The Big Data of Social Groups: Beyond Networks in Structural Theories of Interaction

• In Event: 404 - Section on Social Psychology Paper Session. Computational Social Science and Studying Social Behavior

Abstract

Researchers implicitly employ four very different versions of the social network concept: A network is a set of socially constructed role relations (e.g. friendship), a set of interpersonal affective evaluations (e.g. liking), a pattern of behavioral social interaction (e.g. communication), or an opportunity structure for exchange. Researchers conventionally assume these conceptualizations are interchangeable, given that positive cases (‘ties’) often coincide for these definitions. This paper interrogates the interplay across the four definitions, with specific attention to how they treat ‘null ties’ (as dyads where a specific relationship or affective attachment is not reported, where a specific form of interaction is not observed, or where exchange is exogenously prohibited). Cutting-edge methods for data collection and analysis now allow us to move beyond reified notions of ‘social ties’ (and null ties) and instead directly observe and analyze the dynamic and structural interdependencies of social interaction behavior. Emerging technologies of computational social science -- wearable sensors, logs of telecommunication, online exchange or other interaction – allow us to observe the fine-grained dynamics of interaction over time. Employing these new technologies, perspectives, and analytical methods allows us to refashion dynamic structural theories of exchange that advance ‘beyond networks’ to unify previously disjoint research streams on relationships, interaction, and opportunity structures.

Author

• James A. Kitts, University of Massachusetts