”and
c) allowing for correlations among the corresponding disturbances of the indicators across
time. Obviously, these changes had an impact, as the global fit of the modified model
demonstrated improvement (see Kline, 2016). Despite a significant χ2 (150) = 314.985
(p= .00) due to the large sample size, the approximate fit indices, which are robust to
sample size, showed a good global fit of the model: χ2/df = 2.10, TLI = .98, CFI = .99,
RMSEA = .03 (90% CI: .028 to .038).
Regarding local fit, all standardized factor loadings were higher than .50 and
significant (see Table B2 in the online appendix). The average variance extracted and
reliability of each factor exceeded .60, indicating a sufficient convergent validity of the
individual parameters (see Byrne, 2012, pp. 77–82). Discriminant validity concerning the
different factors was also tested and confirmed based on the Fornell-Larcker criterion
(Fornell & Larcker, 1981). Moreover, a model with all indicators loading only on one
latent factor fit the data worse, speaking against a joint measure of institutional trust. We
inspected factorial measurement invariance (see Widaman & Reise, 1997) by comparing
the model to a restricted version through chi-square difference testing and assumed
configural, weak (equal loadings), partial strong (equal intercepts), and strict (equal
residual variances) invariance over time for our measures (see Table B1 in the online
appendix).
Main Results
After validating our measures, we turn to our first structural model. In order to benefit
from the panel design of our study, we conceptualized it as a so-called autoregressive
model with cross-lagged effects (Cole & Maxwell, 2003; Finkel, 1995; Jöreskog, 1979).
This implied integrating lagged variables into our model, which leads to two types of
relationships: autoregressive and cross-lagged. The autoregressive paths (i.e., Y
T
on Y
T-1
)
express the stability of a variable over time. Additional cross-lagged effects (i.e., Y
T
on
X
T-1
) of other independent variables represent the association between the two variables
from one time to another, controlling for the stability of the particular dependent variable.
Therefore, a cross-lagged panel model (CLPM) provides some (but not necessarily
sufficient) indication for causality regarding the relationship between X and Y.
10 Fabian Zimmermann and Matthias Kohring
In this case, we included our focal variables “trust in traditional news media,”“trust
in politics,”and “disnews believability”at T
1
,T
2
, and T
3
. In our full CLPM, every
upstream variable had a direct effect on every downstream variable, and all exogenous
variables as well as the residuals of all endogenous variables were allowed to correlate
within each wave. Afterward, we added the controls “gender,”“age,”“education,”
“political ideology,”“social media news use,”“traditional news media use,”“news
media literacy,”“disnews exposure,”and “placebo news recall”as exogenous variables
(all correlated) exerting a direct effect on the focal constructs.
6
Proceeding from this baseline CLPM, we tried to find the most parsimonious model
that still provides a good fit to the data. Therefore, we excluded non-significant paths
originating from the controls and restricted all wave-skipping effects (direct paths from
T
1
to T
3
) except the auto-correlational ones to zero. Moreover, we removed all paths
originating from “disnews believability”to the trust variables. These restrictions did not
significantly worsen the model fit. Overall, this SEM provided a good fit to the data: χ2
(427) = 759.726 (p= .00), χ2/df = 1.78, TLI = .97, CFI = .98, RMSEA = .03 (90% CI:
.025 to .032), SABIC = 64,813.791. We chose this over a reversed model with paths from
the trust to the disnews variables eliminated because it showed a significantly worse fit to
the data (see Table B1 in the online appendix).
To test our first two hypotheses, we estimated indirect effects from T
1
to T
3
using
a bootstrap of 10,000 draws. Overall, the independent variables accounted for 53.4% of
variance in the central outcome “disnews believability”at T
3
(see Figure 2). Over and
Figure 2. Most parsimonious CLPM with manifest indicators, error terms, control variables,
covariances between the variables, and wave-skipping auto-regressions omitted (N = 974).
Figure displays standardized regression coefficients; ns = not significant.
†
p<.10*p<.05.**p<.01.
***p<.001.R
2
= coefficient of determination.
Mistrust, Disinforming News, and Vote Choice 11
above the autoregressive impact of the lagged dependent variable and several controls,
“trust in news media”at T
1
had a significant negative total effect (sum of all nonspurious,
time-specific effects) on “disnews believability”at T
3
(B = −.08, β=−.11, SE = .03, p=
.00 [95% CI: −.17 to −.05]). Likewise, the total effect of “trust in politics”at T
1
on
“disnews believability”at T
3
was also negative and significant (B = −.08, β=−.13, SE =
.03, p= .00 [95% CI: −.20 to −.06]). Hence, the less people trust the established news
media and politics, the more they tend to believe online disinformation to be true,
supporting our hypotheses H1 and H2.
In order to approach our further hypotheses, we estimated another structural equation
model including our voting variables. The model differed from the previous one in that
the trust variables were only included at T
1
and “disnews believability”at T
1
and T
2
. Vote
choice at T
3
was introduced as central endogenous variable. As we dealt with an
unordered categorical outcome, we employed multinomial logistic regression for para-
meter estimation. In order to address change in vote decision in the course of the
campaign, we also added voting intention (in the form of our dummy variables) as an
exogenous predictor.
7
Beyond the previously mentioned confounders, we also included
“placebo news believability”to eliminate spurious effects of “disnews believability”on
voting behavior. Moreover, we used “disnews exposure”as a grouping variable to reveal
the separate effects for those people who actually encountered online disinformation
during the campaign.
8
This logistic regression model fit the data better than an intercept-only model without
the predictors’effects on vote choice. Removing the insignificant paths did not signifi-
cantly deteriorate the goodness of fit (see Table B1 in the online appendix). The
McFadden pseudo-R
2
of .47 indicated a high explanatory power (McFadden, 1974).
9
Conducting multinomial logistic regression on vote choice at T
3
, entailing five categories,
provided us with four sets of different estimates. Each of them represents the effect of
a given independent variable on the occurrence of an outcome relative to a fixed base
category (i.e., the CDU/CSU).
The results show quite high autoregressive estimates of voting intention on vote
choice indicating rather low volatility (see Table 1). Most people that planned to elect
a party at the beginning of the campaign seemed to cast their vote for the same party.
Nevertheless, there was a significant impact of believing disinformation on the vote
decision with reference to the CDU/CSU.
10
More precisely, a one-unit increase in
“disnews believability”increased the odds of voting for the AfD as opposed to the
CDU/CSU more than sevenfold (B = 2.03, OR = 7.62, SE = .71, p= .00). Similarly,
the odds of voting for the SPD instead of for the CDU/CSU increased about fivefold (B =
1.67, OR = 5.33, SE = .59, p= .00). The positive effects on voting for an established
opposition party (B = 1.08, OR = 2.96, SE = .58, p= .06) as well as on abstaining (B =
1.42, OR = 4.13, SE = .86, p= .10) fell short of the significance level of 5%. Taken as
a whole, the coefficients suggest that disinformation beliefs lower the odds of electing the
main governing party.
Nevertheless, log-odds and odds ratios are hard to interpret independently and
sometimes misleading when it comes to probability statements. Therefore, we calculated
and plotted the predicted probabilities of voting for a specific party at varying levels of
disnews believability to address our remaining hypotheses and research question. To get
a more fine-grained picture of the relationships, we grouped the probabilities by voting
intention at T
1
while holding all the other covariates constant at their sample means or
modes (see Figure 3). To estimate the average influence of believing disinformation on
12 Fabian Zimmermann and Matthias Kohring
Table 1
Multinomial logistic regression on individual vote choice (T
3
)
Base category: CDU/CSU
SPD Opposition party AfD Abstention
Controls
Gender (= female) (T
1
) -.334 -.388 -.915* -.098
(.335) (.308) (.402) (.375)
Age (T
1
)————
Education (T
1
) -.399* -.120 -.308 -.620**
(.193) (.179) (.226) (.225)
Political ideology (T
1
) -.172* -.228** .243* -.169
(.088) (.071) (.106) (.110)
Traditional news media use (T
1
)————
Social media news use (T
1
)————
News media literacy (T
1
) .210 .233
†
.264 .428*
(.160) (.139) (.190) (.177)
Trust variables
Trust in traditional news media (T
1
)————
Trust in politics (T
1
) -.612** -.720*** -.986*** -1.032***
(.212) (.192) (.260) (.231)
Voting intention (base: CDU/CSU)
SPD (T
1
) 5.270***
(.589)
2.577***
(.527)
1.735* 2.920***
(.875) (.765)
Opposition party (T
1
) 3.195***
(.569)
4.076***
(.449)
2.509*** 2.364**
(.631) (.750)
AfD (T
1
) 2.218* 2.641** 5.140*** 2.715**
(.970) (.804) (.796) (.928)
Undecided (T
1
) 2.085***
(.501)
1.567***
(.414)
1.272* 2.728***
(.612) (.613)
News believability
Placebo news believability -.104 .167 -.362 -.183
(.701) (.536) (1.115) (.949)
Disnews believability (T
2
) 1.673** 1.084
†
2.031** 1.418
†
(.588) (.579) (.706) (.858)
Constant -.384
(1.484)
.527
(1.228)
-2.806
(2.028)
-.019
(1.845)
N 791
Log likelihood -11,495.353
McFadden Pseudo R
2
.47
Values are multinomial logistic regression coefficients with standard errors in parentheses;
dash = variable not included in the model; voting for the CDU/CSU (T
3
) is the base category;
†
p<.10*p< .05. **p< .01. ***p< .001.
Mistrust, Disinforming News, and Vote Choice 13
these probabilities, we also calculated the marginal effects (including its standard errors)
on a specific vote choice relative to voting intention at T
1
(see Table 2).
11
In hypothesis H3a, we claimed that believing disnews decreases the likelihood of voting
for a mainstream political party. With regard to the CDU/CSU (Figure 3, top-left panel), this
Figure 3. Probabilities of voting for a specific party against disnews believability grouped by voting
intention.
Predicted probabilities of voting for the CDU/CSU (top-left panel), the SPD (top-right panel), an
opposition party (middle-left panel), the AfD (middle-right panel), and abstaining (lower panel) at varying
levels of disnews believability; separate predictions for different voting intentions at T
1
with all other
covariates set at their sample means/modes; based on estimates from the MLR model in Table 1.
14 Fabian Zimmermann and Matthias Kohring
hypothesis seems to be proved true. Believing disinformation generally appears to affect the
election chances for the main governing party negatively, as already indicated by the odds
ratios. The corresponding ME were however only significant for people who intended to vote
for the CDU/CSU or were yet undecided at T
1
. On average, their probability to elect the
CDU/CSU decreased by 30.0% (SE = .11, p= .01) and 9.5% (SE = .04, p= .02), respectively,
for a one-unit increase in disnews believability. However, we failed to demonstrate
a simultaneous tendency regarding the other governing party, the SPD (Figure 3,top-right
panel). Surprisingly, disnews believability in fact increased the probability of electing the
SPD in most cases (except for AfD supporters), even though not significantly. Especially,
former CDU/CSU supporters appeared to rather vote for the SPD the more they perceived
disinformation to be true. The marginal effect only slightly exceeded the significance level
(ME = .105, SE = .06, p= .05). As expected, the disinformation effects on the probability of
electing an established opposition party (Figure 3, middle-left panel) were mostly negative,
but altogether insignificant. Hence, H3a was only confirmed with regard to the main
governing party CDU/CSU.
Hypothesis H3b implied a positive effect of disnews beliefs on voting for the right-
wing protest party. In general, the AfD indeed seems to benefit from disinformation
beliefs (Figure 3, middle-right panel). However, the only significant gain in probability
stems from former CDU/CSU supporters. For those, a one-unit increase in disnews
believability raised the likelihood of voting for the AfD by 9.9% (SE = .05, p= .05).
Accordingly, H3b was partly corroborated for voters initially leaning toward the CDU/
CSU.
Lastly, there was no evidence for an impact of disnews on the probability to abstain
from the vote, which answers our RQ1 (Figure 3, lower panel). Regardless of voting
intention, all of the slopes regarding abstention were flat and the marginal effects were
close to zero and highly insignificant.
Table 2
Effects of disnews believability on the probability of voting for a specific party
Vote choice (T
3
)
CDU/CSU SPD Opposition party AfD Abstention
Voting intention (T
1
)
CDU/CSU -.300** .105
†
.069 .099* .026
(.112) (.055) (.102) (.050) (.040)
SPD -.015
†
.086 -.073 .007 -.004
(.009) (.079) (.066) (.010) (.021)
Opposition party -.014 .058 -.081 .032 .005
(.009) (.048) (.062) (.023) (.014)
AfD -.025 -.003 -.156 .195 -.011
(.021) (.027) (.097) (.127) (.029)
Undecided -.095* .099 -.082 .053 .025
(.041) (.081) (.104) (.037) (.105)
Marginal effects of the variable ‘disnews believability’at its sample mean grouped by voting intention
at T
1
; all other covariates are held constant at their means/modes; based on estimates from the MLR model
in Tab le 1; standard errors are in parentheses;
†
p<.10*p< .05. **p< .01. ***p< .001.
Mistrust, Disinforming News, and Vote Choice 15
Overall, the most striking result is that former CDU/CSU supporters were more
likely to refrain from electing this party the more they believed disinformation. Instead,
these voters tended to choose either the AfD or the SPD. To inspect the robustness of this
finding, we reestimated the marginal effects for a group that previous research indicates
to be most susceptible to online disnews, namely the rightist voters.
12
The direction of all
the effects holds true for this part of the electorate (see Table B4 in the online appendix).
However, the impact of disnews on voting for the AfD becomes much stronger and more
significant for CDU/CSU supporters with right-wing attitudes. At the same time, the
influence on electing the SPD almost disappears among this group (see Figure 4). This
indicates that those right-leaning voters who had initially intended to vote for the CDU/
CSU exclusively switched to the AfD when believing disinformation.
Discussion
Political debates in the face of the 2017 German parliamentary election expressed severe
concerns regarding the influence of political disinformation on the Internet. Our study
Figure 4. Effects of disnews believability for former (moderate and right-leaning) CDU/CSU
supporters.
Note. Marginal effects of the variable “disnews believability”on the probability to vote for
a specific party grouped by political ideology; effects only apply to people who intended to vote
for the CDU/CSU at T
1
; all other covariates are held constant at their means/modes; 95%
confidence intervals are reported; based on estimates from the MLR model in Table 1.
16 Fabian Zimmermann and Matthias Kohring
sheds light on this matter in a twofold way. First, we aimed to analyze the possible
institutional antecedents of online disnews. In light of our findings, one cannot ignore that
the success of disinformation is also a defeat for democratic institutions. While there is
certainly not a general loss of institutional trust in Germany, a specific portion of the
German population has become strongly skeptical about legacy news media and the
political system over the last years. From their point of view, professional journalists
and politicians have discredited themselves in covering and dealing with important
political topics such as the refugee situation. As a consequence, these doubly-
mistrustful people are yearning for alternative facts for the purpose of orientation and
confirmation, with the striking result that the less one trusts in news media and politics,
the more one believes in online disinformation. We thus provided empirical evidence for
Bennett and Livingston's (2018) notion of a disinformation order emerging from
a breakdown of institutional trust and forming in opposition to the established informa-
tion system.
Besides these antecedents, we did also reveal the electoral consequences of disnews
beliefs in a multiparty context. Here, our research also makes a significant contribution to
the field of political communication in addressing this relationship over time. By applying
a longitudinal design, we were able to draw conclusions about the impact of political
disinformation on vote switching, even though our results concerning this matter are mixed.
Our data demonstrate that false news intentionally spread on the Internet did play a role in
diminishing loyalty and raising voice in the German election. In contrast, abstention from
the vote (exit option) remained unaffected by online disinformation, probably due to its
rather inflammatory (anti-immigration) narratives. Because of its disruptive, right-leaning
nature, believing disnews apparently alienated voters from the main governing party, i.e.,
the Christian Democrats, and notably drove them into the arms of the AfD. Accordingly,
disinformation beliefs were apparently one of the reasons for the electoral success of the
right-wing populists in the 2017 parliamentary election. At the same time, sticking to
disnews did obviously not encourage a decision against the other governing party, i.e., the
Social Democrats (SPD). They have, if anything, rather benefited from disinformation,
even drawing former supporters of their coalition partner.
There may be a suitable explanation for this quite puzzling observation. In Germany,
disinformation has mainly focused on purported troubles caused by immigration from
Islamic countries. The public debate about the refugee situation has been centered around
Chancellor Angela Merkel, as she was deemed responsible for Germany’s“welcome
policy.”Misinformed individuals might have felt vindicated in primarily blaming Merkel,
the head of the Christian Democrats (CDU/CSU), for the alleged misconduct concerning
Muslim immigrants. Against this background, it makes sense that it was only the CDU/
CSU which considerably suffered from disinformation in Germany. The former suppor-
ters of this party mainly voiced their disnews-induced disaffection by switching to the
most obvious protest party, AfD. This tendency holds especially true for the most
conservative fraction, as the AfD’s right-wing populist claims probably best match its
demands. For the rather moderate portion, even the SPD apparently served as a voice
option due to their attempted replacement of Angela Merkel with their candidate Martin
Schulz, thereby providing an alternative to her policy beyond a rightist ideology.
Although we could not demonstrate a meaningful disloyalty or voice effect of
disnews beyond people formerly supportive of the Christian Democrats, it would be
wrong to conclude that online disinformation did not matter further. It may be reasonable
that the periods between waves were too short to detect a causal impact of disinforming
Mistrust, Disinforming News, and Vote Choice 17
news. According to our data, most of the voters were already positive about their decision
two months before the election day, which counteracts any media effects during that
period. One possibility however is that disnews shaped voting intentions for the AfD (or
for other parties) far before the election, e.g., during the peak of the refugee situation in
2015.
However, our study deliberately focused on vote switching during the peak of the
election campaign because this provided the strictest test for an electoral influence of
disnews. We leave it to future research to pay attention to the long-term effects of online
political disinformation. Another question is whether our results are also applicable to
other democracies. Of course, there are cross-country differences that shape the char-
acteristics of the national disinformation orders (e.g., amount, structure, and narratives)
and its social impact. Nevertheless, its role as a right-wing counter-public should apply
across nations and unfold with similar implications. Besides, when there are certain
disruptions in Germany, where overall institutional trust is comparatively high, electoral
consequences of disnews might probably be even graver in more polarized countries such
as the USA.
As every internet-based research, our study has to face a potential sampling bias due to
recruitment from an online access panel. We nonetheless attempted to ensure a high degree of
representativeness by employing quotas for gender, age, education, and federal state.
Additionally, participants fromthe online access panel wererecruited using online and offline
procedures, which may increase population coverage.
In addition, some of our self-reported measures could have caused difficulties. Our
respondents may have been wary about giving their true voting intention for the AfD because
of a perceived societal stigma as an extremist party. However, some portion of this possible
bias has been compensated for by our longitudinal design. Besides, the share of AfD voters in
our sample (14.8%) slightly exceeded their actual election result (12.6%), which contradicts
the understatement concern. Beyond that, the variable “disnews believability”might partly
represent general xenophobic and populist attitudes. In that case, its effects on vote choice
would not be reducible to disinformation that actually circulated online, but also be biased by
stable traits. We took measures to eliminate this bias as far as possible. First, we restricted our
vote-specific analysis to the subsample that indicated exposure to our disnews headlines,
thereby excluding all people declaring to believe them without in fact having read
some. Second, we included the perceived believability of made-up placebo news conveying
similar narratives to control for potential spurious effects which did not stem from disnews
stories that were indeed distributed during the campaign.
Furthermore, we were not able to capture all disinforming news that was dissemi-
nated online during the election period. Therefore, it may be possible that we missed
some effects by leaving out some false stories. There are two reasons why this seems
unlikely: First, we relied on what the major fact-checking websites in Germany declared
as the most attention-grabbing falsehoods. And second, we intentionally considered
a wide range of narratives supposing the influence of omitted disnews to lead in the
same direction due to their similar message pattern.
Altogether, we provided evidence for the political impact of online disinformation, as
it may affect the individual vote choice based on false information, thereby undermining
important democratic principles. However, to understand the problem in its entirety, we
have to go beyond “fake news”and look at its societal background. That is, we should not
merely understand disinforming news as an isolated phenomenon but rather as a symptom
of a more deep-rooted public disaffection with the news media as well as the political
18 Fabian Zimmermann and Matthias Kohring
system. Therefore, effective measures to combat political disinformation should address
its social root cause by trying to regain trust in democratic institutions.
Notes
1. The “respondi AG”(https://www.respondi.com/) satisfies the ESOMAR guidelines on mar-
ket, opinion, and social research. It combines online and offline procedures to recruit
participants for its online access panel.
2. See Table A1 in the online appendix for a detailed description of the survey measures
including all question wordings and items.
3. We summarized the mainstream opposition parties to prevent estimation problems due to low
individual rates of the single parties.
4. The comparison between actual disnews and made-up placebo news exposure only takes an
overestimation of true exposure due to the headlines’perceived plausibility into account. It
misses a possible underestimation of true exposure by discounting some real disnews articles
that people saw but forgot.
5. See Table B1 in the online appendix for the global fit measures corresponding to all models
that have been estimated in the analysis.
6. The following model estimations were only based on a subsample of N = 974 because of
missing values in the control variable “political ideology.”
7. We did not include a dummy variable for the Christian Democrats (CDU/CSU), as it served as
base category in the upcoming analysis.
8. The following analysis regarding the effects of ‘disnews believability’on vote choice is based
only on the parameter estimates from the group indicating exposure to disinformation.
9. As this model included multinomial logistic regression, chi-square-based fit indices could not
be computed. Therefore, the goodness of fit assessments were based on the log-likelihood
values. Besides, the estimation was only based on a subsample of N = 791 because of missing
values in the variables “voting intention”and “vote choice.”
10. We also calculated MLR models setting the other parties as base category. These can be found
in Table B3 in the online appendix.
11. The computed marginal effects (ME) give the instantaneous rate of change in the probability
of the DV caused by the IV at its sample mean (holding all other covariates constant).
12. This time, we computed the marginal effects of disnews believability (at its sample mean) on
the probability to vote for a specific party holding the other covariates at their means or
modes, but fixing political ideology at 2 SD above its mean.
Disclosure statement
There was no potential conflict of interest.
Funding
This research received no specific grant from any funding agency in the public.
Supplemental material
Supplemental data for this article can be accessed on the publisher’s website at https://
doi.org/10.1080/10584609.2019.1686095.
Mistrust, Disinforming News, and Vote Choice 19
Data availability statement
The data described in this article are openly available in the Open Science Framework at
https://doi.org/10.7801/313
Open Scholarship
This article has earned the Center for Open Science badges for Open Data and Open
Materials through Open Practices Disclosure. The data and materials are openly acces-
sible at https://doi.org/10.7801/313
ORCID
Fabian Zimmermann http://orcid.org/0000-0003-2660-0023
Matthias Kohring http://orcid.org/0000-0001-9819-1906
References
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of
Economic Perspectives,31,211–236. doi:10.1257/jep.31.2.211
Barber, B. (1983). The logic and limits of trust. New Brunswick, NJ: Rutgers University Press.
Barrera, O., Guriev, S., Henry, E., & Zhuravskaya, E. (2018). Facts, alternative facts, and fact
checking in times of post-truth politics. Retrieved from https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=3004631
Bartels, L. M. (2002). Beyond the running tally: Partisan bias in political perceptions. Political
Behavior,24,117–150. doi:10.1023/A:1021226224601
Bennett, W. L., & Livingston, S. (2018). The disinformation order: Disruptive communication and
the decline of democratic institutions. European Journal of Communication,33, 122–139.
doi:10.1177/0267323118760317
Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and
programming. New York, NY: Taylor and Francis.
Choi, J. (2016). Why do people use news differently on SNSs? An investigation of the role of
motivations, media repertoires, and technology cluster on citizens’news-related activities.
Computers in Human Behavior,54, 249–256. doi:10.1016/j.chb.2015.08.006
Citrin, J., & Stoker, L. (2018). Political trust in a cynical age. Annual Review of Political Science,
21,49–70. doi:10.1146/annurev-polisci-050316-092550
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data:
Questions and tips in the use of structural equation modeling. Journal of Abnormal
Psychology,112, 558–577. doi:10.1037/0021-843X.112.4.558
Cook, T. E., & Gronke, P. (2005). The skeptical American: Revisiting the meanings of trust in
government and confidence in institutions. Journal of Politics,67, 784–803. doi:10.1111/
j.1468-2508.2005.00339.x
Craft, S., Ashley, S., & Maksl, A. (2017). News media literacy and conspiracy theory endorsement.
Communication and the Public,2, 388–401. doi:10.1177/2057047317725539
Dechêne, A., Stahl, C., Hansen, J., & Wänke, M. (2010). The truth about the truth: A meta-analytic
review of the truth effect. Personality and Social Psychology Review,14, 238–257.
doi:10.1177/1088868309352251
20 Fabian Zimmermann and Matthias Kohring
DiFonzo, N., Beckstead, J. W., Stupak, N., & Walders, K. (2016). Validity judgments of rumors
heard multiple times: The shape of the truth effect. Social Influence,11,22–39. doi:10.1080/
15534510.2015.1137224
Easton, D. (1975). A re-assessment of the concept of political support. British Journal of Political
Science,5, 435–457. doi:10.1017/S0007123400008309
Egelhofer, J. L., & Lecheler, S. (2019). Fake news as a two-dimensional phenomenon:
A framework and research agenda. Annals of the International Communication Association,
1–20. doi:10.1080/23808985.2019.1602782
Ernst, N., Kühne, R., & Wirth, W. (2017). Effects of message repetition and negativity on
credibility judgments and political attitudes. International Journal of Communication,11,
3265–3285.
Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks, CA: Sage.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable
variables and measurement error. Journal of Marketing Research,18,39–50. doi:10.2307/
3151312
Gamson, W. A. (1968). Power and discontent. Homewood, IL: The Dorsey Press.
Gelfert, A. (2018). Fake news: A definition. Informal Logic,38,84–117. doi:10.22329/il.
v38i1.5068
Gibson, T. A. (2018). The post-truth double helix: Reflexivity and mistrust in local politics.
International Journal of Communication,12, 3167–3185.
Giddens, A. (1990). The consequences of modernity. Stanford, CA: Stanford University Press.
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazar, D. (2019). Fake news on
Twitter during the 2016 U.S. presidential election. Science,363, 374–378. doi:10.1126/
science.aau2706
Guess, A., Nyhan, B., & Reifler, J. (2018). Selective exposure to misinformation: Evidence from the
consumption of fake news during the 2016 U.S. presidential campaign. Brussels, Belgium:
European Research Council. Retrieved from http://www.dartmouth.edu/~nyhan/fake-news-
2016.pdf
Harsin, J. (2015). Regimes of posttruth, postpolitics, and attention economies. Communication,
Culture & Critique,8, 327–333. doi:10.1111/cccr.12097
Hasher, L., Goldstein, D., & Toppino, T. (1977). Frequency and the conference of referential
validity. Journal of Verbal Learning and Verbal Behavior,16, 107–112. doi:10.1016/
S0022-5371(77)80012-1
Hetherington, M. J. (2005). Why trust matters: Declining political trust and the demise of American
liberalism. Princeton, NJ: Princeton University Press.
Hirschman, A. O. (1970). Exit, voice, and loyalty: Responses to decline in firms, organizations, and
states. Cambridge, MA: Harvard University Press.
Hooghe, M., Marien, S., & Pauwels, T. (2011). Where do distrusting voters turn if there is no viable
exit or voice option? The impact of political trust on electoral behaviour in the Belgian
regional elections of June 2009. Government and Opposition,46, 245–273. doi:10.1111/
j.1477-7053.2010.01338.x
Humprecht, E. (2019). Where ‘fake news’flourishes: A comparison across four Western democracies.
Information, Communication & Society,22,1973–1988. doi:10.1080/1369118X.2018.1474241
Igartua, J. J., & Cheng, L. (2009). Moderating effect of group cue while processing news on
immigration: Is the framing effect a heuristic process? Journal of Communication,59,
726–749. doi:10.1111/j.1460-2466.2009.01454.x
Jöreskog, K. G. (1979). Statistical models and methods for analysis of longitudinal data. In
K. G. Jöreskog & D. Sörbom (Eds.), Advances in factor analysis and structural equation
models (pp. 129–169). Cambridge, MA: Abt Books.
Kahne, J., & Bowyer, B. (2017). Educating for democracy in a partisan age: Confronting the
challenges of motivated reasoning and misinformation. American Educational Research
Journal,54,3–34. doi:10.3102/0002831216679817
Mistrust, Disinforming News, and Vote Choice 21
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York,
NY: Guilford Press.
Koch, T., & Zerback, T. (2013). Helpful or harmful? How frequent repetition affects perceived
statement credibility. Journal of Communication,63, 993–1010. doi:10.1111/jcom.12063
Kohring, M., & Matthes, J. (2007). Trust in news media: Development and validation of
a multidimensional scale. Communication Research,34, 231–252. doi:10.1177/
0093650206298071
Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin,108, 480–498.
doi:10.1037/0033-2909.108.3.480
Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., …
Zittrain, J. L. (2018). The science of fake news: Addressing fake news requires
a multidisciplinary effort. Science,359, 1094–1096. doi:10.1126/science.aao2998
Levy, N. (2017). The bad news about fake news. Social Epistemology Review and Reply Collective,
6(8), 20–36. Retrieved from https://social-epistemology.com/2017/07/24/the-bad-news-about-
fake-news-neil-levy/
Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017). Beyond misinformation: Understanding and
coping with the “post-truth”era. Journal of Applied Research in Memory and Cognition,6,
353–369. doi:10.1016/j.jarmac.2017.07.008
Luhmann, N. (1979). Trust and power. Chichester, UK: Wiley & Sons.
Marwick, A., & Lewis, R. (2017). Media manipulation and disinformation online. New York, NY:
Data & Society Research Institute. Retrieved from https://datasociety.net/output/media-
manipulation-and-disinfo-online/
Matthes, J. (2005). The need for orientation towards news media: Revising and validating a classic
concept. International Journal of Public Opinion Research,18, 422–444. doi:10.1093/ijpor/
edh118
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka
(Ed.), Frontiers in econometrics (pp. 105–142). New York, NY: Academic Press.
Meirick, P. C., & Bessarabova, E. (2016). Epistemic factors in selective exposure and political
misperceptions on the right and left. Analyses of Social Issues and Public Policy,16,36–68.
doi:10.1111/asap.12101
Muthén, L. K., & Muthén, B. O. (2015). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén &
Muthén.
Pennycook, G., Cannon, T. D., & Rand, D. G. (2017). Prior exposure increases perceived accuracy
of fake news. Social Science Research Network. Retrieved from https://ssrn.com/
abstract=2958246
Polage, D. C. (2012). Making up history: False memories of fake news stories. Europe’s Journal of
Psychology,8, 245–250. doi:10.5964/ejop.v8i2.456
Prochazka, F., & Schweiger, W. (2019). How to measure generalized trust in news media? An
adaptation and test of scales. Communication Methods and Measures,13,26–42. doi:10.1080/
19312458.2018.1506021
Reedy, J., Wells, C., & Gastil, J. (2014). How voters become misinformed: An investigation of the
emergence and consequences of false factual beliefs. Social Science Quarterly,95, 1399–1418.
doi:10.1111/ssqu.12102
Simmel, G. (2004). The philosophy of money (third enlarged ed., D. Frisby, Ed.). London, UK:
Routledge.
Swami, V. (2012). Social psychological origins of conspiracy theories: The case of the Jewish
conspiracy theory in Malaysia. Frontiers in Psychology,3,1–9. doi:10.3389/fpsyg.2012
Swami, V., Barron, D., Weis, L., & Furnham, A. (2018). To Brexit or not to Brexit: The roles of
Islamophobia, conspiracist beliefs, and integrated threat in voting intentions for the United
Kingdom European Union membership referendum. British Journal of Psychology,109,
156–179. doi:10.1111/bjop.12252
22 Fabian Zimmermann and Matthias Kohring
Swire, B., Berinsky, A. J., Lewandowsky, S., & Ecker, U. K. (2017). Processing political mis-
information: Comprehending the Trump phenomenon. Royal Society Open Science,4, 160802.
doi:10.1098/rsos.160802
Tandoc, E. C., Jr., Lim, Z. W., & Ling, R. (2018a). Defining “fake news”. A typology of scholarly
definitions. Digital Journalism,6, 137–153. doi:10.1080/21670811.2017.1360143
Tandoc, E. C., Jr, Ling, R., Westlund, O., Duffy, A., Goh, D., & Zheng Wei, L. (2018b). Audiences’
acts of authentication in the age of fake news: A conceptual framework. New Media & Society,
20, 2745–2763. doi:10.1177/1461444817731756
Tsfati, Y. (2010). Online news exposure and trust in the mainstream media: Exploring possible
associations. American Behavioral Scientist,54,22–42. doi:10.1177/0002764210376309
Tsfati, Y., & Peri, Y. (2006). Mainstream media skepticism and exposure to sectorial and extrana-
tional news media: The case of Israel. Mass Communication and Society,9, 165–187.
doi:10.1207/s15327825mcs0902_3
Uscinski, J. E., Klofstad, C., & Atkinson, M. D. (2016). What drives conspiratorial beliefs? The
role of informational cues and predispositions. Political Research Quarterly,69,57–71.
doi:10.1177/1065912915621621
Van Spanje, J., & de Vreese, C. (2014). Europhile media and Eurosceptic voting: Effects of news
media coverage on Eurosceptic voting in the 2009 European parliamentary elections. Political
Communication,31, 325–354. doi:10.1080/10584609.2013.828137
Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary frame-
work for research and policy making (Council of Europe report DGI(2017)09). Strasbourg,
France: Council of Europe.
Weaver, D. H. (1980). Audience need for orientation and media effects. Communication Research,
7, 361–376. doi:10.1177/009365028000700305
Weedon, J., Nuland, W., & Stamos, A. (2017). Information operations and Facebook. Menlo Park,
CA: Facebook, Inc. Retrieved from https://fbnewsroomus.files.wordpress.com/2017/04/face-
book-and-information-operations-v1.pdf
Weeks, B. E., & Garrett, R. K. (2014). Electoral consequences of political rumors: Motivated
reasoning, candidate rumors, and vote choice during the 2008 US presidential election.
International Journal of Public Opinion Research,26, 401–422. doi:10.1093/ijpor/edu005
Wells, C., Reedy, J., Gastil, J., & Lee, C. (2009). Information distortion and voting choices: The
origins and effects of factual beliefs in initiative elections. Political Psychology,30, 953–969.
doi:10.1111/j.1467-9221.2009.00735.x
Widaman, K. F., & Reise, S. P. (1997). Exploring the measurement invariance of psychological
instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle, &
S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and
substance abuse research (pp. 281–324). Washington, DC: American Psychological
Association.
Winter, S., Metzger, M. J., & Flanagin, A. J. (2016). Selective use of news cues: A multiple motive
perspective on information selection in social media environments. Journal of
Communication,66, 669–693. doi:10.1111/jcom.1224
Mistrust, Disinforming News, and Vote Choice 23
