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There is a vast research tradition examining the antecedents that lead people to be politically persuaded. However, political opinion and attitude change in social media has received comparatively scarce attention. This study seeks to shed light on this strand of the literature by theoretically advancing and empirically testing a structural equation model linking online social media, and fake news exposure, with political discussion and political persuasion in social media. Drawing on autoregressive causal tests from two waves of US survey panel data collected in 2019 and 2020, our results indicate that online, social media fake news, and political discussion are all positive predictors of individual political attitude change. Furthermore, structural equation tests reveal that online and social media news lead individuals to be exposed to fake news, which, in turn, predict higher levels of political discussion, ultimately facilitating political persuasion in the social media realm. Limitations and further suggestions for future research are also included in the study.
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https://doi.org/10.1177/00027642221118272
American Behavioral Scientist
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DOI: 10.1177/00027642221118272
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Article
Pathways to Political
Persuasion: Linking Online,
Social Media, and Fake News
With Political Attitude Change
Through Political Discussion
Homero Gil de Zúñiga1,2,3 ,
Pablo González-González1,
and Manuel Goyanes1,4
Abstract
There is a vast research tradition examining the antecedents that lead people to be
politically persuaded. However, political opinion and attitude change in social media
has received comparatively scarce attention. This study seeks to shed light on this
strand of the literature by theoretically advancing and empirically testing a structural
equation model linking online social media, and fake news exposure, with political
discussion and political persuasion in social media. Drawing on autoregressive causal
tests from two waves of US survey panel data collected in 2019 and 2020, our results
indicate that online, social media fake news, and political discussion are all positive
predictors of individual political attitude change. Furthermore, structural equation
tests reveal that online and social media news lead individuals to be exposed to fake
news, which, in turn, predict higher levels of political discussion, ultimately facilitating
political persuasion in the social media realm. Limitations and further suggestions for
future research are also included in the study.
1Democracy Research Unit, Political Science Department, College of Law and Public Administration,
University of Salamanca, Salamanca, Spain
2Media Effects Research Lab, Media Studies Department, Donald P. Bellisario College of
Communications, Pennsylvania State University, University Park, PA, USA
3Facultad de Comunicación y Letras, Universidad Diego Portales, Santiago, Chile
4Department of Communication, Universidad Carlos III, Madrid, Spain
Corresponding Author:
Homero Gil de Zúñiga, Department of Political Science, University of Salamanca, Campus Unamuno s/n,
Salamanca, Castilla y Leon 37007, Spain.
Pennsylvania State University, 201 Carnegie Building, University Park, PA 16802-1503, USA.
Emails: hgz@usal.es; hgzn@psu.edu
1118272ABSXXX10.1177/00027642221118272American Behavioral Scientist</italic>Gil de Zúñiga et al.
research-article2022
2 American Behavioral Scientist 00(0)
Keywords
fake news, misinformation, online news, social media news, fake news, political
discussion, political persuasion
Political persuasion permeates through all layers of political life in well-established
democracies. For instance, political elites and elected officials need to persuade their
constituents to support their policies (Mutz et al., 1996), while the media inform and
“persuade” the public opinion by offering public affair facts and promoting rational
arguments across society (Arias, 2019; Graves & Amazeen, 2019). In addition, opin-
ion leaders and lay citizens regularly persuade others about political matters (Cobb &
Kuklinski, 1997; Iyengar & Simon, 2000). In short, citizens end up being influenced
by a myriad of mechanisms, including voluntary news consumption, incidental news
exposure, and political discussion (Mutz et al., 1996; Wood, 2000). Yet, the growth of
social media ecologies and the Internet have also changed how and when these mecha-
nisms operate. People use social networking sites for a variety of purposes: from stay-
ing connected with friends, relatives, and coworkers (Kahne & Bowyer, 2018; Whiting
& Williams, 2013) to consuming entertainment, getting news, or acquiring novel polit-
ical information (Anderson & Caumont, 2014; Shearer & Mitchell, 2021).
This article structurally examines the antecedents of political persuasion in social
media. Specifically, we examine whether online news consumption, social media
news usage, and fake news exposure lead to political persuasion. First, we empirically
test the direct effects of such factors, using data from a two-wave panel survey of resi-
dents in the United States, and developing causal autoregressive models (Ardèvol-
Abreu et al., 2017; Huckfeldt et al., 2005). In addition, we analyzed direct and indirect
effects of the previous exogenous variables alongside political discussion in a struc-
tural equation model, proposing a theoretical framework that illuminates the specific
pathways toward citizens’ political persuasion in social media. Results from the study
lend support for a positive and direct association between online news consumption,
social media news use, fake news exposure, and political persuasion. Additional struc-
tural equation model results illustrate the potential mechanisms explaining people’s
political attitude change through social media fake news exposure and political discus-
sion. Overall, this study contributes to the scholarly conversation on political persua-
sion in social media, arguing that exposure to online, social media, and fake news
across different platforms and through divergent mechanisms contours people’s politi-
cal attitudes.
Online and Social Media Political Persuasion
There is ample evidence that exposure to political news makes it easier for people to
reconsider their political views (Fishkin, 1991; Mutz, 2002). However, news con-
sumption may elicit distinctive effects on persuasion hinging on individuals’ percep-
tions of issue relevance (McCombs & Shaw, 1972), and perhaps what is more important
Gil de Zúñiga et al. 3
for our study, the overall likelihood for individuals to change their judgments based on
new information acquisition (Mutz et al., 1996; Wood, 2000), and exposure to diverse
viewpoints (Feldman, 2011; Ladd & Lenz, 2008).
While most people typically rely on mass media to be informed about public affairs
and politics (Chaffee & Kanihan, 1997; Petty & Cacioppo, 1986; Gil de Zúñiga &
Chen, 2019), the growing emergence of social media has significantly changed this
landscape.
Recent data suggest that the use of social media for news is growing exponentially,
as more than half of the Americans (53%) rely on such platforms (Shearer & Mitchell,
2021).
Accordingly, it has been argued that such exposure may influence public opinion
(Asur & Huberman, 2010; Kim, Hsu, et al., 2013; Weiksner et al., 2008) and poten-
tially persuade people to change their political beliefs (Allcott & Gentzkow, 2017;
Dimitrova et al., 2014; Kim, Chen, et al., 2013; Valenzuela, 2013).
Despite the myriad of definitions (Mutz et al., 1996; Petty, 2018), political persua-
sion has been theorized as the process of attitude change germane to political matters
(Bohner et al., 2008; Diehl et al., 2016; Perloff, 2008), whereby a communicative
intention to influence the judgments or behavior of a “persuade” tend to be the norm
(Burnell & Reeve, 1984; O’keefe, 2015; Perloff, 2020; Simons, 1976). More recently,
and specifically applied to political communication, political persuasion has been
defined as the reconsideration “of one’s political attitudes based on exposure to new
information” (Gil de Zúñiga et al., 2018, p. 303). This definition best fits our analytical
strategy as it weighs the importance of news stimuli in the cognitive process of attitude
change (e.g., Feldman, 2011). In this regard, examining political persuasion is impor-
tant to better understand how political attitudes and behavior may change due to of
exposure to diverse information hubs nurtured by social media ecologies and online
public spaces.
Numerous political communication scholars have previously examined how news
consumption facilitates people’s political persuasion process, and the complementary
fashion in which political discussion attributes reinforce this attitude change (Feldman
et al., 2012; Gerber et al., 2008; Zaller, 1992). Social media facilitate the creation and
eventual consolidation of larger and more diverse, heterogeneous social networks
(Brundidge, 2010a; Diehl et al., 2016) that may share politically dissimilar news con-
tents (Bakshy et al., 2012; Lee et al., 2014; Messing & Westwood, 2014). This expo-
sure to heterogeneous news prompts users to change their attitudes (Chang et al., 2018;
Diehl et al., 2016) either due to the novel information acquisition or cognitively pro-
cessing the information they receive via news exposure and political exposure
discussion.
First, exposure to counter-attitudinal information can lead to ambivalence in one’s
political opinions and beliefs (Mutz, 2006), which may challenge prior assumptions
stored in the memory. Second, the politically grounded reasons exposed by participants
in interpersonal discussions may also challenge one’s opinion. Third, research has
noted that attitude ambivalence is contingent on the framing and context of the issues
tackled and, not surprisingly, on one’s social network diversity (Keele & Wolak, 2008).
4 American Behavioral Scientist 00(0)
Ambivalence energizes a more open view toward opinion-challenging perspectives
which, in turn, fosters attitude change (Mutz, 2006). More recent research has, indeed,
demonstrated a positive relationship between news consumption and political persua-
sion in social media. Evidence found it to be both direct (Gil de Zúñiga et al., 2018) and
indirect mechanisms, mediated by cognitive elaboration about cross-cutting views (Lee
& Choi, 2017), and through network heterogeneity and discussion disagreement (Diehl
et al., 2016). Based on the preceding theoretical discussion, we propose the following
hypotheses:
H1: Online news consumption (W1) will be positively related over time to political
persuasion on social media (W2).
H2: Social media news use (W1) will be positively related over time to political
persuasion on social media (W2).
Fake News Exposure and Political Persuasion
In the social media ecosystem, people will encounter not only verified factual informa-
tion, but also fabricated news (Allcott & Gentzkow, 2017; Lazer et al., 2018). Such
misleading content is usually theorized under the protection of fake news, but there is
no strong consensus on its structural interpretation (Conroy et al., 2015; Tandoc et al.,
2018; Wardle & Derakhshan, 2017). As pointed out in similar research, simply reduc-
ing the dimensions of false information may not be an effective or productive way to
advance research (Weeks & Gil de Zúñiga, 2021). Thus, our empirical efforts focus on
the effects of fake news exposure on political persuasion, understanding “fake news”
as fabricated, false news that could mislead readers by mimicking news media content
(Allcott & Gentzkow, 2017; Lazer et al., 2018). The literature on the potential impact
of fake news on society paints a rather gloomy and pessimistic picture (Balmas, 2014;
Rapp & Salovich, 2018). Fake news’ persuasive influence stems from its scalability, as
fabricated content spreads deeper and faster than authentic news (Vosoughi et al.,
2018). It can be said that the most vulnerable topics are related to health and medicine
(i.e., abortion, vaccination, etc.), because prior knowledge is usually required to deter-
mine the authenticity of the content (Ha et al., 2021), but it also threatens other areas,
including politics. Ultimately, it has been theorized that when fake news is assumed as
authentic, peoples’ attitudes may change, no matter the topic, and this includes, inex-
tricably, voting behavior (Chadwick et al., 2018; Luo et al., 2020). In this regard, dif-
ferent agents across political systems have explored with relative success misleading
narratives for political gains, including, for example, Donald Trump’s presidential
campaigns (Allcott & Gentzkow, 2017; Guo & Vargo, 2020).
Recent work has focused on the potential impact of fake news on unconscious pro-
cesses (Bastick, 2021). It has been argued that fake news might exert a negative influ-
ence on emotions and political attitudes, such as cynicism, political efficacy, and
polarization (Allcott et al., 2019; Azzimonti & Fernandes, 2018; Balmas, 2014),
which, in turn, may affect political behavior. Given that being exposed to new infor-
mation and factual-based news may inspire people to reconsider their political views,
Gil de Zúñiga et al. 5
we also expect that the distorted narratives of fabricated content that mimics real news
may also prompt users to be politically persuaded and, ultimately, change their atti-
tudes toward political issues. More formally:
H3: Online exposure to fake news (W1) will be positively related in time to political
persuasion on social media (W2).
Fake News and Online Discussion as Pathways to Political Persuasion
Social media provides fertile soil to foster peer-to-peer interactions (Kaplan &
Haenlein, 2010; Van Noorden, 2014). Their affordances enable users to engage in
semi-public activities, such as liking and (re)sharing (Khan, 2017). Such interactions
are deemed “semi-public” because users can either make them fully visible or invisi-
ble (Boyd & Ellison, 2007; Ellison et al., 2011). In addition, another interaction mech-
anism that is particularly relevant to our research is the possibility of commenting.
This activity carries the potential of engaging in discussions with other users, involv-
ing explicit political content (Halpern & Gibbs, 2013; Gil de Zúñiga, Ardèvol-Abreu,
& Casero-Ripollés, 2019), that may influence users’ political opinions.
However, the mainstay of the pathways to political discussions in social media is
arguably news use. When citizens consume news, they find information about public
affairs and political arguments with which they tend to agree or disagree, triggering
their willingness to discuss and express their political opinions (Gil de Zúñiga,
Ardèvol-Abreu, & Casero-Ripollés, 2019; Halpern & Gibbs, 2013). Previous work
has yielded strong, consistent evidence on this association (Brundidge, 2010b; Gil de
Zúñiga et al., 2016). Subsequently, after political discussions take place, users involved
could generally want to seek new information and follow the news on a particular
issue (Jacobs et al., 2009; Jahng, 2018), and check, verify, and contextualize what
other users also said about these issues (Lane et al., 2019; Velasquez & Rojas, 2017).
A large body of research has shown that exposure to discussion disagreement can lead
individuals to be more likely to reconsider their views and opinions (Fishkin, 1991;
Mutz & Martin, 2001).
Therefore, it stands to reason that political discussion could mediate between users’
news consumption and their likelihood of being persuaded.
Beyond fact-checked news, social media also enable the distribution of fake news,
prompting users to comment, discuss, or correct its distorted nature (Bode & Vraga,
2018; Colliander, 2019). As discussed previously, misinformation narratives in the
distribution of inaccurate or misleading news may also elicit changes in people’s polit-
ical attitudes, emotions, and behavior (Balmas, 2014; Bastick, 2021; Rapp & Salovich,
2018). However, the question of which role fake news plays in political persuasion as
a broader, more comprehensive framework remains open. Therefore, this study also
seeks to investigate how variables previously articulated in the political persuasion
literature as influential (e.g., online, social media, fake news exposure, and political
discussion) may work in a theoretically structured system. Drawing upon these theo-
retical rationales, we pose the following overarching research question:
6 American Behavioral Scientist 00(0)
RQ1: How do online news consumption (W1), social media news use (W1), fake
news exposure (W1), and online discussion (W1) relate in time to political persua-
sion (W2) in a theoretical causal structure?
Methods
Data for this study came from a three-wave panel survey that was administered in the
United States (June 2019–October 2019–February 2020). Questionnaires were
designed and distributed in Qualtrics at the Principal Investigator’s university. At the
same time, respondent recruitment was performed by IPSOS Austria, from an online
panel mirroring the US general population using stratified sampling using key demo-
graphic variables such as gender, age, and education (see Table 1). Drawing on a panel
curated by IPSOS Austria, 3,000 subjects were sampled based on quotas to represent
US society according to the US Census. In Wave 1, 1,338 cases were validated and 511
in Wave 2, with a retention and cooperation rate of 44.60% and 38.19%, respectively
(see AAPOR, 2018). Only data from the first and second waves were employed in this
study. The following operationalizations were used for the endogenous and exogenous
variables:
Social Media News Use
This construct computes respondents’ news consumption across social media (adapted
from Gil de Zúñiga et al., 2018), including “Facebook,” “Twitter,” “Snapchat,”
“LinkedIn,” “WhatsApp,” “local news on social media,” and “national news on social
media” (13-item averaged scale, 1 = never to 10 = all the time; Cronbach’s α = .91;
M = 3.20; SD = 1.98).
Online News Consumption
This variable taps into respondents’ online news consumption across different plat-
forms, including “online news sites,” “citizen journalism,” and “local news online
sites” (three-item averaged scale, 1 = never to 10 = all the time; Cronbach’s α = .68;
M = 3.80; SD = 2.30)
Fake News Exposure
This construct computes respondents’ self-evaluation of their exposure to “fake
news” (Allcott & Gentzkow, 2017; Lazer et al., 2018). It was measured by asking
respondent how often they think they see “fabricated information that mimics news
media content and could mislead readers,” “articles that originate from satirical
websites but were transformed by others and put in a misleading context,” and “sto-
ries containing deliberatively misleading elements making the reader believe it is
correct” (three-item averaged scale, 1 = never to 10 = all the time; Cronbach’s α = .92;
M = 5.99; SD = 2.32)
Gil de Zúñiga et al. 7
Social Media/Online Political Discussion
This variable measure respondents’ frequency of online and social media political dis-
cussion. Respondents indicated how often they talk “about politics or public affairs”
online, encompassing closer knit networks or strong ties, as well as discussions
Table 1. Demographic Profile of Study Survey, Other Comparable US Survey, and the US
Census.
Study survey,
June 2019 (%) GSS, 2018 (%)
U.S. Census Bureau
(estimates, 2019) (%)
Gender
Female 53.2 55.2 51.5
Male 46.8 44.8 48.5
Race/ethnicity
White 75.2 71.2 77.5
Black or African American 10.1 16.4 13.0
Hispanic or Latino 6.5 5.4 14.6
Asian 5.6 3.1 6.1
Age (years)
18–35 32.3 5.5 30.6
36–55 39.7 33.2 36.7
Older than 55 28.0 38.2 32.7
Education
High school or less 35.2 61.3 38.9
Some college 25.0 8.3 27.8
Bachelor’s degree or more 39.8 30.3 33.3
Household income
Less than $15,000 11.6 21.2 9.1
$15,000–$24,999 9.8 12.7 8.0
$25,000–$49,999 21.1 30.8 20
$50,000–$99,999 33.3 24.0 28.8
$100,000–$149,999 15.9 3.3 15.5
$150,000–$199,999 4.8 7.9 8.3
$200,000 or more 3.5 0.0 10.3
Source (third column). U.S. Census Bureau, Population Division.
Note. Percentages are calculated for population 18 years old and over. In the second column, “Race”
(block #2) was measured by respondents’ first mention, “Some college” (block #4) includes those who
reported “Junior College” as their highest degree in the GSS, and “Household income” (block #5) was
measured in inflation-adjusted constant dollars (See GSS Methodological Reports). The third column
(blocks #1–4) shows official estimates of the resident population by single year for the United States
(2019). For education attainment, Census figures also refer to population age 18 and older, and “Some
college” also includes those with an associate’s degree. Household-level income data (block #5) in the
third column is based on information from the Current Population Survey Annual Social and Economic
Supplements (CPS ASEC).
GSS = general social survey
8 American Behavioral Scientist 00(0)
maintained with looser social connections or weak ties (Gil de Zúñiga, 2017;
Valenzuela et al., 2012) (six-item averaged scale, 1 = never to 10 = all the time;
Cronbach’s α = .92‚ M = 2.92‚ SD = 2.31).
Social Media Political Persuasion
This construct taps on respondents’ likelihood of being persuaded in social media (see
Diehl et al., 2016). “I have changed my opinion based upon something I saw on social
media,” “I have changed my opinion based upon what someone influential posted on
social media,” and “I take part in changing my mind about political issues because of
information or interaction on social media” (three-item averaged scale, 1 = never to
10 = all the time; W1 Cronbach’s α = .89; M = 4.17; SD = 2.46; W2 Cronbach’s α = .91;
M = 3.75; SD = 2.39).
Controls and Analysis Strategy. As prior scholarship has suggested that the association
between explanatory and response variables may also be affected by other exogenous
antecedents (see Feldman, 2011; Gil de Zúñiga et al., 2018; Weeks et al., 2015), a set
of controls were also included: demographics (age, gender, education, income, and
race), media antecedents (social media use, television use for news, newspaper use,
radio use for news), and political antecedents (political interest, ideology, network
size, and online political discussion). For data analysis, we first run a series of panel
autoregressive causal order ordinary least squares (OLS) regressions, with social
media political persuasion in Wave 2 as the dependent variable, controlling for all
prior theoretical antecedents, as well as prior individual levels of political persuasion
on social media in Wave
1. The models include controls in five different blocks (including the autoregres-
sive term), and we ran a series of OLS regressions for each of our variables of
interest (i.e., social media use for news, online news consumption, and fake
news exposure). Then, a structural equation model test was employed to
account for the direct and indirect effects on an overarching model. Different
competing models were also theorized and compared, with the one presented
in this manuscript being the most parsimonious. Thus, the rationale behind our
analytic strategy was: (1) to test the direct effects of each exogenous variable
independently and (2) to model their direct and indirect effects structurally and
more stringently. Direct regression effects were calculated with SPSS 26,
while the structural equation model was run with MPlus 8. Zero-order correla-
tions are also shown in Table 1.
Results
Before properly testing our hypothesis, an overall set of zero-order correlations
(Table 2) show that all exogenous and endogenous variables of interest in our model
are associated with each other in a bivariate relationship. In order to test H1 through
Gil de Zúñiga et al. 9
H3 (Table 3), we first employed a series of panel autoregressive OLS regressions with
social media political persuasion as the dependent variable and our variables of inter-
est, namely, (1) social media news, (2) online news, and (3) fake news exposure as
independent variables. Regarding the effect of online news on social media political
persuasion (H1), the first autoregressive model indicates a statistically significant and
positive effect of online news consumption on social media political persuasion
(β = .133, p < .05; total R2 = 44.5%). Accordingly, those users who consume more
online news in W1 are more prone to be politically persuaded in social media in W2.
Beyond the autoregressive term (i.e., social media political persuasion in Time 1;
β = .398, p < .001; ΔR2 = 36.9%), social media use (β = .140, p < .001), and online dis-
cussion (β = .124, p < .05) were also statistically significant and positive predictors of
social media political persuasion, supporting H1.
The second autoregressive regression model examines the relationship between
social media use for news and social media political persuasion (H2). The analysis
portrayed in Table 3 shows a positive association between social media use for news
and social media political persuasion (β = .214, p < .001; total R2 = 45.2%). Accordingly,
greater use of social media for being informed in W1 is positively associated with
social media political persuasion in W2. The autoregressive term (β = .364, p < .001;
ΔR2 = 36.9%), social media use (β = .088, p < .05), online discussion (β = .098, p < .05)
were also shown to be statistically significant and positive predictors of social media
political persuasion. H2 is therefore also supported.
Finally, the third autoregressive model tests the effect of fake news exposure on
social media political persuasion (H3). The model reveals a positive and statistically
significant association between fake news exposure and social media political persua-
sion (β = .076, p < .05; total R2 = 44%). Accordingly, those who are exposed to fake
news on social media in W1 are more prone to be politically persuaded in social media
in W2. The autoregressive term (β = .419, p < .001; ΔR2 = 36.9%), social media use
(β = .145, p < .001) and online discussion (β = .134, p < .05) were shown to be signifi-
cant and positive predictors of social media political persuasion, while age (β = −.091,
p < .05) was revealed to be a significant but negative predictor. Thus, H3 was
supported.
Among the remaining controls introduced in our model, use of social media
(β = .088, p < .05, cross-sectional; β = .140, p < .001, lagged; and β = .145, p < .001,
autoregressive) and online political discussion (β = .098, p < .05, cross-sectional;
β = .124, p < .001, lagged; and β = .134, p < .001, autoregressive) are all consistent
positive predictors of political persuasion. Age is also a significant predictor, as
younger people tend to change their political opinion more frequently than their older
counterparts in time (β = −.091, p < .05, in the autoregressive model).
In order to examine the structural influence of our exogenous measurements on
the endogenous variable, the study tested different parsimonious theoretical models
that could explain the relationship between news exposure and discussion in pre-
dicting political persuasion in social media (RQ1). Prior Structural Equation
Modeling (SEM) literature suggests that an appropriate structural model fit should
get a comparative fit index (CFI > 0.90), root mean square error of approximation
10 American Behavioral Scientist 00(0)
(RMSEA < 0.08), and Tucker–Lewis Index (TLI > 0.90) (see Awang, 2014).
Accordingly, the model presented here yielded the most parsimonious fit to the
data, when compared to the baseline model, and other competing theoretical mod-
els: χ2(3) = 4.091, p = .251; CFI = 0.995; TLI = .986; RMSEA = 0.027; and
SRMR = 0.014. Figure 1 represents the standardized betas of direct effects, while
Table 4 shows the results from computing the mediation effects. As reflected, all
exogenous variables have a positive and statistically significant effect on the endog-
enous measurements, except social media political persuasion. Only social media
and online political discussion are directly related to persuasion in time. Regarding
social media news, the structural equation model reveals a positive effect on fake
news (β = .031, p < .05), social media online discussion (β = .479, p < .001), and a
positive (covariate) association with online news (r = .616, p < .001).
As for online news consumption, the structural equation model indicates a statisti-
cally significant and positive effect on fake news (β = .116, p < .05), and social media
online discussion (β = .135, p < .01). Finally, the model reveals a positive effect of fake
new exposure on social media online discussion (β = .085, p < .05), and a positive
effect of social media online discussion on social media political persuasion (β = .150,
p < .01). The exogenous variables had significant explanatory power in accounting for
Figure 1. Autoregressive SEM testing social media news, online news, fake news exposure,
and social media/online discussion effects on social media persuasion.
Note. N = 511. Maximum-likelihood estimation. Continuous path entries are standardized SEM
coefficients (StdYX standardization): *p < .05,**p < .01, ***p < .001. The model controls for all
variables included in Table 4: demographic variables (age, gender, education, income, and race),
traditional media use (TV, radio, and newspapers), political orientations (political intertest, discussion
network size, and ideology), and the autoregressive term (social media political persuasion W¹) by
residualizing all observed variables prior to model fitting. All W¹ variables were brought into the
model by mentioning their variance in the MODEL command. The model includes indirect effects on
persuasion (W2) (see TABLE2Table 2). Model bootstrapped 1,000 iterations. Goodness of fit: χ² = 4.09;
df = 3; p = .25; RMSEA = 0.027, CFI = 0.995, TLI = 0.986, SRMR = 0.022. Explained variance of criterion
variables: SM political persuasion (W2), R2 = .023; SM/online political discussion (W1), R2 = .335; fake
news (W1), R2 = .049. CFI = comparative fit index, RMSEA = root mean square error of approximation,
SEM = structural equation modelling, TLI = Tucker–Lewis Index.
Gil de Zúñiga et al. 11
the endogenous measurements (fake news R2 = 4.9%, social media online discussion
R2 = 35.5%, and social media political persuasion R2 = 2.3%)1.
When it comes to indirect effects, the structural equation model shows three posi-
tive mediating paths. The first path reveals that social media news positively affects
social media persuasion through online/social media political discussion (β = .035,
p < .05). Accordingly, those who consume more news in social media are more prone
to engage in online/social media discussion, which, in turn, influence their likelihood
of being persuaded in social media. Similar effects were found in regard to online
news consumption. According to our structural equation model, those who consume
more online news are more likely to participate in online/social media discussions,
which positively affects their likelihood of being persuaded in social media (β = .009,
p < .05). Finally, a more nuanced picture is depicted when considering the mediating
effect of online/social media discussion over the relationship between fake news and
political persuasion (β = .013, p = .06).
Discussion
There is abundant evidence in the literature suggesting that people who more fre-
quently use social media amplify their social ties within these platforms and also tend
to rely on social media to consume information about current news and public affairs
(Gottfried & Shearer, 2016; Kahne & Bowyer, 2018; Shearer & Mitchell, 2021). In
this process of news consumption and political discussion, users’ political attitudes are
susceptible to change (Cobb & Kuklinski, 1997; Iyengar & Simon, 2000). Drawing on
a two-wave panel survey of US residents, this study contributes to better understand
how political persuasion in social media unfolds. First, our study provides illustrative
causal empirical evidence suggesting that higher levels of online news consumption,
social media news use, and fake news exposure directly lead to social media political
persuasion. In addition, the study proposes a comprehensive social media political
Table 2. Zero-Order Correlations.
Variables 1 2 3 4 5 6 7
1. TV news use
2. Newspaper .572***
3. Radio news .426*** .420***
4. Online discussion .345*** .363*** .424***
5. Online news .495*** .514*** .444*** .467***
6. Social media news .421*** .392*** .352*** .621*** .647***
7. Fake news exposure .180*** .107*** .229*** .221*** .212*** .218***
8. Social media political
persuasion
.251*** .225** .204*** .495*** .436*** .640*** .149***
Note. Cell entries are Pearson’s correlation coefficients (W1 = 1,338).
*p < .05, **p < .01, ***p < .001.
12 American Behavioral Scientist 00(0)
Table 3. Autoregressive Models Predicting Political Persuasion.
Social media
political
persuasion W2
Social media
political
persuasion W2
Social media
political
persuasion W2
Block 1: Autoregressive term
Social media political
persuasion W1
0.354***
R236.9%
Social media political
persuasion W1
398***
R236.9%
Social media political
persuasion W1
0.419***
R2 36.9%
Block 2: Demographics
Age −0.055 −0.071 −0.091*
Gender (female) −0.031 −0.029 0.022
Education 0.037 0.034 0.038
Income −0.030 −0.017 −0.026
Race (white) −0.033 −0.036 −0.033
R21.8% 1.8% 1.8%
Block 3: Media antecedents
Social media use 0.088* 0.140*** 0.145***
TV news use 0.048 0.056 0.070
Newspaper use −0.025 −0.033 0.007
Radio news use −0.001 −0.006 −0.001
R23.2% 3.2% 3.2%
Block 4: Political orientations
Political interest −0.065 −0.075 −0.076
Network size 0.026 0.021 0.014
Online discussion 0.098* 0.124* 0.134*
Ideology −0.049 −0.043 −0.061
R21.6% 1.6% 1.6%
Block 5: Variables of interest
Social media news 0.214***
Online news 0.133*
Fake news 0.076*
R21.7% 1% 0.5%
Total R245.2% 44.5% 44%
Note. Sample size = 511 (Wave 2). Cell entries are final-entry OLS standardized beta (β) coefficients.
OLS = ordinary least squares.
*p < .05, **p < .01, ***p < .001.
Gil de Zúñiga et al. 13
persuasion theoretical framework by shedding more light into this relationship with a
structural equation model. Overall, our findings suggest that people are politically
persuaded after consuming news and being exposed to fake news, and engaging in
online/social media discussions, which function as a mediating mechanism.
First, our analyses show that online news use is a significant factor influencing
political persuasion. When people consume online news from (professional) media
outlets, users tend to change their political positions. This finding is not entirely sur-
prising, as prior research has consistently shown that news use nurtures political per-
suasion both in offline and online environments (Feldman, 2011; Fishkin, 1991; Ladd
& Lenz, 2008; Mutz et al., 1996). The main driver here is novel information. When
individuals are exposed to factual information, they incorporate these facts into their
prior cognitive and mental map, making more informed decisions. Some of these deci-
sions lead people to change their attitude toward political matters and policies
(Johansen & Joslyn, 2008).
Second, we also provide evidence that supports a positive association between
social media news use and political persuasion. Also consistent with extant research
(Diehl et al., 2016; Gil de Zúñiga et al., 2018), our findings suggest that news con-
sumption in social media may encourage people to reconsider their political assump-
tions and, ultimately, be persuaded.
Beyond the explanatory power of the novel information individuals acquire to pro-
mote political attitude change, the persuasive process may also hinge on a myriad of
mechanisms, including news trust, peers’ news suggestions, algorithms curation pro-
cess, or/and individual filtration tactics (Fletcher & Nielsen, 2018; Goyanes et al.,
2021; Hermida et al., 2012; Messing & Westwood, 2014). All these agents may also be
responsible for activating a cognitive process that prompts users to reconsider their
political attitudes in time.
Table 4. Indirect Effects of Social Media News (W1), Online News (W1), Fake News (W1),
and SM/Online Political Discussion on Persuasion (W2).
Indirect effects
Point
estimate
95% confidence
interval
Social media news (W1) SM/online political discussion
(W2) SM political persuasion (W2)
0.072** 0.006 to 0.065
Social media news (W1) fake news (W2) SM/online
political discussion (W2) SM political persuasion (W2)
0.002#−0.001 to 0.002
Online news (W1) SM/online political discussion (W2)
SM political persuasion (W2)
0.020* 0.002 to 0.021
Online news (W1) fake news (W2) SM/online political
discussion (W2) SM political persuasion (W2)
0.001 −0.001 to 0.002
Fake news (W1) SM/online political discussion (W2)
SM political persuasion (W2)
0.013#−0.005 to 0.030
Note. Indirect effects calculated from the model shown in Figure 1. Standardized coefficients are
reported. W1 = Wave 1; W2 = Wave 2.
*p < .05, **p < .01, ***p < .001 (two-tailed), #p < .10.
14 American Behavioral Scientist 00(0)
Complementarily to real news, users may also encounter “fake news” (Allcott &
Gentzkow, 2017; Lazer et al., 2018). The low access barriers to information in social
media ecologies enable the massive circulation of misleading content aiming to mod-
ify users’ ideals (Bastick, 2021; Ha et al., 2021). Interestingly, when citizens are
exposed to fake news, they also seem to be politically persuaded in time. This is an
important and novel finding.
A first feasible explanation implies that, although the information is not factual,
some people may be deceived and believe such content is authentic, triggering an
attitude change. This possibility denotes the importance of social media platforms and
policymaker’s efforts in identifying misleading and false content, making social media
users and people aware.
Alternatively, and what we consider more plausibly given our measurement of fake
news, people are able to identify a given content as fake news at the moment of being
exposed to it; however, there are certain features about that fake “information” that
also fosters political attitude change eventually. In other words, individuals may iden-
tify the information as fake at first, but over time, they cannot recall or assess the
provenance of that information, affecting their political attitudes (Jankowski, 2018).
Likewise, the direction of the attitude change is uncertain. It might well be occur-
ring as a change in one’s former attitudes, adopting a counter-prior political position.
However, we should consider the possibility of persuasion happening by embracing a
more polarized opinion consistent with their prior political stance; this would fuel a
reinforcement loop. As social media users can stumble upon fake news shared by like-
minded peers, if its content is consistent with one’s opinions and (political) leanings,
it could potentially reinforce users’ distorted views (Faragó et al., 2020; Moravec
et al., 2018), as trust in the news also matters (Gil de Zúñiga, Ardèvol-Abreu, Diehl,
et al., 2019). In this vein, another potential rationale may stem from users’ inclinations
to believe in any news piece as long as it comes from sources they trust. Put differ-
ently, users may assume as real the information shared by those within their social
network, such as friends, coworkers, and relatives, or other actors such as political
leaders and parties (Anspach, 2017; Turcotte et al., 2015). Many of these are important
considerations for future research, and forthcoming scholarship should pay more
attention to these processes.
Lastly, we aimed to draw a theoretical casual order model to explain how online
news consumption, social media news use, and fake news exposure would relate to
online/social media discussion, and ultimately, to political persuasion. The analysis
shows several insightful pathways. First, online news consumption and social media
news use positively predict discussion in social media/online environments. As peo-
ple get relevant insights from the news, discussions organically emerge. Second,
online news consumption and social media news use also positively predict fake
news exposure. Accordingly, the more the users consume news online and in social
media, the higher their chances of being exposed to misleading content. Third, fake
news exposure also leads to discussions in social media/online environments.
Analogously to real news, fake news stimulates and sparks peer-to-peer discussions,
whether to support or correct the assumptions held in the (fake) contents (Bode &
Gil de Zúñiga et al. 15
Vraga, 2018; Colliander, 2019). Finally, after discussions, some users may change
their political position. This phenomenon might occur because discussing involves
processing new information that challenges one’s previous beliefs. This exposure
might be sufficient to enable attitude ambivalence or directly change one’s political
attitudes and opinions.
The finding that a positive association between news consumption and political
persuasion is encouraging for democratic societies. However, under certain condi-
tions, fake news exposure may exert similar effects. Online/social media discussion
plays a key role here, mediating some of these effects on political persuasion. It is
important to stress that discussion partially mitigates the effect of fake news exposure.
In addition, there might be potential cofounders, such as political orientations or other
political motivations, either dispositional or situational. These could potentially impact
on people’s news consumption online and on social media and further, affect how citi-
zens engage in political discussions. Although this study has included some of these
potential orientations as controls (i.e., political interest, ideology, discussion network
size, etc.), future research should further address this possibility.
In contrast to prior studies (Diehl et al. 2016; Gil de Zúñiga et al., 2018), one of the
primary findings of our article is that social media news use seems not to exert a direct
effect on political persuasion. As explained above, this holds as all the mentioned
variables are considered in our novel, stringent model (Figure 1). Although we do not
focus on the discussion attributes here, this study has unveiled some nuances about the
process of political persuasion that the literature was not fully aware of. Furthermore,
while building on the cited studies, we have considered in our models the different
types of information sources available in the virtual environment, differentiating
between social media news and online news, as well as fake news. This consideration
also contributes to the literature by providing a better glance at how such diverse ways
of being exposed to information online lead to political persuasion. On the whole, our
analysis suggests that political persuasion on social media is most likely to occur when
people (1) consume news online and on social media, (2) are exposed to fake news,
and (3) engage in discussions online/on social media.
As much as this study contributes to better understanding the mechanisms that lead
to individual political attitude change in social media, it is not exempt from noteworthy
limitations and caveats. First, we conduct our analysis on data from a representative
sample of the US general population; this limits our study in comparative terms, and
future research is needed to establish if the causal association reported exists in other
geographic areas. Second, our study’s findings are based upon statistical analyses of
online panel surveys, potentially lacking the causal inference attributable to experimen-
tal methods. Third, our dependent variable (i.e., political persuasion) alongside fake
news exposure is based on self-reports. These kinds of measurements are linked to
several limitations, such as individuals’ cognitive capacity to recall accurately.
Accordingly, respondents might not only correctly remember the extent to which they
were exposed to misleading news pieces, but also may underreport their actual fake
news exposure: social desirability. Finally, we lack information about the content of
discussions, the news consumed, and measures of particular political attitudes or issues
16 American Behavioral Scientist 00(0)
from which persuasion emerges. Therefore, our study may not accurately pinpoint
whether political persuasion is unfolding as changes in individuals’ previous beliefs or
stances regarding a particular issue or whether there is a reinforcement loop of such
beliefs. On the whole, our study fleshes out the idea that political persuasion in social
media unfolds in different pathways hinging on the very nature of news and the political
discussions they trigger.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship,
and/or publication of this article: This work has benefited from the support of the Spanish
National Research Agency’s Program for the Generation of Knowledge and the Scientific and
Technological Strengthening Research + Development Grant PID2020-115562GB-I00. The
first author is funded by the ‘Beatriz Galindo Program’ from the Spanish Ministry of Science,
Innovation & Universities, and the Junta de Castilla y León. The second author is funded by the
Spanish National Research Agency (MCIN/AEI/10.13039/501100011033) and the European
Social Fund Plus (ESF+) (PRE2021-097685). Responsibility for the information and views set
out in this study lies entirely with the authors.
ORCID iD
Homero Gil de Zúñiga https://orcid.org/0000-0002-4187-3604
Note
1. It is noteworthy to state that the R2 (2.3%) explained for social media political persuasion
in the structural equation model is beyond the R2 explained for all other control variables,
included the autoregressive term for a total of R2 of 48.3.
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Author Biographies
Homero Gil de Zúñiga is a Distinguished Research Professor at University of Salamanca and
serves as director of the Democracy Research Unit (DRU). He is also a Professor at Pennsylvania
State University, and a Senior Research Fellow at Universidad Diego Portales, Chile. In general,
his work draws from theoretically driven research, aiming to shed an empirical social scientific
light over how social media, algorithms, AI, and other technologies affect society and
democracy.
Pablo González-González is a doctoral candidate in Political Science at the University of
Salamanca, and a member of the Democracy Research Unit (DRU). His research interests
revolve around quantitative methods applied to political communication, social media, and
political behavior.
Manuel Goyanes (PhD) serves as an assistant professor at Carlos III University in Madrid and
is a former visiting fellow at both the London School of Economics (LSE) and the University of
Vienna. His research addresses the influence of journalism and new technologies over citizens’
daily lives, as well as the effects of news consumption on citizens’ political knowledge and
participation. He is also interested in global inequalities in academic participation, the system-
atic biases towards global South scholars, and publication trends in Communication. His works
appeared in top-tier journals such as News Media & Society, Information, Communication &
Society, Scientometrics, Computers in Human Behaviour, etc. He is editorial board member in
several international journals and the co-PI of a funded interdisciplinary project on fake news
detection on the Internet.
... Finally, as to examining the effects of exposure to fake news as a factor to better determine how pseudo-information should be confronted, Gil de Zúñiga et al., (2022) show the processes embedded in online and social media news consumption and exposure to fake news (self-assessment), particularly in relation to political persuasion. Ultimately, the ecosystem is much more complex than "being exposed to pseudoinformation will make people change their minds politically," where the way of consuming news and discussing politics with others online and in social media is a deeper determining factor than sheer exposure to fake news. ...
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... The survey was fielded at University of Vienna and drew on a national online panel sample which seeks to be representative of the US population. A detailed sociodemographic composition of our sample can be found on (Gil de Zúniga et al. 2021). The panel of subjects was contracted to IPSOS Austria, and the fieldwork was conducted via Qualtrics through the PI's Research Unit in June 2019, with 1338 respondents (COOP2 = 45.5%), of whom 511 repeated in the second wave, in October 2019 (COOP2 = 40.9%). ...
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... To recruit a diverse sample of respondents, we enlisted the services of the international polling organization Ipsos in Austria. Ipsos distributed the survey link to 3,000 of their panel members, seeking to mirror the United States census in terms of demographic variables (i.e., age, gender, and education, see Gil de Z uñiga et al. 2021). For this study, we used data from the first and the second wave 2 , delivered four months apart (June and October 2019, respectively). ...
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... Overall, the sample fairly resembles key demographic breakdowns on U. S. population accruing to the census, and also compares fairly well to survey data collected at a similar time by the Pew American Life Project by RDS (PALP, 2018). To learn more about the data and specific demographic distirbution see Gil de Zúñiga, Goyanes, & González-González (2021). ...
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