MOTIVATIONS FOR SEEKING POLITICAL INFORMATION ONLINE AS PREDICTORS
OF INDIVIDUAL’S CROSS-CUTTING EXPOSURE
By
Sangwon Lee

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Communication—Master of Arts
2015

ABSTRACT
MOTIVATIONS FOR SEEKING POLITICAL INFORMATION ONLINE AS PREDICTORS
OF INDIVIDUAL’S CROSS-CUTTING EXPOSURE
By
Sangwon Lee
For a long period of time, a number of political communication scholars to investigate
specific individual attributes (e.g., political interest) that influence individual’s pattern of
exposure to conflicting political viewpoints. However, no research has been conducted on how
individual’s Internet usage influences cross-cutting exposure, despite the fact that people often
seek out politically similar/dissimilar groups to make a better sense of mediated political
information. Thus, this research investigates correlations between types of motivations for
seeking political information online and people’s cross-cutting exposure to dissimilar political
viewpoints. Respondents recruited from Amazon Mechanical Turk (Mturk), were surveyed about
their motivation for seeking political information online and their interpersonal discussion
networks (N = 123). The results showed that party strength significantly moderated the
relationship between entertainment motivation and cross-cutting exposure. Strong partisans were
less likely to be exposed to conflicting political viewpoints, while weak partisans showed the
opposite tendency.

Copyright by
SANGWON LEE
2015

TABLE OF CONTENTS

LIST OF TABLES .......................................................................................................................... v
LIST OF FIGURES ....................................................................................................................... vi
INTRODUCTION .......................................................................................................................... 1
LITERATURE REVIEW ............................................................................................................... 3
METHOD ....................................................................................................................................... 7
Overview ............................................................................................................................. 7
Procedure ............................................................................................................................ 7
Measurement ....................................................................................................................... 8
Independent variables .................................................................................... 8
Dependent variables .................................................................................... 10
Demographics ............................................................................................. 11
Political interest .......................................................................................... 11
Internet use for seeking for political information ....................................... 12
Partisanship ................................................................................................. 12
Analysis............................................................................................................................. 13
RESULTS ..................................................................................................................................... 14
DISCUSSION ............................................................................................................................... 17
Limitations ........................................................................................................................ 18
CONCLUSION ............................................................................................................................. 20
REFERENCES ............................................................................................................................. 22

iv

LIST OF TABLES

Table 1.

Factor Loadings for Motivations for Seeking Political Information Online ............ 9

Table 2.

Factor Loadings for Items and Reliabilities ............................................................ 14

Table 3.

Predictors of cross cutting exposure- two way interactions.................................... 15

v

LIST OF FIGURES

Figure 1.

Predictors of cross-cutting exposure – two-way interactions ................................ 16

vi

INTRODUCTION
Exposure to a diverse marketplace of idea is considered to be a central element in
political conversations that is needed to maintain a democratic citizenry (e.g., Habermas 1989;
Mutz, 2006; Price & Neijens, 1997). The importance of communication across lines of difference
can be traced back to early days. John Stuart Mill, a British philosopher, asserted the importance
of exposure to the other’s perspective by saying that, “If the opinion is right, [people] are
deprived of the opportunity of exchanging error for truth; if wrong, they lose what is almost as
great a benefit, the clearer perception and livelier impression of truth produced by its collision
with error” (Mill, 1956, p. 21). Mill emphasized two potential benefits of exposure to
oppositional views; the first is to seize the opportunity to change one’s mind and develop an
informed viewpoint, and the second is more to deeply understand one’s own position after
confronting different perspectives (Mill, 1956). Likewise, a number of prominent social theorists
have emphasized that democracy has a future only if citizens start talking to people who have
views that differ from their own (Delli Carpini, 2000; Fishkin, 1991; Habermas, 1989; Mutz,
2002, 2006).
Despite the normative importance of exposure to dissimilar views, in reality, it is
unlikely to occur, due to people’s tendency to select politically like-minded individuals to talk to.
In addition, people often feel uncomfortable (i.e., uneasy and bothered) talking to those whose
political opinions are dissimilar from their own (Bennett, Fisher, & Resnick, 1994; Mutz, 2006;
Mutz & Martin, 2001; Ulbig & Funk, 1999). Consequently, people on both sides of an issue do
not often understand one another and they try to avoid talking to each other, which may threaten
the ideal of a pluralist society (Delli Carpini, 2000; Fishkin, 1991; Habermas, 1989; Mutz, 2002,
2006).

1

Thus, a number of political communication scholars have investigated the conditions
that influence how individuals expose themselves to, and engage with dissimilar political
perspectives (Garrett, 2009; Goldman & Mutz, 2011; Mutz, 2006; Mutz & Martin, 2001; Mutz &
Mondak, 2006; Wojcieszak, 2010; Wojcieszak & Mutz, 2009). Diverse individual attributes such
as political interest (Campbell &Kwak, 2011; Ulbig & Funk, 1999; Xenos & Moy, 2007),
ideological strength (Ulbig & Funk, 1999; Wojcieszak, Baek, & Delli Carpini, 2010), political
expertise (McClurg, 2006), discussion willingness (Kim, Scheufele, & Han, 2011), attitude
certainty (Matthes, 2012), and perceived comfortableness in using new technologies (Bachman,
Kaufhold, Lewis, & Gil de Zuniga, 2010; Bae, Kwak, & Campbell, 2013) have been found to
influence individual cross-cutting exposure.
However, to the best of my knowledge, cross-cutting exposure has not been studied in
conjunction with the political information seeking motives online. Connecting these concepts is
theoretically plausible, as people often seek out politically similar/dissimilar groups to make a
better sense of mediated political information (Eveland & Shah2003; Mutz, 2002; Hogg & Reid,
2006). However few studies have examined whether different motivations for seeking political
information online may predict cross-cutting exposure (except for Kayahara & Wellman, 2007).
Only one study, conducted by Kayahara and Wellman (2007), suggested that word-of-mouth
communication among close relationships lead to view political information online, and it again
motivates people to talk with their friends. They utilized in-depth interview to test this scenario. I
advance this study by testing whether and how motivations for seeking political information
online are related to cross-cutting exposure by utilizing quantitative methods.

2

LITERATURE REVIEW
The notion that individuals actively seek information for certain motivation and
discussing with others to reflect information is based on the assumption of the uses and
gratifications theory. The theory posits that people actively search out media messages to satisfy
certain needs, rather than passively consuming mass media messages (Blumler, 1979; Herzog,
1944; Katz, Blumler, & Gurevitch, 1973; Lin, 1996, 1999; McGuire, 1974; McLeod & Becker,
1981; Rubin, 2009; Ruggiero, 2000). Researchers who have utilized the uses and gratifications
theory have aimed to identify why people choose certain media and what kinds of gratifications
they obtain from that media. Even though the uses and gratifications theory has received a fair
amount of criticism and it has gone through a number of revisions over the past several decades
(for the review, see Lin, 1996, 1999; Rubin, 2009; Ruggiero, 2000), this approach has been
credited as one of the most popular theoretical frameworks in the field of mass communication
research (LittleJohn, 2001). The uses and gratifications approach is based on several common
assumptions. a) The audience is active, b) media usage consists of goal-directed actions, c)
media consumption can fill a wide range of audience’ needs, and d) people have enough selfawareness to know what they need and articulate their reasons for using the media (Kaye &
Johnson, 2002; Lin, 1999).
The uses and gratifications approach has been prevalent in the communication field and
changes in the new media environment have once again made it popular in communication
research (Baran & Davis, 2003; Ruggiero, 2000). Compared to traditional mass media in which
individuals only passively consume the content, online technologies are interactive and they
require audiences to be active users (Baran & Davis, 2003; Kaye & Johnson, 2004; Lin, 1996;
Papacharissi & Rubin, 2000; Ruggiero, 2000). This characteristic makes the uses and

3

gratification approach ideally suited for studying the Internet (Baran & Davis, 2003; Kaye &
Johnson, 2004; Papacharissi & Rubin, 2000; Ruggiero, 2000).
Furthermore, the Internet has changed the political landscape more dramatically than
ever before (Bimber & Davis, 2003; Papacharissi, 2002; Shah, Holbert, & Kawk, 2001). The
Internet provides avenues for personal expression and promotes citizen activity (Bimber &
Davis, 2003; Papacharissi, 2002). A growing number of individuals seek political information on
the Internet (Bimber & Davis, 2003; Johnson & Kaye, 2004; Kaye & Johnson, 2002, 2004; Shah
et al., 2001; Xenos & Moy, 2007).
Some attention has been focused on identifying how individuals use the Internet for
seeking political information (Althaus & Tewksbury, 2000; Kaye, 1998; Kaye & Johnson, 2002,
2004). Various motivations lead individuals to seek political information online. Kaye and
Johnson (2004) have identified four motivations prompting people to seek political information
online: guidance, entertainment/social utility, convenience, and information seeking. Guidance
motivation is to look for political advice, and to seek information to guide individuals’ voting
decisions. Entertainment/social utility motivation is to seek entertaining political information for
amusement and relaxing purposes (i.e., entertainment motivation) or to reinforce one’s decision
for discussion with others (i.e., social utility motivation). Convenience motivation is to easily get
information. Finally, information seeking motivation is to search out specific political
information and to keep an eye on the political landscape (Kaye & Johnson, 2004).
Different motivations for seeking information online may lead to different levels of
diversity in political conversations. Individuals with certain motivations may prefer to talk with
people with similar opinions. On the other hand, individuals with other motivations may prefer to
talk with people who hold a range of political opinions. As there has been no attempt to connect

4

motivations for seeking political information online and the level of diversity in political
conversations, I state the following as a research question.

RQ1: How are the four motivations for seeking political information online related with the
level of cross-cutting exposure?

The first research question was designed to merely investigate main effects of motivations,
an unexplored topic. However, most of previous literature has suggested that some variables
moderate the influence of motivations. Previous literature suggests that party strength plays a
critical role in terms of the level of cross-cutting exposure (Luker, 1984; Sears & Whitney, 1973;
Taber & Lodge, 2006). When forming an attitude on a certain policy (i.e., guidance), strong
partisans are more likely to seek for like-minded information compared to weak partisans
(Iyengar & Hahn, 2009; Taber & Lodge, 2006; Wojcieszak & Mutz, 2009). Furthermore, the
theory of motivated reasoning (i.e., selective processing information to arrive at conclusions
congruent with one’s prior belief) suggests that strong partisans tend to have strong directional
goals (see Kunda, 1990; Taber & Lodge, 2006) and try to reinforce their original opinion by
avoiding cross-cutting exposure, while weak partisans may lack such a strong motivation. Thus,
following hypothesis is proposed.

H1. As the guidance motivation increases, the cross-cutting exposure will decrease
among strong partisans and increases among weak partisans

5

Party strength may also function as an interaction term for entertainment motivation.
Strong partisans may have a strong desire to avoid falling into a “negative feeling” by crosscutting exposure. Previous research on humor indicate that people find jokes that denigrate their
own reference group to be less humorous than jokes that denigrate other people’s reference
groups (Becker, 2014; La Fave, Haddad & Maesen, 1976; Priest, 1966; Weise, 1996). The
studies conducted on political comedy also suggested that strong partisans process political jokes
in a way that minimizes their negative feelings and maximizes their positive feelings, while weak
partisans showed less of a tendency to do so (Becker, 2014; Coe et al., 2008; LaMarre,
Landreville, & Beam, 2009; Zillmann, Taylor, & Lewis, 1998). Thus, I predict that partisanship
strength may moderate the relationship between entertainment motivation for seeking
information online and cross-cutting exposure.

H2. As the entertainment motivation increases, the cross-cutting exposure will decrease
among strong partisans and increases among weak partisans

6

METHOD
Overview
An online experiment was conducted with 152 participants. The initial part captured
measurements for political interest, political information seeking hours, and party affiliation.
Then, participants were asked about motivations for seeking political information online and
about their discussant networks with whom they most frequently discuss politics. Demographic
variables were collected at the end of the survey. The sample consisted of 152 U.S citizens who
are above 18 years old. The sample was restricted to U.S citizens, above 18 years old who are
eligible to vote in presidential elections. Twenty nine cases were dropped because the subjects
could not be identified as any party (i.e., indicated as “pure Independents” or “other party” in the
partisanship question. This produced a final sample size of 123 for hypothesis testing.
Procedure
An online survey was conducted via Amazon Mechanical Turk (Mturk) from February
23 to 28, 2015. Several researchers have criticized the tendency of studies’ heavy reliance on
American college sample and its inaccuracy in measuring public opinion (Buhrmester, Kwang,
& Gosling, 2011; Druckman & Kam, 2010; Larose, 2004; Sears, 1986). Among various ways to
revise this problem of collecting data, Mturk has become increasingly popular in research areas,
allowing for participants that are more demographically diverse than typical American college
samples and standard internet samples (Buhrmester et al., 2011; Paolacci, Chandler, & Ipeirotis,
2010). A number of political scientists conducting research on political opinions and political
issues has widely utilized Mturk (e.g., Berinsky, Huber, & Lenz, 2012; Berinsky & Kindler,
2006). Upon consenting to participate in the study, participants were informed to participate in
political information seeking research. First, self-report surveys were utilized to assess the

7

participants’ motivations for searching for political news on the Internet. Then, participants were
asked about how much time per week they used the Internet to seek political information, their
level of political interest, and political party affiliation. Further, they were instructed to name the
three people whom they most frequently talked about topics on government, election, and
politics. After filling out three discussants, participants were asked to answer the extent of the
agreement about political issues, the extent of the disagreement about political issues, the
presidential candidate they prefer, and the frequency of disagreement about political issues (see
Mutz, 2002). After answering all of these questions to measure extent to cross-cutting exposure,
the participants were asked about the motivations for seeking political information online,
composed of four subscales from Kaye and Johnson’s study (2004) (For more details, see
Appendix 1). These sub-scales include four motivations: guidance, entertainment/social utility,
convenience, and information seeking. Finally, participants filled out their demographic
information, and were paid 25 cents after completing the survey.
Measurement
Independent variables
IVs are motivations for using the Internet for political information (e.g., Kaye, 1998;
Kaye & Johnson, 2002, 2004; Ponder & Haridakis, 2014). I utilized Kaye and Johnson (2004)’s
scale, which is composed of four motivations: guidance motivation, entertainment/social utility
motivation, convenience motivation, and information seeking motivation.
Following the data collection, confirmatory factor analysis (CFA) were conducted with
PACKAGE (Hunter, Cohen, & Nicol, 1982), which provides estimates of factor loadings based
on a centroid method of estimation. This program calculates predicted correlations based on the
specified model, then provides deviations between the actual correlation and the predicted to

8

gauge model fit (Levine & McCroskey, 1990). The specific item retention criteria employed
primary factor loadings of at least .40. Internal consistency and parallelism theorems were
assessed with confirmatory factor analysis using R. Among the original 20 items, eight items
were deleted due to low primary factor loadings or higher factor loadings on the other dimension.
Table 1. Factor Loadings for Motivations for Seeking Political Information Online
Factor Loadings for Motivations for Seeking Political Information Online
‘‘I use the Web for political information...’’
F1
F2
Factor One α = .68
To help me decide how to vote
.12
.54
To judge personal qualities of candidates
.To help me decided about important issues
.42
.67
To see what a candidate will do if elected
.28
.75
For unbiased viewpoints
To find out about issues affect people like myself
-

F3

F4

.29
.33
.53
-

.24
.59
.58
-

Factor Two α = .83
Because it is entertaining
To enjoy the excitement of the election race
To give me something to talk about with others
Because it is exciting
To use as ammunition in arguments with others
Because it helps me relax
To remind me of my candidates strongest points

.24
.33
.37
.38
.14
-

.77
.79
.65
.91
.40
-

.31
.37
.32
.31
.16
-

.04
.16
.33
.45
.15
-

Face Threat Factor Three α = .79
To access information quickly.
Because information is easy to obtain
To see how candidates stand on issues
To access political information from home

.06
.36
-

.39
.29
-

.81
.81
-

.70
.65
-

Factor Four α = .75
To find specific political information that I am looking for
To keep up with main issues of the day
To access political information at any time

.55
.57

.27
.39

.72
.58

.78
.78

9

After dropping these items from the scale that caused large errors, the data fit a fourfactor model. The results revealed an acceptable fit of the data to the model (CFI = .89, RMSE =
.08, SRMR = .06). Tests of parallelism revealed small error rates, and the number of significant
deviations (1 out of the 66 residuals) did not exceed what was expected by sampling error.
Reliability checks were acceptable as well.
For the guidance motivation items, reliability of the three items was acceptable (α = .68),
even though it was not as high as one may desire. Relatively low reliability score can be
improved with the creation of additional items in the future study. The sum of the retained items
was distributed normally (M = 3.61, SD = .82).
For the entertainment/social utility motivation items, reliability of the five items was
good (α = .83). The sum of the retained items was distributed normally (M = 2.89, SD = .91).
For the convenience motivation items, reliability of the two items was good (α = .79). I
decided not to retain more items, as item quality was considered as a more important criterion.
(M = 3.93, SD =.80). For the information seeking items, reliability of the two items was good (α
= .75) and the sum of the retained items was distributed normally (M = 3.90, SD = .81).
After CFA, I utilized the revised version of Motivations for Seeking Political Information
Online as an independent variable in my study.
Dependent Variable
Cross-cutting exposure was the dependent variable. The concept of cross-cutting
exposure was operationalized using Mutz’s definition “the extent to which a person's political
network included exposure to oppositional views, a measure that assessed the extent to which a
source provided dissonant contact independent of the frequency of that contact” (Mutz, 2002).
For the first step, participants were asked to name three discussants whom they most frequently

10

discuss government, election, and politics with. They were allowed to name generators with
initials or nicknames in order to protect their privacy. Then, they were asked with four questions,
“Compared with [discussant], would you say that your political views are much the same,
somewhat different, or very different?”, “Which presidential candidate, if any, does discussant
favor? Obama, Romney, or other?” and “Overall, do you feel discussant shares most of your
views on political issues, opposes them, or doesn’t do either one?”, and “When you discuss
politics with [discussant], do you disagree often, sometimes, rarely or never?” (for more details,
see Mutz, 2002). Participants were asked to answer each questions per each three discussants. If
the opinions between the participants and the discussants were similar or the same, it was coded
as 1. If the opinions were somewhat different, it was coded as 2. If the answers were very
different, it was coded as 3. Then, reliability test was conducted for four questions, and reliability
of the four items was acceptable (α = .68). Then, for calculating cross-cutting exposure, scores
for each discussants per each questions were all summed and averaged.
The scores ranged from 1 to 3 (1 = same, 2 = somewhat different, 3 = very different).
The average score for cross-cutting exposure was 1.6 (SD = .34). Given that a score of 2
indicates the middle point of cross-cutting exposure, the average score indicates that people tend
to talk to homogeneous others, although that score was not extremely skewed.
Demographics
A set of demographic variables served as control variables in this analysis: gender (male
= 62.6%, female = 37.4 %, coded as a dummy variable considering female as a reference
category), age (M = 32.26, SD = 10.49), education (median = 2 year college degree), and
household income (median = $24000-$36000).
Political interest.

11

At the beginning of the survey, participants were asked with a single question,
“Generally speaking, how much are you interested in politics?” with a 5-point scales from “Not
at all” to “very much”, coded with higher values for higher political interest. The average score
was 3.44 (SD = 1.15).
Internet use for seeking for political information
Following the Internet usage time in general, participants were asked with a question,
“How much time per week do you usually pay attention to news about politics?” with a 5-point
scales, “Less than 30 minutes/30-60 minutes/1-2 hours/2-5 hours/More than 5 hours”, coded with
higher values for greater time usage. Time spend on the Internet for political information seeking
was relatively low (median = 30-60 minutes per week).
Partisanship
Conventional measure of party identification was utilized (Brody, 1978; Claggett, 1981;
Shively, 1979). The participants were asked about their party identification and its strength.
Participants were also asked which party they think of themselves as closer to- Democrat, a
Republican, an Independent or What. If participants identified themselves as either Democrat or
Republican, they will be asked to identify themselves as “strong Democrat (Republican)” or
“weak Democrat (Republican)”. If they identify themselves as Independent, they were asked to
identify themselves as “leaning toward Democrats” or “leaning toward Republicans”. Those who
identified themselves as “strong Democrats” or “strong Republicans” were categorized as strong
partisans. Those who identified themselves as “weak Democrats” or “weak Republicans” and
“Independent but leaning toward democrats” or “independent but leaning toward republicans”
were all categorized as weak partisans. Here, Independent leaners were regarded as partisans as

12

they tend to show similar vote choice and policy opinions with partisans (e.g., Lascher & Korey,
2011). Pure Independents were excluded from the analysis.
Party identification was skewed toward the Democrats which reflects skewness toward
Democrat samples in Mturk samples (Berinsky et al., 2012); 54.6% were Democrat or Democrat
leaning, 27.0% were Republican or Republican leaning, and remaining 18.4% were those who
answered as “Independent with no leanings” or “Other”.
Analysis
I analyzed the results using multivariate regression analyses, in which I entered blocks of
variables following their assumed causal order. Blocks were entered as follows. The Model 1
included four motivations (i.e., Guidance motivation, Entertainment motivation, Convenience
motivation, and Information seeking motivation), party affiliation (coded as a dummy variable
considering Democratic Party as a reference category), party strength (coded as a dummy
variable considering weak partisans/leanings as a reference category), political interest, time for
using web for political information seeking, indicators for gender, age in years, household
income, and level of education (8-point scale). The Model 2 added the interaction terms for party
strength X guidance motivation and party strength X entertainment motivation.

13

RESULTS
The first set of regression results is presented in Table 2. The Model 1 includes four
motivation variables and the full set of control variables. The Model 2 adds interaction terms for
party strength X guidance motivation and party strength X entertainment motivation.
The Model 1 which includes all control variables and four motivations, turns out to be not
significant (p = .07, two tailed). I explored RQ 1 to investigate whether there are any motivation
related to cross-cutting exposure. The coefficient estimates shown in Table 2 show that none of
motivations were significant predictors for cross-cutting exposure (p > .05).
Table 2. Predictors of cross-cutting exposure
Cross-cutting exposure
Control variables

B

T value

Age

.10

1.06

Gender(high: male)

-.10

-1.04

Education

-.14

-1.56

Household income

.02

.19

Political interest

.05

.30

Time for using web for political information seeking

.02

.18

Guidance Motivation

.07

.61

Entertainment Motivation

-.06

-.52

Convenience Motivation

.004

.03

Information Seeking Motivation

-.03

-.19

Partisanship (high: Republican party)

.23*

2.33

Party Strength

-.18†

-1.88

Total R2 (%)

6.7%†

Note: Entries are standardized regression coefficients
†p = .10, two tailed; *p < .05, two tailed; **p < .01, two tailed; ***p < .001, two tailed.
14

The addition of interaction terms for party strength X guidance motivation and party
strength X entertainment motivation (Model 2), led to a statistically significant increase in R2
of .098, F(2, 108) = 7.11, p < .001, two sided. Hypothesis 1 predicts that as the guidance
motivation increases, the cross-cutting exposure will decrease among strong partisans and
increases among weak partisans. The results do not provide support for Hypothesis 1 (p > .05).
Hypothesis 2 predicts that as the entertainment motivation increases, the cross-cutting exposure
will decrease among strong partisans and increases among weak partisans. The interaction
between entertainment motivation and party strength is significant (p < .001). An interaction
graph is plotted in Figure1. The two different slopes for strong and weak levels of party strength
indicate that avoiding cross-cutting exposure is greater for respondents with strong levels of
partisanship. The result suggests that as the entertainment motivation increases, the level of
cross-cutting exposure decreases among strong partisans and increases among weak partisans.
Table 3. Predictors of cross-cutting exposure- two way interactions
Cross-cutting exposure
B

T value

Prior blocks (R2; %)

6.7†

Guidance Motivation X Party Strength

.35

.73

Entertainment Motivation X Party Strength

-1.16***

-3.77

Incremental R2 (%)

9.8***

Total R2 (%)

16***

Note: Prior blocks include age, gender, education, household income, political interest, time for using
web for political information seeking, guidance motivation, entertainment motivation, convenience
motivation, information seeking motivation, partisanship, and party strength. Entries are standardized
regression coefficients after controlling for the prior blocks. †p = .05, two tailed; *p < .05, one tailed;
**p < .01, one tailed; ***p < .001, one tailed.

15

Figure 1. Predictors of cross-cutting exposure: two-way interactions.

16

DISCUSSION
As Denton and Woodward (1998) noted, political communication scholars study how
information is produced, disseminated, processed, and influences one’s attitude both through the
media and interpersonal communication. Given a dearth of study that connects the two spheres,
this study attempts to connect one's information seeking through the media (online) to their
interpersonal political discussion off-line.
The main effect of motivations for seeking political information online on cross-cutting
exposure was not found in this study. However, the results showed that entertainment motivation
is negatively related to the extent to cross-cutting exposure, moderated by party strength. Strong
partisans who seek political information online for entertainment motivation are more likely to
avoid cross-cutting exposure in their interpersonal relationships, whereas weak partisans with the
same motivation are more likely to actually engage in cross-cutting exposure in their
interpersonal relationships.
This result may be due to different motivations between strong and weak partisans to
seek for entertaining political information online. Strong partisans may seek entertaining political
information that presents a caricature of people whose political views are on the other side of the
political spectrum (e.g., Becker, 2014; LaMarre, Landreville, & Beam, 2009; Zillmann, Taylor,
& Lewis, 1998), which may also lead to them talking with homogeneous others in interpersonal
discussion networks. On the other hand, weak partisans may be entertained by seeking
information, including political satire in general, or they may at least have less of a desire to
maximize their enjoyment by denigrating the other side, which may lead to them talking about
politics with heterogeneous groups of people.

17

This finding is meaningful considering the scant academic focus on the entertainment
function of political information seeking. Despite the rise of studies that have examined
entertainment media as a source of political information (e.g., Fox, Koloen, & Sahin, 2007;
Hollander, 2005; Holbert, Kwak, & Shah, 2003; Holbert, Shah, & Kwak, 2003; Kim & Vishak,
2008; Moy, Xenos, & Hess, 2005; Prior, 2003; Young, 2004; Young & Esralew, 2011; Zillmann,
2000), a few have attempted to address the entertaining function of political information seeking
online. (except for Kaye & Johnson, 2002, 2004; Ponder & Haridakis, 2014)
Limitations
Although, this research adds some noteworthy findings to the previous literature, it is not
without limitations. First and foremost, it is difficult to measure an individual’s motivation
accurately (for a review, see Kleinginna & Kleinginna, 1981). The motivations categorized in
this study may be problematic in that motivations are sometimes mixed; thus, it might be
difficult for the motivations to be clear-cut. In addition, people may not necessarily recognize
their motivations for engaging in certain types of behavior. Individuals are not be particularly
reflective enough to realize why they act in the way that they do (Locke, 1975; McClelland,
1961; Touré‐Tillery & Fishbach, 2014). In addition, motivations tend to be floating, varying
from state to state (Logan, 1976). Thus, instead of conceptualizing motivation as a dynamic state,
it is worth thinking about an alternative approach, such as conceptualizing motivation as a longstanding personality trait (Lodge & Taber, 2000; Nir, 2011; Smith, Fabrigar, Powell, & Estrada,
2007).
Secondly, the measures of cross-cutting exposure in this study has some limitation.
The scores of the three discussants were averaged to constitute the final cross-cutting exposure
score. In doing so, the variance of the three scores was neglected in this study; that is, unique

18

information about each discussant was lost in the analysis and the interpretation. Therefore, the
average score should be best interpreted as the focal individual's general tendency toward crosscutting exposure. It has limited utility in representing each unique tie of the individual's
discussant network. For the future, the cross-cutting exposure can be assessed by using
dichotomous measures.
Finally, the sample also lacked substantial representativeness. A sample gathered only
through an online crowding source, such as Mturk, is known to have some shortcomings (e.g.,
Berinsky, Margolis & Sances, 2014; Goodman, Cryder, & Cheema, 2013). For instance,
Berinsky et al (2014) pointed out that, because it is impossible for a researcher to monitor the
participants, the respondents may not pay enough attention to survey questions. It would be
desirous to recruit more participants from offline venues in order to make the findings more
rigorous.

19

CONCLUSION
This research examined (a) effect of motivations for seeking political information online
on cross-cutting exposure (b) the moderating role of party strength between motivation for
political information seeking online and cross-cutting exposure. To investigate these questions,
participants from Mturk were surveyed about motivation for political information seeking online,
their personal discussants network, party affiliation with strength, and a number of questions to
control for possible factors which may influence the original relationship. Prior to analysis, the
often utilized Motivations for Seeking Political Information Online Scale has been revised
following the result of Confirmatory Factor Analysis (CFA). After CFA, sub-scales showed
much clearer distinction between the four dimensions. Multivariate regression analyses shows
that main effects of motivations for seeking political information online on cross-cutting
exposure were not significant. Interaction effect of guidance motivation and party strength on
cross-cutting exposure was not significant (H1 not supported), however, moderating role of party
strength was strong between entertainment motivation and cross-cutting exposure.
The data suggests that as the entertainment motivation increases, the cross-cutting
exposure decreases among strong partisans and increases among weak partisans. The results
imply that strong partisans who seek political information online for entertaining motivation,
prefer talking with people who are similar to themselves in political views. On the other hand,
weak partisans with the same motivation prefer talking with people with different perspectives.
In conclusion, this research has two contributions. First, the shortcoming of the previous
motivation scale was addressed and revised. Second, this study investigated the relationships
between motivations for seeking for political information online, party strength, and cross-

20

cutting exposure, which have not been examined in previous research. Therefore, the findings
from this study may provoke further research in the future.

21

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