During its inception, the Internet was commonly conceived as a virtual venue for pluralistic and rational communication (Fuchs, 2014) or deliberation and collaboration (Dahlberg, 2001). But such an ideal expectation of an online public sphere does not often materialize. Recent research suggests a rather balkanized form of public discussion on the Internet.
The term cyberbalkanization2 was first coined in Van Alstyne and Brynjolfsson's early article (1996) to describe the information technology-driven division of virtual space into special interest groups. It is later put into the political context as an online phenomenon in which “people seek out only like-minded others and thereby close themselves off from ideological opposition, alternative understandings, and uncomfortable discussions” (Brainard, 2009, p. 598). Sunstein (2008, p. 94) also describes a similar online phenomenon as a group of bloggers living “in echo chambers of their own design” or “in information cocoons.”
One typical example of cyberbalkanization occurs in the online political debate among English-speaking Americans during presidential elections. Previous results indicate that online discussions follow a bipartisan pattern, within which ideologically compatible online users tend to cite and mention each other more frequently (Adamic & Glance, 2005; Conover et al., 2011)3. However, evidence also suggests that the segregation of communication according to political ideology on social media is largely issue-dependent, showing that such segregation was seen more often in the discussions of political issues, such as the 2012 presidential debates, but less profound in the public's exchanges on other issues, for example the 2013 Boston Marathon Bombing (Barberá et al., 2015).
Cyberbalkanization and Opinion Polarization
Even though cyberbalkanization has been described as a pervasive online phenomenon, whether or not it actually results polarization of people's opinions in real life is subject to further investigation. Opinion polarization is known as a state referring to “the extent to which opinions on an issue are opposed in relation to some theoretical maximum,” and polarization as a process that “refers to the increase in such opposition over time” (DiMaggio, Evans, & Bryson, 1996, p. 693). It has been a global concern.4
At the core of our theoretical concern, the central question of this study is to examine whether or not, and to what extent, cyberbalkanization on social media reflects or contributes to the polarization of the public's views. Sunstein (2009) theorizes the process of selective exposure and polarization of political views, suggesting that Internet communication can increase political polarization because like-minded people tend to discuss political issues with each other, and consequently they end up reinforcing each other, leaving them holding more extreme or more polarized views than they had before (Sunstein, 2009). Sunstein's hypothesis is supported only, however, with “unnatural” experimental results in which “Internet-like” face-to-face interactions with like-minded individuals drove subjects to adopt more extreme views. On the other hand, Farrell argues that even though research clearly supports the case that the Internet can bring like-minded people together, there is insufficient evidence to substantiate Sunstein's hypothesis that cyberbalkanization causes opinion polarization (Farrell, 2012). Specifically, Farrell notes that the causal mechanism of cyberbalkanization and opinion polarization remains uncertain.
Sunstein (2008) lists out three possible reasons to explain why polarization is caused by cyberbalkanization, namely selective exposure, social comparison, and social corroboration. However, his explanations rest only on online information seekers (readers). Lawrence et al. (2010) point out the differences in roles played by readers and authors on social media and such distinction must be considered in studying the process of cyberbalkanization.
Information Seeker's Information Bias
Cyberbalkanization is thought as a mechanism through which an individual's preference towards certain information sources leads to reinforce one's skewed opinion, i.e., a user voluntarily selects like-minded peers for interactions and filters out, consciously or unconsciously, less-preferred contacts, which is an innate aspect of human communication long documented in the literature on selective exposure ( Zillmann & Bryant, 1985). Selective exposure of human communication represents an individual's tendency to favor information that reinforces one's pre-existing views and filters any contradictory content (Klapper, 1960). The process is grounded in the theory of cognitive dissonance (Festinger, 1957), positing individual's preference towards cognitive consistency and avoiding information that likely induces discomfort.
Indeed, selective exposure to information sources seems to play a key role in polarizing the public (Hollander, 2008). Selective exposure to traditional media sources (such as television viewing) has long been seen as a factor that can drive public opinion sectors apart (Iyengar & Hahn, 2009). Even though selective exposure to blogs is found to be pervasive among Internet users (Lawrence, Sides, & Farrell, 2010), the ease of receiving information via the Internet can also facilitate online exposure to opposite viewpoints (Garrett, 2009). Nonetheless, evidence for a causal relationship between exposure to partisan information and change in political attitude or behavior is equivocal (Prior, 2013).
Another reason is social comparison. Individuals in a group opt for adjustment of opinions towards the perceived norm to be perceived well by their fellow group members (Stroud, 2010) and this shift consequently generates opinion polarization. As the third explanation suggested by Sunstein (2008), social corroboration conveys that when an individual's opinion is reconfirmed by the members in a group setting, one gains social acceptance, becomes more confident and thus extreme in belief (Baron et al., 1996).
Some scholars further argue that in the online environment, algorithmic personalization of Internet experience, for example Google's personalized search or Facebook's news feed, might promote exposure to biased information because less preferred information is algorithmically eliminated, a pattern known as the “filter bubble” (Pariser, 2012). However, in a study supported by Facebook (Bakshy, Messing, & Adamic, 2015) and a few independent works (e.g. Flaxman, Goel, & Rao, 2016), the effect of algorithmic personalization on reduction of information consumption diversity appears modest. Human's voluntary selective exposure seems to play a stronger role than the “filter bubble” in promoting cyberbalkanization (Bakshy, Messing, & Adamic, 2015).
Information Source's Selective Sharing
The subject we are studying is Facebook page, not individual Facebook user. Like open Twitter accounts or blogs, Facebook pages act like public media which are designed for one-to-many communication and function as information producers and curators. Apart from publishing original messages to their readers, Facebook pages can also rebroadcast information from other Facebook pages who share common interests or political views whereas ignore the information from pages with differing opinions. This selective sharing of like-minded information on Facebook pages can overemphasize one-sided arguments and effectively downplay counterarguments to their readers. Based on persuasive arguments theory, sharing of new arguments can induce attitude change, particularly in the case of restricted “argument poll,” e.g., abundance of similar arguments and high perceived persuasiveness of arguments (Vinokur & Burstein, 1974). Hence, Facebook pages' selective sharing of posts can reduce diversity of perspectives presented to their readers, i.e., a limited “argument poll.” This restriction of “argument poll” has been found to promote polarization within groups (Hamlett & Cobb, 2006). Sunstein (2000) argues that a limited diversity in perspectives can also promote online enclave deliberation and make reaching consensus difficult. Such a persuasion-based explanation can explain how the ideologically slanted news outlets drive polarization (Prior, 2013), even though exposure to slanted news can drive opinion polarization using non-persuasive routes such as reinforcement of group identity and promotion of motivational reasoning (Prior, 2013).
Empirical studies find that frequency of information shares between like-minded information seekers/producers is associated with online polarization (Conover et al., 2011; Gruzd & Roy, 2014). In particular, Conover et al. (2011) reveal that the network of content sharing exhibits ideologically segregated community structure, but the network of mentioning does not. These studies suggest that the difference in finding between sharing networks and mention networks comes from politically motivated users' ability to insert partisan content into the timeline of users with opposing viewpoint by mentioning their name. Sharing content cannot function as such and therefore it is mostly an act of endorsement.
Early social psychology literature has suggested that effective persuasive communication depends on the source, the message, and the audience, each of which plays an important part (Hovland, Janis, & Kelley, 1953). In this study, we argue that both Facebook's information seekers and Facebook's information producers are formative parts of the mechanism of opinion polarization but they have not been holistically studied. A study has indicated that overlapping ‘audienceship’ is much higher between those Facebook Pages who share with each other more frequently, suggesting that the cyberbalkanization of information producers and that of information seekers are closely related to each other (Chan & Fu, 2017). The conceptual diagram of the holistic integration of the two processes is presented in Figure 1.
In this study, we aim at showing the relationship between cyberbalkanization of information producers and offline opinion polarization. In the subsequent paragraph, cyberbalkanization refers exclusively to cyberbalkanization of information producers. This area has not been studied extensively. Previously, a few survey studies examined the correlations between a set of social network characteristics, including media consumption habits and political attitudes of online information seekers (Dvir-Gvirsman, 2016; Huckfeldt & Sprague, 1987; Lee, 2016; Mutz, 2006). These survey data relied primarily on individually self-reported information. However, social network information derived from survey methodology based on self-reporting suffers from both random and nonrandom measurement errors5.
The availability of social media data can partly resolve the above measurement problems because they provide a relatively complete digital trace of a user's online activities. However, social media data also come with the problem of representativeness, as the population of social media users is not a statistically representative sample of the target population (Tufekci, 2014) and therefore we cannot infer individual political behavior based solely on social media data (Freelon, 2014; Jungherr, 2015). As the current study focuses only on a very specific target population of Facebook pages, it is appropriate to use social media data to study the cyberbalkanization of those pages.
We expected that the indicator of cyberbalkanization (like-minded sharing) is related to opinion polarization but the effect on the public has not been studied. Empirical evidence from our previous pilot study of three months of Facebook data (Chan & Fu, 2015) has shown that the quantity of sharing between like-minded Facebook Pages is a leading indicator of offline opinion polarization. In the current study, we seek to test the same hypothesis with data collected over a longer period (12 months). Therefore, this study seeks to test the following hypothesis.
H1: Cyberbalkanization is positively correlated with opinion polarization
Although some studies have indicated that the online sphere is largely balkanized, many scholars still agree that different political views can still be channeled to individuals via an online social network's strong ties (mostly like-minded close friends and family) as well as weak ties (mostly acquaintances as sources of novel and non-redundant information). The definition of cyberbalkanization only considers the communications between “like-minded others” and therefore only considers strong-tie connections. However, recent studies reveal that social media not only bring like-minded individuals together but also facilitate broader exposure to alternative views, usually coming from weak connections (Grabowicz et al., 2012; Bakshy et al., 2012). Barberá (2014) also suggests that weak ties can moderate political extremism and reduce opinion polarization based on results from mixed methods of survey study and web trace analysis. He proposes two possible mechanisms for the mediation of political extremism by information sourced to weak ties in the social network: 1) “greater awareness of rationales for opposing views” (Mutz, 2006, p. 69) and 2) triggering affection to acknowledge that there are people inside one's social networks who hold opposite views (Iyengar, Sood, & Lelkes, 2012).
With this background, this study proposed a new conceptualization of cyberbalkanization which isolates the contributions of strong- and weak-tie connections. Based on previous studies, we hypothesized that information from strong-tie connections largely reinforces pre-existing views, resulting in more extreme views, but the information from weak-tie connections tends to ease polarization because the information flow facilitates broader exposure to alternative views. The frequency of sharing determines the tie strength. This conceptualization is not optimal: It is possible to have social media users who rarely share with each other on the social network but still share with those who have the same political stance. Nonetheless, our approach isolates the relative contributions of strong-tie connections and weak-tie connections, and the strong- and weak- ties connections is expected to have a stronger correlation with opinion polarization than the strong ties connections alone. Thus, we derive the second hypothesis as follows:
H2: Cyberbalkanization among both weak-ties and strong-ties connections can explain the correlation between cyberbalkanization and opinion polarization better than that found among strong-ties connections alone.
Sunstein's hypothesis suggests that selective exposure to online media content can lead to opinion polarization. One essential criterion for such a causal claim is to establish the temporal precedence of events (Hill, 1965), establishing that an increase or decrease in cyberbalkanization temporally precedes an increase or decrease in opinion polarization. Even though a statistical correlation between the level of cyberbalkanization and opinion polarization might have been established, an alternative explanation for such correlation might be that the degree of cyberbalkanization is simply an online manifestation of offline opinion polarization. In the current study, we intend to show that the relationship between cyberbalkanization and opinion polarization can either be 1) unidirectional, i.e., only cyberbalkanization can lead to future opinion polarization; or 2) bidirectional, with only the relationship between cyberbalkanization and future opinion polarization statistically and practically significant. We must emphasize that, per Hill's criteria of causation (Hill, 1965), establishing a temporal precedence relationship between two events is only one among many essential conditions for causality. It is not our intention to claim causality in this study.
H3: Changes in offline opinion polarization temporally lead to a change in cyberbalkanization.
Nonetheless, no empirical study has so far examined the above three hypotheses with empirical social media data nor investigated the changes in degree of cyberbalkanization, i.e. the extent of strong or weak ties, with respect to the process of opinion polarization, in a longitudinal research setting. This research approach is missing not only because it is difficult to measure cyberbalkanization, which poses a methodological challenge to extract online social network interactions between users in a specific polity, but also because there is no available method to quantify and operationalize the level of cyberbalkanization inside a single communication network that can be effectively tracked across time. Previous approaches to measuring selective exposure (e.g., Clay, Barber, & Shook, 2013) might be adopted to track cyberbalkanization but the main methodological challenge is the feasibility of classifying “pre-existing” stance of the user, which is essential for determining whether communications are among like-minded others or not. The approach using human classification is often labor-intensive. Following another study of selective exposure on Twitter (Himelboim, Smith, & Shneiderman, 2013), we developed a social network analysis methodology to assign a possible stance of users automatically based on the content source they shared.