Structural Influences on the News Finds Me Perception: Why People Believe They Don’t Have to Actively Seek News Anymore

Abstract

Using data from a two-wave panel survey among 18 countries worldwide, this study investigates the individual- and country-level antecedents of the “News Finds Me” perception (NFM). Results show that older, more educated, and individuals belonging to the ethnic majority are less prone to develop the NFM. However, social media (news) use, incidental news exposure, discussion frequency, and group affiliations lead to a higher NFM. In contrast, information elaboration as well as news use online were found to weaken the NFM. Testing various country-level factors, only gross domestic product was found to be negatively related to the NFM. The findings form a theoretical and empirical basis for future studies that aim at investigating news use in today’s high-choice media environment.

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Article
In today’s high-choice media environment, people are con-
stantly exposed to news via diverse media channels, such as
television (TV), radio, smartphones, social media, or other
digital tools (Aalberg et al., 2013; Prior, 2007). The array of
possibilities to get news anywhere and at any time can cause
the perception that one does not have to actively seek the
news anymore to stay informed, and that news will find one
through the various information channels one is exposed to
on a daily basis. Scholars have identified this phenomenon as
the “News Finds Me” perception (NFM). It is defined as the
“extent to which individuals believe they can indirectly stay
informed about public affairs—despite not actively follow-
ing the news” (Gil de Zúñiga et al., 2017, p. 3).
Previous research on unintended news exposure has pre-
dominantly focused on the reported behavior of individuals
coming across news although not intentionally seeking it
(Karnowski et al., 2017; Oeldorf-Hirsch, 2018). This con-
cept, defined as incidental news exposure (INE; Tewksbury
et al., 2001), has been shown to lead to positive effects, such
as awareness for current affairs (Feezell, 2018), learning
about news (Stroud et al., 2019), and even political participa-
tion on- and offline (Kim et al., 2013; Valeriani & Vaccari,
2016). However, while INE implies a (limited) cognitive
awareness of the news one incidentally comes across, less is
known about the perception of people thinking they are
informed without actively seeking the news while being con-
stantly and unintentionally exposed to news.
Recent research on the NFM, in fact, points to negative
effects of the perception on political behavior (e.g., political
knowledge, voting) and news use (Gil de Zúñiga & Diehl,
2019; Gil de Zúñiga et al., 2017; Park & Kaye, 2020; Gil de
Zúńiga et al., 2020). While positive effects of the NFM on
other than political or news consumption behavior cannot be
ruled out at this stage—given the lack of research on the con-
cept—it is equally relevant to find out about the driving
forces that explain the manifestation of this particular per-
ception among people. That way, this study aims at making a
theoretical and empirical contribution to the conceptual
1024966SMSXXX10.1177/20563051211024966Social Media <span class="symbol" cstyle="Mathematical">+</span> SocietyStrauß et al.
research-article20212021
1University of Zurich, Switzerland
2University of Vienna, Austria
3University of Salamanca, Spain
4Pennsylvania State University, USA
5Universidad Diego Portales, Chile
Corresponding Author:
Nadine Strauß, Department of Communication and Media Research,
University of Zurich, Andreasstrasse 15, Zurich CH-8050, Switzerland.
Email: n.strauss@ikmz.uzh.ch
Structural Influences on the News Finds
Me Perception: Why People Believe
They Don’t Have to Actively Seek News
Anymore
Nadine Strauß1, Brigitte Huber2,
and Homero Gil de Zúñiga3,4,5
Abstract
Using data from a two-wave panel survey among 18 countries worldwide, this study investigates the individual- and country-
level antecedents of the “News Finds Me” perception (NFM). Results show that older, more educated, and individuals
belonging to the ethnic majority are less prone to develop the NFM. However, social media (news) use, incidental news
exposure, discussion frequency, and group affiliations lead to a higher NFM. In contrast, information elaboration as well as
news use online were found to weaken the NFM. Testing various country-level factors, only gross domestic product was
found to be negatively related to the NFM. The findings form a theoretical and empirical basis for future studies that aim at
investigating news use in today’s high-choice media environment.
Keywords
News Finds Me perception, incidental news exposure, new use, online behavior, multi-level, cross-country

2 Social Media + Society
development of the NFM to guide future research on news
use.
What is more, studies on the NFM have mostly been
solely conducted in the United States, therefore, leaving
room for discussions whether the NFM is simply an US phe-
nomenon or also prevalent across different countries in the
world. Hence, this study seeks to explain the antecedents of
the perception among individuals in 18 countries worldwide.
In doing so, this study contributes to the growing area of
comparative research in communication science (Esser,
2013). By relying on a two-wave online panel survey across
18 countries, we investigate how (1) individual-level factors
(demographics, media and news use, and network variables)
and (2) macro-level variables (internet connectivity, gross
domestic product (GDP), and press freedom) relate to the
NFM over time. Furthermore, individual country analyses
allow us to identify differences that can be explained by
country-specific contexts (cf. Livingstone, 2003).
Theoretical Framework
“News Finds Me Perception” and Availability
Heuristics
Today, individuals find themselves in a multi-media platform
environment that increases the likeliness of coming across
news without actively seeking it (Fletcher & Nielsen, 2018;
Kim et al., 2013). Not only is the instantaneous flow and the
availability of news suggesting that news is an inherent part of
people’s digital lives (Sheller, 2015); but it also leads people to
believe that they do not have to actively seek news anymore.
Gil de Zúñiga et al. (2017) have labeled this as the “News
Finds Me” perception (NFM). It describes the extent to which
citizens (a) rely on networks and peers to get information and
news about public affairs, (b) their belief that news will “find
them” through their general media use as well as their interac-
tion with peers and other social connections, and (c) their per-
ception that they are well-informed about current public events
and politics although not actively following the news.
A general conclusion a reader might draw here is that the
NFM resembles news avoidance. However, an important
facet to distinguish is that citizens with a strong NFM are not
necessarily disinterested in news or not motivated to follow
the news as such. Following rational-choice theory, news
avoidance would require the active and conscious decision to
avert the consumption of news content (cf. Frey, 1986).
Instead, today’s high-choice media environment conveys the
impression that news is constantly available (Bergström &
Belfrage, 2018), which in turn, fuels people’s perception that
“news will find” them. Furthermore, it leaves individuals
with the impression that they stay informed about current
affairs without having to actively consume news.
A better way to explain the mechanism behind the emer-
gence of the NFM among people is to make use of the theory
of availability heuristics (Tversky & Kahneman, 1973). In the
early 1970s, Tversky and Kahneman have introduced a judg-
mental heuristic that posits that individuals evaluate the prob-
ability of events or things to occur by the direct availability. In
other words, the more easily available something is, or the
more accessible certain issues or things are (e.g., news), the
more likely one believes that these things occur frequently. As
a consequence, this availability heuristic leads to systematic
biases, such as the overestimation of certain things or events to
happen. Not only has the theory been repeatedly tested in
communication research (e.g., violent TV programs: Riddle,
2010; risk events: Sjöberg & Engelberg, 2009; and media
reports on terrorism: Breckenridge et al., 2010), the same
mechanism might also apply when studying the link between
the sheer availability of news and information in today’s news
media environment and individuals’ level of the NFM.
Following availability theory, it can thus be assumed that the
more available news seems to be to individuals (e.g., via social
media), the more likely they might develop the perception that
they are well-informed about politics and public affairs without
reading the news. However, in line with the availability bias,
research has shown this perception might be misleading.
Scholars have shown that individuals scoring high on the NFM
are likely to suffer from a lower political knowledge over time
(Gil de Zúñiga & Diehl, 2019; S. Lee, 2020). Furthermore, anal-
yses of survey data imply that the NFM is negatively related to
political interest and voting behavior (Gil de Zúñiga & Diehl,
2019) as well as news consumption of traditional news media
overall (Gil de Zúñiga et al., 2017; Park & Kaye, 2020). Given
the supposedly detrimental effects of the NFM on democratic
behavior, it becomes of paramount interest to study this percep-
tion and its explanatory factors in more depth.
“News Finds Me” Perception—Individual and
Context-Dependent Factors
To get a deeper understanding of the influencing forces that
could explain the manifestation of the NFM among individu-
als, we make use of the definition of the perception (Gil de
Zúñiga et al., 2017) and derive possible individual- and macro-
level variables that we want to test (cf. social networks, news
use, interaction with peers, and other social contacts). Yet, per-
ceptions or cognitive beliefs, such as the NFM, are usually the
result of dynamic interplays between an autonomous agent
and its environment (cf. Thompson, 2010). Hence, in the pur-
suit of offering both a theoretical and empirical account for the
NFM, we study both individuals’ predispositions (e.g., demo-
graphics, daily media behavior, and attitudes) as well as their
indirect structural influences and interactions with the envi-
ronment (e.g., economic situation, internet connectivity, press
freedom), relevant to news and media use.
Individual-Level Factors
Demographics. Based on a plethora of research, it is well-
known that there are considerable differences between

Strauß et al. 3
individuals and their news consumption behavior based on
their demographic characteristics. For example, traditional
news-seekers were found to be older, better educated, and to
have a higher income (Ksiazek et al., 2010). Furthermore,
women have been found to state more often that they avoid the
news when compared to men, developing the so-called “news-
is-for-men” perception (Toff & Palmer, 2018). Regarding race,
a study from 2019 reported that 60% of Black Americans pre-
fer to get news from TV, compared with 43% of Whites and
45% of Hispanics (Atske et al., 2019). In terms of equality, an
analysis by Kalogeropoulos and Nielsen (2018) for news con-
sumption in the United Kingdom reported that social inequality
in online news consumption is greater than in offline news con-
sumption, with individuals scoring lower on the social-grade
scale use significantly fewer online sources on average.
When it comes to social media, younger people and ado-
lescents are more likely than older generations to use them
for news consumption (e.g., Newman et al., 2018). At the
same time, a recent survey found that about half of US adults
(53%) reported to consume news from social media “often”
or “sometimes” (Shearer & Mitchell, 2021) and more than
eight-in-ten of Americans indicated to get news from digital
devices (Shearer, 2021). Hence, social media is becoming
increasingly common among older age groups, mitigating
the age gap. For more on news and age gap effects, see
Johnson et al. (1998) and Sotirovic and McLeod (2004).
However, given that all these findings relate to actual news
use, and research on the NFM is scarce, we want to explore
the relationship between demographic characteristics of indi-
viduals and the NFM across countries:
RQ1. How are age, gender, education, income, and race
related to the NFM?
Information Elaboration. Second, one of the most important
theoretical aspects of the NFM is the fact that individuals
believe that they are well-informed without actively seeking
news. This perception might be strongly fueled by individu-
als’ tendency to discuss and elaborate on (virtual) conversa-
tions they have with others about politics and public affairs.
Information elaboration has been defined by Eveland (2001)
as “the process of connecting new information to other infor-
mation stored in memory, including prior knowledge, per-
sonal experiences, or the connection of two new bits of
information together in new ways” (p. 573). The concept has
been shown to play a crucial role in facilitating learning from
information (Eveland, 2001; Eveland & Dunwoody, 2002),
and might thus be a mitigating factor in developing the NFM.
Assuming that individuals with a high level of information
elaboration are likely to actively consume and elaborate on
information to satisfy their cognitive needs, we propose:
H1. Information elaboration is negatively related to the
NFM.
News Use. Another important aspect of the NFM is that
individuals believe that they do not have to actively con-
sume news to stay informed. Hence, it becomes necessary
to investigate to what extent actual news use is related to
the NFM. In fact, one of the first studies on the concept has
shown that the NFM is strongly associated with the con-
sumption of news on social media (Gil de Zúñiga et al.,
2017; S. Lee, 2020) but that, reversely, social media news
use is also reinforcing the NFM. Moreover, the study
showed that the NFM is negatively related to traditional
news use, such as TV or radio news. However, given that
Gil de Zúñiga and colleagues have only examined social
media news use as an independent variable, it is still open
for discussion to what extent online news use or news use
via traditional media affects the perception. Hence, based
on the findings from the seminal study (Gil de Zúñiga
et al., 2017), we formulate the following hypotheses and
research question:
H2. Social media news use is positively related to the
NFM.
H3. Traditional news use is negatively related to the NFM.
RQ2. How is online news use related to the NFM?
Online and Social Media Behavior. Two crucial dimensions of
the NFM are that individuals report to stay well-informed
when being online and active on social networks (Gil de
Zúñiga et al., 2017). Hence, the time spent online and on
social media on a daily basis might also be determining fac-
tors for developing the NFM. We thus assume:
H4. (a) Daily hours spent online and (b) daily social media
use are positively related to the NFM.
Incidental News Exposure. As a side product of being online
and on social media, people report to come across news
online (e.g., via shares, comments), even without actively
following news sources (Fletcher & Nielsen, 2018; Kim
et al., 2013; Tewksbury et al., 2001). There is an array of
research that has investigated INE and its effects on knowl-
edge acquisition. While one camp of researchers suggests
that INE on social media supports the learning of political
facts (Bode, 2016; Shehata et al., 2015), others have shown
more recently that being incidentally exposed to news does
not necessarily lead to knowledge gains (Oeldorf-Hirsch,
2018). Given that INE might drive the impression of people
being up-to-date and informed about current events, despite
not actively consuming the information provided by the
news-snippets (cf. Fletcher & Nielsen, 2018; Park & Kaye,
2020), we presume:
H5. INE is positively related to the NFM.

4 Social Media + Society
Network Size. Another central aspect of the NFM is that peo-
ple believe that they receive their news and information from
their peers and friends within their social networks (Gil de
Zúñiga et al., 2017). Hence, the NFM implies a strong reli-
ance on other people and personal connections to stay
informed. In fact, retrieving information from one’s personal
networks is strongly related to network hubs (Eveland et al.,
2011). Network hubs “connect together otherwise separate
parts of the social network and facilitate the widespread flow
of political influence and information” (Eveland et al., 2011
p. 4). Previous research has shown, for example, that large
networks are associated with higher levels of exposure to
political content, more news media use (McLeod et al.,
1999), higher factual knowledge (Kwak et al., 2005), and
more engagement in political conversations or politics in
general (Eveland & Hively, 2009; Kwak et al., 2005).
Although most of the previous findings did not control for
news use (offline/online) when studying the direct relation-
ships, we expect:
H6. Individuals’ political discussion network size is posi-
tively related to the NFM.
Discussion Frequency. Furthermore, we argue that not only the
number of people citizens discuss with politics matters, but
also how frequently they discuss politics with others. Infor-
mal political talk is beneficial in processing information
received via the mass media and linking it to other, already
known things (cf. Andersen & Hopmann, 2018). The ratio-
nale behind is that personal conversation is generally per-
ceived more trustworthy, authentic, and closer when
compared with mass media messages (Katz & Lazarsfeld,
1955). In fact, a wide range of research has shown that politi-
cal talk can increase political knowledge (Nisbet & Scheufele,
2004), strengthen political polarization (Druckman et al.,
2018), and potentially mitigate knowledge gaps (e.g., Ander-
sen & Hopmann, 2018). Hence, arguing based on the find-
ings on political talks—both off- and online—and following
the rationale of the NFM, we hypothesize:
H7. Political discussion frequency (offline/online) is posi-
tively related to the NFM.
Member of Groups. Today, a great share of people around the
world are active on social media and networks (e.g., 2.8 bil-
lion users on Facebook by 2021) and engage in social groups
offline (Walsh, 2004). Online groups, such as on Facebook
or WhatsApp play an important role when it comes to shar-
ing and discussing news with others (Swart et al., 2019;
Zhang et al., 2013). Moreover, being part of clubs, sports
groups and other societies dealing with hobbies, personal
interests, or causes are an important venue for people to
exchange ideas, opinions, and talk about current events
(Walsh, 2004). People who belong to such on- or offline
groups have thus higher chances to get exposed to informa-
tion through these groups, which might in turn foster their
perception that important news will find them through their
social networks. Therefore, we hypothesize:
H8. Being a member of online/offline groups is positively
related to the NFM.
Macro-Level Factors
Internet Connectivity. As perceptions, thoughts and knowledge
are a result of environmental factors (Thompson, 2010), the
NFM might also be influenced by macro-structural and coun-
try-specific parameters. One obvious indicator that may be
positively related to the NFM is internet connectivity; hence,
the availability of access to online information. Data from the
past decades have shown that countries with high internet
access or internet connectivity evince higher internet usage
among individuals (The World Bank, 2018). Likewise, internet
connectivity and access are strongly dependent on an adequate
infrastructure and resources available in the respective country,
thereby influencing levels of education, social development,
economic growth (ITU & UNESCO, 2020)—and also news
use (e.g., fake news: Shirish et al., 2021). It is, therefore, likely
that in countries in which more citizens have access to the inter-
net and in countries in which there are more broadband accesses
registered, the NFM might be more prevalent. Our first hypoth-
esis regarding the macro-level factors thus reads:
H9. Individuals living in a country with higher internet
connectivity show a higher NFM.
Gross Domestic Product. Recent research by the Pew Research
Center has found that people in wealthier countries are more
likely to get news online on a daily basis (Mitchell et al., 2018).
On the individual level, it has also been shown that individuals
with a higher socio-economic status are better in reading and
seeking information, such as reading newspapers (e.g., Van
Eijck & Van Rees, 2000). When it comes to the relationship
between GDP and the NFM, both directions seem plausible:
On one hand, in countries in which economic productivity (cf.
GDP) is high (e.g., the United States, Sweden, the Netherlands,
Australia, or Germany), people might be more likely to actively
access news, and thus less likely to develop the NFM. On the
other hand, GDP has been found to positively influence media
ownership, that is, individuals in countries with higher GDP
have more resources to buy new media devices (Kononova
et al., 2014) that also facilitate social media news use (e.g.,
smartphone, tablets). Hence, it might also be the case that indi-
viduals in high GDP countries are more likely to evince the
NFM. Accordingly, we pose the following research question:
RQ3. How is GDP related to the NFM?

Strauß et al. 5
Press Freedom. Another factor that might influence the NFM
is press freedom. The concept of press freedom or media
freedom entails journalists’ degree of freedom to produce
content and also citizens’ degree of freedom in terms of
accessing media content (McQuail, 2000). In countries with
low press freedom, mobile phones and online platforms play
an important role for citizens to get news from foreign uncen-
sored media and to bypass authoritarian information (Pestin,
2011; Verclas & Mechael, 2008). Indeed, Wei et al. (2014)
have shown that in societies with low press freedom citizens
are more likely to use mobile phones to follow news on
social media. Hence, given that news on social media is
ubiquitous, individuals in countries with low press freedom
might be more likely to develop the NFM. However, since
empirical findings from prior research are lacking in this
context, we pose the following research question:
RQ4. How is press freedom related to NFM?
Method
Sample and Data
As part of the international research project “World Digital
Influence,” a collaboration between the University of Vienna
and Massey University of New Zealand, data for this study
are based on an international two-wave online panel survey
which was conducted in late September 2015 (Wave 1:
N = 20,361) and in March 2016 (Wave 2: N = 8,708). The
selection of countries was based on several criteria. First, the
countries should originate from different continents (from
the Americas, Asia, Europe, and Africa) and second, the
countries had to present different political, economic, and
cultural contexts. Accordingly, countries with different lev-
els of democratic development, economic growth, and cul-
tural indicators were considered. From the 22 countries,
participants from 18 countries gave valid responses in both
waves of the survey for the key variables investigated in this
study. The survey was administered with the help of the poll-
ing company Nielsen.
In cooperation with partners in the selected countries,
Nielsen curates a pool of 10 million individuals across 20
countries from which it draws a final sample, generating a
stratification on the national census with quotas. Ultimately,
the collected data largely resemble the national population
parameters from all countries participating in the study. For
the most part, all survey instruments were the same across
countries and across the two waves, except for a few coun-
tries where additional constructs were measured (e.g.,
WhatsApp use in New Zealand, Spain, and the United
States). The surveys were translated into the native language
of each participating country with the help of project partners
at local universities of each country, respectively. Whenever
possible, back-translation methods were employed (Behling
& Law, 2000). Given the large scale of the survey study, and
considering the reliance on online paneling sampling tech-
niques, response rates are not calculated, but rather coopera-
tion rates (CR3), as suggested by the American Association
for Public Opinion Research. The across-wave cooperation
rate was consistently high, averaging 77% (AAPOR, 2011).
Descriptive statistics for all key variables per country (sorted
by press freedom) are displayed in Table 1. For more infor-
mation on the country samples, the demographics per coun-
try, and comparative data with respect to each country’s
census information, please refer to Gil de Zúñiga and Liu
(2017).
Measures
NFM Perception. Following previous research (Gil de Zúñiga
et al., 2017), the NFM was measured by four items, using a
7-point scale (W1: Cronbach’s α = .76; M = 3.91, SD = 1.18;
W2: α = .77, M = 3.71, SD = 1.20). See Table 2 and the appen-
dix for the survey items, and Figure 1 for the NFM scores in
each country.
Demographics. Across the 18 countries, we asked respon-
dents for a range of socio-demographic data. Age was mea-
sured using an open-ended question (M = 40.96, SD = 14.65)
and gender using a dichotomous variable (0 = male and
1 = female). Of all respondents, 50.4% were female. To cap-
ture respondents’ education, we asked about the highest level
of education they have completed, ranging from “Elemen-
tary school” to “Graduate school or higher” (M = 4.37,
SD = 1.30, median = 4 (high school). Income was measured
by asking an open question about last year’s family’s total
household income before taxes (range of scale: 1 = 0–10 per-
centile; 5 = 91–100 percentile; M = 2.94 [11–30 percentile],
SD = 1.09). Finally, respondents could indicate their race/
ethnicity by choosing between six options (1 = Black,
2 = White or Caucasian, 3 = Hispanic or Latino, 4 = Asian,
5 = Native American, 6 = Pacific Islander, and 7 = Other).
The variable has then been recoded (0 = minority and
1 = majority). Of respondents, 85.0% belonged to the cate-
gory “Majority” in the respective country.
Information Elaboration. To measure information elabora-
tion, we asked respondents to indicate their level of agree-
ment on a 7-point scale to four statements (see the
appendix for the survey items; Cronbach’s α = .92,
M = 3.97, SD = 1.45).
News Use (Social Media, Online, and Traditional). Social media
news use was measured by (a) asking respondents how fre-
quently they get news from social media and (b) how fre-
quently they use social media to get news about current
events from mainstream media (1 = never and 7 = all the time;
Spearman–Brown’s coefficient = .48, M = 4.30, SD = 1.62).
To measure online news use, individuals were asked to indi-
cate how often (1 = never and 7 = always) they get news from

6 Social Media + Society
Table 1. Descriptives of Key Variables per Country.
Country Press
freedom
Information
elaboration
Social media
news use
Online
news use
Traditional
news use
Daily hours back-
translation online*
Daily social
media use
M (SD)M (SD)M (SD)M (SD)M (SD)M (SD)
Estonia 16 3.76 (1.27) 3.98 (1.52) 4.24 (1.28) 4.99 (1.33) 4.10 (3.10) 4.32 (1.74)
New Zealand 20 3.60 (1.54) 3.69 (1.62) 3.52 (1.25) 4.63 (1.27) 5.47 (4.46) 4.25 (1.81)
Germany 20 4.36 (1.37) 3.56 (1.72) 3.81 (1.42) 4.97 (1.32) 5.43 (3.81) 4.28 (2.02)
The United States 21 3.64 (1.67) 3.39 (1.77) 3.37 (1.36) 4.25 (1.38) 6.31 (4.94) 4.12 (1.95)
The United Kingdom 25 3.42 (1.67) 3.04 (1.79) 3.28 (1.39) 4.67 (1.29) 6.03 (4.80) 3.77 (2.09)
Taiwan 26 3.75 (1.29) 4.54 (1.19) 4.20 (1.17) 3.94 (1.07) 6.76 (3.94) 5.33 (1.33)
Japan 26 4.14 (1.17) 3.23 (1.54) 4.03 (1.35) 4.11 (1.36) 6.19 (4.51) 3.55 (1.87)
Spain 28 4.20 (1.45) 4.44 (1.55) 4.26 (1.45) 4.73 (1.25) 6.16 (4.58) 4.98 (1.78)
Italy 31 4.04 (1.51) 4.17 (1.32) 4.13 (1.20) 4.27 (1.05) 6.93 (4.69) 4.81 (1.66)
Korea 33 3.52 (1.43) 3.98 (1.48) 4.40 (1.26) 3.95 (1.34) 5.91 (4.04) 4.35 (1.69)
Philippines 44 4.21 (1.26) 5.56 (1.01) 4.80 (1.19) 4.69 (1.18) 8.98 (5.38) 5.55 (1.13)
Brazil 46 4.31 (1.50) 5.32 (1.25) 5.22 (1.38) 4.78 (1.32) 10.00 (5.54) 5.82 (1.41)
Indonesia 49 4.07 (1.29) 5.36 (1.07) 4.14 (1.31) 5.31 (1.12) 7.26 (4.47) 5.51 (1.28)
Argentina 50 4.45 (1.43) 4.93 (1.44) 4.43 (1.47) 4.70 (1.25) 7.61 (4.94) 5.47 (1.55)
Ukraine 53 3.41 (1.33) 4.07 (1.31) 4.74 (1.20) 3.70 (1.28) 6.81 (4.04) 4.64 (1.48)
Turkey 71 4.44 (1.39) 5.19 (1.27) 4.45 (1.16) 4.44 (1.15) 8.71 (5.13) 5.59 (1.36)
Russia 83 3.94 (1.43) 4.14 (1.46) 4.63 (1.24) 4.42 (1.29) 7.12 (4.28) 4.40 (1.61)
China 87 4.28 (1.19) 5.04 (1.10) 5.15 (1.12) 4.24 (1.20) 6.58 (3.71) 5.23 (1.23)
Country INE Network size
offline**
Network size
online**
Discussion
frequency
offline
Discussion
frequency
online
Member
of offline
groups
Member of
online groups
Total Cases
M (SD)M (SD)M (SD)M (SD)M (SD)M (SD)M (SD)
Estonia 4.38 (1.19) 5.54 (8.51) 2.03 (7.54) 3.62 (1.18) 1.78 (.99) 2.44 (1.52) 2.57 (1.35) 1,168
New Zealand 4.04 (1.21) 4.20 (16.09) 4.95 (31.59) 2.87 (1.23) 1.81 (1.10) 3.07 (1.73) 2.60 (1.61) 1,157
Germany 4.44 (1.29) 5.48 (8.55) 2.96 (8.65) 3.67 (1.41) 2.29 (1.48) 2.35 (1.43) 2.46 (1.34) 1,054
The United States 4.04 (1.31) 3.72 (6.84) 6.19 (32.49) 2.99 (1.29) 1.92 (1.23) 2.86 (1.72) 2.43 (1.68) 1,161
The United Kingdom 3.81 (1.41) 3.38 (7.16) 4.66 (27.60) 2.96 (1.32) 1.78 (1.22) 2.44 (1.48) 2.75 (1.47) 1,064
Taiwan 4.73 (1.07) 3.98 (19.36) 6.77 (27.43) 2.60 (1.09) 2.39 (1.18) 3.98 (1.31) 4.00 (1.24) 1,008
Japan 4.19 (1.22) 2.73 (7.99) 2.94 (20.80) 2.45 (1.15) 1.62 (1.08) 2.59 (1.34) 2.94 (1.24) 975
Spain 4.31 (1.38) 7.19 (15.73) 9.09 (34.94) 3.71 (1.30) 2.50 (1.49) 3.11 (1.59) 3.24 (1.48) 1,020
Italy 4.36 (1.39) 7.89 (22.16) 10.40 (38.08) 3.76 (1.39) 2.50 (1.54) 2.85 (1.69) 2.98 (1.54) 1,041
Korea 4.48 (1.24) 3.52 (30.13) 4.81 (21.77) 2.82 (1.21) 2.47 (1.29) 3.53 (1.39) 3.19 (1.26) 944
Philippines 4.97 (1.22) 12.34 (42.87) 24.36 (62.95) 3.73 (1.26) 3.40 (1.45) 3.19 (1.66) 3.32 (1.59) 1,064
Brazil 5.23 (1.19) 11.34 (30.24) 25.12 (67.73) 4.09 (1.44) 3.37 (1.67) 3.51 (1.72) 3.80 (1.60) 1,086
Indonesia 5.05 (1.13) 11.30 (28.85) 14.22 (39.41) 3.54 (1.31) 3.08 (1.48) 3.77 (1.58) 4.03 (1.39) 1,080
Argentina 4.78 (1.33) 12.35 (25.31) 17.17 (53.49) 3.98 (1.40) 2.60 (1.55) 3.21 (1.74) 3.15 (1.55) 1,146
Ukraine 4.36 (1.17) 8.60 (23.80) 9.60 (34.45) 3.39 (1.33) 2.38 (1.32) 3.29 (1.49) 3.33 (1.38) 1,223
Turkey 4.99 (1.23) 8.04 (14.94) 13.45 (35.62) 3.85 (1.42) 3.02 (1.58) 2.59 (1.76) 2.86 (1.80) 961
Russia 4.60 (1.19) 4.89 (10.56) 5.40 (18.52) 3.14 (1.32) 2.22 (1.32) 2.90 (1.51) 3.12 (1.42) 1,145
China 4.98 (.95) 4.78 (10.97) 9.71 (35.56) 3.28 (1.23) 3.09 (1.34) 3.99 (1.41) 4.13 (1.22) 1,004
Note. Countries sorted by press freedom (high score = low press freedom); *outliers who indicate to spend more than 24 hr online were removed; **high
skewness and kurtosis of network size variables were not adjusted here, but done for the final pooled variable.
(a) online news websites and (b) citizen journalism sites
(Spearman–Brown’s coefficient = .30, M = 4.26, SD = 1.40).
Traditional news use was gauged by asking respondents how
often (1 = never and 7 = always) they get news from (a) TV
(cable or local network news), (b) newspapers (printed ver-
sion), and (c) radio. The three items were averaged (Cron-
bach’s α = .60, M = 4.50, SD = 1.32).
Daily Hours Online. Daily time spent online was measured by
a single item, asking respondents “About how many hours

Strauß et al. 7
Table 2. Statistics for the “News Finds Me” Perception Items in 18 Countries.
Country Cases for
NFM
I rely on my
friends to tell me
what’s important
when news
happens
I rely on information
from my friends based
on what they like or
follow through social
media
I can be well-
informed even
when I don’t
actively follow the
news
I don’t worry
about keeping
up with the news
because I know
news will find me
Overall
scale
Reliability
index
N M (SD)M (SD)M (SD)M (SD)M (SD)A
ALL 19,023 3.85 (1.59) 3.58 (1.61) 4.32 (1.41) 3.90 (1.54) 3.91 (1.18) .76
Argentina 1,130 4.96 (1.36) 4.42 (1.47) 4.80 (1.42) 3.92 (1.66) 4.53 (1.07) .69
Brazil 1,066 4.59 (1.45) 4.07 (1.50) 4.60 (1.50) 3.54 (1.61) 4.20 (1.15) .76
China 984 3.78 (1.44) 4.45 (1.21) 4.43 (1.21) 4.22 (1.31) 4.22 (.99) .77
Estonia 1,155 3.97 (1.42) 3.60 (1.33) 4.11 (1.27) 4.56 (1.25) 4.06 (.99) .74
Germany 1,045 3.77 (1.78) 2.55 (1.61) 4.23 (1.56) 3.46 (1.67) 3.50 (1.22) .72
Indonesia 1,057 3.77 (1.42) 3.82 (1.42) 4.61 (1.23) 4.60 (1.27) 4.20 (1.02) .76
Italy 1,031 3.81 (1.60) 3.42 (1.63) 4.31 (1.40) 3.69 (1.60) 3.81 (1.20) .77
Japan 968 2.96 (1.27) 2.54 (1.40) 3.82 (1.23) 3.48 (1.22) 3.20 (.96) .74
Korea 922 3.97 (1.32) 2.90 (1.50) 3.76 (1.40) 3.69 (1.43) 3.58 (1.08) .76
New Zealand 1,149 3.05 (1.53) 2.67 (1.49) 4.06 (1.45) 3.44 (1.58) 3.31 (1.13) .73
Philippines 1,032 3.99 (1.53) 3.88 (1.48) 4.39 (1.37) 3.98 (1.49) 4.06 (1.18) .82
Russia 1,131 4.22 (1.31) 4.40 (1.23) 4.53 (1.24) 4.65 (1.23) 4.45 (.91) .69
Spain 1,009 4.96 (1.30) 4.39 (1.30) 4.76 (1.26) 4.03 (1.52) 4.54 (.97) .69
Taiwan 994 3.63 (1.26) 4.11 (1.21) 4.22 (1.13) 4.05 (1.27) 4.00 (.91) .74
Turkey 938 4.25 (1.47) 4.24 (1.53) 4.86 (1.50) 3.97 (1.64) 4.33 (1.13) .72
The United Kingdom 1,058 2.84 (1.64) 2.47 (1.55) 4.07 (1.52) 3.41 (1.65) 3.20 (1.24) .78
Ukraine 1,202 3.88 (1.37) 3.96 (1.33) 4.21 (1.27) 4.18 (1.43) 4.06 (.98) .70
The United States 1,152 2.86 (1.65) 2.53 (1.56) 3.95 (1.63) 3.25 (1.66) 3.15 (1.27) .79
Note. All items measured on 7-point scales, where 1 = strongly disagree and 7 = strongly agree. NFM = News Finds Me.
Figure 1. “News Finds Me” perception; displayed are the means for the 18 countries analyzed.

8 Social Media + Society
per day would you say that you are online?.” We removed
outliers (n = 4) who indicated to be online more than 24 hr
(M = 6.79, SD = 4.70). Although being online for 24 hr seems
unrealistically high, some respondents might have thought
that being online also means having one’s phone turned on
during night.
Daily Social Media Use. Respondents were also asked to indi-
cate on a 7-point scale (1 = never and 7 = all the time) how
much they use social media on a typical day (M = 4.77,
SD = 1.76).
Incidental News Exposure. Individuals’ INE was measured by
asking respondents:
Sometimes people encounter or come across news and infor-
mation on current events, public issues, or politics when they
may have been using media for a purpose other than to get
the news. How often does that happen to you with the fol-
lowing media . . .
(1) While watching TV, listening to the radio, or reading the
newspaper, (2) while on social media or the internet. The
7-point scale ranged from 1 = never to 7 = always (Spearman–
Brown’s coefficient = .47, M = 4.54, SD = 1.29).
Network Size Offline. To measure individuals’ network size
offline, respondents were asked: “During the past month,
about how many total people have you talked to face-to-face
or over the phone about politics and public affairs (that is,
not via the internet)?.” Given the open answer option, the
answers were highly skewed and leptokurtic (skew-
ness = 17.29; kurtosis = 495.83). Thus, we removed outliers
who ranged one SD above the mean (M = 6.71, SD = 20.82).
Removing 667 cases, the recoded variable yields an accept-
able distribution (skewness: 1.88; kurtosis: 3.80; M = 4.14,
SD = 4.82).
Network Size Online. Network size online was measured by
asking respondents
Still thinking about the people that you have talked to about
politics or public affairs the past month, about how many
total people would you say you have talked to via the Internet,
including e-mail, chat rooms, social networking sites and
micro-blogging sites?.
Again, we removed outliers who were one SD above the
mean (M = 9.51, SD = 37.07) for the given answers due to
issues with skewness and kurtosis (skewness = 10.05; kurto-
sis = 134.10). The recoded variable shows an improved dis-
tribution (skewness = 2.69; kurtosis = 8.07; M = 3.78,
SD = 6.72).
Discussion Frequency Offline. Respondents were furthermore
asked “How often [they] talk about politics or public affairs
face to face or over the phone with (1 = never; 7 = always) . .
.” (1) spouse or partner, (2) family, relatives, or friends, (3)
acquaintances, and (4) strangers. The four items were
averaged, forming a reliable scale (Cronbach’s α = .82,
M = 3.36, SD = 1.38).
Discussion Frequency Online. Similarly, respondents were
asked “How often [they] talk about politics or public affairs
online with (1 = never; 7 = always) . . . (1) spouse or partner,
(2) family, relatives, or friends, (3) acquaintances, and (4)
strangers.” The four items were averaged (Cronbach’sα = .88,
M = 2.45, SD = 1.46).
Member of Offline Groups. The extent to which individuals
are part of and active in a group offline was gauged by asking
respondents to indicate their disagreement or agreement on a
7-point scale (1 = disagree completely and 7 = agree com-
pletely) with the following two statements: (1) I am a mem-
ber of many different groups and (2) I am active in lots of
different groups (Spearman–Brown’s coefficient = .71,
M = 3.09, SD = 1.64).
Member of Online Groups. Correspondingly, group affiliation
online was captured by asking respondents to indicate their
disagreement/agreement (1 = disagree completely and
7 = agree completely) with the following two statements: (1)
I am a member of many online groups and (2) I am active in
lots of different online groups (Spearman–Brown’s coeffi-
cient = .48, M = 3.15, SD = 1.55).
Country-Level Indicators. Internet connectivity was composed
of two items (Spearman–Brown’s coefficient = .77, M = 72.42,
SD = 20.04), using data from 2015–2016 from the World
Bank for percentage of internet users per country (M = 68.74,
SD = 20.22) and data from webworldwide.io (except for Tai-
wan: data from Akamai), indicating the percentage of broad-
band access per country (M = 76.10, SD = 22.76). GDP per
capita (in thousands; M = 21.98, SD = 15.98) was retrieved
from the website of the World Bank for the year 2015. Press
freedom per country (M = 40.40, SD = 21.11) was retrieved
from the website of Freedom House (freedomhouse.org) for
the year 2015. Higher scores mean less press freedom.
Analysis
To answer the research question and test the hypotheses
posed in this study, we employed multi-level models that are
considered the best method of analysis for a nested dataset
(e.g., 19,301 individuals within 18 countries). And in fact,
the null-models (no covariates included) indicate that the
null hypotheses of no country differences in the dependent
variables (NFMW1/W2) was rejected. The interclass correla-
tion coefficient is .16 (NFMW1) and .20 (NFMW2), respec-
tively, thus indicating that a critical proportion of the variance
in the population is explained by the grouping structure
(Hox, 2002). Given that the variables measured were on dif-
ferent scales (e.g., GDP vs daily social media use), we cen-
tered all variables before running our analyses.

Strauß et al. 9
Furthermore, we also investigated possible causal rela-
tionships by constructing a lagged multi-level model (NFMW2
as dependent variable) and the so-called autoregressive
model (NFMW2 as dependent variable and NFMW1 as inde-
pendent variable). The lagged model is useful to get first
insights into a possible causal relationship between variables
from Wave 1 on variables from Wave 2. However, the model
with the autoregressive term (cf. static-score model) allows a
more accurate estimation of causal inferences. First, because
it uses an autoregressive term that is necessary when there
are “synchronous” or “cotemporal” (Finkel, 1995, p. 13)
effects at play (e.g., social media use affecting NFM, and
also vice versa). Second, because the waves in the panel sur-
vey are not too long (here: approx. 6 months). And third,
because the variables are considered to be rather static, and
do not depend on time or external shocks. While cross-sec-
tional data are not suited to establish causality, autoregres-
sive panel data models are a better way to deal with issues of
endogeneity and causal inference compared with the lagged
model, as they include and control for respondents’ prior
scores on the outcome variable (for more details, see, e.g.,
Greenberg, 2008; Kleinnijenhuis, 2016). For individual
country analyses, we relied on ordinary least squares (OLS)
regression analyses with standardized betas.
Results
In the following, we present three different models for test-
ing each hypothesis: (1) cross-sectional model, (2) lagged
model, and (3) autoregressive model. Overall, the results in
Table 3 show that in the cross-sectional model, 16% of the
variance of the NFM is due to country differences (intraclass
correlation [ICC] = .16). In the lagged and in the autoregres-
sive models, the value of ICC = .20. That is, 20% of the vari-
ance is explained by country differences.
The first research question asked how demographic char-
acteristics are related to the NFM. The findings of the multi-
level models (see Table 3) indicate that age, education, and
race are significantly and negatively related to the NFM.
Thus, people who are older (cross-sectional: B = −.05,
p < .001; lagged: B = −.04, p < .01), more educated (lagged:
B = −.03, p < .05; autoregressive: B = −.02, p < .05), and who
belong to the ethnic majority (autoregressive: B = −.03,
p < .05) are less likely to evince the NFM.
H1 presumed that the stronger information elaboration
among individuals, the lower their NFM. We find support for
this hypothesis in the lagged (B = −.06, p < .001) and autore-
gressive model (B = −.04, p < .01), suggesting a causal rela-
tionship. In H2, we hypothesized that social media news use
is positively related to the NFM. H2 is supported across all
models (see Table 3). H3 suggested that traditional news use
is negatively associated with the NFM. Our findings show no
significant relationship between traditional news and the
NFM; thus, H3 is rejected. RQ2 asked how online news use
is associated with NFM. The results indicate that online news
use is negatively related to NFM for the cross-sectional
(B = −.08, p < .001) and lagged models (B = −.05, p < .01). In
H4, we assumed that (a) daily hours spent online and (b)
daily social media use are positively related to the NFM.
While we do not find support for H4(a), H4(b) is supported
across all models (see Table 3). Furthermore, the results
show that H5, which assumed a positive relationship between
INE and NFM, is supported for the cross-sectional (B = .14,
p < .001) and lagged models (B = .07, p < .001).
In H6, we presumed that the larger individuals’ network
size, the higher their NFM. We tested both, off- and online
network size, but the results do not support the hypothesis
and show that, in fact, the opposite is true (see Table 3).
Higher off- and online networks imply a lower NFM. H7
suggested that the more individuals discuss politics on- and
offline, the higher their NFM. We find support for this
hypothesis only for discussion frequency online, but across
all models (cross-sectional: B = .13, p < .001; lagged: B = .14,
p < .001; and autoregressive: B = .06, p < .01). Similarly, H8
implied that the higher individuals’ group membership
online/offline, the higher their NFM. This hypothesis is
partly supported for the cross-sectional and lagged models,
and both for off- and online group membership.
Eventually, the last set of hypotheses and research ques-
tions inquired how macro-level factors (internet connectiv-
ity, GDP, and press freedom) relate to NFM. The results in
Table 3 show that internet connectivity is not significantly
related to the NFM. Thus, our results do not support H9.
However, findings show that GDP (RQ3) is negatively asso-
ciated with NFM across all models (cross-sectional: B = −.26,
p < .05; lagged: B = −.26, p < .05; and autoregressive: B = −.13,
p < .05). Press freedom, the last macro variable included in
the model (RQ4), is not significantly associated with the
NFM.
We have also simulated OLS regression analyses to study
the individual differences across the 18 countries (see Table 4).
While the overall picture looks similar across countries (cf.
social media news use and INE are the strongest positive factor
influencing the NFM), we identify higher coefficients in coun-
tries where social media use (e.g., Brazil, Argentina, Philippines,
Indonesia) and online media use (e.g., Estonia, China Korea,
the United Kingdom) is reported to be generally high (Newman
et al., 2019). Here, East-Asian countries, such as the Philippines,
stand out with strong positive relationships between social
media use, and INE and the NFM, respectively. In fact, the
Philippines have been labeled the “social media capital of the
world” (Pablo, 2018) which is equally well-reflected in our
results.
Most strikingly are the high coefficients in Ukraine. Here,
INE seems to have a particular strong effect on the manifes-
tation of the NFM, while traditional news use strongly miti-
gates the perception. In fact, TV is still considered the most
popular media among the population in Ukraine (70% use it
as a main source of information; Media Landscapes, 2020).
Another distinct result from our analyses is that information

10 Social Media + Society
elaboration seems to be a strong negative predictor of the
NFM in the United Kingdom. This could possibly be related
to the widespread debate culture in the country (cf. Oxford
Union; Haapala, 2012) that implies in-depth consumption of
news and information that is regularly discussed in proxi-
mate social networks. This finding, however, stands in direct
contrast to the positive relationship between information
elaboration and the NFM in Turkey. One possible explana-
tion here could be the low levels of press freedom in the
country. Given partisan news and the need to switch to social
media to consume alternative and critical news (Newman
et al., 2019), information elaboration could rather refer to the
elaboration of news encountered through social interactions
or social media activities that, in turn, positively fuel the
NFM.
Discussion
In today’s rich information and media environment, citizens
around the world have developed the so-called “News Finds
Me” perception (Gil de Zúñiga et al., 2017). Yet, while recent
research on the phenomenon has mainly focused on the
Table 3. Structural Influences on the NFM Perception.
“News Finds Me” perception
Cross-sectionalW1 LaggedW2 AutoregressiveW2
B (SE)B (SE)B (SE)
Fixed parts
Intercept −.31*** (.06) −.08(.06) .07 (.04)
Block 1: autoregressive term
NFMW1 – – .47*** (.01)
Block 2: demographics
Age −.05*** (.01) −.04** (.02) −.02 (.01)
Gender (0 = male and 1 = female) .01 (.01) .00 (.01) −.01 (.01)
Education −.02 (.01) −.03* (.01) −.02* (.01)
Income −.01 (.01) .01 (.01) .02 (.01)
Race (0 = minority and 1 = majority) .01 (.01) −.03 (.02) −.03* (.01)
Block 3: media and News
Information elaboration −.01 (.01) −.06*** (.02) −.04** (.01)
Social media news use .13*** (.01) .15*** (.02) .08*** (.02)
Online news use −.08*** (.01) −.05** (.02) −.01 (.01)
Traditional news use −.00 (.01) −.02 (.01) −.01 (.01)
Daily hours online −.01 (.01) −.01 (.01) .01(.01)
Daily social media use .06*** (.01) .06** (.02) .03* (.02)
INE .14*** (.01) .07*** (.01) .02 (.01)
Block 4: network variables
Network size offline −.02* (.01) −.04* (.02) −.03 (.01)
Network size online −.03** (.01) −.01 (.02) .00 (.02)
Discussion frequency offline .02 (.01) .04 (.02) .03 (.02)
Discussion frequency online .13*** (.01) .14*** (.02) .06*** (.02)
Member of offline groups .05*** (.01) .03** (.01) .01 (.01)
Member of online groups .05*** (.01) .04** (.01) .01 (.01)
Block 5: country level
Internet connectivity .16 (.08) .10 (.08) .04 (.04)
GDP −.26* (.09) −.26* (.09) −.13* (.04)
Press freedom .05 (.08) .06 (.08) .05 (.04)
Random parts
σ2.70 .71 .56
τ00, country .05 .05 .01
Ncountry 18 18 18
ICC .16 .20 .20
Observations 11,539 4,998 4,964
Note. Cell entries are unstandardized coefficients of multi-level models; standard errors in parentheses; all variables are centered. INE = incidental news
exposure; ICC = intraclass correlation; NFM = “News Finds Me” Perception; GDP = gross domestic product.
*p < .05, ** p < .01, *** p < .001.

Strauß et al. 11
Table 4. Structural Influences on the “News Finds Me” Perception per Country.
Argentina
Key variables
Cross-sectionalW1
β
LaggedW2
β
Autoregressive W2
β
Information elaboration .03 −.11 −.12
Social media news use .04 −.09 −.09
Online news use .03 .10 .09
Traditional news use .04 −.10 −.12
Daily hours online −.05 −.04 −.05
Daily social media use .14* .10 .13
INE .12* .29** .19
Network size offline −.01 −.03 −.11
Network size online −.05 −.04 .04
Discussion frequency off. .04 .12 .17
Discussion frequency on. .04 −.03 −.08
Member of offline groups .10 .09 .04
Member of online groups −.04 −.11 −.14
Brazil
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .10 .05 −.03
Social media news use .07 −.07 −.01
Online news use −.07 −.05 .001
Traditional news use .02 .11 .04
Daily hours online .01 −.005 −.03
Daily social media use .11* .13 .02
INE .10* .22* .13
Network size offline −.08 −.07 .01
Network size online −.09 −.05 .05
Discussion frequency off. .05 −.03 −.08
Discussion frequency on. .11 .16 .09
Member of offline groups .15 .12 .06
Member of online groups .07 .15 .06
China
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .04 .18** .15*
Social media news use .20*** .12 .06
Online news use −.14** −.02 .03
Traditional news use .08* −.004 −.01
Daily hours online −.04 −.05 −.05
Daily social media use .02 −.03 −.03
INE .07 .15* .12
Network size offline .01 .07 .06
Network size online .001 −.01 −.03
Discussion frequency off. .07 .21* .15
Discussion frequency on. .12* −.15 −.12
Member of offline groups .12** .07 .04
Member of online groups .16*** .06 .03
Estonia
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .08 −.01 −.01
Social media news use .21*** .25** .20**
Online news use −.08 −.06 −.04
Traditional news use −.04 −.07 −.03
(Continued)

12 Social Media + Society
Estonia
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Daily hours online .01 −.05 −.06
Daily social media use .05 .01 −.05
INE .24*** .13* .04
Network size offline −.04 .06 .08
Network size online .05 .05 .04
Discussion frequency off. .0001 −.08 −.09
Discussion frequency on. .03 .13* .11
Member of offline groups .02 −.09 −.09
Member of online groups .03 .08 .10
Germany
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration −.01 −.06 −.07
Social media news use .16** .17* .11
Online news use −.15** −.09 −.04
Traditional news use −.04 −.07 −.04
Daily hours online .02 .05 .01
Daily social media use .02 .04 .02
INE .12** .03 .00004
Network size offline −.08 −.05 −.02
Network size online −.09* −.14** −.10
Discussion frequency off. .01 .02 .04
Discussion frequency on. .16** .23*** .15*
Member of offline groups .11* −.01 −.04
Member of online groups .08 .15* .09
Indonesia
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .08 .04 .02
Social media news use −.04 .24* .23*
Online news use .12** −.03 −.09
Traditional news use −.10* .08 .13
Daily hours online −.08* .03 .05
Daily social media use .13** .01 −.004
INE .18*** −.02 −.07
Network size offline −.05 .03 .04
Network size online −.03 −.10 −.10
Discussion frequency off. .001 .06 .04
Discussion frequency on. .16** .10 .09
Member of offline groups .14** .20* .17
Member of online groups .002 −.20* −.21*
Italy
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration −.07 −.11 −.08
Social media news use .22*** .22 .12*
Online news use −.01 −.04 .01
Traditional news use .05 −.02 −.02
Daily hours online −.06 −.05 −.02
Daily social media use .05 .10 .07
INE .05 −.02 −.04
Network size offline −.07 −.06 −.05
(Continued)
Table 4. (Continued)

Strauß et al. 13
Italy
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Network Size Online −.08 .02 .03
Discussion frequency off. .02 .15 .14*
Discussion frequency on. .17*** .14 .03
Member of offline groups .16** .13 .03
Member of online groups .02 .06 .09
Japan
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration −.10* −.14** −.08
Social media news use .03 .04 −.03
Online news use −.004 .05 .07
Traditional news use −.06 .04 .07
Daily hours online −.06 −.10 −.02
Daily social media use .04 .08 .10
INE .03 −.04 −.05
Network size offline −.01 −.05 −.06
Network size online −.06 .01 .04
Discussion frequency off. .07 −.01 −.05
Discussion frequency on. .09 .16* .12
Member of offline groups .27*** .15* .06
Member of online groups .05 .14 .07
Korea
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration −.06 −.10 −.08
Social media news use .28*** .23** .09
Online news use −.15** −.16** −.07
Traditional news use −.04 .07 .06
Daily hours online −.03 .05 .07
Daily social media use .07 .18* .16*
INE .03 .02 .005
Network size offline −.03 −.02 .0005
Network size online .01 .05 .08
Discussion frequency off. .002 −.01 −.02
Discussion frequency on. .24*** .19* .09
Member of offline groups .12* .13* .12*
Member of online groups .11* .02 −.07
New Zealand
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration −.05 −.06 .002
Social media news use .13* .14 .06
Online news use −.16*** −.10 −.03
Traditional news use −.13** −.10 −.03
Daily hours online .03 −.04 −.01
Daily social media use .01 .05 .01
INE .11* −.002 −.03
Network size offline −.05 −.09 −.08
Network size online −.02 .09 .10
Discussion frequency off. −.02 −.02 −.02
Discussion frequency on. .11* −.04 −.07
Member of offline groups −.01 .08 .09
Member of online groups .12* .06 −.01
(Continued)
Table 4. (Continued)

14 Social Media + Society
Philippines
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .13** .03 −.06
Social media news use .08 −.02 −.10
Online news use −.05 −.15 −.08
Traditional news use .001 −.05 −.07
Daily hours online −.02 −.01 −.06
Daily social media use .10* .31* .24
INE .17*** .33* .35**
Network size offline −.01 .01 .01
Network size online .01 −.06 −.05
Discussion frequency off. .01 .20 .18
Discussion frequency on. .06 .18 .16
Member of offline groups .16* .04 .12
Member of online groups −.003 −.07 −.22
Russia
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .06 .05 .02
Social media news use .14** .22** .13
Online news use −.07 −.08 .001
Traditional news use .04 .07 .0003
Daily hours online −.01 .06 .04
Daily social media use .11* .06 .03
INE .19*** .01 −.06
Network size offline −.01 −.04 −.05
Network size online .001 .02 .05
Discussion frequency off. .03 .04 −.01
Discussion frequency on. .06 .04 .03
Member of offline groups .04 −.14 −.17*
Member of online groups .01 .04 .06
Spain
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .09 −.07 −.02
Social media news use .12 .24 .25*
Online news use −.11 −.03 −.02
Traditional news use .07 −.03 .04
Daily hours online .003 −.07 −.04
Daily social media use .15** −.09 −.20*
INE .18*** .13 .07
Network size offline −.02 .07 .05
Network size online .02 .05 .05
Discussion frequency off. −.003 .13 .09
Discussion frequency on. .09 .12 −.002
Member of offline groups −.05 −.10 −.05
Member of online groups .08 −.03 −.04
Taiwan
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .09* −.04 −.07
Social media news use .03 −.11 −.12
Online news use −.004 .16* .14
Traditional news use .003 .01 .002
(Continued)
Table 4. (Continued)

Strauß et al. 15
Taiwan
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Daily hours online −.05 −.03 −.01
Daily social media use .02 .04 .05
INE .20*** .15* .12
Network size offline .03 −.01 −.002
Network size online .01 −.07 −.09
Discussion frequency off. −.08 .10 .11
Discussion frequency on. .21*** .13 .07
Member of offline groups .09 .08 .02
Member of online groups .19*** .14 .06
Turkey
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .06 .23** .24**
Social media news use .08 .10 .08
Online news use −.08 .10 .15
Traditional news use −.02 −.03 −.05
Daily hours online −.08 .01 .07
Daily social media use .06 −.03 −.04
INE .25** .06 −.04
Network size offline .02 −.05 −.08
Network size online −.07 −.10 −.08
Discussion frequency off. −.03 −.02 .001
Discussion frequency on. .15** −.02 −.09
Member of offline groups .05 −.05 .01
Member of online groups .12 .18 .07
The United Kingdom
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration .13** −.23*** −.14**
Social media news use .18** .21** .07
Online news use −.05 −.08 −.03
Traditional news use −.05 −.08 −.05
Daily hours online −.02 −.04 −.01
Daily social media use .05 −.03 −.04
INE .20*** .17** .06
Network size offline −.05 −.11* −.07
Network size online −.03 .06 .06
Discussion frequency Off. −.001 .04 .02
Discussion frequency on. .14** .11 .04
Member of offline groups .10* .04 −.01
Member of online groups −.04 .11 .12*
Ukraine
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration −.05 .32 .32
Social media news use .06 .004 .003
Online news use −.04 −.18 −.17
Traditional news use .04 −.47* −.47*
Daily hours online .04 .10 .10
Daily social media use −.02 −.06 −.06
INE .26*** .62** .62*
Network size offline .06 .02 .02
(Continued)
Table 4. (Continued)

16 Social Media + Society
Ukraine
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Network size online −.09 −.25 −.25
Discussion frequency off. .09 .08 .08
Discussion frequency On. .04 .12 .12
Member of offline groups .08 .39 .40
Member of online groups .13* −.06 −.06
The United States
Key variables
Cross-sectionalW1
β
LaggedW2
β
AutoregressiveW2
β
Information elaboration −.11 −.09 −.04
Social media news use .12* .08 .01
Online news use −.10* −.11 −.03
Traditional news use −.03 −.10 −.04
Daily hours online .08* .02 .01
Daily social media use −.01 .08 .06
INE .17*** .05 −.03
Network size offline −.06 −.08 −.06
Network size online −.12** −.14* −.05
Discussion frequency off. .05 .05 .01
Discussion frequency on. .13** .13 .04
Member of offline groups .03 .08 .04
Member of online groups .14** .12 .07
Note. Cell entries are final-entry OLS standardized coefficients (β); all demographic control variables were included in each OLS regression model;
*p < .05, **p < .01, ***p < .001. INE = incidental news exposure.
Table 4. (Continued)
outcomes or mediating function of the perception (e.g., news
use, political interest, political knowledge, voting; Gil de
Zúñiga & Diehl, 2019; S. Lee, 2020; Park & Kaye, 2020;
Song et al., 2020), less is known about the structural influ-
ences and antecedents of the NFM. In this study, we have
provided compelling insights into the driving factors of the
NFM—both on the individual and the country levels.
Based on the results of the multi-level models, it appears
that particularly older, more educated and individuals
belonging to the ethnic majority in the respective country
evince a lower NFM. These findings are in line with previous
research on socio-demographic data and social media news
use (Newman et al., 2018; Shearer, 2021; Shearer & Mitchell,
2021). For example, a recent study implies that Black and
Hispanic Americans find social media platforms personally
important for political activism (Auxier, 2020), while more
than 50% of Black and Hispanic Americans, respectively,
feel that news media do not understand them (Gottfried &
Barthel, 2020). Although the NFM is a perception and does
not imply actual news use, the findings of this study suggest
that certain demographic groups might be more likely to
believe that “news-will-find” them. However, self-selection
issues of individuals who have participated in the survey,
administered by Nielsen, also need to be taken into consider-
ation when interpreting the results.
Furthermore, the results of the analyses imply that daily
social media use and INE seem to be two strong driving
forces for the NFM. One basic assumption of the NFM is that
it is related to high activity on social media platforms and,
probably therewith, serendipitous exposure to news. Hence,
in line with availability heuristics (Tversky & Kahneman,
1973), the more individuals are active on social media and
equally the more they report to be incidentally exposed to
news, the higher their perception that news-will-find them.
However, an open question that remains is whether or not
specific types of news consumption groups are more likely
than others to develop the NFM, and whether these groups
could explain the adverse or beneficial effects of the percep-
tion on democratic behavior (e.g., political knowledge, par-
ticipation, voting). One future stream of research could deal,
for example, with the differentiating effects of the NFM on
knowledge acquisition among various interest groups for
news topics.
In fact, the findings from this study imply that the NFM
also seems to be influenced by social media news use—an
arena for news that is increasingly characterized by informa-
tion and content tailored to personal interests (Beam &
Kosicki, 2014). However, what needs to be highlighted here
is that the measurement of INE and social media news use
might be impaired in this study to a certain extent. It is likely
that INE also shares some common variance with social
media news use; that is, being incidentally exposed to news
on social media might also be perceived as news consump-
tion on social media. Furthermore, the data of this study from

Strauß et al. 17
2015–2016 can be considered somewhat outdated, given the
fast-paced environment and developments of (social media)
news consumption. Thus, future studies not only need to find
a way to better differentiate between active social media
news consumption and incidental exposure to news online
and on social media, but should also take more recent devel-
opments into consideration, such as news use via new and
emerging platforms (e.g., Instagram, TikTok, Clubhouse).
Furthermore, the finding that traditional news use is not
related to the NFM and that online news use is negatively
related to the perception resonates with the basic assump-
tions of the NFM and previous research. For example, Gil de
Zúñiga et al. (2017) have provided evidence that the NFM
comes along with lower levels of traditional news use over
time. Hence, the absence of a reversed relationship is less
surprising. Similarly, online news use implies an active
search for news on dedicated news websites and thus stands
in direct contrast to the NFM. Moreover, given that the news-
paper readership has increasingly moved to the online sphere
in the past decade (Von Krogh & Andersson, 2016), current
online news use may mirror the active news consumption of
newspapers in the past. In fact, future studies should develop
and employ more nuanced and detailed measurements of
news media habits that will allow to distinguish better
between various news use formats and the NFM.
Regarding discussion frequency and group membership,
we find both to be positively related to the NFM, but not for
discussion frequency offline. Thus, activities online that
involve being in contact with other people (e.g., exchanging
opinions, viewpoints, or information) also increase the per-
ception that “news will find” one. Although we are not able
to make any assumptions about the effects of the NFM in this
study here, we encourage future research to study the various
dimensions of the NFM (e.g., Song et al., 2020) and their
distinct effects on political and non-political behavior.
Another area of research could be to group respondents in
particular “news” personalities (e.g., lurking on social media/
radio/TV for news; relying on news being forwarded by
social networks or push-up notifications; checking online
news/newspapers/TV news regularly, etc.) and identify how
their NFM relates to various knowledge parameters.
One finding of this present study that points into certain
personality groups is that individuals with strong information
elaboration evince lower levels of the NFM. Thus, regardless
of any demographic groupings, individuals who seek to think
and process the information encountered in discussions and in
the news are less likely to believe that “news will find” them.
In fact, previous research has found elaboration to be a crucial
mediator that influences the effect of news media use on politi-
cal behavior and knowledge acquisition (Cho et al., 2009;
Eveland, 2001, 2004)—similar variables that have recently
been proven to be negatively related to the NFM (Gil de
Zúñiga & Diehl, 2019; Gil de Zúńiga et al., 2017).
However, contrary to our expectations, network size
appears to be a negative or no predictor of NFM at all. Thus,
simply controlling for the size of individuals’ networks on
social media and offline does not seem to explain the NFM; it
is rather the active interaction with others and the actual expo-
sure to news on social media that leads people to believe that
they do not have to actively seek the news to stay informed.
Although we found network size to be strongly related to dis-
cussion frequency (offline: r = .48, p < .001 and online: r = .48,
p < .001), there is a limitation regarding the measurement of
network size in this study. We followed discussions that have
suggested to use the summary network size measure (Eveland
et al., 2011), but we might have still faced a measurement
error. Many respondents might have simply indicated the
number of friends they have on social media networks, but not
consciously thought about those they actually talked with
about politics and public affairs in the past month (as indicated
in the question). Future research might therefore benefit from
a control or filter question to rule out false positives.
Eventually, regarding country differences, we have found
evidence that individuals who live in countries with a higher
GDP are less likely to report the NFM. This is also in line
with previous research that has shown that citizens in wealth-
ier countries are more likely to actively read news online
(Mitchell et al., 2018) as well as studies on the individual
level that have provided insights that people with a higher
socio-economic status are more likely to read the news (Van
Eijck & Van Rees, 2000). Interestingly, our findings indicate
that internet connectivity and press freedom are not related to
the NFM. However, the individual country analyses have
shown that there might be more factors at play when investi-
gating country differences regarding the NFM, including
internet and social media penetration, the culture of political
debates and discussions (in person or online) as well as coun-
try-specific political systems and media environments.
In sum, this study has provided a comprehensive over-
view of the individual and country-level antecedents of the
NFM. The perception that news will find one has become a
global phenomenon that is not only fueled by individuals’
tendency to increasingly spent time online, consume news on
social media and discuss current affairs online; the percep-
tion is also associated with countries that have high levels of
social media penetration and lower economic productivity.
Reversely, the ability to elaborate on news and tie new infor-
mation together with things one already knows seem to be a
powerful factor to limit the NFM in today’s ubiquitous infor-
mation environment. Overall, the findings form a theoretical
and empirical basis for future studies that aim at investigat-
ing news use in today’s high-choice media environment.
Declaration of Conflicting Interests
The author(s) 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

18 Social Media + Society
research was supported by Grant FA2386-15-1-0003 from the
Asian Office of Aerospace Research and Development.
Responsibility for the information and views set out in this study
lies entirely with the authors.
ORCID iDs
Nadine Strauß https://orcid.org/0000-0002-5050-7067
Brigitte Huber https://orcid.org/0000-0002-9070-4962
Homero Gil de Zúñiga https://orcid.org/0000-0002-4187-3604
References
Aalberg, T., Blekesaune, A., & Elvestad, E. (2013). Media choice
and informed democracy: Toward increasing news consump-
tion gaps in Europe? The International Journal of Press/poli-
tics, 18(3), 281–303.
American Association of Public Opinion Research (AAPOR).
(2011). Standard definitions: Final dispositions of case codes
and outcome rates for surveys. http://aapor.org/Content/
NavigationMenu/AboutAAPOR/StandardsampEthics/
Standard-Definitions/StandardDefinitions2011.pdf
Andersen, K., & Hopmann, D. (2018). Compensator, amplifier, or
distractor? The moderating role of informal political talk on
the effect of news media use on current-affairs learning among
first-time voters. Political Communication, 35(4), 634–654.
Atske, S., Barthel, M., Stocking, G., & Tamir, C. (2019). 7 facts
about black Americans and the news media. Pew Research
Center. https://www.pewresearch.org/fact-tank/2019/08/07/
facts-about-black-americans-and-the-news-media/
Auxier, B. (2020). Social media continue to be important politi-
cal outlets for Black Americans. Pew Research Center. https://
www.pewresearch.org/fact-tank/2020/12/11/social-media-
continue-to-be-important-political-outlets-for-black-ameri-
cans/
Beam, M. A., & Kosicki, G. M. (2014). Personalized news portals:
Filtering systems and increased news exposure. Journalism &
Mass Communication Quarterly, 91(1), 59–77.
Behling, O., & Law, S. K. (2000). Translating questionnaires and
other research instruments: Problems and solutions. SAGE.
Bergström, A., & Belfrage, M. J. (2018). News in social media.
Digital Journalism, 6(5), 583–598.
Bode, L. (2016). Political news in the news feed: Learning politics
from social media. Mass Communication & Society, 19, 24–48.
Breckenridge, J., Zimbardo, P., & Sweeton, J. (2010). After years
of media coverage, can one more video report trigger heuris-
tic judgments? A national study of American terrorism risk
perceptions. Behavioral Sciences of Terrorism and Political
Aggression, 2(3), 163–178.
Cho, J., Shah, D. V., McLeod, J. M., McLeod, D. M., Scholl, R.
M., & Gotlieb, M. R. (2009). Campaigns, reflection, and delib-
eration: Advancing an O-S-R-O-R model of communication
effects. Communication Theory, 19(1), 66–88.
Druckman, J., Levendusky, M., & McLain, A. (2018). No need to
watch: How the effects of partisan media can spread via inter-
personal discussions. American Journal of Political Science,
62(1), 99–112.
Esser, F. (2013). The emerging paradigm of comparative commu-
nication enquiry: Advancing cross-national research in times
of globalization. International Journal of Communication, 7,
113–128.
Eveland, W. P. (2001). The cognitive mediation model of learning
from news. Communication Research, 28(5), 571–601.
Eveland, W. P. (2004). The effect of political discussion in produc-
ing informed citizens: The roles of information, motivation,
and elaboration. Political Communication, 21(2), 177–193.
Eveland, W. P., & Dunwoody, S. (2002). An investigation of elabo-
ration and selective scanning as mediators of learning from the
web versus print. Journal of Broadcasting & Electronic Media,
46(1), 34–53.
Eveland, W. P., & Hively, M. (2009). Political discussion fre-
quency, network size, and “heterogeneity” of discussion as
predictors of political knowledge and participation. Journal of
Communication, 59(2), 205–224.
Eveland, W. P., Hively, M. H., & Morey, A. C. (2011). What’s
all the hubbub about hubs? Identifying political discussion
network hubs and their characteristics. Paper presented at the
International Communication Association (ICA) 2011, Boston,
MA, United States. http://citation.allacademic.com/meta/
p488854_index.html
Feezell, J. (2018). Agenda setting through social media: The impor-
tance of incidental news exposure and social filtering in the
digital era. Political Research Quarterly, 71(2), 482–494.
Finkel, S. E. (1995). Causal analysis with panel data. SAGE.
Fletcher, R., & Nielsen, R. K. (2018). Are people incidentally
exposed to news on social media? A comparative analysis. New
Media & Society, 20(7), 2450–2468.
Frey, D. (1986). Recent research on selective exposure to informa-
tion. Advances in Experimental Social Psychology, 19, 41–80.
Gil de Zúñiga, H., & Diehl, T. (2019). News finds me perception
and democracy: Effects on political knowledge, political inter-
est, and voting. New Media and Society, 21(6), 1253–1271.
Gil de Zúñiga, H., & Liu, J. (2017). Second screening politics in
the social media sphere: Advancing research on dual screen use
in political communication with evidence from 20 countries.
Journal of Broadcasting & Electronic Media, 61(2), 193–219.
Gil de Zúñiga, H., Strauß, N., & Huber, B. (2020). The prolif-
eration of the „news-finds-me” perception across societies.
International Journal of Communication, 14, 1605–1633.
Gil de Zúñiga, H., Weeks, B., & Ardèvol-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,
22(3), 1–19.
Gottfried, J., & Barthel, M. (2020). Black, Hispanic and white adults
feel the news media misunderstand them, but for very different
reasons. Pew Research Center. https://www.pewresearch.org/
fact-tank/2020/06/25/black-hispanic-and-white-adults-feel-
the-news-media-misunderstand-them-but-for-very-different-
reasons/
Greenberg, D. V. (2008). Causal analysis with nonexperimen-
tal panel data. In M. Scott (Ed.), Handbook of longitudinal
research: Design, measurement, and analysis (pp. 259–278).
Elsevier.
Haapala, T. (2012). Debating societies, the art of rhetoric and the
British House of Commons: Parliamentary Culture of Debate
before and after the 1832 Reform Act. Publica: Revista de
Filosofía Política, 27, 25–35.

Strauß et al. 19
Hox, J. (2002). Multilevel analysis: Techniques and applications.
Lawrence Erlbaum.
ITU & UNESCO. (2020). The state of broadband 2020: Tackling
digital inequalities. A decade for action. https://www.itu.int/dms_
pub/itu-s/opb/pol/S-POL-BROADBAND.21-2020-PDF-E.pdf
Johnson, T. J., Hays, C. E., & Hayes, S. P. (1998). Engaging the
public: How government and the media can reinvigorate
American democracy. Rowman & Littlefield.
Kalogeropoulos, A., & Nielsen, R. K. (2018). Social inequalities in
news consumption. Factsheet October 2018. Reuters Institute
for the Study of Journalism, University of Oxford. https://
reutersinstitute.politics.ox.ac.uk/sites/default/files/2018-10/
Kalogeropolous%20-%20Social%20Inequality%20in%20
News%20FINAL.pdf
Karnowski, V., Kümpel, A. S., Leonhard, L., & Leiner, D. (2017).
From incidental news exposure to news engagement. How
perceptions of the news post and news usage patterns influ-
ence engagement with news articles encountered on Facebook.
Computers in Human Behavior, 76, 42–50.
Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part
played by people in the flow of mass communications. Free Press.
Kim, Y., Chen, H., & Gil de Zúñiga, H. (2013). Stumbling upon
news on the internet: Effect of incidental news exposure and
relative entertainment use on political participation. Computers
in Human Behavior, 29, 2607–2614.
Kleinnijenhuis, J. (2016). Chapter 22: Multilevel regression anal-
ysis. In H. Keman & J. J. Woldendorp (Eds.), Handbook of
research methods and applications in political science (pp.
323–340). Edgar Elward Publishing.
Kononova, A., Zasorina, T., Diveeva, N., Kokoeva, A., &
Chelokyan, A. (2014). Multitasking goes global: Multitasking
with traditional and new electronic media and attention to
media messages among college students in Kuwait, Russia,
and the USA. International Communication Gazette, 76(8),
617–640.
Ksiazek, T. B., Malthouse, E. C., & Webster, J. G. (2010).
News-seekers and avoiders: Exploring patterns of total news
consumption across media and the relationship to civic par-
ticipation. Journal of Broadcasting & Electronic Media, 54(4),
551–568.
Kwak, N., Williams, A., Wang, X., & Lee, H. (2005). Talking poli-
tics and engaging politics: An examination of the interactive
relationships between structural features of political talk and
discussion engagement. Communication Research, 32, 87–111.
Lee, S. (2020). Probing the mechanisms through which social media
erodes political knowledge: The role of the news-finds-me per-
ception. Mass Communication and Society, 23, 810–832.
Livingstone, S. (2003). On the challenges of cross-national com-
parative media research. European Journal of Communication,
18(4), 477–500.
McLeod, J. M., Scheufele, D. A., & Moy, P. (1999). Community,
communication, and participation: The role of mass media
and interpersonal discussion in local political participation.
Political Communication, 16(3), 315–336.
McQuail, D. (2000). McQuail’s mass communication theory. SAGE.
Media Landscapes. (2020). Philippines. https://medialandscapes.
org/country/philippines
Mitchell, A., Simmons, K., Matsa, K. E., & Silver, L. (2018,
January 11). Publics globally want unbiased news coverage,
but are divided on whether their news media deliver. Pew
Research Center. http://www.pewglobal.org/2018/01/11/pub-
lics-globally-want-unbiased-news-coverage-but-are-divided-
on-whether-their-news-media-deliver/
Newman, N., Fletcher, R., Kalogeropoulos, A., Levy, D. A.
L., & Nielsen, R. K. (2018). Reuters Institute digital news
report 2018. http://media.digitalnewsreport.org/wp-content/
uploads/2018/06/digital-news-report-2018.pdf?x89475
Newman, N., Fletcher, R., Kalogeropoulos, A., & Nielsen, R. K.
(2019). Reuters Institute digital news report 2019. https://
reutersinstitute.politics.ox.ac.uk/sites/default/files/inline-files/
DNR_2019_FINAL.pdf
Nisbet, M., & Scheufele, D. (2004). Political talk as a catalyst
for online citizenship. Journalism & Mass Communication
Quarterly, 81(4), 877–896.
Oeldorf-Hirsch, A. (2018). The role of engagement in learning from
active and incidental news exposure on social media. Mass
Communication and Society, 21, 225–247.
Pablo, M. C. (2018). Internet in accessibility plagues “social media
capital of the world.” The Asia Foundation. https://asiafoun-
dation.org/2018/10/24/internet-inaccessibility-plagues-social-
media-capital-of-the-world/
Park, C. S., & Kaye, B. K. (2020). What’s this? Incidental expo-
sure to news on social media, news-finds-me perception, news
efficacy, and news consumption. Mass Communication and
Society, 23(2), 157–180.
Pestin, D. (2011). News on the go: How mobile devices are chang-
ing the world’s information ecosystem. Center for International
Media Assistance and National Endowment for Democracy.
Prior, M. (2007). Post-broadcast democracy: How media choice
increases inequality in Political involvement and polarizes
elections. Cambridge University Press.
Riddle, K. (2010). Always on my mind: Exploring how frequent,
recent, and vivid television portrayals are used in the forma-
tion of social reality judgments. Media Psychology, 13(2),
155–179.
Shearer, S. (2021). More than eight-in-ten Americans get news
from digital devised. Pew Research Center. https://www.
pewresearch.org/fact-tank/2021/01/12/more-than-eight-in-ten-
americans-get-news-from-digital-devices/
Shearer, S., & Mitchell, A. (2021). News use across social media
platforms in 2020. Pew Research Center. https://www.journal-
ism.org/2021/01/12/news-use-across-social-media-platforms-
in-2020/
Shehata, A., Hopmann, D. M., Nord, L., & Höijer, J. (2015).
Television channel content profiles and differential knowledge
growth: A test of the inadvertent learning hypothesis using
panel data. Political Communication, 32, 377–395.
Sheller, M. (2015). News now. Interface, ambience, flow, and
the disruptive Spatio-temporalities of mobile news media.
Journalism Studies, 16(1), 12–26.
Shirish, A., Srivastava, S. C., & Chandra, S. (2021). Impact of mobile
connectivity and freedom on fake news propensity during the
COVID-19 pandemic: A cross-country empirical examination.
European Journal of Information Systems, 30(3), 322–341.
Sjöberg, L., & Engelberg, E. (2009). Attitudes to economic risk
taking, sensation seeking and values of business students
specializing in finance. Journal of Behavioral Finance,
10(1), 33–43.
Song, H., Gil de Zúñiga, H., & Boomgaarden, H. (2020). Social
media news use and political cynicism: Differential pathways

20 Social Media + Society
through “news finds me” perception. Mass Communication
and Society, 23(1), 47–70.
Sotirovic, M., & McLeod, J. M. (2004). Knowledge as understand-
ing: The information processing approach to political learn-
ing. In L. Kaid (Ed.), Handbook of political communication
research (pp. 357–394). Erlbaum.
Stroud, N. J., Peacock, C., & Curry, A. L. (2019). The effects of
mobile push notifications on news consumption and learning.
Digital Journalism, 8, 32–48.
Swart, J., Peters, C., & Broersma, M. (2019). Sharing and discuss-
ing news in private social media groups. Digital Journalism,
7(2), 187–205.
Tewksbury, D., Weaver, A. J., & Maddex, B. D. (2001).
Accidentally informed: Incidental news exposure on the World
Wide Web. Journalism and Mass Communication Quarterly,
78(3), 533–554.
Thompson, E. (2010). Mind in life. Biology, phenomenology, and
the sciences of mind. Harvard University Press.
Toff, B., & Palmer, R. A. (2018). Explaining the gender gap in news
avoidance: “News-is-for-men” perceptions and the burdens of
caretaking. Journalism Studies, 20, 1563–1579.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for
judging frequency and probability. Cognitive Psychology, 5,
207–232.
Valeriani, A., & Vaccari, C. (2016). Accidental exposure to politics
on social media as online participation equalizer in Germany,
Italy, and the United Kingdom. New Media & Society, 18(9),
1857–1874.
Van Eijck, K., & Van Rees, K. (2000). Media orientation and
media use: Television viewing behavior of specific reader
types from 1975 to 1995. Communication Research, 27(5),
574–616.
Verclas, K., & Mechael, P. (2008). A mobile voice: The use of
mobile phones in citizen media. http://www.columbia.edu/
itc/sipa/nelson/inafu6212-001-2012-3/mainSpace/files/A_
Mobile_Voice.pdf
Von Krogh, T., & Andersson, U. (2016). Reading patterns in
print and online newspapers. Digital Journalism, 4(8),
1058–1072.
Walsh, K. C. (2004). Talking about Politics. Informal groups
and social identity in American life. University of Chicago
Press.
Wei, R., Lo, V., Xu, X., Chen, Y., & Zhang, G. (2014). Predicting
mobile news use among college students: The role of press
freedom in four Asian cities. New Media & Society, 16(4),
637–654.
The World Bank. (2018). Individuals using the Internet (% of pop-
ulation). https://data.worldbank.org/indicator/IT.NET.USER.
ZS
Zhang, Y., He, D., & Sang, Y. (2013). Facebook as a platform for
health information and communication: A case study of a dia-
betes group. Journal of Medical Systems, 37(3), 1–12.
Author Biographies
Nadine Strauß (PhD, University of Amsterdam) is an Assistant
Professor of Strategic Communication and Media Management at
the University of Zurich, Department of Communication and
Media Research. Previously, she worked as a Postdoc at the
University of Vienna and as a Marie Sklodowska–Curie research
fellow at the University of Oxford. Her research interests include
journalism studies, (online) news use and financial, and sustain-
able communication.
Brigitte Huber (PhD, University of Vienna) is a Postdoc at the
Department of Communication at the University of Vienna. After
finishing her PhD, she worked at the Department of
Communication Science and Media Research at the Ludwig
Maximilian University of Munich. Her research interests include
political communication, journalism studies, science communi-
cation, and social media.
Homero Gil de Zúñiga (PhD, Universidad Europea de Madrid and
University of Wisconsin) serves as Distinguished Research
Professor at University of Salamanca where he directs the
Democracy Research Unit (DRU), as Professor at Pennsylvania
State University, and as Senior Research Fellow at Universidad
Diego Portales, Chile. His research addresses the influence of new
technologies and digital media over people’s daily lives, as well as
the effect of such use on the overall democratic process.
Appendix
Survey Items
1. Age: How old are you?
18–22
23–35
36–55
56 or older
2. Gender: What is your gender?
Male
Female
Other
3. Education: What is the highest level of education
you have completed?
Less than high school
High school
Some college
Bachelor’s degree
Some graduate education
Professional certificate
Master’s degree
Doctoral degree
4. Income: Last year, what was your family’s total
household income, before taxes?
US$0–14,999
US$15,000–24,999
US$25,000–49,999
US$50,000–99,999
US$100,000–149,999
US$150,000–199,999
US$200,000 or more

Strauß et al. 21
5. Race: What is your race or ethnicity?
Black or African American
White or Caucasian
Hispanic or Latino
Asian or Asian American
Native American
Other
6. News Finds Me Perception
The way people get their news may have changed
because of social networking sites and digital media.
Please tell us how much you agree or disagree with
the following statements (1 = disagree completely
and 7 = agree completely).
I rely on my friends to tell me what’s important
when news happens.
I can be well-informed even when I don’t actively
follow the news.
I don’t worry about keeping up with the news
because I know news will find me.
I rely on information from my friends based on
what they like or follow through social media.
7. Information Elaboration (1 = disagree completely
and 7 = agree completely)
I often find myself thinking about my conversa-
tions with other people about politics and public
affairs after the discussion has ended.
I often think about how my conversations with
other people about politics and public affairs
relate to other things I know.
I often think about what I’ve encountered in
the news, and tie it together with my own
ideas.
I often think about how the news I encountered
relates to other things I know.