Tap, scroll down, chat and more?

Tap, scroll down, chat and more?  Examining the influence of mobile applications and interpersonal discussions towards political participation

By Joseph Yoo, Pei Zheng, Hyeri Jung, Vickie Chen, Shuning Lu and Thomas Johnson

Due to the mobility and portability, mobile technology enables users to gather political information and discuss with others conveniently through applications. Scholars have examined the interplay of news media use and interpersonal discussion to predict political engagement, but few studies have focused on the role of mobile applications as news sources. Based on the Differential Gains and Communication Mediation Models, this study tested the influence of mobile applications on political participation. Results indicated that online discussions both mediated and moderated the relationship between mobile application use and political participation. Offline discussions showed a limited capacity as a mediator.


Mobile communication is ubiquitous. Mobile technology can connect individuals virtually anytime, anywhere and mobile users can easily engage in several interpersonal discussions and information-searching activities. The mobility and portability of mobile devices allows users to use their mobile devices conveniently. Pew (2013) found that 44% of U.S. adults own a smartphone and 22% of U.S. adults also possess a tablet PC. Among them, 62% of smartphone and 64% of tablet owners are consuming news on their mobile devices. Pew (2014) suggested that 81% of cellphone owners used their phones to send or receive text messages and 60% used them for Internet access. Such descriptive results confirm that the main function of a mobile phone is interpersonal communication, followed by information searching.

The basic functions of mobile communication are social and instrumental dimensions. Shah, Kwak, and Holbert (2001) discovered that (1) information exchange about news and public affairs, (2) sociability with family and peers and (3) personal recreation are three main functions for mobile phone uses. Campbell and Kwak (2010a) argued that using mobile devices for information exchange and recreation functions significantly predicted civic engagement. On smartphones or tablet PCs, mobile communication can be achieved by applications, designed to heighten the interactive experience of a touch screen (Johnson & Kaye, 2014). Reliance on information resources and online communication can significantly predict political engagement. While mobile (online) communication is not a face-to-face process, interpersonal communication is still a significant predictor of political participation (Vu, et al., 2013).

Scholars are still confirming the influence of media use and communication activities in predicting political participation. The Differential Gains Model (Scheufele, 2002; Scheufele & Nisbet, 2002) and the Communication Mediation Model (Cho, Shah, McLeod, McLeod, Scholl, & Gotlieb, 2009; Shah, Cho, Eveland & Kwak, 2005) are two main models to explain the influence of media use and communication toward political engagement. The Differential Gains Model argues that political outcomes of media consumption are contingent upon the media’s interaction with interpersonal communication both off and online. More specifically, individuals who frequently discuss political issues in conjunction with consuming media are more likely to participate in both offline and online political activities. The Communication Mediation Model posits that mass communication strongly influences political activities but such a relationship is indirect. According to the Communication Mediation Model, communication is a mediating variable between reliance on mass communication and political behaviors.

While some scholars have examined the effectiveness of the two models based on traditional and Internet news sources, few studies have focused on the role of mobile applications on political engagement. This study examines the influence of mobile application use on political participation, from the perspective of the Differential Gains Model and the Communication Mediation Model.

Literature Review

Mobile Communication and Politics

As one of the fastest diffusing media, mobile phone technology has been noted for its growing influence on social and cultural aspects of people’s daily life (e.g., Campbell & Kwak, 2011; Fortunati, 2002; Katz, 2006; Ling, 2008). Political use of mobile phone technology has also been widely witnessed in recent events throughout the world, such as SARS in China (Pomfret, 2003) and the Arab Spring revolution (Wagner, 2011). This booming phenomenon implies that mobile phones have evolved beyond simple chatting and calling into tools for political activities and mobilization.

Some scholars have pointed out that the affordability of mobile phone technology lends itself to be a promising component of democracy, allowing for widening the public sphere and strengthening civil society via the creation of new networks and the dissemination of information (Rheingold, 2002). Basically, Campbell and Kwak (2011) argued engagement with a different number of network ties contributed to social trust and the desire to contribute for mutual benefits. Those different networks share views and interests, participating in the political process and ultimately making a contribution to democratic society. Among many distinctive aspects of mobile communication, the low cost, easiness and portability lend itself to reach numerous people, even those who are often politically apathetic (Hermanns, 2008) and enables people to engage in civic life anytime and anywhere (Wei & Lo, 2006). Also, such features of mobile applications allow users to have more chances to be involved in a discussion with networks consisted of both strong and weak ties. Furthermore, mobile technology also transforms people from passive receivers into active players in the way that people are triggered to react towards messages received from someone in their network (Green & Gerber, 2004).

However, some are concerned about the dark side of mobile communication. Compared with other media, mobile phone technology is a characteristically personal and privatized one; people use mobile phones primarily for connecting with social contacts (Campbell & Park, 2008). Therefore, by strengthening one’s core social network and bringing together like-minded individuals, the use of mobile phones may result in social insularity and political detachment (Habuchi, 2005). Also noteworthy is that the negative link between recreational use of media and engagement in civic life observed by scholars (Shah, Kwak, & Holbert, 2001; Sotirovic & McLeod, 2001) tends to offer shaky evidence that mobile phone technology will boost civic activity.

The evolving debate on the hopes or fears of mobile phone technology fosters empirical inquiry on the linkage between different mobile usage patterns and civic or political engagement. Early studies addressed the use of typical functions of mobile phones and found that text messaging, compared with voice calling, is more likely to be associated with membership in a community and political organizations (Ling, et al., 2003). Other research revealed that both informational and recreational use of mobile phones are positive predictors of civic and political participation, while relational use is not found to be a strong predictor (Campbell & Kwak, 2010b; Kwak, et al., 2011).

It is worth noting that most studies cited above examined mobile phone use through texting, calling and mobile Internet browsing. With the penetration of the smartphone, recent report shows that there has been a sharp increase in mobile application use among the public in the previous five years (Fielder, 2014). This notable trend paves new ways to examine the link between mobile phone use and political participation. Thus, this study looks closely at the emerging pattern of mobile application use for political news and information and its impact on political participation from the following theoretical approaches.

Differential Gain, Mobile News Use and Political Participation

The Differential Gains Model aims to explain the variations in the relationship between news consumption and participation behaviors (Scheufele, 2002). According to Scheufele (2002), the impact of media content on citizens’ understanding of politics and ultimately on participatory behavior might be contingent on discussing politics with others. In other words, interpersonal discussion moderates the potentially informational influence of mass media on its audiences. Citizens’ understanding of politics depends on an interactive effect between mass and interpersonal communication. Reasons behind the interaction effect are two-fold: first of all, interpersonal discussion helps citizens to elaborate on what they consume in mass media and assist them in reaching a decision about how they might participate (Lemert, 1981). Second, engaging in interpersonal discussion mobilizes news resources by exposing individuals to more diverse, rational and objective perspectives of politics that they may not encounter by reading news themselves. Discussing with others helps people to be aware of politics (Brundidge, Garrett, Rojas, & Gil de Zúñiga, 2014), thus, mobilized and well-informed citizens are a key antecedent of political participation.

The essence of differential gains is the relation between information and participation (Scheufele, 2002). Information is essential. The fundamental assumption is that more informative individuals, who obtain information from either news media or interpersonal discussion, are more likely to participate in politics. With the development of media technologies, the Internet, and more recently mobile apps, have supplanted traditional mass media. Print and broadcast news still have barriers for less educated or less knowledgeable individuals, while digital media is believed to be more accessible to everyone, given it’s cheaper (or even free), faster and the content is more straightforward than traditional media. Mobile apps are believed to provide mobilizing information (Lemert, 1981) that enable citizens to participate meaningfully in politics on a day-to­day basis. Therefore, mobile phones and apps help spread news to a larger and more diverse population, laying a more solid foundation for citizen participation.

Interpersonal discussions among citizens have been treated as the “soul of democracy” in research on media, interpersonal communication, and democratic citizenship (Brundidage et al., 2014). Initially, the Differential Gains Model was limited to examining the interaction between traditional news media reliance and face-to-face communication (Scheufele, 2000, 2002); however, the emerging media technologies have kept updating the model with the latest media platforms and communication patterns. Studies have recognized that the Internet serves as a discursive space for users to express opinions and interact with each other (Mitra & Watts, 2002). While the effect of online discussion on political participation is complex (Brundidge, 2010), studies in general agree that online discussion could stimulate political participation and civic engagement (Shah, et al., 2005) with the Internet’s capacity of turning disparate groups or communities into an “electronic commonnation.” (Scheufele, 2002, p.49). Specifically, chatting online (Hardy & Scheufele, 2005), emailing articles to friends, participating in online forums (Kim & Johnson, 2006), using blogs (Kim, Johnson & Kaye, 2013), and commenting on political blogs replace or supplement face-to-face discussion to influence participatory behaviors (Brundidge, et al., 2014; Gil de Zúñiga et al., 2009).

Based on discussions about the influence of mobile phones explained by the Differential Gain Model, this study advanced two hypotheses:

H1: The interaction between mobile application use and online communication is positively related to H1a) online and H1b) offline participation.

H2: The interaction between mobile application use and face-to-face communication is positively related to H2a) online and H2b) offline participation.

Communication Mediation Model

With the advent of the two-step flow model (Katz & Lazarsfeld, 1955), the idea that interpersonal discussion mediates the relationship between news consumption and individual engagement became axiomatic. Based on this idea of the Communication Mediation Model (McLeod et al., 2002; Sotirovic & McLeod, 2001), scholars have examined the complementary relationships between mass media and interpersonal discussion. One of the implied premises of the model asserts that although the influence of mass media on political participatory behavior is strong, it is often mediated by an individual’s discussion about politics with others.

There are two explanations for the positive mediating effect of interpersonal discussion on the relation between news consumption and political participation. First, consuming news information offers rich topics to elicit political conversations among people (Delli Carpini, 2000), drawing attention to important issues, enriching political knowledge, emphasizing opportunities for political activities, and eventually igniting participatory engagement (McLeod, Scheufele, & Moy, 1999). Second, scholars acknowledge that by reinforcing interpersonal discussion as a mediating role, it can facilitate the effect of news media consumption on civic participation (Cho et al., 2009; Lee, 2009). News consumption and interpersonal discussion are not competing but complementary factors that both have the ability to produce political engagement (Chaffee & Frank, 1996).

Communication scholars acknowledge that interpersonal discussion is a critical component of a wide range of media effects (Kim, Wyatt, & Katz, 1999). Like the Differential Gains Model, individuals who are engaged in interpersonal discussion are able to use complex concepts, make deep logical connections among them, and create consistent and reasoned argumentations (Cappella, Price, & Nir, 2002). Furthermore, online political communications mediate some of the effects of political participation (Cho et al., 2009; Shah et al., 2007).

Many scholars have tried to advance the Communication Mediation Model by identifying multitudinous features rooted in media and interpersonal discussion that may yield distinct outcomes (Eveland & Hively, 2009; Kwak et al., 2005). Not only traditional news media such as television and newspapers, but also online media are sources of political information, and foster political discussion and participation (Shah et al., 2005). With the advent of the Internet and the development of communication technologies, mobile devices such as smartphones and tablets have become sources for the public to consume political information. Although previous works have extended the scope of the Communication Mediation Model by incorporating various media channels and interpersonal discussion, there is limited discussion on how the model might vary depending on a newer form of media technology: the mobile apps in which people use both to seek out information as well as discuss politics. Based on the discussions about Communication Mediation Model, this study established two hypotheses.

H3: Online communication mediates the relationship between mobile application use and a) online and b) offline political participation.

H4: Face-to-face communication mediates the relationship between mobile application use and a) online and b) offline political participation.


Data Collection

To answer the research questions empirically, an online survey was conducted from one week before to one week after the 2012 presidential election. The total sample size was 1,267.

This study used’s Mechanical Turk (MTurk) to recruit respondents. MTurk is a crowdsourcing website that provides easy access to large and diverse respondents (Mason & Suri, 2012). Respondents from MTurk have diverse backgrounds in terms of age, ethnicity, and socioeconomic status. Previous studies showed the consistency in behaviors between users from MTurk and offline users. That is, MTurk users and offline users have similar behavior patterns (Berinsky, Huber, & Lenz, 2012; Mason & Suri, 2012; Messing & Westwood, 2012; Rand, 2012; Riordan & Kreuz, 2010). Although respondents from online panel is not representative, it is widely used in survey and experiment research (Vu, et al., 2013), suggesting that Mturk is acceptable in research. The respondents of this study were restricted to U.S. voters.

Independent Variables

Reliance on mobile applications. Respondents were asked how much they rely on smartphone/tablet apps for political news and information. Smartphone/tablet reliance was measured on a 5-point scale (1=never rely on, 5=heavily rely on; M = 1.97, SD = 1.22).

Political discussion variables. Online discussion and face-to-face discussion were measured in the survey. For online discussion, respondents were asked the level of interaction (sending comments, sending links, and reading) when they access different online sources, including political blogs, social network sites and Twitter on a 5-point scale (1=never interact; 5=very high interaction. Blog: M = 2.46, SD = 1.76, SNS: M = 2.95, SD = 1.61, Twitter: M = 2.69, SD = 1.89, Cronbach’s α = .72). Face-to-face discussion was measured by asking respondents how much they rely on face-to-face discussion with others for political news and information, using a 5-point Likert scale (1= never; 5= always. M = 3.24, SD = .94).

Interaction variables. Online discussion and face-to-face discussion with reliance on news apps were used to create interaction terms. To avoid multi-collinearity, the news app reliance variable and discussion variables were transformed into z-scores. Two interaction variables were created: news app reliance with online discussion, and news app reliance with face-to-face discussion.

Dependent Variables

Two dependent variables, online political participation and offline political participation were examined separately in this study. Online political participation was created by combining six questions, which asked respondents to mark their level of activity on each political activity (1=not involved at all, 10=involved all of the time). The six items are 1) Contacted via the Internet by a national, state or local government official about an issue, (2) Contributed money via the Internet to a political candidate or a party or any political organization or cause, (3) Attended online a political meeting in support or against a particular candidate, party, or issue, (4) Signed or distributed an online petition, (5) Tried to persuade someone online (i.e. email, Twitter, Facebook, Skype) to vote for or against a political issue, cause or candidate, and (6) Informed someone else using an online source (i.e. web, email, Twitter, SNS), about a political event as it was happening (Cronbach’s α = .79, M = 2.49, SD = 2.55).

Offline political participation was created by combining six 10-point items, which asked respondents to mark their level of activity for each political activity (1=not involved at all, 10=involved all of the time). The six items are (1) Contacted by telephone, mail or in person by a national, state or local government official about an issue, (2) Contributed money by mailing a check or calling in a credit card number to a political candidate or a party or any political organization or cause, (3) Attended in person a political meeting in support or against a particular candidate, party or issue, (4) Signed or distributed a printed petition, (5) Tried to persuade someone by telephone to vote for or against a political issue, cause, or candidate, (6) Informed someone else by telephone about a political event as it was happening (Cronbach’s α = .83, M = 2.83, SD = 2.76). The minimally acceptable reliability of Cronbach’s α is .7(Peterson, 1994)

Controlled Variables

Demographics. Gender, age, race, education and the level of incomes were measured. Overall, 51.7% of the respondents were male and 48.3% of the respondents were female. Respondents’ ages ranged between 18 and 80 (M=33.89, Median = 31). While 78% of the respondents were Caucasian/White; 7.8% were African American/Black; 6.7% were Asian/Pacific Islander; 4.4% were Hispanic/Spanish/ Latino. Race was recoded as White (78%) and non-White (22%). Education was measured on a six-point scale: less than high school (1.3%), high school graduate (11.4%), some college (38.2%), four-year college degree (34.3%), master’s degree (11.5%), and terminal degree (i.e., Ph.D., M.D., J.D., Ed.D.) (3%). The education median was “some college.” Income was measured by an open-ended question on estimated annual income for 2012 (M = $47,577, Median = $32,000).

Political predisposition. Three political variables were examined: political interest, political ideology and party ties. Political interest was created by combining two questions. Respondents were asked to rate their political interest in general and in the 2012 presidential election on a 10-point scale from 1=not at all interested to 10=very interested. Reliability tests indicated a strong internal consistency between the two questions (Cronbach’s α= .88). The minimally acceptable reliability of Cronbach’s α is .7(Peterson, 1994). Political ideology was measured on a 5-point scale from 1=very liberal to 5=very conservative (M = 2.63, SD = 1.07). Party ties were measured on a 10-point scale, from 1=no political party ties to 10=very strong political party ties (M = 5.8, SD = 2.79).

Data Analysis

In order to test the Differential Gains Models (H1 and H2), this study conducted hierarchical regression analysis using SPSS. Demographics were entered in the first block, and three political predisposition variables in the second block as controlled variables. In the third block, the reliance on mobile application use was entered and the fourth block included online and face-to-face discussion. Interactions of mobile app use with the two forms of discussion were entered in the fifth block with the two interactions measured separately. For H3 and H4, path analyses were conducted on AMOS. Demographic information and political predispositions were controlled. The exogenous variable is reliance on news apps, and the mediation variables are two forms of interpersonal discussion. The exogenous variables are the two forms of political participation.


Hypothesis 1 and 2 asked about the interaction effects of mobile application use and online and face-to-face communication in predicting online and offline political participation. The results showed that the interaction of reliance on news app and online discussion was significantly related with online political participation (ß = .087, p<.01) and offline political participation (ß = .129, p<.001). It suggested a strong tendency of getting involved in online and offline political participation among those who rely on mobile applications and discuss political issues online. In other words, when using mobile apps for news, people who discuss politics online will be more likely to participate politically both online and offline than those who are less involved in online political discussion. However, the interaction effects between reliance on mobile apps and face­to-face discussion did not predict both online and offline political participation. Thus, while Hypothesis 1 is supported, Hypothesis 2 is rejected. Besides, the Differential Gains Models showed that the main effects of mobile application use (ß = .081, p<.01) and online discussions (ß = .457, p<.001) were significant in predicting online political participation. Also, in predicting offline participation, the main effects of mobile application use (ß = .083, p<.01) and online (ß = .384, p<.001) and offline (ß = .096, p<.001) discussions were statistically significant. The relationship in Figures (1 and 2) indicates that there is a positive relationship between mobile app use for news and political participation online and offline, yet online discussion speeds up the whole process.





Hypothesis 3 and 4 examined the mediation effects of mobile application use and two forms of discussion in predicting online and offline political participation. To examine Hypothesis 3 and 4, this study conducted two path analyses. First, full (saturated) models were calculated with all exogenous, control and endogenous variables. After trimming out insignificant paths on the full models, actual (parsimonious) models were created.


To examine the fit between the original data and the three actual models, this study compared the performance of those models with fit measures, including the ratio of the normed chi-squared statistics to the degrees of freedom for the model (CMIN/df), the Normed Fit Index (NFI), the Comparative Fit Index (CFI), and the Root Mean Squared Error of Approximation (RMSEA). The results, in Table 2, suggested that the online participation model reflected the original data marginally (NFI=.943, CFI=.945, RMSEA= .117) but offline participation doesn’t (NFI=.878, CFI=.883, RMSEA=117).1


Before conducting the path analysis, this study measured the direct effect of reliance on news apps on online and offline political participation. There were significant direct relationships between reliance on application use and online participation (ß= .266, p<.01) and offline participation (ß = .250, p<.01).

In predicting online participation, online discussion was found to be a mediator, while the influence of face-to-face discussion on the relationship between mobile application use and online participation was not statistically significant. The mediator, online discussion, decreased the standardized beta of the direct path from reliance on smartphones toward online participation into .082 (compared with the ß = .266 for the direct path), and this direct effect remains significant. This supported the partially mediated effect of online discussion between the relationship between reliance on news apps (ß = .429, p<.001) and online political participation (ß = .462, p<.001), controlling age, race and party ties.

Both online and face-to-face discussions were significant mediators of the relationship between mobile application use and offline participation. The direct effect of mobile application use toward offline political participation decreased (from ß = .250 to .078, p<.01) after adding online and face-to-face discussions into the model as mediators. Online discussion was a significant mediator between mobile application use (ß = .427, p<.001) and offline participation (ß = .399, p<.001). Also, face-to-face discussion mediated the relationship between mobile application use (ß = .224, p<.001) and offline participation (ß = .104, p<.001). To conclude, online discussion partially mediated the relationship between mobile app use and online participation, the relationship between mobile app use and offline participation, while face-to-face discussion partially mediated the relationship between mobile app use and offline participation. Thus, while Hypothesis 3 was completely supported, Hypothesis 4 was partially supported.



This study examined the influence of mobile applications as well as interpersonal discussions toward political engagement. Theoretical arguments based on Differential Gains (Scheufele, 2002; Scheufele & Nisbet, 2002) and the Communication Mediation Model (Cho, Shah, McLeod, McLeod, Scholl, & Gotlieb, 2009; Shah, Cho, Eveland & Kwak, 2005) were employed to explain the influence of mobile applications on political activities. As expected, the relationship between reliance on mobile applications and political participation was both mediated and moderated by interpersonal discussion. Specifically, reliance on mobile applications and online discussion showed significant moderation and mediation effects in predicting both online and offline political participation. Such synergy effects can be attributed to the portability, connectivity, and personalization of mobile devices. Quick and convenient access to online information through mobile devices enables users to be engaged in any kind of political activities (Wei & Lo, 2006). They might easily contact officials, donate money, attend online meetings, sign or distribute petitions, and ask others to vote or refer to online sources through their mobile devices. Such active online participation also extends to the real world, where mobile devices have been credited with mobilizing citizens on a range of events from SARS to the Arab Spring (Pomfret, 2003; Wagner, 2011).

Online discussion had a stronger mediating and moderating effect on political participation than face-to-face. In the Differential Gains Model, online discussion, but not face-to-face discussion interacted with news app reliance to influence both offline and online participation. Similarly, for the Communication Mediation Model, online discussion mediated the relationship between online and offline participation while face-to-face communication only served as a significant mediator of online participation. The differences between results for face-to-face and online discussion may reflect the technological affordances of mobile technology. While smartphones in particular allow for both interpersonal discussion and information search, it is primarily used for online discussion, particularly sending texts back and forth to friends, answering and creating e-mails as well as using it to call individuals (Pew, 2014). Similarly, while recent studies have found mixed support for differential gains effects with online and social media (Brundidge et al., 2014; Kim, et al., 2010; Vu et al., 2013), this study showed more evidence of a differential gains effect, at least among online discussion because of the prominent role interpersonal discussion plays in mobile technology.

Also, online discussion usually has a larger network size in general than offline ones. While online discussion conducted more through weak ties, offline discussion usually engages people who know each other better. Usually, individuals from a smaller network size are connected through strong ties. To some extent, weak tie connections have a larger capacity for discussion, compared to smaller offline discussion based on strong ties, to predict our dependent variable: participation.

Some researchers have argued that the Internet may have a greater effect on online than offline political participation. For instance, Gil de Zuniga and colleagues (2010) argued “online participation may open a different pathway to participation, as some of the costs associated with this online participation may not be so high” (p. 38). Although costs associated with participatory behaviors are higher and additional efforts are in offline participation, this study did not find noticeable differences between the effects of mobile app use and interpersonal communication on offline and political participation. This is in line with studies that have looked at social mobilization that argue that because mobile news apps are portable and provide always-on connectivity and location-based services (Weiss, 2013; Wolf & Schnauber, 2014) they are ideal for mobilizing individuals in online activities such as elections or protests (Yamamoto, Kushin & Dalisay, 2013).


This study cleared differentiated online communication and face-to-face discussions during the analysis. Shah et al. (2005) argued that the Internet could complement face-to-face political talk. Our study found this argument was somewhat true: while online discussion could mediate and moderate the influence of mobile application use towards both forms of participation, face-to-face discussions did not mingle with using mobile apps to predict online participation. Like Hong et al. (2013), arguing that online communication has become much more important in leading people to be engaged in political activities, this study confirmed the overarching role of online discussions in combining with the media to increase political activities.

This study has several limitations. First of all, this study was a cross-sectional analysis, which means that the study cannot examine cause and following effect in analyzing the influence of mobile application use and interpersonal discussions on political participation. It is uncertain whether mobile application usage would lead to political discussion with others, or whether the causal direction can be reversed. Even though second-screen activities on mobile devices are common these days, which means that information gathering activities and discussion happens at the same time, there might be clear causal relationships. Also, this survey was conducted during the 2012 presidential election period, when more politically active discussants were prevalent. A multi-wave panel study can complement such shortcomings, by measuring time-relational and constant effects of mobile application usages. Moreover, this study was not a random sample, but gathered information though the popular crowdsourcing site Amazon Mechanical Turk. Studies have found that on most demographic measures, MTurk samples are representative of the U.S. population, and that it provides more representative results than other types of convenience samples (Berinsky, Huber, & Lenz, 2012; Mason & Suri; 2012, Messing & Westwood, 2012; Rand, 2012; Riordan & Kreuz, 2010). However, caution still must be taken in generalizing the results to online populations.

Beyond several limitations, this study confirmed the influence of mobile application use and derived political discussion among networks to the political participation. Campbell and Kwak (2011) originally examined the influence of mobile phone use, an alternative form of interpersonal communication, along with network sizes in predicting political participation. While mobile phone use could cause political discussions with the networks with family, relatives and close friends having strong ties, mobile applications provide a discussion platform with more heterogeneous people with weak ties. This study showed that with the emergence and widespread use of mobile applications for political discussions, mobile applications could mobilize weak ties, supported by more significantly strong effects of online discussions on online participation. This study could be differentiated from former studies based on the limited capacity of mobile phones. Thus, news media companies should take into consideration that mobile news applications can elicit more engagement in their published news. It would also be better for news media outlets to develop more sophisticated news applications that allow application users to join continuous discussions within their networks. Such simultaneous activities combining news consumption and discussions could result in robust discourses and engender more active political participation.

Further analysis should incorporate ideas about the nature of political discussions. Specifically, political discussions can provide a chance for gaining access to cross­cutting information or restricting access to similar ideas, which might lead to the heterogeneity of discussions or insularity of like-minded groups, ultimately influencing the level of participation. The examination of the nature of political discussions related to mobile communication usage could enrich the academic focus on mobile communication, which has been shown to be a strong influence on political activities.


1.The Normed Fit Index (NFI) is the proportion of improvement of the overall fit of the model relative to the baseline model. The Comparative Fit Index (CFI) is an updated criterion of NFI, taking the sample size into account. Values close to 1 generally are a good fit. The RMSEA explains the error of approximation in the population and inquiries “how well the model, with unknown but optimally chosen parameter values, would fit the population covariance matrix” (Browne & Cudeck, 1993). Guidelines to interpret the Root Mean Squared Error of Approximation (RMSEA) are: RMSEA ≤ .05 = good fit, .05 < RMSEA < .08 = reasonable fit and RMSEA ≥ .08 = poor fit.


Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research:’s Mechanical Turk. Political Analysis, 20(3), 351-368.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sage Focus Editions, 154, 136-161

Brundidge, J. (2010). Encountering ‘‘difference’’ in the contemporary public sphere: The contribution of the Internet to the heterogeneity of political discussion networks. Journal of Communication, 60(4), 680-700.

Brundidge, J., Garrett, R. K., Rojas, H., & Gil de Zúñiga, H. (2014). Political participation and ideological news online: “Differential gains” and “differential loses” in a presidential election cycle. Mass Communication and Society, 17(4), 464-486.

Campbell, S. W., & Kwak, N. (2010a). Mobile communication and civic life: Linking patterns of use to civic and political engagement. Journal of Communication, 60(3), 536-555.

Campbell, S. W., & Kwak, N. (2010b). Mobile communication and social capital: An analysis of geographically differentiated usage patterns. New Media & Society, 12(3), 435-451.

Campbell, S. W., & Kwak, N. (2011). Mobile communication and civil society: Linking patterns and places of use to engagement with others in public. Human Communication Research, 37(2), 207­.

Campbell, S. W., & Park, Y. J. (2008). Social implications of mobile telephony: The rise of personal communication society. Sociology Compass, 2(2), 371-387.

Cappella, J., Price, V., & Nir, L. (2002). Argument repertoire as a reliable and valid measure of opinion quality: Electronic dialogue during campaign 2000. Political Communication, 19(1), 73-93.

Chaffee, S. H., & Frank, S. (1996). How Americans get political information: Print versus broadcast news. The annals of the American Academy of Political and Social Science, 546, 48-58.

Cho, J., Shah, D., McLeod, J., McLeod, D., Scholl, R., & Gotlieb, M. (2009). Campaigns, reflection and deliberation: Advancing an O-S-R-O-R model of communication effect. Communication Theory, 19(1), 66-88.

Delli Carpini, M. (2000). Youth, civic engagement, and the new information environment. Political Communication, 17(4), 341-349.

Eveland, W., Hayes, A., Shah, D., & Kwak, N. (2005). Understanding the relationship between communication and political knowledge: A model comparison approach using panel data. Political Communication, 22(4), 423-446.

Eveland, W., & Hively, M. (2009). Political discussion frequency, network size, and “heterogeneity” of discussion as predictors of political knowledge and participation. Journal of Communication, 59(2), 205-224.

Fielder, R. (2014). Majority of smartphone owners are now routinely using news apps: 2014 RJI Mobile Media Research Report 3. Retrieved on November 23, 2014 from

Fortunati, L. (2002). The mobile phone: Towards new categories and social relations. Information, Communication & Society, 5(4), 513-528.

Gil de Zúñiga, H., Puig-i-Abril, E., & Rojas, H. (2009). Weblogs, traditional sources online and political participation: An assessment of how the Internet is changing the political environment. New Media & Society, 11(4), 553-574.

Gil de Zúñiga, H, Veenstra, A., Vraga, E., & Shah, D. (2010). Digital democracy: Reimagining pathways to political participation. Journal of Information Technology & Politics, 7(1), 36 -51.

Gil de Zúñiga, H., Jung, N., & Valenzuela, S. (2012). Social media use for news and individuals’ social capital, civic engagement and political participation. Journal of Computer-Mediated Communication, 17(3), 319-336.

Green, D. P., & Gerber, A. S. (2004), Get Out the Vote! How to Increase Voter Turnout, Washington, DC: Brookings Institution Press.

Habuchi, I. (2005). Accelerating reflexivity. In M. Ito, D. Okabe, & M. Matsuda (Eds.), Personal, portable, pedestrian: Mobile phones in Japanese life (pp. 165–182). Cambridge, MA: MIT Press.

Hardy, B. W., & Scheufele, D. A. (2005). Examining differential gains of Internet use: Comparing the moderating role of talk and online interactions. Journal of Communication, 55, 71-84.

Hermanns, H. (2008). Mobile democracy: Mobile phones as democratic tools. Politics, 28(2), 74-82.

Johnson, T. J. & Kaye, B. (2014). An appetite for political information? Characteristics and media habits of mobile news app users. Submitted to the annual meeting of the International Communication Association.

Katz, E. (2006). Magic in the Air. New Brunswick, NJ: Transaction Publishers.

Katz, E., & Lazarsfeld, P. (1955). Personal influence: The part played by people in the flow of mass communications. Glencoe, IL: The Free Press.

Kaye, B. K., & Johnson, T. J. (2011). Net gain? Selective exposure and selective avoidance on online political information. In F. Comunello (ed.), Networked sociability and individualism: Technology for personal and professional relationships, (pp. 218-237). Hershey, PA: IGI Global.

Kim, D., Johnson, T. J., & Kaye, B. K. (2010, September). Something ventured, something gained. Web Journal of Mass Communication Research, 24. Retrieved from

Kim, J, Wyatt, R., & Katz, E. (1999). News, talk, opinion, participation: The part played by conversation in deliberative democracy. Political Communication, 16(4), 361-385.

Kwak, N., Campbell, S. W., Choi, J., & Bae, S. Y. (2011). Mobile communication and public affairs engagement in Korea: An examination of non-linear relationships between mobile phone use and engagement across age groups. Asian Journal of Communication, 21(5), 485-503.

Kwak, N., Williams, A., Wang, X., & Lee, H. (2005). Talking politics and engaging politics: An examination of the interactive relationships between structural features of political talk and discussion engagement. Communication Research, 32(1), 87-111.

Lee, F. (2009). The impact of political discussion in a democratizing society: The moderating role of disagreement and support for democracy. Communication Research, 36(3), 379-399.

Lemert, J. B. (1981). Does mass communication change public opinion after all? A new approach to effects analysis. Chicago, IL: Nelson Hall.

Ling, R. (2008). New tech, new ties: How mobile communication is reshaping social cohesion. Cambridge, MA: MIT Press.

Ling, R., Yttri, B., Anderson, B., & DiDuca, D. (2003). Mobile communication and social capital in Europe. In K. Nyiri (Ed.), Mobile democracy: Essays on society, self, and politics. Vienna: Passagen Verlag.

Mason, W., & Suri, S. (2012). Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods, 44(1), 1-23.

McLeod, J. M., Kosicki, G. M., & McLeod, D. M. (2002). Resurveying the boundaries of political communication effects. In J. Bryant & D. Zillman (Eds.), Media effects: Advances in theory and research (2nd ed., pp. 215–267). Mahwah, NJ: Lawrence Elbaum Associates, Inc.

McLeod, J. M., Scheufele, D. A., & Moy, P. (1999). Community, communication,and participation: The role of mass media and interpersonal discussion in local political participation. Political Communication, 16(3), 315–336.

Messing, S., & Westwood, S. J. (2012). Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online. Communication Research, DOI: 0093650212466406.

Mitra, A., & Watts, E. (2002). Theorizing cyberspace: The idea of voice applied to the Internet discourse. New Media & Society, 4, 479-498.

Peterson, R. A. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 381-391.

Pew Research Center for the People and the Press (2013). 12 trends shaping digital news. Retrieved from

Pew Research Center for the People and the Press (2014). Cell phone activities 2013. Retrieved from

Pomfret, J. (2003, 13 May). Outbreak gave China’s Hu an opening, The Washington Post.

Price, V., Cappella, J., & Nir, L. (2002). Does disagreement contribute to more deliberative opinion? Political Communication, 19(1), 95-112.

Rand, D. G. (2012). The promise of Mechanical Turk: How online labor markets can help theorists run behavioral experiments. Journal of Theoretical Biology, 299, 172-179.

Rheingold, H. (2002). Smart mobs: The next social revolution, Cambridge, MA: Perseus.

Riordan, M. A., & Kreuz, R. J. (2010). Emotion encoding and interpretation in computer-mediated communication: Reasons for use. Computers in Human Behavior, 26(6), 1667-1673.

Scheufele, D. A. (2000). Talk or conversation? Dimensions of interpersonal discussion and their implications for participatory democracy. Journalism and Mass Communication Quarterly, 77(4), 727-743.

Scheufele, D. A. (2002). Examining differential gains from mass media and their implication for participatory behavior. Communication Research, 29(1), 46-65.

Scheufele, D. A., & Nisbet, M. (2002). Being a citizen online: New opportunities and dead ends. Harvard International Journal of Press/Politics, 7(3), 55-75.

Schmitz Weiss, A. (2013). Exploring news apps and location-based services on the smartphone. Journalism & Mass Communication Quarterly, 90(3), 435–456.

Shah, D. V., Cho, J., Eveland, W. P., & Kwak, N. (2005). Information and expression in a digital age. Modeling internet effects on civic participation. Communication Research, 32(5), 531-565.

Shah, D. V., Cho, J., Nah, S., Gotlieb, M., Hwang, H., Lee, N, & McLeod, D. (2007). Campaign ads, online messaging, and participation: Extending the communication mediation model. Journal of Communication, 57(4), 676-703.

Shah, D. V., Kwak, N., & Holbert, L. (2001). “Connecting” and” disconnecting” with civic life: Patterns of internet use and the production of social capital. Political Communication, 18(2), 141-162.

Sotirovic, M., & McLeod, J. M. (2001). Values, communication behavior, and political participation. Political Communication, 18(3), 273-300.

Vu, H., Yoo, J., Stephens, M., Mourao, R. R., Baresh, B. & Johnson, T. J. (2013) Communication’s next top model: Comparing the Differential Gains and Communication Mediation Models as predictors of political participation and knowledge. Association for Education in Journalism and Mass Communication Conference 2013. Political Communication Interest Group. Washington, DC. August 8-11, 2013. Top Paper Award.

Wagner, B. (2011). The Arab Spring “I have understood you”: The co-evolution of expression and control on the internet, television and mobile phones during the Jasmine Revolution in Tunisia. International Journal of Communication, 5(8), 1295-1302

Wei, R., & Lo, V. H. (2006). Staying connected while on the move: Cell phone use and social connectedness. New Media & Society, 8(1), 53-72.

Wolf, C., & Schnauber, A. (2014). News consumption in the mobile era: The role of mobile devices and traditional journalism’s content with the user’s information repertoire. Digital Journalism. DOI:10.1080/21670811.2014.942497 (Advance online publication).

Wyatt, R. O., Katz, E., & Kim, J. (2000). Bridging the spheres: Political and personal conversation in public and private spaces. Journal of Communication, 50(1), 71-92.

Yamamoto, M., Kushin, M. J., & Dalisay, F. (2013). Social media and mobiles as political mobilization forces for young adults: Examining the moderating role of online political expression in political participation. New Media & Society, DOI:1461444813518390 (Advance online publication).

Zhang, W., Johnson, T. J., Seltzer, T., & Bichard, S. (2010). The revolution will be networked: The influence of social network sites on political attitudes and behavior. Social Science Computer Review, 28(1), 75-92.



Joseph Jai-sung Yoo is a PhD student at the School of Journalism, University of Texas at Austin. His research areas of interest include political communication, telecommunication policy, especially regarding Net neutrality, network analysis, and sports communication.

Pei Zheng is a PhD student at the School of Journalism, University of Texas at Austin. Her research interests are in political communication, media technologies and media effects. She also focuses on how social media affect people’s political life.

Hyeri Jung is a PhD student at the School of Journalism, University of Texas at Austin. Her main research interests revolve around soft power and pop culture as political and diplomatic tools in international settings and the U.S. media’s foreign news coverage.

Victoria Y. Chen is a PhD student at the School of Journalism, University of Texas at Austin. Her research interests are in media economics, political communication and news consumption. Her research addresses causes of news homogeneity under the Hierarchy of Influences model. Her future research will deal with the news industry from media economics perspectives.

Shuning Lu (M.A., Fudan University, China) is a doctoral student at the School of Journalism, University of Texas at Austin. Her research interests include political communication, journalism studies and social media.

Thomas J. Johnson is the Amon G. Carter Jr. Centennial Professor in the School of Journalism at the University of Texas. His research areas include the uses and effects of new media, particularly social media, in United States and foreign politics.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s