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The Effect of Network Structure on Preference Formation
Samara Klar
Assistant Professor
School of Govt. & Public Policy
University of Arizona
[email protected]
Yotam Shmargad
Assistant Professor
School of Information
University of Arizona
[email protected]
Abstract: How does network structure influence opinion? Relying on theories of preference
formation and social networks, we randomize a sample of adults into a highly clustered network
(in which one’s connections are connected to each other) or a low-clustered network (in which
one’s connections are less connected to one another). We then seed competing pieces of
information in each network: one underrepresented viewpoint is seeded less often, while a
dominant viewpoint is seeded more often. We track their diffusion and demonstrate that in low-,
as opposed to high-, clustered networks, individuals become equally exposed to both dominant
and underrepresented views. As a result, those in low-clustered networks learn more and become
open to persuasion. In high-clustered networks, underrepresented views are drowned out and
prohibit learning and attitude change. This study has implications for how underrepresented
viewpoints can influence opinion and, more broadly, for how network structure influences
preference formation.
Word Count (including figures and tables): 7,325
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Introduction
There may be two sides to every argument but, most often, one perspective is more
prevalent within one’s social network. As Stimson (2004) explains, “it is quite wrong to assume
that all issues get two-sided treatment. There are many matters where one side is dominant and is
the only message the public hears” (p. 18). When one side dominates, says Stimson, only that
perspective is heard and it thus becomes more popular, “which makes it less likely that the other
side will be heard, and on and on” (p. 19). Not only is it difficult to access counter-attitudinal
information but it is also thought to be undesirable. Individuals prefer information that supports
their pre-existing beliefs (Sears and Freedman 1967) and they dismiss views that do not support
their own (Redlawsk 2002, 2004). Although modern media increase accessibility to opposing
perspectives, access to both cable news (Prior 2007; Lelkes et al. 2013) and online news (Baum
and Groeling 2008) nevertheless appear to exacerbate the homogeneity of an individual’s
information environment.
Normatively, the prevalence of biased information environments is troubling. American
legal scholar Karl Llewellyn used the term “the threat of the available” (1931) to describe “the
almost inevitable tendency” to “turn to the most available material and to study that—and to
study it exclusively” (p. 95).Not only do biased information environments encourage attitudinal
polarization (Klar 2014) and depress knowledge (Jerit and Barabas 2012), but the dominance of
one particular perspective coupled with the subsequent subordination of others can lead to
inequality (Mansbridge 1983) and segregation (Mendelberg and Olseke 2000).
In this paper, we take an optimistic turn by building on research that suggests the
structure of our networks might help to mitigate biased information environments. Based on
existing work on both social networks and preference formation, we theorize that certain network
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structures (in which one’s connections are connected to each other) exacerbate the dominance of
dominant information, while others (in which one’s connections are unlikely to know each other)
enable the diffusion of underrepresented information. As a result, we expect that an individual’s
opinion is, in part, determined by the structure of the network in which she finds herself. To test
these hypotheses, we employ an experimental study in which we randomize a sample of adults
into online social networks where their connections are either highly connected to one another
(high-clustering) or are instead connected to more distant parts of the greater network (lowclustering). We asymmetrically seed competing pieces of political information within each
network – one argument is seeded less often, while the opposing argument is seeded more often
– and we track the diffusion of this information over a 10-day period. We first demonstrate that
in low-clustered networks individuals quickly become exposed to both the dominant and the
underrepresented information, whereas high-clustered networks perpetuate the dominance of one
side only. As a result of these structural differences, individuals in low-clustered networks learn
more about both sides of an issue, are more interested in learning about the issue, and become
more sympathetic toward the under-represented viewpoint. In high-clustered networks, on the
other hand, infrequently seeded information is persistently drowned out by the most prevalent
piece of information, leading to less engagement with the issue and less sympathy for the underrepresented perspective. Our study provides important empirical evidence for how
underrepresented viewpoints can gain the most traction in particular network structures. More
broadly, we demonstrate that changes to network structure can mitigate the dominance of one
perspective over the other to affect preference formation.
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Exposure to New Information
Encounters with unfamiliar or disagreeable information are often studied in the social
sciences because these instances provide individuals with opportunities to become more
informed. Early psychological theories of cognitive consistency (Festinger 1957), however, tell
us that individuals are primarily interested in reconciling new information with their pre-existing
beliefs. In doing so, they tend to dismiss information that might otherwise cast doubt on their
predetermined allegiances (Redlawsk 2002, 2004). As a result, individuals accept attitudinally
congruent information (for example, that their own political party is the better of the two parties)
and cast aside opposing information without even-handedly evaluating the information with
which they are presented (Lodge and Taber 2000; Taber and Lodge 2006). For example,
Democrats tend to express greater outrage for the number of casualties in the Republican-led
military effort in Iraq than do Republicans; yet, Republicans tend to express greater outrage for
the number of casualties in the Democratic-led military effort in Bosnia than do Democrats
(Gaines et al. 2007).1
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It is important, as Bartels (2002) and Gerber and Green (1999) spiritedly discuss, to be sure that
rational perceptual differences are not mistaken for biases, or perhaps to be cognizant that bias is
not itself indicative of a negative personality-type. As Green and Gerber point out, when a
partisan prefers a partisan candidate’s policy choices due to a common preference for a certain
policy, this is not due to bias in a negative sense but rather to like-minded thinking. But, as
Bartels responds, when a partisan prefers a partisan candidate’s decisions – no matter how
inconsistent they may be with the party’s values – one cannot deny the presence of biased
reasoning. In this case, “the appropriate conclusion to draw…is not that perceptual biases do not
exist but that perceptual biases may be sometimes rational” (Bartels 2002, 126).
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Not only do individuals resist persuasion in the face of counter-attitudinal information,
but indeed we resist any opportunity to encounter the information in the first place. This
phenomenon is referred to as selective exposure. Sears and Freedman (1967) called it “one of the
most widely accepted principles in sociology and social psychology” (194) and he defined it as
“any systematic bias in audience composition.” In somewhat of a paradox of American political
behavior, selective exposure is most pronounced among those who are most politically
sophisticated (Redlawsk 2002; Mutz 2006). This suggests a rather stark conclusion: individuals
are happiest to remain in silos of homogeneous information.
Yet more recent work suggests that individuals do not realistically possess sufficient
control to block out incongruent messages in their day-to-day lives (e.g. Huckfeldt and Sprague
1987). Even if they prefer agreeable information, it is simply not possible to avoid opponents. A
variety of methods are used to demonstrate that incongruent information sneaks its way in to an
individual’s information environment in unexpected ways. For example, Walsh (2004) finds,
through her ethnographic work, that political discussions arise in casual groups that may not
have formed based on political commonalities. In online message boards, politics similarly arise
incidentally among groups that were formed based on non-political interests (Wojcieszak and
Mutz 2009). Despite our best attempts, counter-attitudinal information tends to permeate our
information environments, and social network theorists provide useful analyses of how this
might occur.
The Role of Weak Times in Enabling Information Exposure
A key contributor to the transmission of novel information is what network theorists call
a “weak tie.” Weak ties – as opposed to strong ties – are individuals with whom we maintain less
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intimate relationships. The strength of a tie, as defined by Granovetter (1973), relies upon the
amount of time, the emotional intensity, and the intimacy of a relationship (p. 1361). Our strong
ties, by virtue of being our very close friends, tend to know each other. Our weak ties are more
likely to be acquaintances and they “are less likely to be socially involved with one another than
are our friends (strong ties)” (Granovetter 1983, p. 201). The advantage of weak ties thus lies in
their tendency to connect individuals to regions of the social network to which they otherwise
would not have access and, subsequently, to expose them to rare or unusual information.
Weak ties are particularly important to the study of information exposure because they
facilitate exposure to dissimilar views (Barbera 2014). Studying information diffusion on
Twitter, Barbera finds that more weak ties in a social network lead to more moderate views
because a greater proportion of novel information can permeates an individual’s network. That
is, individuals with more weak ties receive more counter-attitudinal information and
subsequently moderate their opinions over time. Although individuals may prefer to avoid
counter-attitudinal information, elements of our social networks allow it to happen nonetheless.
Social media appear to exacerbate the tendency for counter-attitudinal information to
seep into our information environments by increasing the number of weak ties to whom we have
access (Barbera 2014). Bakshy et al. (2012) employ a large-scale experimental study of
Facebook users to demonstrate that, in fact, weak ties drive the majority of information diffusion
on Facebook. Of the many consequences that social media might have on our lives, this access to
new information is perhaps the most consequential for preference formation. Although online
social networks are, to be sure, distinct from face-to-face interactions, they nonetheless provide
us with important opportunities to measure the information to which we are exposed and the
attitudinal consequences of that exposure.
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The Influence of Social Network Structure on Information Exposure
Weak ties provide access to new information for a very specific reason: they connect us
to parts of the network to which we would not otherwise be connected. To use the terminology
employed by Huckfeldt et al. (1995), weak ties make one’s network less socially cohesive.
Huckfeldt and his co-authors find with survey data that the distribution of information that flows
through a socially cohesive network tends to be independent from the true opinion distribution in
the larger environment. Information that flows through a less cohesive network is more
representative of the opinion distribution in the larger environment, the authors find. Huckfeldt
and his co-authors characterize a socially cohesive network as one in which main discussants are
intimate connections (for example, relatives). Less socially cohesive networks feature more
distant connections who are non-relative and not close friends (p. 1041).
This distinction between close-knit and more distant types of networks relies on the
extent to which our ties connect us to similar versus dissimilar parts of our network. Highlyclustered networks are those in which an individual’s connections are largely connected to one
another (what Huckfeldt et al. might call “socially cohesive). Low-clustered networks, on the
other hand, are characterized by ties that are not connected to each other but rather to far-ranging
parts of the extended network. We can quantify the degree to which a network is clustered
through a measure called the clustering coefficient. The clustering coefficient is the number of
connections that exist among one’s contacts divided by the number of connections that could
exist among those contacts (Watts and Strogatz 1998). A network with a clustering coefficient of
1 would mean that every pair of connections are also connected to one another. As clustering
coefficients move from 0 to 1, the ties become more inter-connected and thus less likely to bring
in information from most distant parts of the greater network.
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A lower clustered network should therefore facilitate greater exposure to both sides of an
issue, which are known to permeate different and distant parts of a network (Conover et al. 2011;
Smith et al. 2014). We thus expect that individuals who are embedded in a low-clustered
network are more likely to face exposure to underrepresented viewpoints, as compared with
individuals in a high-clustered network who are more likely to see the same dominant viewpoints
repeatedly (Hypothesis 1). The consequences of network structure, however, are exceedingly
difficult to measure. Namely, an individual who chooses to embed herself in a low-clustered
network may very well be different from someone who chooses to form a higher clustered
network. Any attitudinal or behavioral differences between these individuals are thus impossible
to determine through observational data. In this study, we employ an experimental design to
overcome this challenge. In so doing, we can measure the degree to which the information that
travels through different types of networks affects preference formation.
The Influence of Network Structure on Learning
A higher clustered network should expose individuals to more homogeneous information,
whereas a lower clustered network opens routes to novel information. This access to opposing
viewpoints is significant, as it “ultimately enable[s] and motivate[s] individuals to become more
knowledgeable and participatory citizens.” (Scheufele et al 2004, p. 332). Exposure to opposing
viewpoints appears to “contribute to people’s ability to generate reasons, and in particular
reasons why others might disagree with their own views” (Price et al. 2002). Several scholars
have demonstrated the importance of access to new information on citizen learning and
engagement.
For example, Levitan and Wronski (2014) employ an “information board” in their study
and allow participants to choose short arguments to read about a policy issue. Participants also
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provided the researchers with a list of their top five discussants along with their discussants’
ideological positions. Results demonstrated that individuals whose networks featured a wider
range of ideological viewpoints were more likely to seek out information during the exercise. To
untangle the causal issue of whether the network led to the individual’s curiosity (or vice versa),
the authors followed up with an experimental study in which undergraduate students were
informed that their fellow group members either agreed or disagreed with their own views.
Again the authors found that individuals assigned to the heterogeneous network were most likely
to seek out information regarding policy issues.
Indeed, recent work has challenged the assumption that individuals are conflict avoidant.
Neblo and his coauthors (2010) presented two samples of adult participants the opportunity for
exposure to diverse information about politics. They find not only a keen interest in diverse
settings but a thorough enjoyment for the experience. Rather than shutting out the novel
information they receive, Neblo et al’s participants amend their viewpoints on politics and on
politicians. Klar (2014) demonstrates experimentally that, when brought face-to-face with
members of an opposing party for extremely brief discussions of politics (approximately 5
minutes), participants express a desire to engage in more diverse – and not homogeneous –
discussions in the future. Finally, Stromer-Galley and Wichowski (2003) conduct in-depth
interviews with users of “political discussion spaces” and they find that participants both seek
out and appreciate exposure to a diversity of perspectives. Kehne et al. (2012) find a strong
correlation between an interest in politics and exposure to diverse political perspectives over
social media, suggesting that this type of exposure is received positively. Overall, scholars in
political science and communications demonstrate that individuals are open to exposing
themselves to counter-attitudinal information. We couple these existing pieces of evidence with
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Granovetter’s (1973) finding that novel information is most often spread via distant ties.
Together, this suggests that individuals whose social networks are lower in clustering are more
likely to learn new information (Hypothesis 2a) and to place great importance on the issue
(Hypothesis 2b) as a result of this exposure, as compared with those in high-clustered networks.
The Influence of Network Structure on Preference Formation
We now come to the intersection between network structure and preference formation.
Do low-clustered networks allow underrepresented viewpoints to influence individuals’
preferences through greater exposure? While homogeneous information settings tends to solidify
preexisting beliefs (Klar 2014; Druckman and Nelson 2003; Visser and Mirabile 2004), diverse
settings stimulate more even-handed and considered choices (Price et al. 2002; Nir 2005; Levitan
and Visser 2008). Mutz (2002) finds that cross-pressures from within one’s own network cause
attitudes to become more ambivalent, which loosens prior attachments and pre-existing opinions.
Similarly, Klar (2014) demonstrates how diverse information settings can moderate attitudes
within the group. As Klar shows, even the strongest of partisans become more open-minded
when it comes to policy issues (specifically energy policy and health care). Individuals might not
seek out novel information, but existing evidence suggests that exposure to novel information
nonetheless can influence opinion.
Whereas Huckfeldt et al. (1995) demonstrate that networks that are low in social
cohesion are better able to represent the opinion distribution in the larger environment, we expect
to find an additional and previously untested benefit of these low-clustered networks: distribution
of opinion should be independent from the balance of information that has been seeded into a
network. Low-clustered networks, by virtue of connecting individuals to novel information,
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provide individuals with increased exposure to underrepresented viewpoints. We thus expect
that individuals in low-clustered networks are more likely to be exposed to and, subsequently, to
develop opinions that reflect underrepresented viewpoints, as compared with those in highclustered networks (Hypothesis 3).
Summary of Hypotheses
Hypothesis 1: Individuals embedded in low-clustered networks experience greater exposure to
underrepresented political viewpoints, as compared with individuals embedded in high-clustered
networks.
Hypothesis 2a: Individuals embedded in low-clustered networks experience a greater amount of
learning when it comes to policy issues discussed within their network, as compared with
individuals in high-clustered networks.
Hypothesis 2b: Individuals embedded in low-clustered networks place a greater sense of
importance on the issue being discussed within their network, as compared with individuals in
high-clustered networks.
Hypothesis 3: Individuals embedded in low-clustered networks are more likely to be persuaded
by underrepresented counter-attitudinal viewpoints, as opposed to those in high-clustered
networks.
Procedure
Prior (2013) identifies the great challenge of studying the persuasive effects of
information exposure: “Empirical analysis is severely hampered by a seemingly simple problem:
we do not know how many and what kind of people are exposed to which messages” (102). Even
when we are able to find out both individuals’ opinions as well as the information to which they
have been exposed, there are myriad confounds that could be responsible for both the
information that one sees as well as for the attitudes of the information-seer. Existing work often
relies on self-reports of the ideological viewpoints within one’s social network (e.g., Huckfeldt et
al. 2002; Mutz 2002; Nir 2005) or manipulates the degree to which respondents anticipate
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exposure to particular views but never actually engage in it (Groenendyk 2012; Tetlock and Kim
1987; Tetlock 1983).
In our study, we are able to observe the information to which our participants are exposed
through a carefully controlled experimental design. By conducting an experiment, we exploit the
“twin advantages of randomization and control” (Chong and Druckman 2007, 637). Specifically,
we randomized participants into networks of specified clustering and we carefully controlled the
balance of viewpoints that were seeded into each network. We borrow this design from Centola
(2010), who similarly assigned subjects to one of two different online social networks (one high
in clustering and one low in clustering) to track the spread of a particular behavior through each
network type. Just as Centola does in his study, we assign a sample of adult participants to one of
two online social networks. Our aim is to investigate which network structure enables exposure
to underrepresented information and with what effect.
We began by sending a brief survey to a sample of 756 respondents recruited via
ResearchNow, a public opinion research firm that recruits respondents through an online panel.
The respondents were asked for their basic demographics, and were then invited to participate in
a 10-day trial of a new online social network, which we called Political Pulse. Compensation for
the first survey was $2.50 and the respondents were informed that they would be compensated
with an additional $17.50 at the end of the 10-day study. Three hundred and seventy eight
respondents (50%) agreed to participate in the study. Demographic comparisons (see Online
Appendix for complete details) reveal that the sample of respondents who volunteered to
participate look remarkably like the larger group we solicited. Both the panel we recruited from
as well as our selected participants lean slightly Democratic, though they are evenly balanced
between liberals and conservatives. They are more interested in politics than we would find in a
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nationally representative sample, and they discuss politics a bit more often. The majority of
participants report to be in their mid-40s and report a middle to upper-class income range.
Respondents who agreed to participate in Political Pulse completed an initial survey that
measured their basic demographics and party identification (see Online Appendix for all surveys
referenced in this paper). At this time, respondents were also given the opportunity to construct a
“personal profile page” for the social network site, in which they were asked to provide brief
information about their likes and dislikes (for example, “If you could visit any country in the
world, which would you choose?” and “If you could meet any political figure (living or dead),
who would it be?”
We first randomly selected 60 respondents to a “control group.” These individuals did not
participate in the10-day study, but instead completed a survey that measured our dependent
variables of interest.2 This serves as a useful baseline against which we can compare our
treatment groups. We then randomly assigned our remaining 318 participants to one of two
networks: a high-clustered network and a low-clustered network.
Recall that a high-clustered network is one in which an individual’s connections are
highly connected to each other. In a low-clustered network, an individual’s connections are not
connected to each other but are rather connected to distant parts of the greater network. To
manipulate the clustering of each network, we controlled who each individual was connected to.
We first assigned each participant to a particular node (or position) in the online network. We
then connected each participant to 6 other participants. In the low-clustered condition,
2
As they were promised the compensation of $17.50 to participate in a 10-day study, these
control group respondents were given full compensation for completing their survey.
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individuals’ connections were not connected to one another but rather to other parts of the larger
network, resulting in an average clustering coefficient of 0.02. In the high-clustering condition,
individuals’ connections were connected to one another and to connections nearby, resulting in a
clustering coefficient of 0.4). In carrying out this procedure, we closely followed Centola’s
(2010) design. By randomizing participants in our study to one of these two types of networks,
we ensure that, on average, the two groups do not vary on key covariates that might otherwise
determine political attitudes. By comparing key demographics across the two conditions, we
indeed find highly balanced groups.
Figure 1: Depiction of low-clustered network (left) and a high-clustered network (right)
employed in this study
The second element of our experimental design is the information we seeded into each
network. On the first day of our study, we seeded information about two policy debates into each
network: the advantages and disadvantages of genetically modified organisms (GMOs) and the
advantages and disadvantages of electric cars. For each issue, we randomly selected two
individuals in each network to receive information about one perspective on the policy (either
pro or con). We then randomly selected only one individual in each network to receive
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information about the opposing perspective. Overall, three individuals in each network were
seeded with information on the first day of the study: two received one perspective on an issue
and one received the opposing perspective. This ensured that one policy perspective was
dominant in each network, while one was under-represented, just as we typically find in social
settings (Stimson 2004).
Specifically, two individuals in each network received a link to a news video arguing in
favor of GMOs; one individual in each network received a link to a news video arguing against
GMOs; two individuals in each network received a link to an article arguing against electric cars;
and one individual in each network received a link to an article arguing in favor of electric cars.
For consistency, the more pro-environmental viewpoint on each policy issue was always the
underrepresented viewpoint, while the anti-environmental viewpoint was always the dominant
viewpoint (see Online Appendix for all content seeded in Political Pulse).
We chose to test our hypotheses on two separate issues (GMOs and electrics cars) and
with 2 separate media (articles and videos) to bolster the robustness of our findings. First, it is
important for the realism of our study that our participants be exposed to real-world policies,
rather than fictitious issues. Second, we are able to test whether our hypotheses hold across two
different issues. And, finally, we are able to test whether our hypotheses hold across two
different media (articles and videos).
We chose GMOs and electric cars specifically, as we did not want to use highly salient
issues about which individuals would hold firmly crystallized opinions. Indeed the issue of
GMOs has been relatively absent from major mainstream news. By conducting a brief content
analysis using the search software Lexis Nexis, we found that during the 1-year period prior to
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our study, the New York Times included only 101 mentions of “genetically modified foods,”
“genetically modified organisms,” or “GMOs.” This can be compared with 1,602 mentions of the
terms “global warming” or “climate change.” Similarly, electric cars remain relatively out of the
public eye – during the same time period, the Times mentioned them in their pages only 309
times. On one hand, relatively less crystallized opinions regarding these issues encourages
experimental treatment effects but, given the brevity of the study and the subtlety of the
treatments, we believed that issues about which individuals hold strong attitudes would be
inappropriate.
We launched the 10-day study on November 17, 2013. Once an individual viewed a piece
of content, their contacts were informed via email. Every participant received an email once a
day (at 9:00AM or at 2:00PM) that listed which connections had seen which piece of content, as
well as a link to each piece of content. When an individual chose to click on the link to view the
information, his or her connections received an email the next day alerting them to the fact that
he had seen it, as well as a link to the content (see Figure 2 for an illustration).
Figure 2. Image of Email from Political Pulse Social Network
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It is crucial to note that, in the daily email they received from Political Pulse, each participant
was provided with a unique link to access each piece of information. Using Google Analytics
website tracking, we were then able to see exactly who was exposed to each piece of information
and exactly when the exposure occurred.
We allowed the information to flow through the networks in this manner for 10 days, and
then we ended the study with a final survey that asked participants to rate several aspects of the
experience, as well as their opinions on the issues they may have read about or seen. Once this
second study was completed, respondents received the remainder of their monetary
compensation.
The social network we employ is thus distinct from popular networks one might think of
– for example, Facebook or Twitter – in that sharing information in Political Pulse is passive, and
not active. Although we often think of posting photos and liking articles as a defining element of
online social networks, social networks are “formally defined as a set of nodes (or network
members) that are tied by one or more types of relations (Wasserman and Faust 1994)” (Marin
and Wellman 2011, p. 11). In order to eliminate homophily – the great bane of researchers
interested in studying information diffusion – we designed this carefully controlled experimental
network setting. To be sure, the particular mechanics of this specific network might restrict
respondent behavior in particular ways, and these are considerations which we address in the
discussion section of this paper.
Before we turn to the experimental results, we first will review the elements of control
and manipulation that are featured in this complex experimental design. Many of the elements in
this design were carefully controlled: each individual had 6 connections and each of these
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connections were given the exact same set of fake names (3 male names and 3 female names).
Despite the fact that each respondent completed a profile page for realism, the only profile pages
that respondents actually viewed were fabricated by the researchers so as to control for any
differences among connections. That is, respondents’ decisions to view a piece of information
could not be attributed to the fact that particularly attractive or unattractive connections had
previously seen it.
The content and balance of the information was controlled across both groups, as well:
each network was twice seeded with a pro-GMO video, once seeded with an anti-GMO video,
twice seeded with an anti-electric car article, and once seeded with a pro-electric car article.
Finally, the length of the stud y was controlled: after 10 days, the networks were both shut down.
The only element that was manipulated in this design was the network structure (high vs. low
clustering). Therefore, we can attribute any differences in aggregate behaviors of group members
to the structure of the network (ie. average treatment effect).
Results
Hypothesis 1: Information Exposure
Our first hypothesis states that individuals embedded in the low-clustered network will
experience greater exposure to underrepresented information, as opposed to those in the highclustered network. In Figure 3 and Figure 4, we depict the cumulative unique views that each
piece of information received in each network throughout the study. We begin with Figure 3,
which displays the rate at which individuals viewed the dominant information about genetically
modified organisms (pro; denoted with the black triangle) and the underrepresented information
about genetically modified organisms (con; denoted with the grey square). The number of days
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that participants are active in the study runs along the x-axis. Along the y-axis we illustrate the
cumulative unique views.
We begin first with the high-clustered network (left side). This is the network in which
connections are highly connected to one another. Figure 3 illustrates that throughout the course
of the study, the dominant information (supporting GMOs) is consistently seen more frequently
than is the underrepresented information (opposing GMOs). By the end of the study, the
dominant information is viewed more than twice as frequently as the underrepresented
information.
On the right side of Figure 3, we can see how information spreads through the lowclustered network. Recall that, in low-clustered networks, individuals gain access to different
parts of their network by virtue of the fact that their connections are not connected to one
another. As a result, we can see that the underrepresented information (opposing GMOs) spreads
at roughly the same pace as the dominant information.
Figure 3. In the High-clustered Network, the dominant perspective (“Support GMOs”) is seen
twice as frequently as the underrepresented perspective (“Against GMOs”).
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Figure 4 illustrates the rate at which participants were exposed to both the dominant
perspective on electric cars (anti) and the underrepresented perspective (pro). In this case, the
results are even more striking. In the high-clustered network (left side), the dominant perspective
quickly overtook the underrepresented perspective and, by the end of the study, was viewed
nearly 4 times as frequently. In the low-clustered network (right side), on the other hand, the
underrepresented perspective quickly spread. By the end of the study, there was again no
difference in how often individuals in the low-clustered network were exposed to the dominant
and underrepresented perspectives.
Figure 4. In the High-clustered Network, the dominant perspective (“Against Electric Cars”) is
seen twice as frequently as the underrepresented perspective (“Support Electric Cars”).
We are thus able to demonstrate that low-clustered networks allow underrepresented
information to gain the same exposure as a competing piece of information with twice as many
seeds. This suggests that organizations or campaigns competing with fewer resources can
compensate for their resource disadvantage by seeding their messages in low-clustered networks.
In our next set of tests, we examine whether the exposure to underrepresented information
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actually has any influence over the degree to which individuals learn new information, engage
with the issue, and form opinions.
Hypotheses 2a and 2b: Learning and the Perception of Importance
It is important for our study that respondents held preexisting opinions regarding the
issues to which they were exposed prior to exposure. The purpose of our control group is to
ensure that this is so. Our control group demonstrates that indeed the majority of our participants
have an opinion about GMOs and electric cars: 67 percent of our sample expressed an opinion on
the former (ie. was not neutral), while 78 percent expressed an opinion on the latter. Our control
group also demonstrates that opinion on these issues is divided across party lines. In Figure 5 we
show the average reported support for each issue among Democrats and Republicans who were
assigned to the control group.
Figure 5. Republicans in the control group exhibit significantly greater support for genetically
modified organisms and slightly, but not significantly, less support for electric cars. Vertical
lines indicate 95% confidence interval around the mean.
21
Republicans (black dot) express statistically significantly greater support for genetically
modified organisms (left side of figure) than do Democrats (grey dot). Republicans also express
slightly, but not significantly, less support for electric cars. Given these partisan differences, one
might expect heterogeneous treatment effects in learning and opinion change across party
identification. We thus present our results for Hypotheses 2 and 3 for Democrats and
Republicans separately.
We state in Hypothesis 2a that individuals embedded in low-clustered networks
experience a greater amount of learning. Figure 6 depicts participants’ responses to the following
question: “Through this study, did you learn new information about genetically modified
organisms?” A fully-labeled 7-point response scale was provided. Both Democrats and
Republicans in low-clustered networks (grey triangle) reported statistically significantly more
learning than did those in high-clustered networks (for Democrats, p=0.005; for Republicans,
p=0.003; all directional tests of significance are one-tailed).
22
Figure 6. Both Democrats and Republicans reported greater learning in the low-clustered
network than in the high-clustered network. Vertical lines indicate 95% confidence interval
around the mean.
We asked this same question regarding electric cars and found identical results (see
Figure 7). Among both Democrats (p=0.002) and Republicans (p=0.015), respondents reported
higher rates of learning in low-clustered networks.
Figure 7. Both Democrats and Republicans reported greater learning in the low-clustered
network than in the high-clustered network. Vertical lines indicate 95% confidence interval
around the mean.
Overall, we find support in 4 out of 4 cases that low-clustered networks increase learning when
one side of an issue is seeded twice as frequently as the other. This suggests that by virtue of
increasing exposure to underrepresented perspectives, low-clustered networks also increase the
degree to which network participants learn about these perspectives.
We next turn to the degree to which low-clustered networks increase engagement with
each issue. We asked each respondent at the end of the 10-day study the importance that they
place on each issue. As we state in Hypothesis 2b, we expect that the greater exposure that low23
clustered networks facilitate to both sides of an issue will lead respondents to place greater
importance on the information. Figure 8 displays the importance that Republicans and
Democrats in each network place on the issue of genetically modified organisms.
Figure 8. Network structure has no effect on perceived importance of GMOs among Democrats,
but low-clustered networks do increase importance for Republicans. Vertical lines indicate 95%
confidence interval around the mean.
Contrary to Hypothesis 2b, we see no effect of low-clustered networks on perceived
importance of GMOs for Democrats. It could be that there is a ceiling effect in this case – that is,
for Democrats, GMOs are already a highly important issue. Indeed our control group confirms
that Democrats, on average, rate GMOs at 5.31 on a 7-point importance scale (s.e. 0.17). Among
Republicans, we see that low-clustered networks, by virtue of increasing exposure to both side of
an issue, do also increase the perceived importance of the issue (p=0.04). This may be due to a
generally decreased sense that GMOs are important, and thus greater distance for the perception
of importance to climb. Our control group demonstrates that, in fact, Republicans rate the
24
importance of GMOs as a 4.0 on the 7-point scale (s.e. 0.35), statistically lower than do
Republicans in the low-clustered network (but not the high-clustered network).
Regarding electric cars, we find that the lower-clustered network increases perceived
importance for both Democrats and for Republicans (Figure 9).
Figure 9. Network structure increases the perceived importance of electric cars among both
Democrats and Republicans. Vertical lines indicate 95% confidence interval around the mean.
Increased exposure to both sides of the issue, which the lower-clustered network facilitates,
increases the perception among Democrats (p=0.051) and Republicans (p=0.003) that the issue
of electrics cars in important. Like before, we see a smaller (and less statistically significant)
shift among Democrats, which we again can attribute to a greater baseline perception of
importance for this issue.
Hypothesis 3: Preference Formation
Finally, we come to our most conservative test of the effect of network structure: tests of
persuasion. With our third hypothesis, we expect that individuals embedded in low-clustered
25
networks develop opinions that are more sympathetic toward underrepresented viewpoints, as
opposed to those in high-clustered networks. Recall that in each network, one perspective is
seeded half as frequently as the others. As we demonstrated in our first hypothesis, low-clustered
networks allow for underrepresented information to attain the same level of exposure among
individuals as do dominant perspectives. In high-clustered networks, on the other hand,
underrepresented information remains under-exposed throughout the duration of the study. Our
second set of hypotheses demonstrated that this distinction in network structure leads individuals
to perceive greater learning from their connections and a greater sense that the issues discussed
are important. Our third hypothesis states that this difference in exposure will also result in larger
opinion shifts in favor of the underrepresented viewpoints in low-clustered networks.
To test how exposure to the underrepresented viewpoint influenced preference formation
in each network, we can examine the percentage of respondents who agreed with the
underrepresented viewpoint after the study was completed. Again, we analyze Democrats
separately from Republicans.
Figure 10 shows the percentage of Democrats and Republicans in both the low-clustered
(grey triangle) and high-clustered (black circle) networks who report, after the study was over,
that they oppose genetically modified organisms. Recall that this was the underrepresented
viewpoint in each network. We find that, in fact, both Democrats and Republicans become more
sympathetic toward the under-represented viewpoint (against GMOs) after the increased
26
exposure that the lower-clustered network allowed. Forty percent of Democrats who participated
Figure 10. Both Democrats and Republicans in the low-clustered network, as compared to those
in the high-clustered network, become more supportive of the under-represented information to
which they were exposed (“Against GMO”). Vertical lines indicate 95% confidence interval
around the mean.
in the high-clustered network oppose GMOs, whereas 57.4% of Democrats who participated in
the low-clustered network oppose GMOs. We do note that these results are statistically
significant, but only at marginal levels (p=0.058 for Democrats and p=0.073 for Republicans).
Nonetheless, a test of opinion change regarding an issue about which individuals hold preexisting opinions is perhaps the most conservative we can administer.
Regarding electric cars, our hypothesis is not supported. Despite the greater exposure to
both sides of the issue, an increase in self-reported learning, and an increased in the important
placed on the issue, both Democrats and Republicans in the low-clustered network showed no
27
difference in their ultimate support for electric cars, as compared with those in the high-clustered
network. Figure 11 displays these results.
Figure 11. For both Democrats and Republicans, participation in the low-clustered network has
no effect on supportive for the under-represented information to which they were exposed (“Pro
Electric Cars”). Vertical lines indicate 95% confidence interval around the mean.
These tests of Hypothesis 3 therefore provide mixed results. Regarding the first issue, genetically
modified organisms, we do find that low-clustered networks facilitate persuasion in favor of the
under-represented viewpoint among both Democrats and Republicans. For the issue of electric
cars, however, we find no such support. A summary of our hypotheses and results in provided
below in Table 1.
Table 1. Summary of Hypotheses and Results
28
Hypothesis 1:
Low-Clustered
Networks Enable
Exposure to
Underrepresented
Information
Result
Hypothesis 2a:
Low-Clustered
Networks Facilitate
Greater Learning
GMOs Cars
GMOs
Strong Strong Strong
Support Support Support
Hypothesis 2b:
Low-clustered
Networks
Encourage
Perceived
Importance of
Issues Discussed
Cars
GMOs Cars
Strong Mixed
Strong
Support Support Support
Hypothesis 3:
Low-Clustered
Networks Facilitate
Preferences in Favor
of UnderRepresented
Information
GMOs
Cars
Mixed
Not
Support supported
Conclusion and Discussion
This study bridges two important topics in the social sciences: preference formation and
social networks. While studies on the former tend to suggest that biased information
environments are nearly unavoidable thanks to selective exposure (Sears and Freedman 1967)
and the perpetual dominance of majority viewpoints (Stimson 2004), more recent analyses of
network structure have begun to reveal the ways in which social cohesion helps to shape
preferences (Huckfeldt et al. 1995) and how certain social settings facilitate exposure to novel
information (Bakshy et al. 2012; Barbera 2014). In this study, we demonstrate empirically that
low-clustered social networks not only increase exposure to new information, but they allow
underrepresented opinions to achieve equal footing with dominant perspectives. Moreover, we
demonstrate that exposure to underrepresented viewpoints have importance consequences:
individuals report that they learn more from this exposure, they place greater importance on the
issues being discussed and, in addition, preferences appear to shift in favor of the underrepresented perspective.
It is important to note that the experimental networks in this study are free of homophily.
That is, the respondents could not choose their own connections. Nevertheless, we find that
individuals in low-clustered networks are more likely to learn from new information. This
29
suggests that when like-minded individuals clique together – be it like-minded citizens or inparty elites – information exposure is limited and opinions are more likely to resist change. This
finding has implications beyond the opinion change we explore here but also relates to political
polarization among the mass public, as well as Congressional elites. Of course, this study has not
only broad implications, but also several limitations that are important to consider.
First, online social networks are distinct from face-to-face discussion networks. Previous
work highlights important implications of face-to-face interaction (e.g. Klar 2014), though none
that seem to cast doubt on the findings presented in this study. The online social network
employed in this study is anonymous, and anonymity itself might encourage participants to
engage in behaviors that they might not otherwise were their identity revealed. Yet, nevertheless,
online social networks do allow for empirical studies that have important implications for more
traditional networks. Indeed much of what we know regarding political mobilization (Bond et al.
2012), health behavior (Centola 2010; Christakis et al. 2008), and the contagion of emotions
(Kramer et al. 2014) comes to us through experimental studies of online networks.
There is one peculiarity regarding the social network we employ that does merit
consideration: in our social network, individuals automatically see whatever their contacts read.
Therefore, sharing is passive and not active. On one hand, this feature replicates a common
feature on many social networking sites: “auto-posting.” Facebook, for example, routinely autoposts on a newsfeed the songs that users listen to through music applications, the articles users
read online, and the photos they like or comment on within the network.3 In this sense, the
3
The degree to which Facebook auto-posts users’ behavior has varied over time and has faced
criticism (for media coverage, see http://www.theverge.com/2014/5/27/5754862/facebook-givesup-on-automatically-sharing-everything-you-do-online-open-graph )
30
passive sharing of news articles on Political Pulse is grounded in reality. Nevertheless, passive
sharing does expedite the diffusion of information beyond what we might expect in conditions
that require active sharing. To our knowledge, this experimental design was the first of its kind in
the field of political science and it merits increasingly complex design features in future
iterations. Scholars will be well-served to investigate the degree to which individuals actively
share counter-attitudinal information and who precisely is most likely to do so. This important
extension merits further scholarly inquiry.
Overall, the study we accomplished provides a unique experimental analysis of how
social network structure influences preference formation. The union of these two literatures is
well over-due. Despite the limitations that come with controlled laboratory settings, the
implications of this study are broad and they are significant for the continued study of preference
formation in the social world in which we live.
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35
Supplementary Information
Sections Included:
I. Demographics of Initial Recruitment Panel, Sample Assigned to Low-Clustered Network, and
Sample Assigned to High-Clustered Network.
II. Time 1 Survey: Asked of Respondents Randomly Assigned to Control Group
III. Time 1 Survey: Asked of Respondents Randomly Assigned to Treatment Groups
IV. Time 2 Survey: Asked of Respondents Randomly Assigned to Treatment Groups
V. Content of Information Diffused Across Political Pulse Network
I.
Demographics of Initial Recruitment Panel, Sample Assigned to Low-Clustered
Network, and Sample Assigned to High-Clustered Network.
Initial Sample
(n=756)
% Democrat
% Republican
% Liberal
% Moderate
% Conservative
Mean Political
Interest (s.e.)i
Mean Political
Discussion (s.e.)ii
Mean Age (s.e.)iii
Mean Income (s.e.)iv
43.31
37.08
41.36
17.94
40.69
5.33 (0.05)
Sample Assigned to Sample Assigned to
Low-Clustered
High-Clustered
Network
Network
(n=159)
(n=159)
47.48
41.46
33.09
39.03
42.75
41.46
19.57
16.26
37.68
42.28
5.45 (0.13)
5.36 (0.15)
4.12 (0.07)
3.96 (0.17)
4.04 (0.19)
4.75 (0.05)
2.01 (0.02)
4.48 (0.13)
2.02 (0.06)
4.77 (0.15)
1.98 (0.06)
i
Political Interest Measure: How interested are you in national politics? Are you uninterested or
interested?
1 – Extremely Uninterested
2 – Moderately Uninterested
3 – Slightly Uninterested
4 – Neutral
5 – Slightly Interested
6 – Moderately Interested
7 – Extremely Interested
ii
Political Discussion Measure: How many days per week do you usually discuss politics with
your family and friends?
1 – 0 days
36
2 – 1 day
3 – 2 days
4 – 3 days
5 – 4 days
6 – 5 days
7 – 6 days
8 – 7 days
iii
Age Measure: What is your age?
1 – Under 18 years
2 – 19-25 years
3 – 26-35 years
4 – 36-45 years
5 – 46-55 years
6 – 56-65 years
7 – 66-75 years
8 –75 years or older
iv
Income Measure: What was your approximate household income in the past year (before
taxes)?
1 – Less than $25,000
2 – $26,000-$100,000
3 – $101,000-$250,000
4 – Over $250,000
Time 1 Survey: Asked of Respondents Randomly Assigned to Control Group
You have been randomly selected to complete an anonymous opinion survey.
When it comes to genetically modified foods, how informed are you? Would you say you
are uninformed or informed about genetically modified foods?
1.
2.
3.
4.
5.
6.
7.
Extremely uninformed
Moderately uninformed
Slightly uninformed
Neither uninformed nor informed
Slightly informed
Moderately informed
Extremely informed
Would you say you oppose or support genetically modified foods?
1. Definitely oppose
37
2.
3.
4.
5.
6.
7.
Mostly oppose
Slightly oppose
Neither oppose nor support
Slightly support
Mostly support
Definitely support
When it comes to electric cars, how informed are you? Would you say you
are uninformed or informed about electric cars?
1.
2.
3.
4.
5.
6.
7.
Extremely uninformed
Moderately uninformed
Slightly uninformed
Neither uninformed nor informed
Slightly informed
Moderately informed
Extremely informed
Would you say you oppose or support electric cars?
1.
2.
3.
4.
5.
6.
7.
Definitely oppose
Mostly oppose
Slightly oppose
Neither oppose nor support
Slightly support
Mostly support
Definitely support
Generally speaking, are you a Republican, a Democrat, or an Independent?
1.
2.
3.
4.
5.
6.
7.
Strong Democrat
Weak Democrat
Independent leaning to Democrat
Independent
Independent leaning to Republican
Weak Republican
Strong Republican
When it comes to your political views, would you say you are conservative, liberal, or moderate?
1. Extremely liberal
2. Liberal
3. Slightly liberal
38
4.
5.
6.
7.
Moderate
Slightly conservative
Conservative
Extremely conservative
What is your age?
1.
2.
3.
4.
5.
6.
7.
8.
Under 18 years
19-25 years
26-35 years
36-45 years
46-55 years
56-65 years
66-75 years
76 years or older
What is your gender? Male / Female
With which racial group do you primarily identify?
1.
2.
3.
4.
5.
6.
7.
African American or Black
Asian American or Pacific Islander
Caucasian or White
Hispanic or Latino
Middle Eastern
Native American
Other
What is the highest level of education you have completed?
1.
2.
3.
4.
5.
II.
Some high school
High school diploma
Some college
College diploma
Graduate degree (e.g. MA, JD, PhD)
Time 1 Survey: Asked of Respondents Randomly Assigned to Treatment
Conditions
Welcome to Political Pulse!
This is an online social network that allows respondents to view information about politics and
current events.
39
As a participant in this study, you will be assigned to a private social network for approximately
7-10 days. You will receive a maximum of 1 email per day that provides news stories for you to
read. Your real name will never be collected or recorded. No identifying information will be
released to your network. Your email address will never be shared.
If you would like to participate in this study, please carefully enter the email address below
where you would like to be contacted for your participation. A valid email address is
required for your compensation.
Welcome! We hope you enjoy your brief experience as a Political Pulse member!
We are interested in learning about the participants in Political Pulse. Please remember that this
information is completely anonymous and it will not be shared with anyone in your
personal network.
Generally speaking, would you say you are a Republican, a Democrat, or an Independent?
1.
2.
3.
4.
5.
6.
7.
Strong Democrat
Weak Democrat
Independent leaning to Democrat
Independent
Independent leaning to Republican
Weak Republican
Strong Republican
When it comes to your political views, would you say you are conservative, liberal, or moderate?
1.
2.
3.
4.
5.
6.
7.
Extremely liberal
Liberal
Slightly liberal
Moderate
Slightly conservative
Conservative
Extremely conservative
When it comes to your political views on social issues (things like same-sex marriage and
abortion), would you say you are conservative, liberal, or moderate?
1.
2.
3.
4.
5.
6.
Extremely liberal
Liberal
Slightly liberal
Moderate
Slightly conservative
Conservative
40
7. Extremely conservative
When it comes to your political views on economic issues (things like taxes and the economy),
would you say you are conservative, liberal, or moderate?
1.
2.
3.
4.
5.
6.
7.
Extremely liberal
Liberal
Slightly liberal
Moderate
Slightly conservative
Conservative
Extremely conservative
How interested are you in national politics? Are you uninterested or interested?
1. Extremely uninterested
2. Moderately uninterested
3. Slightly uninterested
4. Neither uninterested nor interested
5. Slightly interested
6. Moderately interested
7. Extremely interested
How many days per week do you usually discuss politics with your family and friends?
1.
2.
3.
4.
5.
6.
7.
One day a week or less
Two days per week
Three days per week
Four days per week
Five days per week
Six days per week
Every day
What is your age?
1.
2.
3.
4.
5.
6.
7.
8.
Under 18 years
19-25 years
26-35 years
36-45 years
46-55 years
56-65 years
66-75 years
76 years or older
41
What is your gender? Male / Female
With which racial group do you primarily identify?
8. African American or Black
9. Asian American or Pacific Islander
10. Caucasian or White
11. Hispanic or Latino
12. Middle Eastern
13. Native American
14. Other
What is the highest level of education you have completed?
6. Some high school
7. High school diploma
8. Some college
9. College diploma
10. Graduate degree (e.g. MA, JD, PhD)
Political Pulse is a private social network. You will be randomly connected to 6 anonymous
participants. Whenever your contacts read or view information on Political Pulse, you
will receive an email from REDACTED FOR ANONYMOUS REVIEW that allows you to view
this same information. If you choose to view the information, your contacts will be sent a
message indicating that you have viewed it.
You will receive a maximum of 1 email per day for a maximum of 10 days.
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III.
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Thank you for participating in the first pilot study of Political Pulse! We hope you enjoyed it.
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IV.
Content of Information Diffused Across Political Pulse Network
Anti Electric Car Article:
“Electric Cars: Don’t Get Taken for a Ride”
by Becket Adams
The Blaze
There has been a lot of talk about electric cars lately and none of it has been positive. In light of
GM’s multitudinous Volt issues, the demise of Aptera Motors, and Tesla Motors’ recent share
downgrading, some analysts have been asking, ”Why won’t people buy electric?”
In theory, an electric motor is a great idea, right? Think about it: instant torque, few moving
parts, and relatively easy maintenance. But in reality, there are so many downsides to owning an
electric vehicle (EV) that the fact that major car companies have brought them to market without
first addressing these issues is baffling.
[Editor's note: Hybrids, although successful, have been excluded from this discussion because of
their reliance on gas; they cannot be considered "true" electric vehicles.]
Here are some of the reasons why buying an EV is a terrible idea (as listed by Business Insider):
1. Limited Range
Most EVs run out of energy before they can get anywhere. Obviously, this is a major problem
for anyone interested in buying a car. Although Tesla Motors advertises a two-seat roadster with
a 300 mile range, this can only be achieved through “careful driving” (something a roadster isn’t
exactly designed for). Not to mention that the car itself is a $100,000 investment. How many
consumers have access to that kind of capital? Though, to be fair, Teslas are marketed towards a
niche audience (i.e. people who can invest six figures in a car). So, let’s look at something a bit
more middle-class friendly. Because it costs about the same as a similarly sized gas-powered
vehicle ($30,000), perhaps the Nissan Leaf would be a good choice for the Eco-conscientious
consumer. If by “good choice” you mean “a car that can only get about 100 miles before running
out of juice,” then, yeah, the Leaf’s the way to go. Who in the world wants a car with a 100-mile
range?
“On the flip slide, standard cars regularly get a range of 300-400 miles, and on certain occasions
can get up into the 800 mile range,” writes Travis Okulski of Business Insider. ”EVs are
perfectly adequate to go around town or to run short errands. But the car is a harbinger of
freedom; the idea behind it was to free people from the grid and get them exploring.”
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Unless you’re willing to shell out six figures for a Tesla, be prepared to be restricted by the more
affordable EVs.
2. Long Charge Times
Yeah, okay, so an EVs’ range is terrible (especially when compared to what’s available with a
gas-powered vehicle). Just recharge the car! Easier said than done. In the case of the Leaf,
recharging the battery can take up to 20 hours on a 120 volt outlet, according to Nissan. Upping
the voltage isn’t much better.
“On a 240 volt, it takes seven hours, and a 480 volt fast charge station takes 30 minutes. In our
instant gratification broadband society, even waiting 30 minutes is an eternity,” writes Business
Insider. Compare that to the 5-7 minutes it takes to fill a gas tank. Not much of a competition
anymore, is it?
3. Infrastructure
Let’s say you’re content with being restricted to 100 miles before having to wait anywhere
between 30 minutes and 20 hours to recharge your car (because, you know, you don’t ever have
to be anywhere). The next problem is finding somewhere where you can actually charge said car.
By using Nissan’s ChargePortal website, this author was able to locate 35 charging stations
within in a 10-mile radius of his office. That’s not too bad considering that Washington, D.C., is
arguably the most EV-friendly city in the country. That’s not to say that everyone in D.C. drives
electric, but rather that the EV industry has many influential and powerful friends in the nation’s
capitol.
But that’s not really the point (that will come later). The point is that in this same 10-mile radius,
there are 83 gas stations. Get that? For every charging station, there are almost three filling
stations.
Furthermore, according to U.S. Census data, there are approximately 125,000 filling stations
across the United States. By the end of 2012, it is expected that there will only be 13,000 electric
car charge points.
Basically, if your EV ever runs out of power–which it will given its small range–you had better
hope that it’s near one of the very, very few charging stations.
4. Cost
Consider the following: the Chevrolet Volt (electric) and Cruze (gas) are approximately the same
size. Yet, even if you factor in the Volt’s $7,500 tax credit, the Volt is still $14,000 more
expensive than the Cruze. Is that premium worth it? Considering the fact that the Cruze gets
excellent mileage (City/Gas mileage: 26/36), the answer is “no.” Why? “If we say that gas costs
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$4 per gallon, the Cruze would need to be filled up 225 times before that $14,000 gap is brought
to $0,” writes Business Insider. “According to GM, the Cruze’s range per tank is estimated to be
390 miles, so that means the 225 fill ups would occur over the course of 88,000 miles.”
Translation: 88,000 miles is almost eight years of driving, and according to The New York
Times, Americans keep their cars for an average of around nine years. If the Volt actually keeps
for that long without, you know, the battery exploding, then it will only be saving you money for
one year.
5. Pollution
EV advocates love to brag about how their cars emit little to no pollution. In a way, this is true.
The Volt, for instance, emits very little pollution. However, their bragging rights go up in flames
like a Volt battery when one considers the following: How was that car made?
How is that car maintained? How were the batteries made? How are the batteries charged?
How are the batteries disposed of?
“Unless you have your own solar generator, the likelihood is that the electric car is actually being
charged by coal or gas power, which are the most prevalent power generating stations in the
world,” Okulski points out.
Think about it: it is only because of the existence of “planet destroying” fossil-fuels that “green”
vehicles are available. If Washington and environmental advocates had their way and everyone
started buying electric, do you have any idea how much that would increase the pollutive output
of factories manufacturing ”green” vehicles?
Then there is the question of the car’s battery. The nickel-hydride battery used in electric cars are
created in a number of processes (such as nickel mining) that some claim add to the world’s
overall pollution. Also, keep in mind that to complete the battery construction process, they have
to be shipped all over the world. How do you suppose they are delivered?
And these are just some of the issues that face the supposedly eco-friendly car. We haven’t even
touched on the subject of disposing of the toxic, non-degradable materials used in the batteries
(there currently isn’t a “green” process).
6. Government
As mentioned earlier, Washington is an EV-friendly city. Proof of this can be seen in the
government’s attempt to boost sales by offering EV tax credits (some as high as $7,500).
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Granted, the tax credit will make some of the price tags a little more reasonable for consumers.
But how long will they last? Considering how poor EV sales are right now, it’s only safe to
assume that they will get worse once the tax credit expires.
And that’s not even the real problem. The real problem lies in artificially manipulating the worth
and price of the vehicles.
“With a third party reducing the costs, manufacturers are not encouraged to research and
innovate in order to bring the true initial buy in down.” Business Insider points out.
Indeed, it would seem that the best thing that the Feds could do, if they really want to encourage
EV sales, is get out of the way. They should allow the manufacturers to figure out the actual
demand and worth of their cars. Obviously, and this is reflected in auto sales, a $30,000 EV with
a 100-mile range doesn’t stand a chance against a similarly priced gas-powered vehicle.
Therefore, manufacturers need to figure out how to either bring down the price of the EV to
make the 100-mile range worth it, or figure out how to improve its mileage. Taking $7,500 off
the tag price will only do so much for so long.
7. Ease of Gasoline
Let’s face it: gas works. It works well and we know it. Gas is readily available, gas stations fuel
us up in under minutes and, considering the technology and labor that goes into making it
available on the market, gas prices are fairly reasonable. Plus, compared to the EV, we get more
bang for our buck with gas-powered vehicles. “But c’mon! All new technologies have a couple
kinks that need to be worked out before they really take off!” you might say.
Although this is true, this cannot be said of electric vehicles. This argument falls on its face when
one realizes that, according to PBS, the “electric car will be celebrating its 180th birthday next
year.” Wait, what?
Yep. Electric cars have been around a lot longer than gas-powered vehicles. “In the last 180
years, there have never been any EVs that can be considered a resounding commercial success,”
writes Okulski. “There have been breakthroughs and revolutionary models, even cars that have
given hope that electric would soon be the new standard, but none of them have had the desired
impact.”
Given the incredible head start EV technology has had on gas-powered cars, you’d think that the
problems mentioned in the above would have been worked out (or at least prepared for) by now.
Therefore, to echo sentiments voiced earlier in this article, it’s truly puzzling that some of these
companies decided to bring their EVs to market without first addressing these obvious and
longstanding flaws.
Pro Electric Car Article:
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“Electric Cars: A Smart Investment and a Sweet Ride”
By Felix Kramer and Max Baumhefner
Think Progress
When it comes to consumer products, environmentalists generally don’t encourage people to buy
new and buy now.
But that’s what we’re about to do because electric cars are significantly cleaner than gasoline
vehicles, and driving one can save you serious cash at the pump.
Perhaps you’ve already thought about buying an electric car, but dismissed the idea for one
reason or another. Let’s look at some common misconceptions, and offer some good reasons
why you might want to reconsider:
“I should drive my current car into the ground.”
“Hold on,” you say to yourself, “I already own a car that gets 25 miles a gallon. I want to get my
money’s worth from the investment.” The sooner you start saving gas, the better it is for the
planet and your pocketbook. There’s no use in throwing good money after bad at the pump, and
the sooner you sell your current car, the less money you’ll lose to depreciation. “I’d just be
switching my pollution from the tailpipe to the power plant.”
If you want to go green, driving on electricity is a clear winner. Using today’s average American
electricity mix of natural gas, coal, nuclear, hydro, wind, geothermal, and solar, an electric car
emits half the amount of climate-changing carbon pollution per mile as the average new vehicle.
In states with cleaner mixes, such as California, it’s only a quarter as much. To find out how
clean your electric car would be today, plug your zip code into the EPA’s “Beyond Tailpipe
Emissions Calculator.” You should also know that, because old coal plants are increasingly
being retired and replaced by cleaner and renewable resources, plug-in cars are the only cars that
become cleaner as they age.
“What I save on gas, I’ll pay in electricity.”
On average US residential electricity rates, driving one of today’s electric cars is the equivalent
of driving a 27 mile-per-gallon car on buck-a-gallon gasoline. It’s been that way for the last four
decades, and is forecasted to stay that way for the next three decades. Experts basically throw up
their hands when asked to predict the price of gas next year, let alone 30 years from now.
One thing we do know: the price at the pump will jump up and down due to geopolitical events
beyond our control. If you’re tired of that rollercoaster, call your local utility to ask about
electricity rates designed for plug-in cars.
“I’ll hold off until prices go down and there are more places to charge.”
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If you’re thinking you’d be better off waiting for a cheaper, better electric car, and a charging
station on every block, consider the following:
Modern electric cars start well below $30,000. Even better, there’s a federal tax credit worth
$7,500, and states like California have rebates of up to $2,500 — which means you can buy an
electric car for under $20,000, or lease one at a very attractive price. Still thinking of waiting for
a better deal? Those incentives won’t last forever.
A variety of high-quality electric cars are available today. There are over 80,000 of them on
America’s streets, with the Chevy Volt, Nissan Leaf, Toyota Prius Plug-in, and Tesla Model S
leading the pack. Public charging stations are proliferating rapidly, but you don’t need to wait for
them to be as abundant as gas stations. Drivers of plug-in cars enjoy fuel that comes to them,
relying on home charging to meet the vast majority of their needs.
“I often need to drive farther than electric vehicles can go without recharging.”
Broadly speaking, electric cars come in two flavors: all-electric and plug-in hybrid. The second
has no range limitations whatsoever; they have batteries sufficient for normal trips (between 10
and 40 miles, depending on the model), and they become efficient gasoline hybrids for longer
trips. If you want one car to do it all, a plug-in hybrid like the Chevy Volt, Toyota Prius Plug-in,
Honda Accord Plug-in, Ford Fusion Energi, or Ford C-Max Energi is a great option.
If, however, your household has more than one vehicle, an all-electric is an ideal “second car”
you’ll end up using most of the time. All-electrics, such as the Nissan Leaf, Ford Focus EV,
Mitsubishi-i, BMW Active-E, Fiat 500 EV, Coda, Chevy Spark EV, Honda Fit EV, or Tesla
Model S, have ranges between 60 and 265 miles, more than enough for the daily commute.
When it comes time for the long road trip, you can always take the other car. When you get
behind the wheel of an electric car, you’ll experience the joy of full torque from a standstill and a
super-quiet cabin. You may have a hard time going back to a machine that relies exclusively on
thousands of explosions of fossil fuel every minute.
If you’d like to try a plug-in outside of a dealership, you can find an owner on
DrivingElectric.org to give you a spin. You’ll be surprised in ways you could never expect, and
you’ll never get tired of driving on a clean fuel for the equivalent of buck-a-gallon gas.
Transcript of Anti Genetically Modified Foods Video
“GMOs Must Go”
Transcript: In a land of supersize, approximately 85 percent of all processed foods contain
genetically modified organisms. GMO is an acronym that owes its notoriety largely to the
agriculture giant Monsanto: a multi-national, billion-dollar corporation generating global
criticism revolving around the safety of its products and growing monopoly over the world’s
food supply.
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“They are able to patent genetically modified seeds and with a very strong patent so farmers can
only lease the seeds from Monsanto each year and they can’t save the seeds.”
Researchers have documented dozens of health risks associated with the consumption of
modified foods and the majority of Americans have campaigned for GMO foods to be labeled,
just like these organic fruits are labeled. But so far, the will of the people has been silenced by
the money of Monsanto.
According to OpenSecrets.org, the company spent nearly 6 million dollars last year lobbying
federal lawmakers and food regulators. The payoff came this year with the passing of the socalled “Monsanto Protection Act,” a bill that gives bio-tech companies immunity from lawsuits
pertaining to the production and sale of genetically modified seeds.
“The new reality of the world is that chemical companies are now feeding us and our families.
It’s now sort of laboratory to table, rather than farm to table.”
And in an effort to widen its power and profit, the agriculture giant has recently purchased a
corporation which sells climate data to farmers. The price tag of 930 million dollars wasn’t a
problem for Monsanto, which grossed a reported 13.5 billion dollars in revenue last year.
But decades before GMOs and fears about modified foods came along, Monsanto was already in
business. It helped bring pesticides, Agent Orange, and terminator seeds to the market.
Agent Orange was used by the US military during the Vietnam War where it is estimated to have
killed hundreds of thousands of people. Its effects are still being felt today. Vietnam says some
half a million children have suffered birth defects due to herbicide.
Monsanto’s current practices have ignited protests around the globe. Millions are taking to the
streets demanding that big food comes clean by either labeling genetically engineered products
or not selling them at all.
Pro Genetically Modified Foods Video
“GMOs Must Grow”
Transcript: For ACSH, I’m Ana Simovska with your news. In the battle over genetically
modified foods, it seems that Team GMO has just scored a couple of points. The UK’s
Environmental Secretary, Owen Patterson, made a powerful speech today outlining the
importance of using and developing GM technology. Patterson said: “If we use cultivated land
more efficiently, we could free up space for biodiversity, nature, and wilderness.”
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But it’s not just Owen Patterson praising the potential GMOs have. GMO pioneers, including an
employee from the controversial company Monsanto, were awarded the World Food Prize – a
prestigious honor known as the Nobel Prize for agriculture.
The team was awarded the prize for developing the biotechnology thirty years ago that has led to
the creation of genetically engineered crops and pesticides. The discovery is recognized as
creating more food for more people in more places.
While critics question the need and safety of GMOs, some scientists say different. Megan Clark,
the Head of Australia’s National Science Agency, talked about the importance of increased food
production in the upcoming years.
She says: “It is hard for me to comprehend that in the next fifty years we will need to produce as
much food as has been consumed over our entire human history.”
Here is some perspective from ACSH’s Dr. Ruth Kava: “I’m sure that the anti-GMO activists
will say that this is all industry-supported and it’s all somehow manipulated, but I don’t think
that that’s true. I think that this I really a revolutionary kind of enterprise. Genetically
engineering food crops that can really help prevent world hunger in the future.”
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