Beyond social media news use
algorithms: how political
discussion and network
heterogeneity clarify incidental
Political Science, Democracy Research Unit (DRU), University of Salamanca,
Department of Communication, Carlos III University of Madrid, Madrid, Spain and
Political Science, Democracy Research Unit (DRU), University of Salamanca,
Salamanca, Spain, and
Homero Gil de Z
Political Science, Democracy Research Unit (DRU), University of Salamanca,
Film Production and Media Studies, Pennsylvania State University,
Pennsylvania, USA and
Facultad de Comunicaci
on y Letras, Universidad Diego Portales, Santiago, Chile
Purpose –Traditionally, most readers’news access and consumption were based on direct intentional news
seeking behavior. However, in recent years the emergence and popularization of social media platforms have
enabled new opportunities for citizens to be incidentally informed about public affairs and politics as
by-product of using these platforms. This article seeks to shed light on how socio-political conversation
attributes may explain incidental exposure to information.
Design/methodology/approach –Drawing on US and UK survey data, the authors explore the role of
political discussion and discussion network heterogeneity in predicting individuals’levels of incidental
exposure to news. Furthermore, the authors also test the role of social media news use as a moderator.
A hierarchical OLS regression analysis with incidental news exposure as dependent variable was conducted as
well as analyses of moderation effects (heterogeneity*social media and political discussion*social media) using
the PROCESS macro in SPSS.
Findings –Findings reveal that heterogeneous networks are positively related to incidental news
exposure in the UK, while sheer level of political discussion is a positive influence over incidental news
exposure in the US. Social media news use moderates the relationship between political discussion and
incidental news exposure in the UK. That is, those who are highly exposed to news on social media and
discuss less often about politics and public affairs, they tend to be incidentally exposed to news online
the most. Meanwhile, the interaction of social media news and discussion heterogeneity showed
significant results in the US with those exhibiting high levels of both also receiving the biggest share
Originality/value –This study contributes to closing research gaps regarding how and when people are
inadvertently exposed to news in two Western societies. By highlighting that beyond the fate of algorithmic
information treatment by social media platforms, discussion antecedents as well as social media news use play
an integral part in predicting incidental news exposure, the study unravels fundamental conditions underlying
the incidental news exposure phenomenon.
Keywords Incidental news exposure, Social media, Political discussion, Discussion heterogeneity
Paper type Research paper
The current issue and full text archive of this journal is available on Emerald Insight at:
Received 17 April 2020
Revised 25 August 2020
3 November 2020
7 December 2020
Accepted 5 January 2021
Online Information Review
© Emerald Publishing Limited
In recent years, readers’consumption patterns have experienced a substantial change due to the
growing popularization of social media platforms for news sharing, distribution and consumption
(Gottfried and Shearer, 2017;Mitchell and Page, 2014). According to recent data, 72% of the
American population use social networking sites (Pew Research Center, 2019). By 2012, nearly half
reported receiving news through social media channels, a number that jumped from 62% in 2016
to 67% in 2017 (Gottfried and Shearer, 2017). Although most news readers still consume news
through direct access or search engines, social media platforms are becoming an increasingly
important source for most citizens to be informed about public affairs and politics (K€
umpel et al.,
2015;Song et al.,2020). In this context, social networking sites are an integral part of daily life for
many people and, especially, for news access and consumption (Song et al., 2020).
While news gathering has mostly been considered a purposeful and directed activity,
the concept of Incidental News Exposure (INE) appears to challenge this notion
(Tewksbury et al., 2001;Kim et al., 2013). Although the seminal idea had already been
introduced in the 1950s (Downs, 1957), with the rise of the Internet and social media use,
INE has also experienced a stark growth (Morris and Morris, 2017). The number of users
getting their daily news online is increasing along with the number of users who claim to
get information by accident on social media (Feezell, 2018), for instance, as by-product of
using them or just utter serendipitous inadvertent exposure (Boczkowski et al., 2018;
Fletcher and Nielsen, 2018). This pervasive opportunity to encounter news content,
governed by algorithmic automated mechanisms (Bakshy et al., 2015), and the ambient
nature of news in the digital realm (Hermida, 2010a,2010b) may lead some citizens to
believe they do not need to regularly and directly follow the news to stay informed. Citizens
can be exposed to news without any explicit effort of their own, through the use of social
networking sites and the Internet eliciting the perception that the news, if important, will
eventually get to them –news finds me perception (Gil de Z
niga et al., 2017;Song et al.,
2020). This media shift suggests that the role of incidental or inadvertent news exposure
may have significant potential for providing information about public affairs and politics
(Hsu et al., 2013;Valeriani and Vaccari, 2016).
However, beyond the establishment of an overly influential effect on news exposure via
algorithmically embodied mechanisms, so far the literature has suggested limited evidence
on the political and social antecedents of this phenomenon (Heiss et al., 2019a, b;Yamamoto
and Morey, 2019). This study seeks to alleviate this gap by focusing on the potential effects
that political discussion, network heterogeneity and social media news use may have on INE.
Do people who tend to discuss politics more often encounter more information about public
affairs inadvertently? To what extent does having more heterogeneous discussion networks
contribute to this phenomenon? And finally, to what extent are these effects of discussion
network attributes over INE contingent on more algorithm driven levels of social media
Drawing on survey data simultaneously collected from two countries, we empirically
tested these relationships in a model that includes demographic information as control
variables and media and political variables as antecedents. Results indicate that higher
levels of political discussion online and offline lead to higher levels of incidental news
exposure in the US, but not in the UK. In addition, we show that those who tend to develop
more heterogeneous discussion networks online and offline, also tend to be associated with
higher levels of incidental news exposure in the UK, but not in the US. Finally, the study
showcases the role of political discussion on INE is contingent upon social media news use.
The interaction terms revealed a cleaved and statistically significant moderation effect of
social media use for news on political discussion in the UK (Holbert and Park, 2020). This
also lends relative support to a positive influence of social media algorithms in presenting
information inadvertently to users who more often engage with news across these
1. Incidental news exposure
1.1 Definition and main characteristics
Incidentally stumbling onto news is not a recent phenomenon (Karnowski et al., 2017).
The first studies focusing explicitly on INE date back to the 1950s. According to Karnowski
et al. (2017), one of the pioneers was Downs (1957), who proposed a concept of “sought-for
data”and passively acquired “accidental data”(Downs, 1957, p. 223). While originally Downs’
study was focused on news readers who were incidentally exposed to news while watching
television or walking by a news kiosk, novel possibilities of incidental exposure have recently
spurred a burgeoning stream of studies centered around social media platforms (Boczkowski
et al., 2018;Feezell, 2018;Fletcher and Nielsen, 2018;Goyanes and Demeter, 2020;K€
2019) or the Internet more generally (Kim et al., 2013;Yadamsuren and Erdelez, 2010).
Tewksbury et al. (2001) conducted one of the first studies on incidental news exposure
focusing exclusively on the Internet, with data from the Pew Research Center. Their aim was
to uncover differences in incidental exposure triggered by people’s web usage patterns,
describing it as people encountering “current affairs information when they had not been
actively seeking it”(p. 534). The definition was adapted by several communication scholars
evol-Abreu et al., 2019;Karnowski et al., 2017;Lee and Kim, 2017) aiming at
studying the main political effects of INE. A brief perusal of these studies suggests that the
foundational element in its definition are individuals’directionality and actions; specifically,
media users’orientation to do something different, and thus non-news related, when they
were suddenly confronted with or exposed to news, both online and offline.
2. Networks and discussion heterogeneity
One of the most important antecedents of INE previous studies seem to agree upon is the type
of ties a person interacts with (Ahmadi and Wohn, 2018;Lee and Kim, 2017). Extant research
has suggested that there are two types of political discussion ties, strong and weak ones.
Strong ties consist of friends and family and are expected to influence political behaviors
(Lee et al., 2015;Min and Wohn, 2018). The network of weak ties (Granovetter, 1973) on the
other hand consists of interpersonal relations with people from a variety of social, economic
or cultural backgrounds (La Due Lake and Huckfeldt, 1998;Lee and Kim, 2017). These social
connections who are not too central in people’s lives such as acquaintances or co-workers,
lead to exposure to more diverse information (Gil de Z
niga and Valenzuela, 2011;
Granovetter, 1973;La Due Lake and Huckfeldt, 1998).
Previous research has underlined the importance of weak tie social connections and
heterogeneity for a functioning democracy (Ardevol-Abreu et al., 2018;Gil De Z
Gil de Z
niga, 2017). Discussing politics with heterogeneous networks has been positively
linked to deeper cognitive elaboration of the discussion content and arguments (Eveland and
Hively, 2009), exposure to diverse viewpoints and sources of information (Brundidge, 2010)
and incidental news exposure (Ahmadi and Wohn, 2018;Lee and Kim, 2017). Heterogeneous
networks result in a positive influence on political difference and knowledge (Lee and Kim,
2017), caused by a higher level of mental activity, stimulated by heterogeneous discussions
Furthermore, network heterogeneity exhibits a positive direct as well as indirect relation
with political participation (Eveland and Hively, 2009;Scheufele et al., 2006). And not only
that, higher levels of network heterogeneity lead to more interest in politics overall (Guidetti
et al., 2016), which in turn explained increased online activism (Guidetti et al., 2016), a greater
likelihood of voting, as well as a decreased penchant of political inaction. Brundidge (2010)
also suggests that beyond news exposure, political difference and campaign information are
also largely unexpected while discussing other topics online. In line with this reasoning,
Lee and Kim (2017) found discussion to be a strong predictor of incidental news, emphasizing
the role of social media ties in generating unexpected news inputs. This is not entirely
surprising, as prior scholarship (La Due Lake and Huckfeldt, 1998), suggests that weak ties
provide a wide array of information sources, as discussion networks with these ties are less
likely to feature redundant sources and expertise. All in all, heterogeneous networks are
characterized by higher diversity of opinions as opposed to those who are related to more
homogeneous networks (Yoo and Gil de Z
Beyond all these pro-civic linkages attested by heterogenous and diverse political
discussions, and what is perhaps most relevant for this study, is that higher levels of network
diversity have been linked to better information distribution (Mutz, 2002) and greater
exposure to diverse viewpoints and information sources (Wojcieszak and Mutz, 2009).
Accordingly, we expect that those citizens who hold more diverse heterogeneous networks
and are consequently exposed to different political news, may also encounter accidental news
more easily as a result of their diverse social media connections. In a more formal hypothesis:
H1. People with more heterogeneous political discussion networks are more prone to be
incidentally exposed to news in (a) the United Kingdom and (b) the United States.
3. Political discussion
Political discussion is deemed one of the pillars of a healthy and deliberative democracy
(De Tocqueville, 1863) and its importance has been addressed by decades’worth of research
(Yoo and Gil de Z
niga, 2019). One of the salient research foci was to establish the connection
between news consumption and political participation, with Sotirovic and McLeod (2001)
further highlighting the importance of political discussion as a mediating mechanism
between news and participation. Results showed this effect can differ substantially
depending on the news outlet: Internet hard-news use, for example, exhibited a significant
relationship with political participation while no effect was found for television hard-news
use (Bode et al., 2014;Moeller et al., 2018). Along these lines, recent studies highlighted the role
of digital media in explaining political discussion, showing that not only face-to-face
discussions but also computer-mediated interactions exhibit a significant effect on
participatory outcomes (Chan et al., 2017;Gil de Z
niga et al., 2010;Hardy and
In this regard, extant research suggest that the influence of social media may be linked to
the networks heterogeneity as it funnels and enhances people’s frequency of political
discussions (Brundidge, 2010;Min and Wohn, 2018). Diverse networks showed positive
effects in the offline realm as well, influencing people’s levels of knowledge and political
difference (Lee and Kim, 2017). More diverse political discussion stimulates mental activity
(Brundidge, 2010), making people more aware of novel information, which in turn elevates the
odds of incidentally encountering news on the topic. Likewise, social media and other
communication technologies greatly facilitate and foster political discussion online
Through political discussion, social media is a space in which users may engage and
establish anonymous interactions with others, regardless of whether they agree in their
political views. In this sense, social media resembles everyday offline conversations of
political topics more closely (Hampton, 2016;Hampton et al., 2017). Additionally, information
and participation are closely intertwined on social media (Heiss and Matthes, 2019;Valeriani
and Vaccari, 2016). According to Heiss et al. (2019a, b), for those who are initially less involved
in politics, incidental news on social media can function as an equalizer toward users with
more initial interest by providing information and opportunity for discussion. Therefore,
those who are more interested in politics are also more prone to be incidentally exposed to
news (Heiss and Matthes, 2019). As individuals with heterogeneous networks are more likely
to receive diverse information (Ahmadi and Wohn, 2018) and thus discuss politics more
frequently (Kwak et al., 2005) online as well as offline, we also expect that those who discuss
politics more frequently may more easily be incidentally exposed to news. We also expect
such effect to be statistically significant in both the US and the UK. Extant research that has
extensively shown countries with a robust freedom of speech are more prone to engage in
political discussions (Barnidge et al., 2018), both privately and publicly. As both the US and
the UK are Western democratic countries and liberal media systems (Hallin and Mancini,
2004) which have a neutral commercial press, internal pluralism and a strong
professionalization of the news industry, it is reasonable to expect a positive impact of
political discussion on incidental news in both countries. In a more formal hypothesis:
H2. People with higher levels of political discussion online and offline are more prone to
be incidentally exposed to news in (a) the United Kingdom and (b) the United States.
4. The moderating role of social media
As previously stated, the study argues that citizens who discuss politics more often or do so
with more heterogeneous networks will be more likely to be incidentally exposed to news.
Social media, as a space that has dramatically changed the way in which users access and
consume news (Gil de Z
niga and Diehl, 2017;Hermida et al., 2012), may also play a
contingent role, as those citizens who discuss politics more often and have greater
heterogeneous networks may incidentally encounter more or less news depending on their
intrinsic social media use. In fact, previous studies suggest that social media platforms are the
main hub for INE: while 66 % of Facebook users get news from the site, 62 % of those claim
that the news exposure was incidental while doing something else (Gottfried and Shearer,
2016,2017). Similar results were found for Instagram and YouTube with 63% and 58% of the
respective news exposure being incidental.
As social media has turned into a major platform for not just social interaction but also
information sharing, many researchers have stressed the connection between these platforms
and incidental exposure to news (Fletcher and Nielsen, 2017;K€
2018;Valeriani and Vaccari, 2016). Lee and Kim (2017) specifically highlight that diverse
networks as found on social media heighten the chances of incidental news exposure, thus
underlining once more the bond between social media and INE. Therefore, it may be expected
that the relationship between both political discussion and discussion heterogeneity and INE
will increase at all levels of social media use, but such increase will be higher when social
media use is high as well (Holbert and Park, 2020). Hence, we expect social media to act as
contributory moderator in two ways:
H3. Social media use for news is a contributory moderator of the relationship between
political discussion and incidental news exposure in (a) the United Kingdom and
(b) the United States.
H4. Social media use for news is a contributory moderator of the relationship between
discussion heterogeneity and incidental news exposure in (a) the United Kingdom
and (b) the United States.
To gather empirical data from the US and the UK the Digital Media Research Program
(DMRP) at the University of Texas at Austin conducted two surveys. Both were administered
online with the software Qualtrics. Participants for the US sample came from the pool of
registered participants of an online panel conducted by Nielsen. A stratified quota (based on
demographics: gender, age, education and income) was applied to choose respondents from
over 200,000 people, ensuring national representativeness. For the US survey, the total
number of cases (2,060), was gathered between December 15, 2013 and January 5, 2014. 247
cases had to be deleted due to the data being incomplete or invalid. The response rate
amounts to 34.6% (calculated following recommendations from the American Association of
Public Opinion Research (2011), which is within the acceptable parameters for web-based
surveys (Sax et al., 2003). In comparison with the US census, participants of this sample are
slightly younger, more educated and there were fewer Hispanic respondents. The split
between women (49.5%) and men (49.6%) is even (for specific information on how the sample
differs from the census please check Salda~
na et al., 2015).
For the UK data, participants were also chosen from a panel administered by Survey
Sampling International, working with proprietary cluster census matching, Dynamix. Thus,
a generalizable representation of the UK population can be guaranteed. Data collection was
carried out simultaneously with the US data to retain parity and ended in March 2014. Out of
the 1,529 respondents, 451 observations had to be deleted due to invalid or incomplete data.
Applying once more the response rate calculator (American Association of Public Opinion
Research, 2011), the result is 34.6%. The respondents were slightly older and more educated
than in the UK census and women as well as white people are overrepresented (for specific
information on how the sample differs from the census please check Salda~
na et al., 2015).
5.2 Independent and dependent variables
5.2.1 Incidental news exposure. Our dependent variable measures how often respondents
encounter or come across news when using “online portals”,“search engines”,“email,“
“blogs,““social networking sites”or “micro-blogging sites (i.e. Twitter)”, on a ten-point Likert
scale ranging from 1 5never to 10 5all the time (six-item averaged scale; UK: Cronbach’s
50.89; M53.85; SD 52.19; US: Cronbach’s
50.85; M53.66; SD 52.00).
5.2.2 Political discussion. This variable measures how often respondents talk about politics
or public affairs online and offline with their “spouse or partner”,“family relatives”,“friends”
and “acquaintances,“on a ten-point Likert scale ranging from 1 5never to 10 5all the time
(four-item averaged scale; UK: Cronbach’s
50.84; M54.40; SD 52.35; US: Cronbach’s
50.81; M54.44; SD 52.29)
5.2.3 Discussion heterogeneity. This variable measures the level in which respondents
engage in conversations with people that hold different political views and are from a
different social class and race, both online and offline (Yoo and Gil de Z
Questions tap on how often respondents “talk about politics or public affairs online and
offline with people who disagree with you?”,“talk about politics or public affairs online and
offline with people whose political views are different from yours?,““talk about politics or
public affairs online and offline with people from a different race or ethnicity?”and “talk
about politics or public affairs online and offline with people from a different social class?”on
a ten-point Likert scale ranging from 1 5never to 10 5all the time (four-averaged scale; UK:
50.94; M53.24; SD 52.44; US: Cronbach’s
50.93; M53.60; SD 52.42).
To control for potential confounds, the statistical models also include a set of variables that
might explain the relationship between our variables of interest and our independent
variable. Specifically, the first block of control variables includes socio-demographics: age,
gender, income, education and race. A second block of variables included political
antecedents and users’online and offline connections: network size, political interest,
political efficacy and political ideology. Finally, a third block of media use was introduced:
traditional media, online media and social media use for news as well as overall social
5.3.1 Political discussion network size (log). This variable measures respondent’s total
number of political discussion connections both online and offline (sheer political discussion
size). Specifically, the variable taps on about how many total people have respondents talked
to “face-to-face or over the phone”and “via the internet, including e-mail, chat rooms, social
networking sites and micro-blogging sites”about politics or public affair. Since the outcome
of the variable was positively skewed, we transform it to a log10 (UK: M50.53; SD 50.26;
Spearman-Brown coefficient: 0.67; US: M50.34; SD 50.37; Spearman-Brown
5.3.2 Political interest. This variable measures respondents’interest in politics and was
measured by asking: “How interested are you in information about what’s going on in politics
and public affairs?”and “How closely do you pay attention to information about what’s going
on in politics and public affairs?”on a ten-point Likert scale ranging from 1 5never to 10 5a
great deal (two-averaged scale; UK: Spearman-Brown coefficient 50.96 M55.67; SD 52.60;
US: Spearman-Brown coefficient 50.96; M56.59; SD 52.69)
5.3.3 Political efficacy. This variable measures respondents’perception of their capacity to
influence politics (Gil de Z
niga and Diehl, 2017). Specifically, it was measured by asking
respondents their agreement or disagreement with the following items: “People like me can
influence government,”“I consider myself well qualified to participate in politics,““I have a
good understanding of the important political issues facing our country”,“No matter whom I
vote for, it will not make a difference,”“Parties are interested in people’s votes rather than
their opinions”and “People like me do not have any say in what the government does”,ona
ten-point Likert scale ranging from 1 5totally disagree to 10 5totally agree, with the final
three items being reverse-coded (six-averaged scale; UK: Cronbach’s
SD 51.65; US: Cronbach’s
50.64; M55.07; SD 51.65)
5.3.4 Traditional media use. This variable measures the level of news consumption in
traditional media. Specifically, it was measured by asking respondents how often do they get
news from “network news TV (e.g. US: ABC, CBS, NBC, UK: BBC 1, ITV, Channel 4),”
“national newspapers (e.g. US: New York Times, Washington Post, USA Today, UK:
Guardian, Times, Telegraph),”“local newspapers (e.g. US: Oregonian, Houston Chronicle,
Miami Herald, UK: Lancashire Evening Post, Shropshire Star),”“cable news (e.g. US: CNN,
Fox News, MSNBC, UK: Sky News, CNN, BBC News),”“radio news (e.g. US: NPR, talk shows,
UK: BBC Radio 4, BBC Radio 5, BBC World Service),”“Print for news”,“Television for news”
and “radio for news”, on a ten-point Likert scale ranging from 1 5never to 10 5all the time
(eight-averaged scale; UK: Cronbach’s
50.80; M55.73; SD 51.96; US: Cronbach’s
50.75; M55.06; SD 51.88)
5.3.5 Online media use. This variable measures the level of news consumption in online
media. Specifically, it was measured by asking respondents how often they get news from
“online news sites (e.g. Gawker, Politico, BuzzFeed / Indymedia UK, BBC News Online,
Guardian Online),”“citizen journalism sites (e.g. US: CNN’s iReport, Examiner.com,
UK: Blottr, Guardian Witness, Citizen News UK),”“hyperlocal news sites (e.g. US: Patch.
com, UK: The Port Talbot Magnet, Your Thurrock, Brixton Blog).”In addition, we include
how often they use “news aggregators (e.g. Google News, etc.) to get news”and “sites and
apps that collect news, such as Flipboard, or Pulse”(five-averaged scale; UK: Cronbach’s
50.82; mean 52.79; SD 51.81; US: Cronbach’s
50.76; mean 52.44; SD 51.65)
5.3.6 Social media for news. This variable measures the level of news consumption by
means of social media use. It was measured by asking respondents how often they use
“LinkedIn”to get news. In addition, this variable incorporates two more items of social
media use for news: “How often do you use social media to stay informed about current events
and public affairs?”and “How often do you use social media to stay informed about your local
community?”(five-averaged scale; UK: Cronbach’s
50.92; M52.50; SD 51.77;
50.89; M52.08; SD 51.42).
5.4 Statistical analysis
In order to test our hypotheses, we conducted a hierarchical OLS regression analysis with
incidental news exposure as dependent variable both in the UK and the US (Tables 1 and 2).
The independent variables were introduced in five different blocks. The first block of
variables comprised the set of demographics, the second included political antecedents, the
third comprised media antecedents, the fourth block our variables of interest: political
discussion and discussion heterogeneity and the fifth block the interaction terms: discussion
heterogeneity*social media and political discussion*social media. We test the moderation
effects both in the US and the UK using the PROCESS macro in SPSS (Hayes and Preacher,
2013; Model 1; 5.000 bootstrap samples).
Our first hypothesis proposed that people who reported higher levels of discussion
heterogeneity online and offline are more prone to be incidentally exposed to news in (a) the
Incidental news exposure Interaction 1 Interaction 2
Block 1: Demographics
Age 0.097** 0.016** 0.016**
Gender 0.008 0.034 0.016
Income 0.011 0.014 0.013
Education 0.066* 0.086* 0.082*
Race 0.031 0.225 0.207
(%) 15.3% 15.3% 15.3%
Block 2: Political antecedents
Network size (log) 0.014 0.119 0.096
Political interest 0.040 0.032 0.027
Political efficacy 0.025 0.030 0.023
(%) 13% 13% 13%
Block 3: Media news antecedents
Traditional news 0.143*** 0.155*** 0.156***
Online news 0.309*** 0.361*** 0.358***
Social media news 0.224*** 0.284*** 0.305***
General social media 0.159*** 0.106*** 0.103***
(%) 33% 33% 33%
Block 4: Variables of interest
Political discussion 0.010 0.012 0.009
Discussion heterogeneity 0.126** 0.108** 0.114**
(%) 0.9% 0.9% 0.9%
Block 5: Interaction terms
Discussion heterogeneity * Social media news (2) 0.012
Political discussion * Social media news (1) 0.032*
(%) 0.1% 0.4%
(%) 62.2% 62.3% 62.6%
Note(s): Sample Size 51,078. Cell entries are final-entry OLS standardized coefficients; *p< 0.05.
**p< 0.01. ***p< 0.001
Results of the
regression analysis for
the UK sample
United Kingdom and (b) the United States. The regression analysis shows a statistically
significant and positive association between discussion heterogeneity and incidental news
exposure in the UK (β50.126; p< 0.01), but not in the US. Therefore, H1a was supported,
while H1b was not. For the UK sample, younger adults (β50.097; p< 0.01), those with
lower levels of education (β50.066, p< 0.05), consume news through traditional media
(β50.143; p< 0.001), online media (β50.309; p< 0.001) and social media (β50.224;
p< 0.001) but also use social media for other purposes than news (β50.159, p< 0.001) are
also more likely to be incidentally exposed to news. For the US sample, similarly, younger
adults (β50.108; p< 0.001) and those who consume news through traditional media
(β50.109; p< 0.001), online media (β50.244; p< 0.001) and social media (β50.322;
p< 0.001), as well use social media for other purposes than news (β50.116, p< 0.001) are also
more likely to encounter or come across news while doing other things (see Tables 1 and 2).
The second hypothesis (H2), proposed that people that reported higher levels of political
discussion online and offline will be more prone to be incidentally exposed to news in (a) the
United Kingdom and (b) the United States. Results of the hierarchical regression analysis
reveal a statistically significant and positive association between political discussion and
exposure Interaction 1 Interaction 2
Block 1: Demographics
Age 0.108*** 0.013*** 0.013***
Gender 0.040 0.153 0.154
Income 0.012 0.019 -0.020
Education 0.001 0.003 0.001
Race 0.002 0.015 0.014
(%) 5.7% 5.7% 5.7%
Block 2: Political antecedents
Network size (log) 0.009 0.042 0.054
Political interest 0.025 0.017 0.017
Political efficacy 0.041 0.046 0.046
(%) 10.4% 10.4% 10.4%
Block 3: Media news antecedents
Traditional media 0.109*** 0.118*** 0.117***
Online media 0.244*** 0.314*** 0.314***
Social media for news 0.322*** 0.527*** 0.522***
Social media use overall 0.116*** 0.072*** 0.073***
(%) 31.4% 31.4% 31.4%
Block 4: Variables of interest
Political discussion 0.067* 0.059* 0.058*
Discussion heterogeneity 0.039 0.034 0.033
(%) 0.5% 0.5% 0.5%
Block 5: Interaction terms
Discussion heterogeneity * Social media for news
Political discussion * Social media for news (1) 0.024
(%) 0.2% 0.1%
(%) 48% 48.2% 48.1%
Note(s): Sample Size 51,813. Cell entries are final-entry OLS standardized coefficients; *p< 0.05.
**p< 0.01. ***p< 0.001
Results of the
regression analysis for
the US sample
incidental news exposure in the US (β50.067; p< 0.05), but not in the UK. Therefore, H2b
was supported, while H2a was not (see Tables 1 and 2).
Our third hypothesis (H3) predicted that social media use for news is a contributory
moderator over the relationship between political discussion and incidental news exposure in
(a) the United Kingdom and (b) the United States. Results of the interaction terms reveal that
social media use for news is a cleaved moderator for the UK (β50.032; p< 0.05) while
yielding a non-significant effect in the US. Therefore, neither H3a nor H3b were empirically
supported. For the interpretation of the analysis we plot the significant interaction term for
the UK (Figure 1). Social media use for news acts as a cleaved moderator, positively affecting
the relationship between political discussion and incidental news exposure when social media
use for news is low, but negatively affecting it when social media news for news is high.
Finally, our fourth hypothesis (H4) proposed that social media use for news is a
contributory moderator of the relationship between discussion heterogeneity and incidental
news exposure in (a) the United Kingdom and (b) the United States. Results of the regression
analysis reveal that social media use for news is a contributory moderator in the case of the
US (β50.025; p< 0.05) but is non-significant in the UK. In Figure 2 we plot the moderation
effects in the US. As can be observed in Figure 2, the relationship between discussion
Moderating effect of
social media for news
on political discussion
and INE in the UK
Moderating effect of
social media for news
heterogeneity and INE
in the US
heterogeneity and incidental news exposure is positive regardless of the levels of social media
use for news, but the effects appear to be stronger when social media use for news and
discussion heterogeneity are high, following as a result a convergent positive moderation
typology. Thus, H4a was rejected, while H4b was confirmed.
Drawing on cross-sectional survey data, this article investigates the role of political
discussion and discussion heterogeneity in accounting for citizens’levels of incidental news
exposure, comparing its effects in two different countries: the US and the UK. Our findings
offer four inter-related contributions on this line of inquiry, arguing that, beyond algorithmic
mechanisms, citizens’socio-political conversational attributes are also relevant to explain the
impact and reach of INE.
Our study first indicates that people’s frequency of discussion levels are an influential
factor for INE. That is, those who discuss habitually about politics will tend to find
themselves exposed to more incidental news. However, the positive association between
political discussion and incidental news exposure is only statistically significant for the US,
but not for the UK. The UK results falling short of significance might be attributed to the
unique nature of Britons when it comes to discussing about politics. Considering the
frequency of discussion in European Union countries, the United Kingdom is ranked among
the lower half (Nir, 2012). Moreover, younger people seem to be the least likely to discuss
politics in the UK (Paskeviciute and Anderson, 2005). However, younger age generally
appears to be an important predictor for incidental news (Tewksbury et al., 2001), as also
attested in our data (age, predicting INE, β50.099; p < 0.01).
Discussion heterogeneity, however, appears to be a strong predictor only for UK
respondents. Although extant research on discussion heterogeneity was primarily focused on
the US, results from Italy and Hong Kong (Guidetti et al., 2016;Tang and Lee, 2013) yielded
similar results (Bello, 2012;Scheufele et al., 2006;Yoo and Gil de Z
niga, 2019). Therefore, it
might not be surprising for the UK. It was, however, unexpected that the analysis for the US
did not yield significant results. As recent results show, apart from demographics, other
factors like media preferences or trust (Goyanes, 2020) can be significant predictors of
incidental news. Therefore, the competing effects of media and political antecedents could
have led to different results for both countries, obscuring the significance of discussion
heterogeneity in the case of the US. Future research should attempt to further disentangle this
Furthermore, social media news use has emerged as another conditional factor when it
comes to explaining the reach of INE, exerting a statistically significant influence on the
relation between political discussion and INE. However, previous studies already showcased
that findings are not one-size-fits-all phenomena that apply to every country equally (Fletcher
and Nielsen, 2017,2019;Salda~
na et al., 2015), suggesting the effects of social media on
incidental news exposure are somewhat context-driven. In fact, our findings indicate that
despite the conditional role of social media for news in accounting for INE at different levels of
political discussion and discussion heterogeneity, such effects may vary according to the
country of analysis, slightly changing both the moderation typology and the significance
It is also noteworthy that social media use for news emerged as a cleaved moderator for
political discussion and incidental news in the UK. Lower levels of news consumption on
social media yield a positive influence on the relationship between political discussion and
INE, whereas higher levels result in a negative effect (see Figure 1). Those who discuss
politics more often and use social media often for news, exhibit lower levels of incidental news
exposure. In contrast, those who discuss politics more often and have lower levels of social
media for news are more likely to experience INE. Results show that the levels of incidental
news exposure are much greater at all levels of political discussion when citizens’use of social
media for news is high. Therefore, despite the cleaved role of social media in the relation
between social political discussion and incidental news, such platforms still account for much
of the variance. What our results suggests is that those who have higher levels of political
discussion and social media use for news may be more prone to seek news intentionally,
possibly leaving less space for inadvertent exposure on social media (Fletcher and Nielsen,
2018;Goyanes, 2020), whereas those with low levels of social media news consumption may
receive the biggest share of their information incidentally, especially through political
discussion as an amplifying mechanism.
Finally, the interaction of discussion heterogeneity and social media news followed the
theorized expectations in the study and showed significant results for the US. Those who
consume the most news on social media and have the most heterogeneous networks will be
the ones exposed to the highest amount of incidental news as well (Figure 2). Thus, a diverse
network of contacts along with avid news consumption online and on social media becomes a
key combination to explain incidental news exposure. While this is in line with our prediction
for the US, the UK sample did not produce any significant results.
The fact that the significant interaction effects of social media use for news differ in the UK
and in the US might be partly due to unique news consumption habits. UK adults consume
more social media news than their US counterparts (Salda~
na et al., 2015), which might lead to
a different incidental news exposure. Additionally, the different political information
environments pertaining to each country (Aalberg et al., 2010) might also contribute to a
variation in results. Both countries differ substantially in terms of the amount of (political)
news broadcasted each day with a more commercialized media system in the US, while in
Europe public broadcasters are more prominent (Aalberg et al., 2010;Esser et al., 2012;
Iyengar et al., 2010;Str€
ack and Dimitrova, 2006). Thus, the way people interact
(e.g. discuss politics) based on these underlying factors could differ as well, affecting the
levels of INE. All these remain, of course, as good lead points for future research.
As revealing as these findings may be, several limitations of this study are noteworthy.
First, survey data for this study is cross-sectional, precluding us to completely infer causal
associations. Therefore, we cannot rule out the possibility that causal orders might be
reversed. Future studies should thus focus on longitudinal data collection. Second, we rely on
self-reported assessments on citizens’levels of incidental news exposure, a measure that
might be biased. To avoid self-reported measures, experimental designs and real time
measures need to be promoted (Heiss et al., 2019a, b;Heiss and Matthes, 2019;Yoo and Gil de
niga, 2019), although initial efforts to compare citizens’self-reported data and
non-obtrusive measurements such as social media users traced and “logged use data,”
yielded optimistic and hopeful findings for survey research (Haenschen, 2020;Jones-Jang
et al., 2020). Finally, another relevant limitation relevant for this study is the choice of
countries. As most other research, we focused on developed Western democracies. Results in
other countries could be rather different and, thus, warrant further exploration.
Finally, the interpretation of the findings is subject to the data collection temporality.
While data were collected in 2014, we are confident our findings remain prevalent. First, the
study reflects on processes of overall effects rather than demographic structural exposure to
information. Second, looking at statistics concerning social media, it is evident that no overtly
drastic changes occurred in usage patterns in the US and the UK since then (Perrin and
Anderson, 2019;Pew Research Center, 2019). As results from the Pew Research Center
indicate (Gottfried and Shearer, 2016;Shearer and Matsa, 2018), news use across social media
platforms is rather stable through the years, with slightly higher numbers in more recent data
(62% of Americans name social media as a news source in 2016 in contrast with 68% in 2018).
Results stemming from the EU’s Eurobarometer indicate similar tendencies outside of the US
(Publications Office of the European Union, 2018), leading us to once more underline the
significance of our results despite slightly older data. If anything, the effects found in this
study may appear even more stalwartly nowadays due to the slight increase of social
The way different social media platforms operate and how users interact with them is
subject to constant change. While the data employed in this study is not as recent as to
capture all the innovative changes in social media algorithms, recent studies show that if
anything, due to changes in algorithms and interaction patterns users might now be
confronted with even more incidental news (Park and Kaye, 2018,2020) than they were
during the data collection period of our study. Despite these limitations, this study
contributes to closing research gaps regarding how and when people are inadvertently
exposed to news in two Western societies. By highlighting the influence of discussion
antecedents as well as social media news use, the study unravels fundamental conditions
underlying the INE phenomenon.
Aalberg, T., van Aelst, P. and Curran, J. (2010), “Media systems and the political information
environment: a cross-national comparison”,The International Journal of Press/Politics, Vol. 15
No. 3, pp. 255-271, SAGE Publications.
Ahmadi, M. and Wohn, D.Y. (2018), “The antecedents of incidental news exposure on social media”,
Social Media þSociety, Vol. 4 No. 2, pp. 1-8.
American Association of Public Opinion Research (2011), Standard Definitions: Final Dispositions of
Case Codes and Outcome Rates for Surveys, AAPOR, available at: https://www.aapor.org/
evol-Abreu, A., Diehl, T. and Gil de Z
niga, H. (2019), “Antecedents of internal political efficacy
incidental news exposure online and the mediating role of political discussion”,Politics, Vol. 39
No. 1, pp. 82-100.
Ardevol-Abreu, A., Diehl, T. and Gil De Z
niga, H. (2018), “Building Social Capital. How the news and
the strength of the ties in the political discussion foster reciprocity”,Revista Internacional de
ıa, Vol. 76 No. 1, p. e083, Consejo Superior de Investigaciones Cient
Bakshy, E., Messing, S. and Adamic, L.A. (2015), “Exposure to ideologically diverse news and opinion
on Facebook”,Science, Vol. 348 No. 6239, pp. 1130-1132.
Barnidge, M., Huber, B., Gil de Z
niga, H. and Liu, J.H. (2018), “Social media as a sphere for ‘risky’
political expression: a twenty-country multilevel comparative analysis”,The International
Journal of Press/Politics, Vol. 23 No. 2, pp. 161-182, SAGE Publications.
Bello, J. (2012), “The dark side of disagreement? Revisiting the effect of disagreement on political
participation”,Electoral Studies, Vol. 31 No. 4, pp. 782-795.
Boczkowski, P.J., Mitchelstein, E. and Matassi, M. (2018), “News comes across when I’m in a moment
of leisure’: understanding the practices of incidental news consumption on social media”,New
Media and Society, Vol. 20 No. 10, pp. 3523-3539.
Bode, L., Vraga, E.K., Borah, P. and Shah, D.V. (2014), “A new space for political behavior: political
social networking and its democratic consequences”,Journal of Computer-Mediated
Communication, Vol. 19 No. 3, pp. 414-429.
Brundidge, J. (2010), “Encountering ‘difference’in the contemporary public sphere: the contribution of
the internet to the heterogeneity of political discussion networks”,Journal of Communication,
Vol. 60 No. 4, pp. 680-700.
Chan, M., Chen, H.-T. and Lee, F.L.F. (2017), “Examining the roles of mobile and social media in
political participation: a cross-national analysis of three Asian societies using a communication
mediation approach”,New Media and Society, Vol. 19 No. 12, pp. 2003-2021.
De Tocqueville, A. (1863), Democracy in America, Sever and Francis, Cambridge.
Downs, A. (1957), An Economic Theory of Democracy, Harper and Row, New York, NY (accessed 30
Esser, F., de Vreese, C.H., Str€
ack, J., van Aelst, P., Aalberg, T., Stanyer, J., Lengauer, G.,
Berganza, R., Legnante, G., Papathanassopoulos, S., Salgado, S., Sheafer, T. and Reinemann, C.
(2012), “Political information opportunities in Europe: a longitudinal and comparative study of
thirteen television systems”,The International Journal of Press/Politics, Vol. 17 No. 3,
pp. 247-274, SAGE Publications.
Eveland, W.P. and Hively, M.H. (2009), “Political discussion frequency, network size, and
‘heterogeneity’of discussion as predictors of political knowledge and participation”,Journal
of Communication, Vol. 59 No. 2, pp. 205-224.
Feezell, J.T. (2018), “Agenda setting through social media: the importance of incidental news exposure
and social filtering in the digital era”,Political Research Quarterly, Vol. 71 No. 2, pp. 482-494.
Fletcher, R. and Nielsen, R.K. (2017), “Are news audiences increasingly fragmented? A cross-national
comparative analysis of cross-platform news audience fragmentation and duplication”,Journal
of Communication, Vol. 67 No. 4, pp. 476-498.
Fletcher, R. and Nielsen, R.K. (2018), “Are people incidentally exposed to news on social media? A
comparative analysis”,New Media and Society, Vol. 20 No. 7, pp. 2450-2468.
Fletcher, R. and Nielsen, R.K. (2019), “Generalised scepticism: how people navigate news on social
media”,Information, Communication and Society, Vol. 22 No. 12, pp. 1751-1769.
Gil De Z
niga, H. (2015), “Toward a European public sphere? The promise and perils of modern
democracy in the age of digital and social media”,International Journal of Communication,
Vol. 9 No. 1, pp. 3152-3160.
Gil de Z
niga, H. (2017), “Attributes of interpersonal political discussion as antecedents of cognitive
elaboration”,Revista Espanola de Investigaciones Sociologicas, Vol. 157 No. 157, pp. 65-81.
Gil de Z
niga, H. and Diehl, T. (2017), “Citizenship, social media, and big data: current and future
research in the social sciences”,Social Science Computer Review, Vol. 35 No. 1, pp. 3-9, SAGE
Gil de Z
niga, H. and Valenzuela, S. (2011), “The mediating path to a stronger citizenship: online and
offline networks, weak ties, and civic engagement”,Communication Research, Vol. 38 No. 3,
Gil de Z
niga, H., Veenstra, A., Vraga, E. and Shah, D. (2010), “Digital democracy: reimagining
pathways to political participation”,Journal of Information Technology and Politics, Vol. 7 No. 1,
Gil de Z
niga, H., Weeks, B. and Ard
evol-Abreu, A. (2017), “Effects of the news-finds-me perception in
communication: social media use implications for news seeking and learning about politics”,
Journal of Computer-Mediated Communication, Vol. 22 No. 3, pp. 105-123.
Gottfried, J. and Shearer, E. (2016), News Use across Social Media Platforms 2016, Pew Research
Center, available at: https://www.journalism.org/2016/05/26/news-use-across-social-media-
platforms-2016/ (accessed 30 May 2019).
Gottfried, J. and Shearer, E. (2017), Americans’Online News Use Is Closing in on TV News Use, Pew
Research Center, available at: https://internet.psych.wisc.edu/wp-content/uploads/532-Master/
Goyanes, M. (2020), “Antecedents of incidental news exposure: the role of media preference, use and
trust”,Journalism Practice, Vol. 14 No. 6, pp. 714-729, Routledge.
Goyanes, M. and Demeter, M. (2020), “Beyond positive or negative: understanding the
phenomenology, typologies and impact of incidental news exposure on citizens’daily lives”,
New Media and Society.
Granovetter, M.S. (1973), “The strength of weak ties”,American Journal of Sociology, Vol. 78,
Guidetti, M., Cavazza, N. and Graziani, A.R. (2016), “Perceived disagreement and heterogeneity in
social networks: distinct effects on political participation”,The Journal of Social Psychology,
Vol. 156 No. 2, pp. 222-242.
Haenschen, K. (2020), “Self-reported versus digitally recorded: measuring political activity on
Facebook”,Social Science Computer Review, Vol. 38 No. 5, pp. 567-583, SAGE Publications.
Hallin, D.C. and Mancini, P. (2004), Comparing Media Systems: Three Models of Media and Politics,
Cambridge University Press, Cambridge.
Hampton, K.N. (2016), “Persistent and pervasive community: new communication technologies and
the future of community”,American Behavioral Scientist, Vol. 60 No. 1, pp. 101-124.
Hampton, K.N., Shin, I. and Lu, W. (2017), “Social media and political discussion: when online presence
silences offline conversation”,Information, Communication and Society, Vol. 20 No. 7,
Hardy, B.W. and Scheufele, D.A. (2005), “Examining differential gains from internet use: comparing
the moderating role of talk and online interactions”,Journal of Communication, Vol. 55 No. 1,
Hayes, A.F. and Preacher, K.J. (2013), “Conditional process modeling: using structural equation
modeling to examine contingent causal processes”, in Hancock, G.R. and Mueller, R.O. (Eds),
Structural Equation Modeling: A Second Course, 2nd ed., IAP Information Age Publishing,
Charlotte, pp. 219-266.
Heiss, R. and Matthes, J. (2019), “Does incidental exposure on social media equalize or reinforce
participatory gaps? Evidence from a panel study”,New Media and Society, Vol. 21 Nos 11–12,
Heiss, R., Knoll, J. and Matthes, J. (2019a), “Pathways to political (dis-) engagement: motivations
behind social media use and the role of incidental and intentional exposure modes in
adolescents’political engagement”,Communications (accessed 21 September 2019).
Heiss, R., Schmuck, D. and Matthes, J. (2019b), “What drives interaction in political actors’Facebook
posts? Profile and content predictors of user engagement and political actors’reactions”,
Information, Communication and Society, Vol. 22 No. 10, pp. 1497-1513.
Hermida, A. (2010a), “Twittering the news”,Journalism Practice, Vol. 4 No. 3, pp. 297-308.
Hermida, A. (2010b), “From TV to twitter: how ambient news became ambient journalism”,Media/
Culture Journal, Vol. 13 No. 2 (accessed 22 September 2019).
Hermida, A., Fletcher, F., Korell, D. and Logan, D. (2012), “Share, Like, Recommend: decoding the
social media news consumer”,Journalism Studies, Vol. 13 Nos 5–6, pp. 815-824.
Holbert, R.L. and Park, E. (2020), “Conceptualizing, organizing, and positing moderation in
communication research”,Communication Theory, Vol. 30 No. 3, pp. 227-246.
Hsu, S.H., Kim, Y. and Gil de Z
niga, H. (2013), “Influence of social media use on discussion network
heterogeneity and civic engagement: the moderating role of personality traits”,Journal of
Communication, Vol. 63 No. 3, pp. 498-516.
Iyengar, S., Curran, J., Lund, A.B., Salovaara-Moring, I., Hahn, K.S. and Coen, S. (2010), “Cross-National
versus individual-level differences in political information: a media systems perspective”,
Journal Of Elections, Public Opinion, and Parties, Vol. 20 No. 3, pp. 291-309, Routledge.
Jones-Jang, S.M., Heo, Y.J., McKeever, R., Kim, J.H., Moscowitz, L. and Moscowitz, D. (2020), “Good
news! Communication findings may be underestimated: comparing effect sizes with
self-reported and logged smartphone use data”,Journal of Computer-Mediated
Communication, Vol. 25 No. 5, pp. 346-363, Oxford Academic.
Karnowski, V., K€
umpel, A.S., Leonhard, L. and Leiner, D.J. (2017), “From incidental news exposure to
news engagement. How perceptions of the news post and news usage patterns influence
engagement with news articles encountered on Facebook”,Computers in Human Behavior,
Vol. 76, pp. 42-50.
Kim, Y., Chen, H.T. and Gil de Z
niga, H. (2013), “Stumbling upon news on the Internet: effects of
incidental news exposure and relative entertainment use on political engagement”,Computers
in Human Behavior, Vol. 29 No. 6, pp. 2607-2614.
umpel, A.S. (2019), “The issue takes it all?: incidental news exposure and news engagement on
Facebook”,Digital Journalism, Vol. 7 No. 2, pp. 165-186.
umpel, A.S. (2020), “The Matthew effect in social media news use: assessing inequalities in news
exposure and news engagement on social network sites (SNS)”,Journalism, Vol. 21 No. 8,
pp. 1083-1098, SAGE Publications.
umpel, A.S., Karnowski, V. and Keyling, T. (2015), “News sharing in social media: a review of current
research on news sharing users, content, and networks”,Social Media þSociety, Vol. 1
No. 2, pp. 1-14.
Kwak, N., Williams, A.E., Wang, X. and Lee, H. (2005), “Talking politics and engaging politics: an
examination of the interactive relationships between structural features of political talk and
discussion engagement”,Communication Research, Vol. 32 No. 1, pp. 87-111.
La Due Lake, R. and Huckfeldt, R. (1998), “Social capital, social networks, and political participation”,
Political Psychology, Vol. 19 No. 3, pp. 567-584.
Lee, J.K. and Kim, E. (2017), “Incidental exposure to news: predictors in the social media setting and
effects on information gain online”,Computers in Human Behavior, Vol. 75, pp. 1008-1015.
Lee, H., Kwak, N. and Campbell, S.W. (2015), “Hearing the other side revisited: the joint workings of
cross-cutting discussion and strong tie homogeneity in facilitating deliberative and
participatory democracy”,Communication Research, Vol. 42 No. 4, pp. 569-596.
McClurg, S.D. (2003), “Social networks and political participation: the role of social interaction in
explaining political participation”,Political Research Quarterly, Vol. 56 No. 4, pp. 449-464.
Min, S.J. and Wohn, D.Y. (2018), “All the news that you don’t like: cross-cutting exposure and political
participation in the age of social media”,Computers in Human Behavior, Vol. 83, pp. 24-31.
Mitchell, A. and Page, D. (2014), State of the News Media: Overview, Pew Research Center, available at:
Moeller, J., K€
uhne, R. and Vreese, C.D. (2018), “Mobilizing youth in the 21st century: how digital media
use fosters civic duty, information efficacy, and political participation”,Journal of Broadcasting
and Electronic Media, Vol. 62 No. 3, pp. 445-460.
Morris, D.S. and Morris, J.S. (2017), “Evolving learning: the changing effect of internet access on
political knowledge and engagement (1998–2012)”,Sociological Forum, Vol. 32 No. 2,
Mutz, D.C. (2002), “Cross-cutting social networks: testing democratic theory in practice”,American
Political Science Review, Vol. 96 No. 1, pp. 111-126.
Nir, L. (2012), “Cross-national differences in political discussion: can political systems narrow
deliberation gaps?”,Journal of Communication, Vol. 62 No. 3, pp. 553-570.
Oeldorf-Hirsch, A. (2018), “The role of engagement in learning from active and incidental news
exposure on social media”,Mass Communication and Society, Vol. 21 No. 2, pp. 225-247,
Park, C.S. and Kaye, B.K. (2018), “News engagement on social media and democratic citizenship: direct
and moderating roles of curatorial news use in political involvement”,Journalism and Mass
Communication Quarterly, Vol. 95 No. 4, pp. 1103-1127.
Park, C.S. and Kaye, B.K. (2020), “What’s this? Incidental exposure to news on social media,
news-finds-me perception, news efficacy, and news consumption”,Mass Communication and
Society, Vol. 23 No. 2, pp. 157-180, Routledge.
Paskeviciute, A. and Anderson, C.J. (2005), “Macro-politics and micro-behavior: mainstream politics
and the frequency of political discussion in contemporary democracies”,Social Logic of Politics:
Personal Networks as Contexts for Political Behavior, pp. 228-248, Temple University Press.
Perrin, A. and Anderson, M. (2019), Share of US Adults Using Social Media, Including Facebook, Is
Mostly Unchanged since 2018, Pew Research Center, available at: https://www.pewresearch.org/
unchanged-since-2018/ (accessed 15 April 2020).
Pew Research Center (2019), “Demographics of Social Media Users and Adoption in the United States,
Pew Research Center: Internet, Science & Tech, available at: https://www.pewinternet.org/fact-
sheet/social-media/ (accessed 18 September 2019).
Publications Office of the European Union (2018), Media Use in the European Union, Website,
Publications Office of the European Union, available at: http://op.europa.eu/en/publication-
detail/-/publication/a575c1c9-58b6-11e8-ab41-01aa75ed71a1 (accessed 15 April 2020).
na, M., McGregor, S. and Gil de Z
niga, H. (2015), Social Media as a Public Space for Politics:
Cross-National Comparison of News Consumption and Participatory Behaviors in the United
States and the United Kingdom, Vol. 9, pp. 3304-3326.
Sax, L.J., Gilmartin, S.K. and Bryant, A.N. (2003), “Assessing response rates and nonresponse bias in
web and paper surveys”,Research in Higher Education, Vol. 44 No. 4, pp. 409-432.
Scheufele, D.A., Hardy, B.W., Brossard, D., Waismel-Manor, I.S. and Nisbet, E. (2006), “Democracy
based on difference: examining the links between structural heterogeneity, heterogeneity of
discussion networks, and democratic citizenship”,Journal of Communication, Vol. 56 No. 4,
Shearer, E. and Matsa, K.E. (2018), News Use across Social Media Platforms 2018, Pew Research
Center’s Journalism Project, available at: https://www.journalism.org/2018/09/10/news-use-
across-social-media-platforms-2018/ (accessed 15 April 2020).
Song, H., Gil de Z
niga, H. and Boomgaarden, H.G. (2020), “Social media news use and political
cynicism: differential pathways through ‘news finds me’perception”,Mass Communication and
Society, Vol. 23 No. 1, pp. 47-70.
Sotirovic, M. and McLeod, J.M. (2001), “Values, communication behavior, and political participation”,
Political Communication, Vol. 18 No. 3, pp. 273-300.
ack, J. and Dimitrova, D.V. (2006), “Political and media systems matter: a comparison of
election news coverage in Sweden and the United States”,Harvard International Journal of
Press/Politics, Vol. 11 No. 4, pp. 131-147, SAGE Publications.
Tang, G. and Lee, F.L.F. (2013), “Facebook use and political participation: the impact of exposure to
shared political information, connections with public political actors, and network structural
heterogeneity”,Social Science Computer Review, Vol. 31 No. 6, pp. 763-773.
Tewksbury, D., Weaver, A. and Maddex, B.D. (2001), “Accidently informed: incidental news exposure
on the world wide web”,Journalism and Mass Communication Quarterly, Vol. 78 No. 3,
Valeriani, A. and Vaccari, C. (2016), “Accidental exposure to politics on social media as online
participation equalizer in Germany, Italy, and the United Kingdom”,New Media and Society,
Vol. 18 No. 9, pp. 1857-1874.
Wojcieszak, M.E. and Mutz, D.C. (2009), “Online groups and political discourse: do online discussion
spaces facilitate exposure to political disagreement?”,Journal of Communication, Vol. 59 No. 1,
Yadamsuren, B. and Erdelez, S. (2010), “Incidental exposure to online news”,Proceedings of the
American Society for Information Science and Technology, Vol. 47 No. 1, pp. 1-8.
Yamamoto, M. and Morey, A.C. (2019), “Incidental news exposure on social media: a campaign
communication mediation approach”,Social Media þSociety, Vol. 5 No. 2, pp. 1-12.
Yoo, S.W. and Gil de Z
niga, H. (2019), “The role of heterogeneous political discussion and
partisanship on the effects of incidental news exposure online”,Journal of Information
Technology & Politics, Vol. 16 No. 1, pp. 1-16.
Rebecca Scheffauer can be contacted at: Rebecca.Scheffauer@gmx.at
For instructions on how to order reprints of this article, please visit our website:
Or contact us for further details: email@example.com