Collaborative Research: A New Infrastructure for Monitoring Social Class Networks.

Stanford University, Stanford CA

Investigators

Abstract

SES-1357488 David Grusky Stanford University SES-1357442 Michael Macy Cornell University Over the last 15 years, an ever larger and more diverse population is choosing to interact using social media that record the digital traces of their communications, a development that opens up unprecedented opportunities to study the network foundation of social class relations. Although there is a long tradition of research examining whether social classes in the United States are well-formed, it has been based exclusively on survey and Census data and, by necessity, has ignored the network foundations of class structure and formation. This research takes advantage of the rising amount of interaction with social media to examine that network structure at population scale. The resulting methods will provide the basis for a new and novel research infrastructure for investigating inter-personal interaction within and between social classes in the United States. By using data from a complete crawl of U.S. Twitter users, it becomes possible to measure class barriers to interpersonal interaction. The centerpiece of this approach is the development of methods to measure the class situation of users with profile data, lexical analysis of message content, and housing valuations for geo-located users. To supplement and validate these behavioral measures, a survey will be administered to a random sample of network edges. A similar analysis of Facebook users will be carried out. The resulting data will be used to complete the first network-based analyses of the extent and patterning of the U.S. class structure. In conventional ?static analyses? of the class structure, the size of inter-class differences in behaviors and attitudes (e.g., childrearing practices, political attitudes) is emphasized, while the patterning of inter-class contact and networks that link classes together is ignored. The key question, therefore, is whether the proposed network analyses of class yield a different portrait of the structure of social classes than the static analyses that have dominated decades of class research in the U.S. At the same time, some network-based analyses of class have been attempted in the past, analyses that have relied on an idiosyncratic range of network behaviors that may be discerned with survey methods (especially, assortative mating where people marry persons with similar education and occupational characteristics, and intergenerational social mobility). The analyses undertaken here will reveal whether social media reduces class barriers to interaction relative to the level of class homophily (the tendency of people to associate with similar people) revealed in face-to-face networks available in survey data. These analyses will provide the foundation of a new network-based analysis of class structure. Broader Impacts If class barriers are comparatively weak in on-line interactions, standard measurements of class structure will provide an increasingly misleading portrait of civil society and its inclusiveness. It is also plausible, however, that the powerful search algorithms of online platforms allow people to efficiently cull for alters who are similar to themselves. If the latter proves to be the case, it means that the rise of new social media are, contrary to the conventional view, increasing class homophily and polarizing class relations. The research also has a methodological payoff. Because a network-based analysis of social class structure requires high-quality measurements of the class situation of media users, much of the research will focus on developing the methods that make such measurement possible. The social class of users and alters will be imputed by (a) linking geo-located users to their neighborhoods and housing values, (b) exploiting available profile data, (c) carrying out a lexical analysis of message content, and (d) administering surveys to users. These methods, which may be extended to carry out analogous imputations of race, gender, and other ascribed traits, will be of use to researchers in the social sciences, computer science, information science, and other disciplines facing the stock situation in which direct information on individual traits is scarce. The project will also provide new research opportunities for graduates and undergraduates at Cornell University and Stanford University.