793427
research-article2018
SCXXXX10.1177/1075547018793427Science CommunicationWalter et al.
Research Note
Communication
Ecologies: Analyzing
Adoption of False Beliefs
in an Information-Rich
Environment
Science Communication
2018, Vol. 40(5) 650­–668
© The Author(s) 2018
Article reuse guidelines:
sagepub.com/journals-permissions
https://doi.org/10.1177/1075547018793427
DOI: 10.1177/1075547018793427
journals.sagepub.com/home/scx
Nathan Walter1, Sandra J. Ball-Rokeach2,
Yu Xu2, and Garrett M. Broad3
Abstract
The continued fragmentation of information and the proliferation of
communication resources necessitate a shift toward perspectives that situate
communication practices in a multilevel ecosystem. The current article
offers a method to map and analyze communication ecologies—defined as
the networks of communication connections that individuals depend on in
order to construct knowledge and achieve goals—as social networks. To
demonstrate the potential of communication ecologies as an analytical tool
in science communication, we report on the results of a feasibility study
(N = 654) in the context of climate science and vaccine safety. The article
discusses the theoretical and practical implications of the communication
ecology approach.
Keywords
misinformation, climate science, vaccine safety, communication ecologies
1Northwestern
University School of Communication, Evanston, IL, USA
of Southern California, Los Angeles, CA, USA
3Fordham University, Bronx, NY, USA
2University
Corresponding Author:
Nathan Walter, Northwestern University School of Communication, 70 Arts Cir Dr,
Evanston, IL 60208, USA.
Email: nathanw@usc.edu
Walter et al.
651
Many educated and sensible individuals hold erroneous beliefs that have
direct consequences for their support of problematic political actions and
public policies (Hochschild & Einstein, 2015). Even an overwhelming scientific consensus regarding issues such as climate change and vaccine safety
has done little to tame the ever-growing flow of misinformation. Given the
centrality of misinformation in public discourse and its presumed effects on
political behavior, one might assume that the literature can reassuringly point
to sociodemographic and communication-related antecedents that predict
adoption of misinformation. Yet questions pertaining to the relationship
between exposure to political communication and adoption of misinformation remain largely unanswered (Hart, Nisbet, & Myers, 2015), a gap that is
not merely a product of the complexities introduced by recent social and
technological shifts. It appears that a significant part of this gap stems from
the questionable validity of current methods and measures adopted to adequately capture the multilevel and dynamic nature of media exposure in a
changing environment. As argued by de Vreese and Neijens (2016), “Today’s
media landscape, in which individuals are exposed to a diversity of messages
anytime, anywhere, and from a great variety of sources . . . has complicated
the measurement of media exposure” (p. 69). The increasing polarization of
the American public further complicates this situation, as the growing variety
of information is not directly translated into more informed beliefs, with people being increasingly exposed to idea-confirming streams of content, often
without assessing their quality (Bennett & Iyengar, 2008; Webster & Ksiazek,
2012).The current study attempts to clarify the relationship between exposure
to information resources and political outcomes (i.e., climate science and
vaccine safety) by advancing a multilevel and ecological method to measure
and analyze science communication in an information-rich environment.
Misinformation in the United States
Almost 170 years ago, Tocqueville observed that the American political system largely relies on the knowledge and education of its electorate, arguing
that “it cannot be doubted that in the United States the instruction of the
people powerfully contributes to support the democratic republic” (de
Tocqueville, 1848/1966, p. 279). In direct contrast to this utopian view, studies find that half of the American public consistently endorses at least one
conspiracy theory (Oliver & Wood, 2014) and that when tested on knowledge
of specific policies only 3% get more than half the facts right (Kuklinski,
Quirk, Jerit, Schwieder, & Rich, 2000).
Scientific topics are rife with conspiratorial thinking and misinformation,
perhaps accentuated by the fact that the U.S. public has become increasingly
polarized in their attitudes toward science-related issues such as climate
652
Science Communication 40(5)
change and vaccination (Pew Research Center, 2017a, 2017b). It is often suggested that Conservatives—unlike Liberals or Moderates–have become skeptical and distrustful of science (Gauchat, 2012). This is especially evident with
respect to climate science, as people who embrace a laissez-faire vision of the
free market are less likely to accept that anthropogenic greenhouse gas emissions are warming the planet than people with an egalitarian-communitarian
outlook (Lewandowsky, Gignac, & Oberauer, 2013; Yale Climate Change
Communication, 2016). With that said, if misinformation associated with climate science is ascribed to the political right, then erroneous information
about vaccine safety is a bipartisan phenomenon (Lewandowsky et al., 2013).
Although misinformation about vaccine safety has existed since the dawn of
vaccines (Myers & Pineda, 2009), it has exponentially increased after the
Lancet published (and then retracted) an infamous paper proposing a connection between receipt of the measles-mumps-rubella (MMR) vaccine and
autism (Poland & Spier, 2010). Furthermore, vaccine-related misinformation
is effectively disseminated on social media and presented as facts by prominent individuals from both sides of the political aisle (Myers & Pineda, 2009).
Anecdotally, during the 2016 U.S. presidential elections, the two candidates
who vocally embraced myths about vaccines were supported by the far left
(i.e., Jill Stein) and the far right (i.e., Donald Trump).
Interestingly, adoption of misinformation and its imperviousness to correction is not simply a matter of a lack of education or interest. Instead, the phenomenon seems to increase with greater levels education, science literacy, and
issue-specific knowledge (Ahern, Connolly-Ahern, & Hoewe, 2016; Hamilton,
2011; Kahan, Peters, Dawson, Slovic, 2017; Kahan et al., 2012), suggesting
that holding incorrect beliefs reflects acceptance of alternative information
rather than an outright deficit of knowledge or ability. In the past, consumption
of media coverage was used as the chief, and often only, predictor of beliefs in
global warming. For instance, Feldman, Maibach, Roser-Renouf, & Leiserowitz
(2012) argued that there is a strong relationship between cable news viewership
and perceptions of global warming, as viewership of Fox News was negatively
correlated with global warming acceptance. While establishing a link between
cable news viewership and beliefs about science is an important task, such findings also point to at least two vital unanswered questions.
First, it is unclear whether mainstream media is the only information
resource that shapes people’s opinions about science. In fact, the lack of evidence regarding the link between exposure to mainstream media and people’s
opinions about other controversial scientific issues, such as vaccination, might
suggest that additional microlevel (e.g., sociodemographic characteristics)
and mesolevel (e.g., local newspapers) resources play a critical role in shaping
individuals’ worldviews. For example, resistance to vaccination has been
Walter et al.
653
associated with distinct geographical pockets—usually rural and wealthy
enclaves—which points to the possibility that mesolevel information resources
can be more important than macrolevel resources, such as cable news.
Second, it is important to examine whether adoption of misinformation is
linked to particular information platforms or whether several different
resources can mutually contribute to the adoption of incorrect beliefs. For
instance, daily viewing of cable news is, perhaps, not sufficient to encourage
skepticism toward vaccine safety; however, when this behavior occurs in tandem with exposure to a local newspaper that gives voice to vaccine deniers
and weekly visits to a church with a large number of vaccine skeptics, then
people might gradually become skeptics themselves. This suggests that exposure to science information should be evaluated through an ecological lens,
enabling us to examine the interplay between different information resources
and their mutual influence on relevant outcomes.
Conceptualizing Communication Ecology as a Method
Drawing on insights from media system dependency and communication
infrastructure theory, the communication ecology approach suggests that if
we are to understand how individuals obtain misinformation, we must cast a
much wider net (Wilkin & Ball-Rokeach, 2011). A communication ecology
has been defined as “a network of communication resource relations constructed by individuals in pursuit of a goal and in context of their communication environment” (Ball-Rokeach, Gonzalez, Son, & Kligler-Vilenchik,
2012, p. 4). Rather than focusing on a single source of media exposure or
interpersonal interaction, a communication ecology represents the “multimodal communication connections, shaped by particular social and cultural
conditions, that are actually employed by an individual as a means to construct knowledge and to achieve goals” (Broad et al., 2013, p. 328). As a
multilevel network, an individual’s communication ecology can include
communication resources that cut across the micro- (based in sociodemographic characteristics and interpersonal connections), meso- (including
local media and community-based organizations or institutions), and macro(e.g., mainstream legacy media) levels. Guided by this individual construct,
the communication ecology approach, therefore, “explores how individuals
actively select from the web of available communication sources to achieve
goals in their everyday lives” (Wilkin, 2013, p. 189).
Unlike past approaches to media exposure in the media effects tradition,
the communication ecology approach acknowledges the fact that information
resources do not exist in a vacuum but rather that they are interdependent
(Altheide, 1995). Moreover, by analyzing the relative importance and balance
between different media sources for an individual’s knowledge construction
654
Science Communication 40(5)
practices, communication ecologies not only provide a way out of the quagmire of media exposure, but they also enable us to determine which combinations of resources lead to desired (or undesired) outcomes. Essentially, the
distinction between traditional measures of media exposure and the communication ecology approach is analogous to the distinction between cause and
effect. That is to stay, instead of asking what are the consequences of being a
heavy viewer or being exposed to a specific message, the communication
ecology approach adopts an alternative perspective by asking what types of
outcomes can be traced back to differences in an individual’s constructed
media environments? In the following section, we propose a network approach
to measure and analyze the effects of different types of individual communication ecologies on misinformed scientific beliefs.
Operationalizing Communication Ecologies as Networks
Communication ecologies are best understood and conceptualized as social
networks (Broad et al., 2013; Friedland, 2016). After all, ecology deals with
the relations of organisms to one another and to their physical environment;
thus, social networks are implied in this definition (Literat & Chen, 2014).
Analogous to other social networks, information resources can be analyzed
within a broader communication ecology that extends beyond a particular
individual and allows for comparisons between individuals within that ecological context (Houston et al., 2015; Wilkin, Ball-Rokeach, Matsaganis &
Cheong, 2007). For instance, if person X begins her morning by reading the
Economist, while person Y enjoys drinking her morning coffee with Fox &
Friends, our ability to infer their beliefs on various topics is limited. However,
if we also learn that person X reads The Huffington Post and donates money
to Planned Parenthood, whereas person Y streams conservative talk radio and
is a member of the Pro-Life Action League, the likelihood of accurately positioning them on various issues substantially increases. As this example illustrates, different communication resources are intersecting as they operate
within the same ecological system, such that the ability to link these diverse
sources together allows analysts to create a more complete information
profile.
Another important methodological advancement is related to the multilevel
nature of information ecosystems. Specifically, communication ecologies operate on three different levels: micro, meso, and macro. Each level is associated
with unique information resources, such that mainstream media operate at the
macrolevel, ethnic-local media and local organizations are situated at the mesolevel, and the microlevel contains interpersonal and intrapersonal characteristics. To this end, a fundamental aspect of the communication ecology approach
is its ability to examine interactions between different levels of analysis. For
Walter et al.
655
instance, recent climate opinion maps indicate that political ideology is not the
only predictor of global warming acceptance. In fact, geographical location can
sometimes be used to predict whether people think that global warming is happening. Even in a conservative state such as Texas, people who live near the
coastline tend to accept global warming at much higher rates (~75%) than those
who live in Central Texas (~55%) (Yale Climate Change Communication,
2016). This is not particularly surprising if one recognizes that the macrolevel
impacts of conservative media can be moderated by the micro- and mesolevel
discussions that predominate the communication environment of a coastal area
in which climate risk is more visible.
To date, the communication ecology approach has been employed in a
variety of empirical and applied contexts (Broad et al., 2013; Houston et al.,
2015). However, despite its fundamentally networked nature, previous
research has not actually integrated the concept with network science. In an
effort to empirically assess the feasibility of the communication ecology
approach for understanding knowledge construction and scientific beliefs,
we propose the following research questions:
Research Question 1: Do mesolevel and macrolevel communication ecologies, along with microlevel characteristics (e.g., sociodemographic variables) predict beliefs in (a) climate science and (b) vaccine safety?
Research Question 2: Are there interactions between the mesolevel and
the macrolevel communication ecologies regarding beliefs in (a) climate
science and (b) vaccine safety?
Method
Participants and Procedure
To answer our research questions, we conducted an online survey among 654
U.S. adults.1 Respondents were recruited via Qualtrics Panels and compensated for their time. The average participant was 49.07 (standard deviation
[SD] = 15.91) years old with 13.98 (SD = 3.04) years of schooling. In terms
of gender, the majority of the sample identified as female (77.2%), and 82%
identified as non-Hispanic White (82%), followed by Black (8.9%), Hispanic
(5.4%), and Asian (1.7%). The most common religious affiliation was
Catholic (24.8%), Christian other2 (22.2%), Protestant (21.4%), Unaffiliated
(13.3%), Atheist (7.2%), and Jewish (2.1%). After respondents consented to
participate in the study, they were instructed to carefully read the questions
and respond to the best of their ability. The questionnaire was divided into
three parts. First, the items assessed respondents’ beliefs in climate science
656
Science Communication 40(5)
and vaccine safety. The second part of the questionnaire was dedicated to
measurements of meso- and macrolevel communication ecologies. The last
part instructed participants to respond on a battery of sociodemographic
measures.
Measures
The scales used to gauge respondents’ beliefs were based on five-item scales
provided in Lewandowsky et al. (2013). All the dependent variables were
assessed with 7-point Likert scale–type answer options, where 1 = strongly
disagree and 7 = strongly agree. The specific items included “humans are
too insignificant to have an appreciable impact on global temperature” (climate science; mean [M] = 3.47, SD = 1.40; α = .79) and “the risk of vaccinations to maim and kill children outweighs their health benefits” (vaccine
safety; M = 2.97, SD = 1.37; α = .80). Both scales were coded such that
higher scores indicated higher levels of misinformation. In addition, the
questionnaire measured respondents’ political ideology with a seven-item
scale adopted from Lewandowsky et al. (2013), where higher scores indicated increased support for liberal ideology (M = 3.76, SD = 1.33; α = .82).
In particular, the items included “on balance, I lean politically more to the left
than to the right” and “I cannot see myself ever voting to elect conservative
candidates.” Furthermore, given its affinity to the concept of misinformation,
we also included a 5-point scale of conspiratorial mind-set (M = 3.28, SD =
1.64; α = .89). The specific items included “the Apollo moon landings never
happened and were staged in a Hollywood film studio” and “the assassination
of John F. Kennedy was not committed by the lone gunman Lee Harvey
Oswald but was rather a detailed organized conspiracy to kill the President.”
Mesolevel communication ecology was measured by providing a list of
different categories of mesolevel resources and asking respondents whether
they are affiliated or exposed to any of these resources. The specific categories were based on a list of the most popular types of groups and organizations in America (Pew Research Center, 2011), as well as a list of the most
popular local news sources in America (Pew Research Center, 2016).
Mesolevel resources included six different categories of local groups (i.e.,
recreational groups, cultural/ethnic groups, religious groups, neighborhood
groups, political groups, and educational groups) and three categories of
media resources (i.e., local TV stations, local radio stations, and local newspapers). This resulted in a bimodal network that included two different types
of nodes (people and mesolevel resources). Essentially, this is an affiliation
network that linked people with mesolevel resources. Next, we transformed
the bimodal networks into unimodal networks that constituted the extent to
Walter et al.
657
which two individuals share similar mesolevel resources. Specifically, the
self-reported results of the mesolevel communication ecologies were transformed into matrices that estimated the extent to which the 654 respondents
in our study shared mesolevel resources. For example, if respondent X and
respondent Y were both exposed to local TV and local radio, their mutual
communication ecology score was 2, and if respondent X and respondent Z
shared five different mesolevel resources, their mutual score was 5.
The macrolevel ecology was gauged by providing respondents with a list
of seven macrolevel resources (i.e., websites/apps, social networking sites,
national network TV stations, national cable TV stations, national radio stations, national print newspapers, and national magazines) and asking them to
choose the ones that are relevant to them.3 While we recognize that the category of social networking sites contains information resources that cut across
the micro, meso, and macrolevels, its inclusion as a macrolevel resource is
consistent with previous research and takes into account the major role played
by mainstream media in setting the agenda for social media conversations
(Feezell, 2018; Harder, Sevenans, & Van Aelst, 2017). Later, as with the
mesolevel ecology, the resulting affiliation network was transformed into a
unimodal network, where respondents were linked to other respondents
through their shared resources. Finally, we assessed the political orientation
of the communication ecologies (i.e., meso and macro) by asking respondents to rate the overall political orientation of each level of analysis.
Specifically, at the mesolevel (M = 5.32, SD = 2.38) respondents were
asked, “Thinking about the local groups and organizations that you just listed,
how would you rate their overall political orientation, from conservative (1)
to liberal (10)?” Analogously, at the macrolevel (M = 5.65, SD = 2.24)
respondents were asked, “Thinking about the media sources that you just
listed, how would you rate their overall political orientate, from conservative
(1) to liberal (10)?”
Analysis
After constructing and transforming the networks with UCINET 6 (Borgatti,
Everett, & Freeman, 2002), we used the Quadratic Assignment Procedure
(QAP; Double Dekker Semi-Partialling MRQAP) to predict beliefs in climate science and vaccine safety as a function of communication ecologies, as
well as other relevant variables, including ethnicity, religion, gender, political
ideology, conspiratorial mind-set, income, education, and age. Though the
QAP regression shares many of the characteristics associated with simple
linear regressions, it also accounts for the fact that observations are interdependent, and the analysis uses a matrix-structured data (Krackhardt, 1988).
658
Science Communication 40(5)
Finally, in order to probe the role played by specific resources, post hoc tests
were conducted with independent samples t tests and two-way analyses of
variances.
Results
To assess the ability of communication ecologies and sociodemographic
characteristics to predict beliefs in climate science, we generated 2,000 permutations of the 654 × 654 matrix. Thus, significance is determined by comparing the properties of the observed network with randomly simulated
networks that share the same number of nodes. The unit of analysis for the
QAP regression was the dyad (i.e., a pair of respondents). The test statistics
compared the mean of the difference in the absolute value of the attribute
with the mean of the difference between randomly generated pairs. For
instance, considering the fact that the education matrix represents the absolute difference in years of schooling between every possible pair of respondents, a value of 0.25 for education would mean that, on average, each year
of schooling brings climate science–associated beliefs closer among respondents by 0.25 points. While usually positive coefficients are indicative of
greater similarity in outcomes and negative coefficients correspond with dissimilarity, this is somewhat different for the measurements of communication
ecologies. Because the matrix of the mesolevel and the macrolevel communication ecologies was computed as the sum of shared resources (as opposed
to variables that represent an absolute difference, e.g., education), negative
coefficients would indicate more similar beliefs among members of a dyad.
Put differently, negative coefficients suggest that increase in similarity of
communication ecologies (i.e., additional shared resources) results in less
difference in relevant attributes (e.g., more similar beliefs in climate science).
Thus, for example, a −0.50 value for macrolevel communication ecology
means that, on average, each shared macrolevel resource brings beliefs in
climate science closer by 0.50 points. Following this logic, shared communication ecologies (as expressed in high levels of homophily within dyads) are
expected to predict a decrease in absolute differences in outcomes; thus, in
this case, negative coefficients are desirable.
As indicated in Table 1, the significant sociodemographic predictors of
beliefs regarding climate science were political ideology (b = .38, p = .005)
and education (b = −.12, p = .008). Specifically, the results suggested that,
on average, possessing a more conservative ideology was associated with an
increase in misconceptions regarding the role played by humans in climate
change (r = .56, p = .005). In addition, the negative coefficient of level of
education indicates that an increase in years of schooling was associated with
659
Walter et al.
Table 1. Quadratic Assignment Procedure (QAP) Regression for Beliefs in
Climate Science by Sociodemographic Variables and Communication Ecologies.
Predictor
Intercept
Ethnicity
White
Black
Hispanic
Religion
Catholic
Protestant
Christian (other)
Unaffiliated
Atheist
Gender
Female
Political ideology
Conspiratorial mind-set
Income
Education
Age
Meso ecology
Macro ecology
Meso politics
Macro politics
Meso ecology × meso politics
Macro ecology × macro politics
Meso ecology × Macro ecology
R2
Block 1, b
Block 2, b
Block 3, b
Block 4, b
1.02
1.14
1.08
1.07
−.05
−.001
.02
.05
−.001
.01
.04
−.001
.01
.04
−.002
.01
−.08
−.03
−.05
.02
−.07*
−.07
−.03
−.05
.02
−.07
−.08
−.03
−.05
.02
−.06
−.08
−.03
−.06
.01
−.06
−.04
.37***
−.001
.006
−.12*
.004
−.04
.38***
.001
.007
−.12*
.001
−.04
−.04*
.06*
.01
−.04
.38***
.002
.007
−.12*
.001
−.07*
−.10*
.06**
.06*
−.001
−.18*
.13***
14***
–.04
.38***
.001
.007
−.12**
.001
−.08*
−.13**
.06*
.06**
−.003
−.18*
.001
.19***
.15***
Note. Small categories for ethnicity (e.g., Asian) and religion (e.g., Jew) were included in the
model but they are not represented in the table.
*p < .05. **p < .01. ***p < .001 (two-tailed tests).
bigger gaps in beliefs regarding climate science, such that the beliefs of more
educated individuals were more polarized than those of their less educated
counterparts. In particular, each additional year of schooling increased the
gap in beliefs by .12 points. Furthermore, the results recorded a significant
effect of the mesolevel ecology (b = −.08, p = .02) and the macrolevel ecology (b = −.13, p = .007). Overall, the QAP regression model was able to
explain 19% of the total variance associated with beliefs in climate science.
660
Science Communication 40(5)
To examine what specific mesolevel resources had an influence on beliefs
in climate science, we conducted a series of post hoc t tests with various
resources as grouping variables and climate science beliefs as outcomes. As
suggested by the post hoc tests, beliefs in climate science were significantly
associated with exposure to local TV t(652) = 2.24, p = .025, local newspapers t(652) = 2.69, p = .007, affiliation with local religious groups t(652) =
1.99, p = .048, and affiliation with local political groups t(652) = 3.91, p =
.001. Interestingly, while exposure to local TV (M = 3.38, SD = 1.37 vs. M
= 3.86, SD = 1.46), local newspapers (M = 3.29, SD = 1.36 vs. M = 3.69,
SD = 1.42), and affiliation with local political groups (M = 2.92, SD = 1.25
vs. M = 3.53, SD = 1.41) tended to reduce misinformation, affiliation with
local religious groups (M = 3.58, SD = 1.38 vs. M = 3.26, SD = 1.43) was
associated with adoption of incorrect beliefs. Keeping in mind that macrolevel resources of information significantly interacted with political orientation, the post hoc tests for this group of variables were conducted with a
two-way analysis of variances, treating communication resource as a fixedfactor, median-split political orientation as a random-factor, and climate science beliefs as an outcome. The results suggested that the only significant
interaction was between political orientation and exposure to cable
TV—F(1,650) = 4.80, p = .03. Specifically, conservatives who included
cable TV in their political diet (M = 3.79, SD = 1.49) were significantly
more likely to adopt misinformed beliefs, compared with liberals who
watched cable TV (M = 3.07, SD = 1.23) and conservatives or liberals who
did not watch cable TV (M = 3.45, SD = 1.37; M = 3.33, SD = 1.41, respectively). Figure 1 outlines the main findings with respect to the sociodemographic and the communication variables that predict adoption of
misinformation in the context of climate science.
With regard to misinformation in the context of vaccine safety, religious
affiliations, including Catholic (b = −.06, p = .02), Protestant (b = −.08, p
= .01), and other Christians (b = −.10, p = .009), as well as having a conspiratorial mind-set (b = .26, p = .001) were significantly associated with
misinformation (Table 2). Individuals who identified as Protestants,
Catholics, or other Christians were more likely to perceive vaccines as being
unsafe compared with atheists/agnostics or unaffiliated respondents
(Mdifference = 0.82, 0.52, and 0.61, respectively, p < .01). Furthermore, having a conspiratorial mind-set was also associated with an increase in concerns over vaccine safety (r = .11, p = .01). Although there were no
significant interactions among different levels of analysis, the mesolevel
ecology (b = −.13) was a significant predictor of beliefs in vaccine safety.
In total, the regression model accounted for 9% of the variance in beliefs
associated with vaccine safety.
Walter et al.
661
Figure 1. Communication ecology for resources significantly predicting climate
science misinformation.
Probing the results with independent samples t tests indicated that exposure to local newspapers t(652) = 3.32, p = .001 and affiliation with local
political groups t(652) = 3.06, p = .003, were significant predictors of concerns over vaccine safety. In particular, while exposure to local newspapers
(M = 2.77, SD = 1.27 vs. M = 3.11, SD = 1.42) reduced support for erroneous beliefs, affiliation with local political groups seemed to predict adoption
of misinformation (M = 3.40, SD = 1.10 vs. M = 2.92, SD = 1.38). Figure
2 summarizes the main findings with respect to the sociodemographic and the
communication variables that contribute to adoption of misinformation in the
context of vaccine safety.
Discussion
The current media environment is less focused, more blurred, and highly
bifurcated (Dilliplane, Goldman, & Mutz, 2013), which adds additional levels of complexity to the already existing doubts associated with measurements and research designs (Prior, 2009a, 2009b). That being so, we need to
critically reconsider what we want to know when we are asking about media
influence (Hornik, 2016)—are we interested in assessing exposure to a specific message or do we want to examine exposure to ideas in the communication environment? While the former is often attempted through simple
662
Science Communication 40(5)
Table 2. Quadratic Assignment Procedure (QAP) Regression for Beliefs in
Vaccine Safety by Sociodemographic Variables and Communication Ecologies.
Predictor
Intercept
Ethnicity
White
Black
Hispanic
Religion
Catholic
Protestant
Christian (other)
Unaffiliated
Atheist
Gender
Female
Political ideology
Conspiratorial mind-set
Income
Education
Age
Meso ecology
Macro ecology
Meso politics
Macro politics
Meso ecology × meso politics
Macro ecology × macro politics
Meso ecology × macro ecology
R2
Block 1, b
Block 2, b
Block 3, b
Block 4, b
1.20
1.38
1.37
1.40
−.014
−.01
−.03
−.04*
−.01
−.03
−.03
.001
−.03
−.03
.001
−.03
−.06
−.05
−.10*
.03
.05
−.07*
−.06*
−.10*
.04
.05
−.06*
−.08*
−.11*
.05
.05
−.06*
−.08*
−.10**
.06
.05
−.04
.03
.24***
−.02
−.03
.003
−.03
.03
.26***
−.02
−.03
.001
−.12**
.05
.05**
.04*
−.02
.02
.28***
−.02
−.03
.001
−.11*
.04
.05*
.03
.001
.003
.06***
.08***
.08***
−.02
.02
.26***
−.02
−.02
.001
−.13*
.07
.05*
.04*
.001
.003
.01
.09***
Note. Small categories for ethnicity (e.g., Asian) and religion (e.g., Jew) were included in the
model but they are not represented in the table.
*p < .05. **p < .01. ***p < .001 (two-tailed tests).
evaluations with varying degrees of success, measuring ideas in the communication environment is much more challenging. This article has posited that
we need to adopt an ecological approach that investigates the constellation of
multilevel and multimodal communication resources with which individuals
connect in order to construct knowledge and achieve goals. In this spirit, the
current study integrated the communication ecology perspective with network theory to map people’s information resources and assess their effects on
relevant outcomes related to scientific misinformation.
Walter et al.
663
Figure 2. Communication ecology for resources significantly predicting vaccine
safety misinformation.
A straightforward way to capture the potential contribution of the communication ecology approach is to compare its results with what is often
concluded while using traditional public opinion surveys. For instance, the
current study found that educated conservatives who are affiliated with local
religious groups and tend to watch conservative national cable TV are more
likely to report on incorrect beliefs with respect to the human fingerprint on
global warming. Conversely, educated liberals who are affiliated with local
political groups, watch local TV, and read local newspapers are more likely
to agree with the assertion that humans contribute to global warming. While
previous studies that examined beliefs in climate science highlighted the link
between political ideology and education (Hart et al., 2015), as well as the
role played by cable TV (Feldman et al., 2012), the literature has been largely
silent on the influence of mesolevel resources in shaping such beliefs.
Likewise, in stark contrast to previous studies that measured the presumed
effects of various information resources in isolation, the ecological approach
advocated here does not assume independence between information
resources. In line with the information-rich reality of the modern world, the
proposed approach accounts for the fact that communication is always interdependent, either because legacy media provide content for social media
(Perloff, 2015) or due to the fact that media diets tend to be relatively homogeneous (Jamieson & Cappella, 2008).
664
Science Communication 40(5)
With respect to vaccine safety, the results indicated that Christians with a
conspiratorial mind-set were more likely to doubt the safety of vaccines. In
concurrence with the literature, political ideology and macrolevel resources
were not significant predictors of beliefs in vaccine safety, while having a
conspiratorial mind-set was associated with rejection of science
(Lewandowsky et al., 2013). With that in mind, the current study also
advances the literature as it goes further to outline the mesolevel resources
that predict people’s beliefs in vaccine safety. Specifically, affiliation with
local political groups was negatively associated with trust in vaccines,
whereas local newspapers emerged again as an information resource that carries positive influence for people’s belief in science. Keeping in mind that the
resurgence of antivaccination movements is associated with specific geographical enclaves (Hochschild & Einstein, 2015), it stands to reason that
local politics may play a much more important role in shaping vaccine-related
beliefs than people’s political leanings as democrats or conservatives. In line
with the common notion that all politics is local, communication ecologies
treat political ideology not as a monolithic construct but rather as a multilevel
predictor that can be manifested in various choices.
This is also the place to raise some important caveats. First, the analysis is
only as good as the data that it uses. Many of the problems that are associated
with self-report data will not and have not disappeared. Yet there is some
evidence to suggest that listing communication resources, as was done in our
networked approach, can increase the validity of self-report measures of
media exposure (Andersen, de Vreese, & Albæk, 2016). Second, the feasibility of the communication ecology method was tested with a nonrepresentative sample; thus, the results should be interpreted with caution. After we are
able to positively assess the methodological feasibility, the next step would
be to examine the external validity of the results with a more representative
sample. Finally, it is important to note that in our model social media was
conceptualized as a macrolevel communication resource, even though such
networks seem to cut across levels. This decision was based on the finding
that individuals who report that they get news from the Internet and social
media are, de facto, frequently accessing newspaper, broadcast television,
and cable TV news (Perloff, 2014). Clearly, people find social media useful,
in part because they are more likely to be exposed to news recommended by
individuals in their social networks (Curry, 2016); however, it is still debatable whether this content is any different from a repackaged version of traditional news media such as CNN or the New York Times. All in all, we
intentionally chose oversimplifications over complicated scenarios, and not
all the alternatives were adequately discussed; yet, the main purpose of this
article was to spark curiosity, and to the extent that we achieved this goal, we
were successful in our endeavor.
Walter et al.
665
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 a summer research fellowship provided by the Annenberg School of Communication and
the USC Graduate School.
Notes
1.
2.
3.
Based on GPower 3.1.9.2 estimation for r = .10; α = .05; 1−β = .80.
This category encompasses different non-Catholic and non-Protestant beliefs
across the Christian church.
Similarly to the macrolevel communication ecology, the specific items on the list
were based on a list provided by Pew Research Center (2016).
References
Ahern, L., Connolly-Ahern, C., & Hoewe, J. (2016). Worldviews, issue knowledge, and the pollution of a local science information environment. Science
Communication, 38, 228-250.
Altheide, D. L. (1995). An ecology of communication: Cultural formats of control.
New York, NY: Aldine de Gruyter.
Andersen, K., de Vreese, C. H., & Albæk, E. (2016). Measuring media diet in a
high-choice environment - testing the list-frequency technique. Communication
Methods and Measures, 10, 81-98. doi:10.1080/19312458.2016.1150973
Ball-Rokeach, S. J., Gonzalez, C., Son, M., & Kligler-Vilenchik, N. (2012, May).
Understanding individuals in the context of their environment: Communication
ecology as a concept and method. Paper presented at the 62nd annual conference
of the International Communication Association, Phoenix, AZ.
Bennett, W. L., & Iyengar, S. (2008). A new era of minimal effects? The changing
foundations of political communication. Journal of Communication, 58, 707731. doi:10.1111/j.1460-2466.2008.00410.x
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software
for social network analysis. Harvard, MA: Analytic Technologies.
Broad, G. M., Ball-Rokeach, S. J., Ognyanova, K., Stokes, B., Picasso, T., &
Villanueva, G. (2013). Understanding communication ecologies to bridge communication research and community action. Journal of Applied Communication
Research, 41, 325-345. doi:10.1080/00909882.2013.844848
Curry, K. (2016, September 30). More and more people get their news via social
media. Is that good or bad? The Washington Post. Retrieved from https://www.
washingtonpost.com/news/monkey-cage/wp/2016/09/30/more-and-more-peo-
666
Science Communication 40(5)
ple-get-their-news-via-social-media-is-that-good-or-bad/?noredirect=on&utm_
term=.a91fe7e21a13
de Tocqueville, A. (1966). Democracy in America. New York, NY: Harper & Row.
(Original work published 1848)
de Vreese, C. H., & Neijens, P. (2016). Measuring media exposure in a changing communications Environment. Communication Methods and Measures, 10, 69-80.
doi:10.1080/19312458.2016.1150441
Dilliplane, S., Goldman, S. K., & Mutz, D. C. (2013). Televised exposure to politics: New measures for a fragmented media environment. American Journal of
Political Science, 57, 236-248.
Feezell, J. T. (2018). Agenda setting through social media: The importance of incidental news exposure and social filtering in the digital era. Political Research
Quarterly, 71, 482-494. doi:10.1177/1065912917744895
Feldman, L., Maibach, E. W., Roser-Renouf, C., & Leiserowitz, A. (2012). Climate
on cable: The nature and impact of global warming coverage on Fox News, CNN,
and MSNBC. International Journal of Press & Politics, 17, 3-31.
Friedland, L. A. (2016). Networks in place. American Behavioral Scientist, 60, 24-42.
doi:10.1177/0002764215601710
Gauchat, G. (2012). Politicization of science in the public sphere: A study of public trust
in the United States, 1974 to 2010. American Sociological Review, 77, 167-187.
Hamilton, L. C. (2011). Education, politics and opinions about climate change evidence for interaction effects. Climatic Change, 104, 231-242.
Harder, R. A., Sevenans, J., & Van Aelst, P. (2017). Intermedia agenda setting in the social media age: How traditional players dominate the news
agenda in election times. International Journal of Press/Politics, 22, 275-293.
doi:10.1177/1940161217704969
Hart, P. S., Nisbet, E. C., & Myers, T. A. (2015). Public attention to science and political news and support for climate change mitigation. Nature Climate Change, 5,
541-545. doi:10.1038/nclimate2577
Hochschild, J. L., & Einstein, K. L. (2015). Do facts matter? Information and misinformation in American politics. Political Science Quarterly, 130, 585-624.
Hornik, R. (2016). Measuring campaign message exposure and public communication environment exposure: Some implications of the distinction in the context of
social media. Communication Methods and Measures, 10, 167-169. doi:10.1080
/19312458.2016.1150976
Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Goldstein Hode, M.,
Halliwell, M. R., . . . Griffith, S. A. (2015). Social media and disasters: A
functional framework for social media use in disaster planning, response, and
research. Disasters, 39, 1-22.
Jamieson, K. H., & Capella, J. N. (2008). The echo chamber: Rush Limbaugh and
the conservative media establishment. New York, NY: Oxford University Press.
Kahan, D. M., Peters, E., Dawson, E. C., & Slovic, P. (2017). Motivated numeracy
and enlightened self-government. Behavioural Public Policy, 1, 54-86.
Walter et al.
667
Kahan, D. M., Peters, E., Wittlin, M., Slovic, P., Ouellette, L. L, Braman, D., &
Mandel, G. (2012). The polarizing impact of science literacy and numeracy on
perceived climate change risks. Nature Climate Change, 2, 732-735.
Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression
analysis of dyadic data. Social networks, 10, 359-381.
Kuklinski, J. H., Quirk, P. J., Jerit, J., Schwieder, D., & Rich, R. F. (2000).
Misinformation and the currency of democratic citizenship. Journal of Politics,
62, 790-816.
Lewandowsky, S., Gignac, G. E., & Oberauer, K. (2013). The role of conspiracist
ideation and worldviews in predicting rejection of science. PLoS ONE, 10,
e75637-e75637. doi:10.1371/journal.pone.0075637
Literat, I., & Chen, N. N. (2014). Communication infrastructure theory and entertainment-education: An integrative model for health communication. Communication
Theory, 24, 83-103. doi:10.1111/comt.12011
Myers, M., & Pineda, D. (2009). Misinformation about vaccines. In A. D. T. Barrett
& L. R Stanberry (Eds.), Vaccines for biodefense and emerging and neglected
diseases (pp. 255-270). London, England: Elsevier.
Oliver, J. E., & Wood, T. J. (2014). Conspiracy theories and the paranoid style(s) of
mass opinion. American Journal of Political Science, 58, 952-966. doi:10.1111/
ajps.12084
Perloff, R. M. (2015). Mass communication research at the crossroads: Definitional
issues and theoretical directions for mass and political communication scholarship in an age of online media. Mass Communication and Society, 18, 531-556.
Pew Research Center. (2011). The social side of the Internet. Section 1: The state
of groups and voluntary organizations in America. Retrieved from http://www.
pewinternet.org/2011/01/18/section-1-the-state-of-groups-and-voluntary-organizations-in-america/
Pew Research Center. (2016). The modern news consumer. Retrieved from http://
www.journalism.org/2016/07/07/pathways-to-news/
Pew Research Center. (2017a). How much does science knowledge influence people’s views on climate change and energy issues?. Retrieved from http://www.
pewresearch.org/fact-tank/2017/03/22/how-much-does-science-knowledgeinfluence-peoples-views-on-climate-change-and-energy-issues/ft_17-03-21_
sciknowledgepartisan_climate/
Pew Research Center. (2017b). Parents of young children are more “vaccine hesitant.” Retrieved from http://www.pewresearch.org/fact-tank/2017/02/06/parents-of-young-children-are-more-vaccine-hesitant/
Poland, G. A., & Spier, R. (2010). Fear, misinformation, and innumerates: How the
Wakefield paper, the press, and advocacy groups damaged the public health.
Vaccine, 28, 2361-2362.
Prior, M. (2009a). The immensely inflated news audience: Assessing bias in selfreported news exposure. Public Opinion Quarterly, 73, 130-143.
Prior, M. (2009b). Improving media effects research through better measurement of
news exposure. The Journal of Politics, 71, 893-908.
668
Science Communication 40(5)
Webster, J. G., & Ksiazek, T. B. (2012). The dynamics of audience fragmentation:
Public attention in an age of digital media. Journal of Communication, 62, 39-56.
doi:10.1111/j.1460-2466.2011.01616.x
Wilkin, H. A. (2013). Exploring the potential of communication infrastructure theory
for informing efforts to reduce health disparities. Journal of Communication, 63,
181-200. doi:10.1111/jcom.12006
Wilkin, H. A., & Ball-Rokeach, S. J. (2011). Hard-to-reach? Using health access status as a way to more effectively target segments of the Latino audience. Health
Education Research, 26, 239-253. doi:10.1093/her/cyq090
Wilkin, H. A., Ball-Rokeach, S. J., Matsaganis, M. D., & Cheong, P. H. (2007).
Comparing the communication ecologies of geo-ethnic communities: How people stay on top of their community. Electronic Journal of Communication, 17.
Retrieved from http://www.cios.org/ www/ejc/v17n12.htm
Yale Climate Change Communication. (2016). Yale climate opinion maps: U.S. 2016.
Retrieved from http://climatecommunication.yale.edu/visualizations-data/ycomus-2016/
Author Biographies
Nathan Walter is an assistant professor in the School of Communication at
Northwestern University. His research concerns the effects of mass media, evaluation
of strategic health messages, naive theory, and correction of misinformation.
Sandra J. Ball-Rokeach received her PhD in sociology from the University of
Washington in 1968. Her current research examines (1) the communication ecologies
that distinguish how gentrifiers respond to the host community and how the gentrified
respond to gentrifiers and (2) the challenge of operating a nonprofit community news
site to serve as a bridge between race/ethnic silos.
Yu Xu is a PhD candidate in the Annenberg School for Communication and
Journalism at the University of Southern California. His research interests focus on
organizational communication, organizational ecology, network evolution, and computational methods.
Garrett M. Broad is an assistant professor in the Department of Communication and
Media Studies at Fordham University. His research investigates the role of storytelling and communication technology in promoting networked movements for social
change, with a focus on environment and food system issues.