Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends-except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes. T he homogeneity of social networks is one of the most striking regularities of group life (1-4). Across countless social settings-from high school to college, the workplace to the Internet (5-8)-and with respect to a wide variety of personal attributesfrom drug use to religious beliefs, political orientation to tastes in music (1, 6, 9, 10)-friends tend to be much more similar than chance alone would predict. Two mechanisms are most commonly cited as explanations. First, friends may be similar due to social selection or homophily: the tendency for like to attract like, or similar people to befriend one another (11, 12) . Second, friends may be similar due to peer influence or diffusion: the tendency for characteristics and behaviors to spread through social ties such that friends increasingly resemble one another over time (13, 14) . Though many prior studies have attempted to disentangle these two mechanisms, their respective importance is still poorly understood. On one hand, analytically distinguishing social selection and peer influence requires detailed longitudinal data on social relationships and individual attributes. These data must also be collected for a complete population of respondents, because it is impossible to determine why some people become friends (or change their behaviors)* unless we also know something about the people who do not. On the other hand, modeling the joint evolution of networks and behaviors is methodologically much more complex than nearly all past work has recognized. Not only should such a model simulate the ongoing, bidirectional causality that is present in the real world; it must also control for a number of confounding mechanisms (e.g., triadic closure, homophily based on other attributes, and alternative causes of behavioral change) to prevent misdiagnosis of selection or influence when another social process is in fact at work (15). Using a unique social network dataset (5) and advances in actor-based modeling (16), we examine the coevolution of friendships and tastes in music, movies, and books over 4 years. Our data are based on the Facebook activity of a cohort of students at a diverse US college (n = 1,640 at wave 1). Beginning in March 2006 (the students' freshman year) and repeated annually through March 2009 (the students' senior year), we recorded network and profile information from Facebook and supplemented it with academic and housing data from the college (SI Materials and Methods, Study Population and Profile Data). Our research was approved by both Facebook and the college in question; no privacy settings were bypassed (i.e., students with "private" profiles were considered missing data); and all data were immediately encoded to preserve student anonymity. Because data on Facebook are naturally occurring, we avoided interviewer effects, recall limitations, and other sources of measurement error endemic to survey-based network research (17). Further, in contrast to past research that has used interaction "events" such as e-mail or instant messaging to infer an underlying structure of relationships (7, 18), our data refer to explicit and mutually confirmed "friendships" between students. Given that a Facebook friendship can refer to a number of possible relationships in real life, from close friends or family to mere acquaintances, we conservatively interpret these data as documenting the type of "weak tie" relations that have long been of interest to social scientists (19). Though network homogeneity has been a perennial topic of academic research, prior attempts to separate selection and influence suffer from three limitations that cast doubt on the validity of past findings. These limitations are summarized by Steglich et al. (15), who introduce the modeling framework we use. First, prior approaches to network and behavioral coevolution inappropriately use statistical techniques that assume all observations are independent-an assumption that is clearly violated in datasets of relational data. Second, prior approaches do not adequately control for alternative mechanisms of network and behavioral change that could also produce the same findings. For instance, two individuals who share a certain behavior may decide to become friends for other reasons (e.g., because they have a friend in common or because they share some other behavior with which the first is correlated), and behaviors may change for many other reasons besides peer influence (e.g., because of demographic characteristics or because all individuals share some baseline propensity to adopt the behavior). Third, prior approaches do not account for the fact that the underlying processes of friendship and behavioral evolution operate in continuous time, which could result in any number of unobserved changes between panel waves. In response, Snijders and colleagues (16, 20, 21) propose a stochastic actor-based modeling framework. This framework considers a network and the collective state of actors' behaviors as a joint state space, and models simultaneously how the network evolves depending on the current network and current behaviors, and how behaviors
Besides, people tend to befriend others who share similar traits, known as homophily in sociology REF .
Social selection and peer influence in an online social network
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Psychology Medicine