Computers in Human Behavior 29 (2013) 2134–2142
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Perceived bridging and bonding social capital on Twitter: Differentiating between followers and followees Matthias Hofer ⇑, Viviane Aubert Institute of Mass Communication and Media Research, University of Zurich, Switzerland
a r t i c l e
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Article history:
Keywords: Twitter Bonding online social capital Bridging online social capital Followers Followees
a b s t r a c t The present study investigates the influence of Twitter use and the number of followers and followees on perceived bridging and bonding online social capital. Data from a convenience sample of Twitter users (N = 264) indicate that bonding social capital is associated with the number of followers whereas bridging social capital is influenced by the number of followees. Thus, the directed friendship model on Twitter affects different forms of social capital differently. In addition, the study found a negative curvilinear effect of the number of followees on bridging and the number of followers on bonding online social capital. This indicates that the number of followees/followers has positive effects on online bridging/bonding social capital, but only to a certain point. The paper concludes with a discussion of the results in light of theoretical considerations and of implications for future research on the effects of Twitter on social capital. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction The Internet provides a vast array of services and applications, including news sites, trading services, entertainment offerings, and innumerable forums and chat rooms. Among the most popular web-based services are social network sites, such as Facebook, Tumblr, and Friendster, which are becoming increasingly important in our everyday communications and social interactions (Cheung, Chiu, & Lee, 2011). The social implications of these communication technologies are indisputable, as they have the potential for users to not only maintain pre-existing relationships, but also to establish new networks with people they have never met before. These new networks can either be consisting of weak or strong ties and accordingly lead to so-called bridging or bonding social capital online (Amichai-Hamburger, Kingsbury, & Schneider, 2013; Ellison, Steinfield, & Lampe, 2006, 2011; Zhong, 2011). The concept of social capital is well suited to the study of the social implications of social network sites (e.g., Ellison, Steinfield, & Lampe, 2007; Kaigo, 2012). Most research has focused on how social network maintains pre-existing offline connections, as well as its impact on other offline outcomes such as civic participation (Valenzuela, Park, & Kee, 2009) and social self-esteem (Valkenburg, Peter, & Schouten, 2006). However, social capital also exists in online communities that have no or only few offline roots (Kobayashi, 2010; Kobayashi, Ikeda, & Miyata, 2006; Zhong, 2011).
⇑ Corresponding author. Tel.: +41 44 635 20 63. E-mail address:
[email protected] (M. Hofer). 0747-5632/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.chb.2013.04.038
The present study focuses on the formation of online social capital (i.e. social capital developed as a result of meeting people online) through use of Twitter. More specifically, we are interested in two forms of online social capital: (1) perceived online bridging social capital and (2) perceived online bonding social capital (cf. Chang & Zhu, 2012; Putnam, 2000; Williams, 2006). Whereas the former facilitates the dissemination of information and solidarity within rather weak ties and is likely to lead to social and political participation, the latter is found within homogenous networks of like-minded people and is likely to result in emotional support for the individual. Therefore, in order to obtain a clear picture of social effects of online communication via social network sites, one should take into account both forms of social capital. We argue that through its directed friendship model, Twitter use affects bridging and bonding online social capital online differently. 2. Bridging and bonding social capital The concept of social capital can be traced to the beginning of the 20th century, when it was first mentioned by Hanifan (1916). Hanifan defined social capital as ‘‘good-will, fellowship, mutual sympathy and social intercourse among a group of individuals and families who make up a social unit’’ (p. 130). However, the concept was later refined by Bourdieu (e.g., 1986) and Coleman (1988; see also Lin, 2003). According to Bourdieu (1986), social capital consists of all the resources owned by an individual because of his or her social contacts. Similarly, Coleman (1988) conceptualizes social capital in functional terms: ‘‘Social capital is defined by its function. It is not a single entity but a variety of different
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entities, with two elements in common: they all consist of some aspect of social structures, and they facilitate certain actions of actors—whether person or corporate actors—within the structure’’ (p. 98). Thus, social capital is neither time- nor place-invariant and requires constant reinvestment by an individual in her or his social networks. Putman provides an important extension to the concept of social capital (e.g., Putnam, 2000). He defines social capital as social networks and their associated norms of trust and reciprocity. Thus, social capital comprises both the network itself and its more far-reaching effects (cf. Putnam, 2000; Williams, 2006). Different networks (or different ties) may have different social capital implications. Therefore, Putnam (2000) distinguishes between two forms of social capital, namely bridging and bonding. Bridging social capital is inclusive and occurs by the formation of rather weak ties between people from different networks. These ties and the resulting network may be useful for numerous reasons, such as for finding a job. The concept of bridging social capital and its social benefits are rooted in Granovetter’s seminal work (1973), in which the author argues that weak ties are especially suitable for reaching populations that are beyond networks of strong ties. Weak ties not only facilitate the flow of information on a macro-level, but also help an individual to broaden his or her horizons. Williams (2006) conceptualizes bridging social capital as consisting of four components: (1) outward looking (i.e. broadening one’s horizons by challenging one’s conventional perceptions), (2) contact with a broader range of people with different backgrounds, (3) viewing oneself as part of a larger group, which goes along with a feeling that ultimately everyone is connected, and (4) diffuse reciprocity with a broader community (this addresses generalized norms of reciprocity). Bonding social capital, on the other hand, is more exclusive and ‘‘reinforces exclusive identities and homogeneous groups’’ (Putnam, 2000, p. 22). This bonding type of social capital is found in dense social networks of like-minded individuals, such as among close friends or family. These strong ties have an emotional rather than informational quality for the members of the network. Unlike bridging social capital, bonding social capital is more likely to occur in networks with limited diversity. Bonding social capital consists of (1) emotional support, (2) access to scarce or limited resources within close-knit networks, (3) ability to mobilize solidarity, and (4) out-group antagonism (Williams, 2006). In summary, social capital is a multidimensional construct consisting of diverse aspects of social structure (Lee & Lee, 2010). Social capital can be divided into bridging and bonding forms. Both forms of social capital may have positive effects on an individual’s well-being, life satisfaction, and even self-esteem (Helliwell & Putman, 2004).
3. Online and offline social capital In his seminal work, Putnam (2000) describes trends in the U.S. towards declining political involvement and active civil engagement, and increasing disconnectedness of individuals from family, friends, and neighbors. Putnam attributes this decrease in social capital to an increase in entertainment-oriented TV use (but see Paxton, 1999). This dystopian view of the effects of media is also held among other Internet researchers (Kraut et al., 1998; Nie, 2001; Nie & Hillygus, 2002). However, Internet use has not only been linked to decreases, but also to increases in social capital. Consequently, there are two competing views on the effects of the Internet on social capital, namely a dystopian and a utopian.
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3.1. The dystopian view In their seminal HomeNet study, Kraut et al. (1998) suggest that use of the Internet is detrimental to social connectedness and psychological well-being. Their data showed that increased Internet use was associated with less time spent with family and friends and a higher level of perceived stress. Similarly, Nie and Hillygus (2002) present evidence corroborating the time displacement hypothesis, which states that time spent online competes with face-to-face social time and therefore results in a decrease in social capital. Thus, more time spent in front of a computer screen corresponds with reduced social interaction, which in turn results in a decrease in social capital. 3.2. The utopian view The dystopian perspective that the Internet reduces social capital has been widely criticized on a number of grounds (e.g., Bargh & McKenna, 2004). Not only since the proclamation of virtual communities (Rheingold, 1993), researchers upholding the utopian view have considered the Internet as a means of social interaction. Online, one can meet and build networks of like-minded individuals. Pénard and Poussing (2005), for example, suggest that use of the Internet has positive effects on social capital. Other researchers have suggested that use of the Internet supplements existing ties (Wellman, Quan-Haase, Witte, & Hampton, 2001). It has been argued that the Internet provides the possibility of staying connected with friends and family members who are otherwise not accessible (Kavanaugh, Carroll, Rosson, Zin, & Reese, 2005). In a follow-up study, Kraut et al. (2002) found evidence that Internet use is positively associated with community involvement. One reason for these diametrically opposing views on the effects of the Internet on social capital may be related to the absence of a differentiation between online and offline social capital (Williams, 2006). Discerning not only between bridging and bonding, but also between online and offline, social capital provides a more nuanced picture of the effects of the Internet on social capital. Accordingly, Williams (2006) argues that most of the findings that show the harmful effects of the Internet on social capital are based on bonding social capital offline as the sole dependent variable. However, use of the Internet might also contribute to new social capital online (cf. Ellison et al., 2007, 2011; see also Steinfield, Ellison, & Lampe, 2008); that is, it is possible to create new ties online with individuals whom one has never met (Best & Krueger, 2006; Zhong, 2011). In other words, through the Internet, one might create new networks and therefore generate new bridging social capital online. Accordingly, Best and Krueger (2006) found that the amount of time Internet users spend with new online ties is a positive predictor of both generalized trust and generalized integrity as components of social capital. Not only bridging, but also bonding social capital can be generated online: socially marginalized individuals, for instance, might find like-minded people or people with similar problems online and thus develop a sense of belongingness to a virtual community (cf. Blanchard, 2007). Therefore, following Williams (2006), we further distinguish between online and offline social capital (both bridging and bonding). Another reason for the tension between utopian and dystopian views may be an overgeneralization of the term Internet use. It has been argued that whether Internet use has a positive or a negative effect on social capital largely depends on how one uses the Internet (Zhao, 2006). For example, using the Internet mainly for entertainment (e.g., video streaming), instead of interactive and communicative and thus social reasons, might have a negative effect on social capital. However, when the Internet is used as a communication technology such that individuals are able to maintain
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existing ties, then the Internet has a positive effect on existing bridging or bonding social capital. In other words, using the Internet as a communication medium, one can maintain offline social capital (both bridging and bonding) in the online world, but also generate new ties, new social networks, and ultimately new social capital online (Amichai-Hamburger et al., 2013; Trepte, Reinecke, & Juechems, 2012; Vergeer & Pelzer, 2009; Zhong, 2011). Social network sites are one type of Internet platform that are suitable for both maintaining existing offline ties (both strong and weak) and also creating new online ties, as they enable users to communicate and connect with one another (for an overview, see Boyd & Ellison, 2007). Consequently, through the use of network sites, one might generate and maintain both bridging and bonding social capital, both online and offline. Social network sites thus have the potential not only to reshape social networks, but also to build new networks online (Ellison et al., 2007; Lee, Moore, Park, & Park, 2012; Steinfield et al., 2008; Subrahmanyam, Reich, Waechter, & Espinoza, 2008; Zhong, 2011).
4. Twitter use and social capital In addition to building and maintaining social relationships, social network sites can increase the size of their users’ networks by many times as compared with offline mechanisms (Ellison et al., 2011; Ye, Fang, He, & Hsieh, 2012). In this way, social network sites facilitate social interactions online. However, different networking sites vary with respect to their thematic focus or to their ‘‘structural variations around visibility and access’’ (Boyd & Ellison, 2007, p. 213; Hughes, Rowe, Batey, & Lee, 2012). One famous example that may be considered is the social network site Twitter. Twitter is a micro-blogging platform that enables its users to post short text messages (of up to 140 characters). Since its foundation in 2006, Twitter has quickly gained in popularity (Chen, 2011; Farhi, 2009), and by early 2012, Twitter had over 500 million registered users. In 2011, Twitter had an average of over 23,000 unique visitors per month (Nielsen Wire., 2011). Although it does not fulfill all the requirements of a social network site (cf. Boyd & Ellison, 2007), Twitter ‘‘displays recognizable users’ profiles publicly and allows users to connect to each other’’ (Recuero, Araújo, & Zago, 2011), which makes it suitable for online social purposes, similar to other social network sites. Previous research on the social implications of social network site usage (Ellison et al., 2007; Subrahmanyam et al., 2008) has shown that the use of these sites is positively related to a person’s social capital. Donath and Boyd (2004) argue that social network sites provide the technological features for their users to build and maintain large networks of social ties, which supplements their offline networks. Specifically, individuals can remain in contact with more members of their online networks than with their offline counterparts. Similar to classical social network sites, Twitter can increase social capital (Kaigo, 2012; Ye et al., 2012) by helping users create new ties and maintain existing ones. In addition, on Twitter one can connect with other users more easily than on sites such as Facebook (Ye et al., 2012), as there is no need for approval of a follower’s request (except if the user has explicitly enabled the privacy protection for his or her tweets). Therefore, individuals can enlarge their online networks more quickly than on sites such as Facebook. Furthermore, Twitter users can write short messages to report on their daily activities and express their opinions and ideas. They can also read about and take part in other people’s lives and therefore feel part of the lives of their online contacts (Java, Song, Finin, & Tseng, 2007). Accordingly, Kaigo (2012) argues that Twitter may increase both a person’s bridging and bonding online social capital. Therefore, we expect the use of
Twitter to be positively related to its users’ online bridging (H1a) and bonding (H1b) social capital. 4.1. Twitter followers vs. followees Boyd and Ellison (2007) argue that, despite their fairly consistent technological features (i.e., different possibilities of creating a profile and establishing connections), social networking sites may differ structurally with regard to the ways that relationships are established and made visible. One of the main features that distinguishes Twitter from other social network sites such as Facebook or Friendster is its directed friendship model (Marwick & Boyd, 2010). That is, users can either follow or be followed. Unlike on Facebook or on Friendster, on Twitter there is no requirement for a user who is followed by another user to follow him or her back. Relationships on Twitter require no reciprocation. Becoming a follower on Twitter receives all of the messages (tweets) of the followed user (Kwak, Lee, Park, & Moon, 2010). Thus, the links of the Twitter network are directed (Huberman, Romero, & Wu, 2009). Therefore, it is crucial to differentiate between the two types of connections on Twitter: followers and followees. The term followers is used to describe a users’ audience, whereas followees are the accounts that he or she is subscribed to. The non-reciprocal friendship model of Twitter is expected to have different outcomes on bridging and bonding online social capital. In other words, we expect that it makes a difference whether one follows or is being followed on Twitter. 4.2. Bridging social capital as a result of following other Twitter users Twitter provides ample opportunities to connect with a broad range of people by becoming their followers. By doing so, users receive information that was not previously available to them. For instance, individuals can follow music or film stars or journalists, to learn about new developments and thereby broaden their horizons by looking outside of their ‘‘narrow daily existence’’ (Williams, 2006, p. 600). Furthermore, individuals may feel part of a broader group. Taking into account Putnam’s (2000) definition of bridging social capital that is found in rather weak but inclusive networks that are well suited to ‘‘information diffusion’’ (p. 22), we argue that following other users on Twitter has a positive effect on a user’s bridging social capital online. Therefore, we hypothesize that the more followees a user has on Twitter, the more bridging social capital this user perceives (H2). 4.3. Bonding social capital as a result of being followed on Twitter Research by Marwick and Boyd (2010) indicates that Twitter users imagine their audience as consisting of so-called ‘‘ideal’’ readers; that is, as persons who are similar to themselves. They further conceptualize their audience as individuals who ‘‘share their perspective and appreciate their work’’ (p. 120). Twitter users with numerous followers are especially prone to imagine their audience as a community with whom they can connect. By tweeting, one can express one’s opinions, problems, and experiences. The emotional support provided by so-called on-line support groups is one example of the bonding provided by online communities (Beaudoin & Tao, 2007; Lieberman & Winzelberg, 2009; Pfeil & Zaphiris, 2009; Preece, 1998). Twitter can also be used to converse about daily problems and experiences and to broadcast personal interests and opinions to one’s followers (Geser, 2010). In any case, being followed can create a certain sense of ‘‘bonding neighborliness’’ (Beaudoin, 2011, p. 159) by providing a sense that one is writing for an exclusive audience that somehow supports what one is ‘‘tweeting’’—just as in writing for friends and family (cf. Stefanone & Jang, 2007). Moreover, one might even mobilize solidarity or
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social support for a cause or opinion (cf. Kaigo, 2012). Additionally, tweets might be ‘‘retweeted’’, which can further provide a sense of social support as retweeting indicates that one’s followers like or agree with one’s tweet. Taken together, these considerations indicate that being followed on Twitter can generate a sense of online bonding social capital. Therefore, we hypothesize that the more followers a user has on Twitter, the more bonding social capital the user perceives (H3).
4.4. Too much of a good thing Hypotheses H2 and H3 postulate linear relationships between the number of followees and online bridging social capital, and the number of followers and online bonding social capital. However, previous research has suggested non-linear relationships between these variables (Donath & Boyd, 2004; Ellison et al., 2011; Tong, van der Heide, Langwell, & Walther, 2008). Donath and Boyd (2004), for instance, point to the phenomenon of so-called ‘‘Friendster whores’’ (p. 80), a pejorative term used to describe Friendster users who collect Friends not because of an interest in the person but as an end in itself. Concerning online bridging social capital, Ellison et al. (2011) argue that despite the benefits of technology in creating ties, one can have too many friends on Facebook. Similarly, on Twitter one can be a follower of so many people that obtaining meaningful information is compromised. As a consequence of following too many people, one must spend so much time on Twitter reading tweets that social capital suffers. Simply put, the user just can’t handle the information overflow. Following too many users can be experienced as tiring and tedious and can, in turn, negatively affect social capital. Similarly, Tong et al.’s (2008) notion of ‘‘friending out of desperation’’ (p. 542) might also be applicable in the context of Twitter: ‘‘following out of desperation’’. In other words, some users might follow others just to have more followees. This in turn might have a negative effect on social capital. Therefore, we hypothesize that the relationship between the number of followees and online bridging social capital is curvilinear, reaching a point where an increase in the number of followees is no longer associated with appreciably higher perceived online social capital (H2a). Concerning online bonding social capital, the perceived emotional support resulting from being followed may become less strong when a user has too many followers. Ellison et al. (2011) state that posts sent on Facebook are less likely to be read by strangers and that the likelihood of having strangers among one’s friends on Facebook increases with the number of friends. As applied to Twitter, this would mean that the provision of social support (as a component of bonding social capital) by being followed is meaningful only up to a certain point. If a user’s follower network becomes too large, strong ties become less likely. A similar argument is presented by the phenomenon of ‘‘Dunbar’s number’’ (i.e., the cognitive limit to the number of meaningful social relationships; Dunbar, 1998). As applied to the number of Twitter followers: As the number of followers becomes large, the network of followers becomes more abstract, which increases the user’s psychological distance from his or her followers. Moreover, on Twitter, followers are not necessarily real persons, but can also be so-called bots that automatically follow users. Certainly, such bots would not elicit a feeling of connectedness among those they follow. Therefore, we hypothesize a negative curvilinear relationship between the number of followers on Twitter and online bonding social capital: The relationship between the number of followers and online bonding social capital will be curvilinear, reaching a point where an increase in the number of followees is no longer associated with higher perceived online bonding social capital (H3a).
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5. Method The present study employed a cross-sectional survey design. Data for the study were gathered through an online questionnaire administered during February and March 2011. A link to the online questionnaire was sent by e-mail, posted on Facebook, or (re)tweeted. It was ensured that the questionnaire could only be assessed once per IP. Thereby it was ensured, that no one participated twice.
5.1. Participants A total of 295 Twitter users participated in the study and filled in the questionnaire. Data from 31 participants were eliminated because the respondents either had insufficient time to complete the survey (indicating that they did not read the questions carefully), or showed response sets (Osborne & Blanchard, 2011). The resulting dataset included data from 264 respondents; the average age was 34.17 years (SD = 9.13), 32.2% (n = 85) were female, and 64.8% were college educated. 217 (82.2%) participants were Swiss, 35 (13.3%) were German, and 12 (4.5%) were Austrian.
5.2. Measures Perceived online bridging social capital was measured using items from Williams’ (2006) Internet Social Capital Scale (ISCS). We adapted the scale by changing the wording to reflect the context of the study (e.g., ‘‘Interacting with people on Twitter makes me feel like part of a greater community.’’). Participants answered on a five-point Likert scale (1 = I strongly disagree to 5 = I strongly agree). Perceived online bonding social capital was measured using items from Williams (2006) (e.g., ‘‘On Twitter, there are several people I trust to help solve my problems.’’). The ISCS has been validated with data gathered through a university message board (Williams, 2006). The items have been adapted to measure social capital on Facebook (Ellison et al., 2007, 2011). However, given the different context of the present study, we factor analyzed the items to ensure that they represented two separate dimensions. We conducted a principal component analysis with oblimin rotation to explore the factor structure of the items. Three items were excluded due to double loadings. The resulting two factors accounted for 52.30% of the variance (see Table 1). Twitter use was assessed by asking participants to estimate the number of minutes they spend on Twitter in an average day (M = 37.72, SD = 18.64).
5.2.1. Followers and followees on Twitter Participants were asked how many followers and followees they have on Twitter. The focus of the present study was to explain how bridging and bonding social capital is differently associated with the numbers of followers and followees, respectively. To separate the number of exclusively followers and followees, followers should not also be followees and vice versa. Therefore, to identify the numbers of followers, participants were asked how many of their followers were also followees. The same was done for followees. Finally, we subtracted the number of overlapping followers and followees from the total number of followers and followees, respectively (MFollowers = 249.60; SDFollowers = 569.77; MFollowees = 180.65; SDFollowees = 248.34; for a similar approach, see Subrahmanyam et al., 2008).
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Table 1 Item loadings and descriptives of perceived online social capital. Perceived bridging online social capital Interacting with people on Twitter makes me feel connected to the bigger picture Interacting with people on Twitter makes me feel like part of a larger community On Twitter I come in contact with new people all the time Interacting with people on Twitter gives me new people to talk to Interacting with people on Twitter reminds me that everyone in the world is connected Interacting with people on Twitter makes me interested in things that happen outside of my town Interacting with people on Twitter makes me want to try new things Interacting with people on Twitter makes me interested in what people unlike me are thinking I am willing to spend time to support general community activities on Twitter If I needed an emergency loan of 500 CHF/Euros, I know someone on Twitter I can turn to The people I interact with on Twitter would share their last dollar with me There is someone on Twitter I can turn to for advice about making very important decisions When I feel lonely, there are several people on Twitter I can talk to The people I interact with on Twitter would put their reputation on the line for me There are several people on Twitter I trust to help solve my problems The people I interact with on Twitter would help me fight an injustice The people I interact with on Twitter would be good job references for me Percentage of Variance accounted for Eigenvalues Cronbach’s a Mean (SD)
Perceived bonding online social capital
.843 .832 .728 .714 .679 .658 .644 .639 .639 .811 .780 .769
35.64 6.058 .880 3.18 (.83)
.755 .735 .660 .581 .518 16.66 2.832 .854 2.44 (.86)
Note: Loadings below |.30| are blanked.
5.2.2. Perceived offline bridging social capital To control for offline social capital, we used items from the ISCS. Participants responded on a five-point Likert scale (1 = I strongly disagree to 5 = I strongly agree).
and the descriptives (means and standard deviations) are depicted in Table 2.
5.2.3. Perceived offline bonding social capital Bonding social capital offline was also assessed with items from the ISCS. A principal component analysis with oblimin rotation resulted in a two-factor solution with 17 items. Three items were excluded due to double loadings. The resulting two factors accounted for 48.38% of the variance. The factor structure, the alpha-values,
To address our hypotheses and research questions, we conducted a seemingly unrelated regression (SUR; Zellner, 1962, 1963) analysis using STATA 11.2. This analytical approach allows more than one dependent variable and takes into account possible contemporaneous correlations of error in the terms of the dependent variables. Unlike in OLS, models for different dependent variables
6. Results
Table 2 Item loadings and descriptives of perceived offline social capital. Perceived bridging offline social capital Interacting with people offline makes me feel connected to the bigger picture Interacting with people offline makes me feel like part of a larger community Interacting with people offline makes me interested in things that happen outside of my town Interacting with people offline reminds me that everyone in the world is connected Interacting with people offline makes me interested in what people unlike me are thinking Interacting with people offline gives me new people to talk to Interacting with people offline makes me want to try new things Offline I come in contact with new people all the time I am willing to spend time to support general community activities The people I interact with offline would share their last dollar with me There is someone offline I can turn to for advice about making very important decisions When I feel lonely, there are several people offline I can talk to If I needed an emergency loan of 500 CHF/Euros, I know someone offline I can turn to The people I interact with offline would help me fight an injustice The people I interact with offline would put their reputation on the line for me There are several people offline I trust to help solve my problems The people I interact with offline would be good job references for me Percentage of Variance accounted for Eigenvalues Cronbach’s a Mean (SD) Note: Loadings below |.30| are blanked.
Perceived bonding offline social capital
.777 .777 .768 .737 .725 .717 .701 .593 .488
33.55 5.703 .873 3.62 (.73)
.745 .741 .724 .707 .669 .656 .587 .437 14.83 2.521 .828 4.37 (.57)
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M. Hofer, V. Aubert / Computers in Human Behavior 29 (2013) 2134–2142 Table 3 Seemingly unrelated regression analysis predicting perceived bridging and bonding online social capital. Dependent variables Predictors Age Sex (1 = female) Education Perceived bonding offline social capital Perceived bridging offline social capital Twitter use per day (min) Number of followers Number of followees Number of followers (squared term) Number of followees (squared term) Total R2adjusted
b Perceived bridging online social capital
b Perceived bonding online social capital .17** .07 .04* .14** .05 .05 .65** .08 .38+ .19 .157***
.08 .01 .09* .02 .13* .33*** .05 .48** .05 .27* .248***
Note: bs refer to the final standardized coefficients after the entry of all variables. * p < .05. ** p < .01. *** p < .001. + p < .1.
can be calculated simultaneously. As a result, the SUR estimator leads to efficient parameter estimates (Zellner, 1963). The Breusch–Pagan test of independence indicates that the residuals of bridging and bonding online social capital are not independent (v2(1) = 20.752, p < .001). Addressing H1a, H2, and H2a, we examined online bridging social capital as the dependent variable. Control variables (gender, age, and education), offline social capital (bridging and bonding), Twitter use, the number of followers and followees, and the squared number of followers and followees were included as independent variables (see Table 3). The final model had an adjusted R2 of .248. The results suggest that of the demographic variables, only education affects online bridging social capital. Thus, fewer years of school of a Twitter user is positively associated with more bridging social capital. Offline bridging social capital was positively related to its online counterpart (b = .13, p < .05). Thus, participants with more offline bridging social capital also had more online bridging social capital. In other words, participants who viewed themselves as being part of a larger group or having diffuse reciprocity with a larger community offline also had the perception of having that online. Hypothesis H1a predicted that Twitter use is positively related to bridging social capital online. This hypothesis was confirmed: Twitter use significantly predicts online bridging social capital (b = .33, p < .001). Participants who spent more time on Twitter in an average day reported more perceived online bridging social capital. Regarding hypotheses H2 and H2a, we confirmed the hypothesis that the number of followees predicts bridging social capital online (H2) (b = .48, p < .01). Thus, participants who follow more users possess higher online bridging social capital. That is, the number of followees is positively related to being outward looking and having the impression that one is in contact with a broader range of people with different backgrounds. However, this effect appears to diminish as the number of followees is too large as indicated by the negative coefficient of the squared term (b = .27, p < .05). In other words, when the number of followees becomes large, bridging social capital is decreasing. As indicated in Table 3, neither the number of followers nor the squared number of followers had an effect on online bridging social capital. Concerning online bonding social capital (and using the same independent variables), the final model had an adjusted R2 of .158. Analyses revealed that age had an effect on bonding social capital online. The younger the Twitter users, the higher the level of perceived bonding social capital (b = .17, p < .01). As was the case for online bridging social capital, online bonding social capital was also positively related to its offline counterpart (b = .14, p < .01). Thus, participants with more perceived solidarity or
emotional support offline also perceived more solidarity and support online. Contrary to hypothesis H1b, Twitter use did not affect bonding social capital online (b = .05, ns). Surprisingly, time spent on Twitter did not seem to play a role in a participant’s online social capital. Concerning the effect of the number of followers and the squared term of the number of followers, our data seem to support both H3 and H3a. Participants with more followers possess more online bonding social capital (b = .65, p < .01), but only to a certain degree, as indicated by the negative coefficient of the squared term (b = –.38, p = .06).1 However, the non-linear effect was only marginally significant and thus should be treated with caution.
7. Discussion The overarching goal of the present study was to examine the effect of the number of followers and followees of Twitter users on perceived bridging and bonding online social capital. Both forms of social capital can be generated on Twitter; however, participants reported higher levels of perceived online bridging social capital than of online bonding social capital (see Table 1). Although we hypothesized that Twitter use would lead to both online bridging and online bonding social capital, Twitter use influenced bridging social capital only. This result corroborates results of previous research by Kobayashi (2010), who showed that mainly bridging social capital is generated online. Contrary to hypothesis H1b, Twitter use did not affect bonding social capital. This result can be explained as follows: as we did not distinguish between the two forms of ‘‘Twitter use’’, which are tweeting (being followed) and reading tweets (following others), the absence of a relationship between Twitter use and online bonding social capital might indicate that Twitter is mostly used in a passive way (i.e. reading or following). In other words, being a lurker (and not a poster) has beneficial social capital outcomes despite the rather pejorative connotation (cf. Preece, Nonnecke, & Andrews, 2004; Rau, Gao, & Ding, 2008). However, this conclusion is highly speculative and 1 This curvilinear relationship may be caused mainly by four influential cases (indicated by 2 < Cook’s distance
