Empirical Models of Social Learning in a Large, Evolving Network.
PLoS One
; 11(10): e0160307, 2016.
Article
em En
| MEDLINE
| ID: mdl-27701430
ABSTRACT
This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Rede Social
/
Aprendizado Social
/
Modelos Teóricos
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article