Groups and network clusters - Collective Emotions

GROUPS AND NETWORK CLUSTERS 133

outgroups), and more generally leads to enactment of the activated social identity (Turner et al., 1987).

Self-categorization also infl uences emotion because events that are relevant to one’s group become personally relevant when one sees oneself as a group member. For example, Doosje, Branscombe, Spears, and Manstead (1998) showed that people may feel guilty for misdemeanors committed by their group rather than by the self, depending on their level of identifi cation with the group (see also Ferguson & Branscombe, Chapter 17, this volume). Moving from group to intergroup emotions, actions of outgroups also accrue particular emotional signifi cance when people self-categorize in terms of social identity (e.g., Yzerbyt, Dumont, Gordijn, & Wigboldus, 2002).

Th e intergroup emotion literature since Durkheim (1912/1915) has generally focused on how social identities give rise to intergroup emotion, but Livingstone, Spears, Manstead, Bruder, and Shepherd (2011) demonstrated that emotion can shape social identities as well. For example, when an individual gets emotional about something, and the emotional experience is shared by others, the “emotional fi t” of the experience self-implicates a group with similar passions. While the sharedness of the emotional experience helps to shape one’s social identity, the content of the shared emotion suggests avenues for collective action. Th us, emotions can help demarcate the boundaries of ingroups and outgroups.

If experiencing emotions gives people a new sense of “us” and “them,” as suggested by Livingstone and colleagues (2011), it can also guide daily interactions and reconfi gure everyday relations. For example, someone who feels compassion toward one group and contempt toward another would be likely to treat people in them diff erently: some individuals might receive welcoming sympathy, others hostile rejection. By making people selectively attract or repel each other, emotions can sort them into like-minded clusters.

Network emotion

Th e strength of the social network approach is that people can be connected to groups with varying levels of embeddedness through their interpersonal relations (Granovetter, 1985) in addition to their perceived group memberships. Suppose that Fig. 9.2 represents a network of individuals who self-categorize themselves as either “black” or “gray.” In the graph, the circles represent individuals and lines the friendship relations among them.

Th e large dotted circles indicate network clusters that can be mathematically identifi ed by actors’ dense interrelations, and their sparse relations with actors outside the cluster.

(Th e dashed line from A to C represents a potential relation and will be discussed later.) Even a brief visual examination of the network conveys a lot of information. For example, there is a single cut-off point: removing actor B would separate the network into three disconnected components. Th us, actor B occupies an important role in the network and can broker information and opportunities between the clusters (Burt, 1992). In social network analysis, there are measures to quantify how easily networks can be cleaved into separate clusters and how unambiguous the resulting divisions would be. In other words, analyzing the number of relations between people in a network can put precise numbers on the level of integration within and between clusters (Porter, Onnela, & Mucha, 2009).

Another feature of the network in Fig. 9.2 is that the clusters are not entirely based on whether the actors identify themselves as black or gray: while two of the clusters are homogenous in terms of their members’ group identifi cation, the third cluster contains both black and gray members. Instead of using information about diff erent levels of identifi cation, a network analyst would use the observed structure of relations to predict actor C’s emotions toward actor A. For example, since C is friends with B, who in turn is friends with A, it follows that C is likely to become friends with A as well (i.e., transitivity of relations). Th is prediction can be made from the pattern of relations alone without any reference to the individual characteristics, such as group identifi cation, of actors C or A.

Similarly to the emotional fi t principle, which states that emotions can infl uence social identity (Livingstone et al., 2011), we propose that the “structural fi t” of one’s network position can be an additional infl uence on social identity, self-categorization, and emotion. Sidanius, Van Laar, Levin, and Sinclair (2004) found that students in ethnically segregated student groups felt more victimized. From our perspective, mapping the par- ticipants’ positions in the actual social network on campus could provide valuable information to supplement data on individual ethnic identity and intergroup attitudes. For example, in Fig. 9.2, actors Y and Z—who are both far removed from intergroup contact opportunities—might harbor more negative emotions toward their respective outgroups

Z A

Fig. 9.2 Community structure versus identiﬁ cation with a group.

GROUPS AND NETWORK CLUSTERS 135

than the actors bridging the gap. While similar predictions could be made based on purely individual-level variables, such as social identity, a relations-based network approach could provide additional research traction to investigators who are interested in a holistic view of emotions in their natural, social habitat.

Fortunately, network-oriented emotion research is gaining a foothold. In terms of theory, emotion has already been rooted in dyadic exchanges with network-wide eff ects (e.g., Lawler, 2001; Lawler, Th ye, & Yoon, Chapter 13, this volume), and framed as a social infection transmitted through the conduits of relations (Hill, Rand, Nowak, & Christakis, 2010). Empirical evidence is also accumulating in favor of networked emotion. For example, people connected to many happy people and those who are central in the network are more likely to become happy in the future (Fowler & Christakis, 2008). Conversely, Schaefer, Kornienko, and Fox (2011) found that depressed adolescents withdrew from friendships over time, leaving them in network positions that were less conducive to new friendships. Since these marginalized network positions reduced opportunities to social- ize with non-depressed peers, the arrangement aided depressed adolescents to fi nd each other instead.

Th e challenge for future research is to further liberate emotions from their intrapersonal confi nes and examine how they operate as functions of relations in a broader network. One rudimentary step toward this goal would be to collect data about discrete other-oriented emotions that actors feel towards each other. For example, a researcher could construct two longitudinal networks, one consisting of the relations based on anger felt between the actors, and another based on friendship relations. RSiena soft ware (Ripley et al., 2012) is capable of analyzing how relations in one network infl uence relations in another. With RSiena, it would be possible to empirically establish, for example, the veracity of the dictum “the enemy of my enemy is my friend,” (i.e., whether actors who agree on their target of anger are more likely to forge friendships as a consequence).

Networks of people and groups

In our terminology, a group in the social-identity sense of the word is a cognitive category to identify with, whereas a cluster is a specifi c set of actors in which one belongs by the vir- tue of one’s relations. As briefl y mentioned earlier, emotions from the relation-alignment perspective are always about some socially relevant object. In terms of the networks discussed so far, this object has been a real-world person.

Parkinson and colleagues (2005) apply the distinction between emotion’s subject (i.e., who is experiencing the emotion) and object (i.e., at whom or at what the emotion is directed) to emotions occurring in social contexts, yielding at least fi ve types of emotions including group and interpersonal ones (Iyer & Leach, 2008). Intergroup emotions are experienced by individuals who self-categorize as members of an ingroup and have an outgroup as the object of their emotion. Personal emotions directed at outgroups deal with situations in which individuals perceive others as outgroup members without including themselves in an ingroup. Th e same applies to personal emotions directed at in-groups, but in this case, individuals experience emotions about their own ingroup

C D C

E F E F

4 3 2 1

People-network projection Affiliation network of people and groups Group-network projection 1

3 2 4

Fig. 9.3 Two projections of a two-mode afﬁ liation network.

without identifying with it. Group-based emotions directed at ingroups are similar, except that the subject of the emotion identifi es with the ingroup. Finally, group-based emotions directed at individuals consider situations in which an individual who identifi es with the group experiences an emotion about an individual, such as British citizens mourning the death of Princess Diana.

Two-mode networks have the potential to provide a single framework to analyze how people relate both to other people in their immediate social environment and to ideas that they endorse or identify with (whether they are groups, ideas, events, or other types of objects). When a two-mode network represents groups and members (i.e., each relation represents an actor’s affi liation to a group), they are called affi liation networks (see Fig. 9.3). To construct a two-mode network, people report groups to which they belong (the middle panel of Fig. 9.3). From these data, it is possible to reconstruct networks of people based on their shared group endorsements. Likewise, the two-mode data can be also projected into a one-mode network of groups in which the groups are connected through shared members. Although affi liation networks are not a new methodology to investigate how individuals and groups interpenetrate each other (Breiger, 1974), more advanced methods for two-mode networks are still developing (Latapy, Magnien, &

Vecchio, 2008) and waiting for application to collective emotion. For example, collect- ing data about people’s actual interpersonal relations, their identifi cations with various groups, and the emotions they feel towards people and groups would allow empirical comparison of emotions based on identifi cation and relational patterns within one ana- lytic framework.

Conclusion

In this chapter, we have argued that the interpersonal function of emotion centers on the relations that people have with each other. As an emergent property of interlocking interpersonal relations, the natural habitat of emotion is the social network. As a theo- reti cal framework and a methodological toolkit, the social network approach also focuses on relations, which makes it a promising avenue to study relational emotions. We posit that emotions are oriented to their interpersonal eff ects and function to align relations by bottom-up implicit, or top-down explicit, processes. By looking at anger and contempt, we illustrated the mutual attunement involved in these social emotions and their rela tional implications.

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By tracing the processes involved in emotion on three levels of analysis, we showed that emotion can confi gure the pattern of relations in structures beyond interpersonal, dyadic encounters. Since emotion at the group level has typically been approached from a social identity or self-categorization perspective, we point out theoretical and practical diff erences between groups as cognitive categories and social networks as empirically observed relations among specifi c social actors.

Although the social identity and social network approaches may seem very diff erent at fi rst, they do not need to be at odds with each other. Instead, they can supplement each other without the need to overhaul either in a fundamental way. Since social identity/self-categorization theorists consider self-categorization as inherently variable and context-dependent, and studies have highlighted the role of networks in enacting, trig- gering, and defi ning identities (Deaux & Martin, 2003), dynamically evolving social networks can serve as the variable social context behind these shift ing self-categorizations.

Especially two-mode networks should provide new opportunities for integrative research in group emotion.

We suggest that SNA can supplement the social identity approach by providing theoretical and methodological help that permits the comparison of clusters of people as emergent network structures and groups of people as cognitive categories. Furthermore, “structural fi t” (i.e., actors’ position in a network in relation to empirically defi ned clusters and identity-based groups) might aff ect how actors in a network experience emotions and categorize themselves. In closing, we encourage researchers who are interested in groups, emotions, and relations to consider incorporating ideas and tools from social network analysis to their practice.

Acknowledgments

Th e authors would like to express their gratitude for receiving support from the ESRC grant RES-060-25-0044, Emotion Regulation of Others and Self (EROS): A Collaborative Research Network.

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Dalam dokumen Collective Emotions (Halaman 158-168)