Social selection and peer influence in an online social network

Abstract

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 ad...

Evolution properties of online user preference diversity

“…Recent advances suggest that, the human social behaviors present remarkable distinct properties against the differences in age, sex and personality etc., both on the collective and individual levels [42,[46][47][48]. Consequently, how to identify the evolution characteristics of online user preference with these attributes are still open questions, which requires not only the details of the user profiles but also the trade-off between the sample size and the statistical methods.…”

“…Online user preference could be affected by the social influence [8,41,42] and the recommendation systems [43][44][45]. Recent advances suggest that, the human social behaviors present remarkable distinct properties against the differences in age, sex and personality etc., both on the collective and individual levels [42,[46][47][48].…”

Life course similarities on social networking sites

“…Contagion refers to the fact that as members of a friendship circle get married or have children, this behaviour is likely to encourage spread, putting social pressure or incentives on others within that group to move into the same life stage, alternatively to leave the group in order to affiliate with another (Christakis and Fowler, 2007). This distinction is related but not identical to the tendency of familiarity to breed similarity and vice versa: close people tend to become more similar over time (Lewis et al, 2012) and maintain friendships better with those who are similar (Burt, 2000;Tuma and Hallinan, 1979). Additionally, friendship itself is both selective and contagious: friendship networks may merge, so that people easier become friends with the friends of a friend, which further enhances the similarities between the original friend dyad (Wimmer and Lewis, 2010).…”

Predicting Online Community Churners Using Gaussian Sequences

“…McGowan et al [6] also predicted churners from a telecommunications provider by experimenting with different dimensionality reduction and boosting methods. Lewis et al [5] examined Facebook networks of college students over a 4 year period and found an association between friendship maintenance and geographical proximity and shared tastes. Quercia et al [7] analysed Facebook friendships and users' personality traits, finding that churn was likely to happen if the ages of connected users differed and if one of the users was neurotic or introverted.…”