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dc.contributor.authorBener, Ayşe Başar
dc.contributor.authorÇağlayan, Bora
dc.contributor.authorHenry, Adam Douglas
dc.contributor.authorPrałat, Paweł
dc.date.accessioned2017-01-12T22:44:12Z
dc.date.available2017-01-12T22:44:12Z
dc.date.issued2016-10-04
dc.identifier.citationEmpirical Models of Social Learning in a Large, Evolving Network 2016, 11 (10):e0160307 PLOS ONEen
dc.identifier.issn1932-6203
dc.identifier.pmid27701430
dc.identifier.doi10.1371/journal.pone.0160307
dc.identifier.urihttp://hdl.handle.net/10150/621953
dc.description.abstractThis 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.
dc.description.sponsorshipADH gratefully acknowledges funding from the University of Arizona’s Institute of the Environment (www.environment.arizona.edu). ABB, BC, and PP gratefully acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC; www.nserccrsng.gc.ca), under Engage Grant number EGP 448184-13. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.language.isoenen
dc.publisherPUBLIC LIBRARY SCIENCEen
dc.relation.urlhttp://dx.plos.org/10.1371/journal.pone.0160307en
dc.rights© 2016 Bener et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.en
dc.titleEmpirical Models of Social Learning in a Large, Evolving Networken
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Sch Govt & Publ Policyen
dc.identifier.journalPLOS ONEen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-11T16:50:19Z
html.description.abstractThis 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.


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