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.

Keywords

News Finds Me perception incidental news exposure new use online behavior multi-level cross-country

In today’s high-choice media environment, people are constantly 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 following the news” (Gil de Zúñiga et al., 2017, p. 3).

Previous research on unintended news exposure has predominantly focused on the reported behavior of individuals coming across news although not intentionally seeking it (Karnowski et al., 2017; Oeldorf-Hirsch, 2018). This concept, 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 participation 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 constantly 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 concept—it is equally relevant to find out about the driving forces that explain the manifestation of this particular perception among people. That way, this study aims at making a theoretical and empirical contribution to the conceptual 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 phenomenon 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 interaction with peers and other social connections, and (c) their perception 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 emergence 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 judgmental heuristic that posits that individuals evaluate the probability 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, analyses 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 perception 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 individuals, 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, perceptions 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 pursuit of offering both a theoretical and empirical account for the NFM, we study both individuals’ predispositions (e.g., demographics, daily media behavior, and attitudes) as well as their indirect structural influences and interactions with the environment (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 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 prefer 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 consumption in the United Kingdom reported that social inequality in online news consumption is greater than in offline news consumption, 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 adolescents 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 individuals 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 individuals’ tendency to discuss and elaborate on (virtual) conversations 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 information stored in memory, including prior knowledge, personal 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 consume 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 consumption 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 factors 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 knowledge 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.

Network Size

Another central aspect of the NFM is that people 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 reliance 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 relationships, we expect:

H6. Individuals’ political discussion network size is positively 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. Informal 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 rationale behind is that personal conversation is generally perceived more trustworthy, authentic, and closer when compared with mass media messages (Katz & Lazarsfeld, 1955). In fact, a wide range of research has shown that political talk can increase political knowledge (Nisbet & Scheufele, 2004), strengthen political polarization (Druckman et al., 2018), and potentially mitigate knowledge gaps (e.g., Andersen & Hopmann, 2018). Hence, arguing based on the findings on political talks—both off- and online—and following the rationale of the NFM, we hypothesize:

H7. Political discussion frequency (offline/online) is positively 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 billion 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 sharing 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 information 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 country-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 internet and in countries in which there are more broadband accesses registered, the NFM might be more prevalent. Our first hypothesis 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 individuals 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?

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 uncensored 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 levels of democratic development, economic growth, and cultural 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 polling 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 countries 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 techniques, response rates are not calculated, but rather cooperation 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 information on the country samples, the demographics per country, and comparative data with respect to each country’s census information, please refer to Gil de Zúñiga and Liu (2017).

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
Country	Press freedom	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
Country	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	Total Cases
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.

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 appendix for the survey items, and Figure 1 for the NFM scores in each country.

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
Country	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.

Demographics

Across the 18 countries, we asked respondents for a range of socio-demographic data. Age was measured 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 capture respondents’ education, we asked about the highest level of education they have completed, ranging from “Elementary 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 percentile; 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 category “Majority” in the respective country.

Information Elaboration

To measure information elaboration, we asked respondents to indicate their level of agreement 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 frequently they get news from social media and (b) how frequently 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 indicate how often (1 = never and 7 = always) they get news from (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 version), and (c) radio. The three items were averaged (Cronbach’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 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 indicate 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 information 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 following 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 (skewness = 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 acceptable 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; kurtosis = 134.10). The recoded variable shows an improved distribution (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 completely) with the following two statements: (1) I am a member 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 coefficient = .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 Taiwan: data from Akamai), indicating the percentage of broadband 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 (NFM_W1/W2) was rejected. The interclass correlation coefficient is .16 (NFM_W1) and .20 (NFM_W2), respectively, 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 different scales (e.g., GDP vs daily social media use), we centered all variables before running our analyses.

Furthermore, we also investigated possible causal relationships by constructing a lagged multi-level model (NFM_W2 as dependent variable) and the so-called autoregressive model (NFM_W2 as dependent variable and NFM_W1 as independent 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 survey 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-sectional data are not suited to establish causality, autoregressive 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 testing 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 autoregressive models, the value of ICC = .20. That is, 20% of the variance is explained by country differences.

Table 3.

Structural Influences on the NFM Perception.

	“News Finds Me” perception
	Cross-sectional^W1	Lagged^W2	Autoregressive^W2
	B (SE)	B (SE)	B (SE)
Fixed parts
Intercept	−.31*** (.06)	−.08(.06)	.07 (.04)
Block 1: autoregressive term
NFM^W1	–	–	.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
σ ²	.70	.71	.56
τ _{00, country}	.05	.05	.01
N _country	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.

The first research question asked how demographic characteristics 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 autoregressive model (B = −.04, p < .01), suggesting a causal relationship. 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 questions inquired how macro-level factors (internet connectivity, 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 associated 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 countries 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.

Table 4.

Structural Influences on the “News Finds Me” Perception per Country.

Argentina Key variables	Cross-sectional^W1 β	Lagged^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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
Estonia Key variables	Cross-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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
Italy Key variables	Cross-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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
Philippines Key variables	Cross-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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
Taiwan Key variables	Cross-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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
Ukraine Key variables	Cross-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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-sectional^W1 β	Lagged^W2 β	Autoregressive^W2 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.

Most strikingly are the high coefficients in Ukraine. Here, INE seems to have a particular strong effect on the manifestation of the NFM, while traditional news use strongly mitigates 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 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 proximate social networks. This finding, however, stands in direct contrast to the positive relationship between information elaboration and the NFM in Turkey. One possible explanation 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 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 influences 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 consideration 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 perception on democratic behavior (e.g., political knowledge, participation, 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 information 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 consumption on social media. Furthermore, the data of this study from 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 developments 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 assumptions 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 newspaper 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 perception 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 political 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 exposure 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 discussion 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 wealthier 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 investigating 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 country-specific political systems and media environments.

In sum, this study has provided a comprehensive overview 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 perception 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 information together with things one already knows seem to be a powerful factor to limit the NFM in today’s ubiquitous information environment. Overall, the findings form a theoretical and empirical basis for future studies that aim at investigating news use in today’s high-choice media environment.

Footnotes

Appendix

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 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

Brigitte Huber

Homero Gil de Zúñiga

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 sustainable 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 communication, 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.

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