Abstract not found

We study the social structure of Facebook “friendship ” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes—gender, class year, major, high school, and residence—at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on the user characteristics. We thereby compare the relative importances of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further. Preprint submitted to

If universities expose students to a more diverse set of peers, do students form more diverse social networks in subsequent interactions? We address this question by exploiting unique data on social contacts from Facebook for a university that randomly assigns students to dormitories. We find that the exposure to members of a different race increases the number of different race friends in the dorm environment, but does not increase the diversity of social networks outside that environment.

We combine data collected on friendship networks and individual characteristics with experimental observations from a sequence of dictator games run at an all-girls school in Pasadena, California. Our analysis provides two sets of insights. First, we find that dictator giving is primarily explained by social distance, defined as the length of the shortest path connecting a dictator and recipient in their (elicited) network of friends. In fact, dictator offers follow a simple inverse distance law. Second, while student demographics play a minor role relative to network effects in explaining offer amounts, individual heterogeneity is important for the network formation process. In particular, we detect significant homophilous behavior – students tend to connect to others similar to them. In addition, the network data reveal a strong preference for cliques – students tend to connect to those that are already close. The study serves as one of the first to identify network architecture with individual behavior and outcomes in a strategic context.

the fact that Facebook is a visually-driven website, suggests that Facebook serves to inadvertently or covertly perpetuate two racist or discriminatory norms: the colorblind mentality and racialized visual classification of others. This paper quickly traverses through a basic theory of identity that dictates that persons incur an on-going dialectic between individual and social identity, both on and offline. It contends that Facebook is an advantageous place to perform and portray one’s social identity and is largely visually-driven, therefore the interface is of paramount importance. Racial, ethnic, or national identity is of significance to many individuals and the lack of a place to express this is a form of discrimination. Such a category should be added to Facebook’s set of basic identity options. Guidelines for a successful implementation are provided.

The authors of this paper believe that social incentives can be used to obtain numerous facial images of faces and they propose a computational method for using these images.

We employ a standard identification strategy from the peer effects literature to investigate the importance of network definitions in estimation of endogenous peer effects. We use detailed information on friends in the Adolescent Longitudinal Health Survey (Add Health) to construct two network definitions that are less ad hoc than the school-grade cohorts commonly used in the educational peer effects literature. We demonstrate that accurate definitions of the peer network seriously impact estimation of peer effects. In particular, we show that peer effects estimates on educational achievement, smoking, and drinking are substantially larger with our more detailed measures than with the school-grade cohorts. These results highlight the need to further understand how friendships form in order to fully understand implications for policy that alters the peer group mix at the classroom or cohort level.

We develop a social network model of occupational segregation between different social groups, generated by the existence of positive inbreeding bias among individuals from the same group. If network referrals are important for job search, then expected homophily in the contact network structure induces different career choices for individuals from different social groups. This further translates into stable occupational segregation equilibria in the labor market. We derive the conditions for wage and unemployment inequality in the segregation equilibria and characterize first and second best social welfare optima. Surprisingly, we find that socially optimal policies involve segregation.

In light of increasing racial tensions in recent years on the University of Illinois campus, the

We combine data collected on friendship networks and individual characteristics with experimental observations from a sequence of dictator games run at an all-girls school in Pasadena, California. Our analysis provides two sets of insights. First, we find that dictator giving is primarily explained by social distance, defined as the length of the shortest path connecting a dictator and recipient in their (elicited) network of friends. In fact, dictator offers follow a simple inverse distance law. Second, while student demographics play a minor role relative to network effects in explaining offer amounts, individual heterogeneity is important for the network formation process. In particular, we detect significant homophilous behavior – students tend to connect to others similar to them. In addition, the network data reveal a strong preference for cliques – students tend to connect to those that are already close. The study serves as one of the first to identify network architecture with individual behavior and outcomes in a strategic context.