In this study, we explore a novel approach to measure fine-grained (photo-level) diversity using Instagram. We compare and contrast these new measures of diversity with traditional metrics (i.e. census). We discuss the merits and shortcomings of supplementing traditional census figures with these new measures. Further, we explore the predictive capacity that this new metric has over socio economic outcomes, namely income inequality. We find that using our ne-grained metric for measuring diversity in interactions produces very different results compared to traditional census measures. We also determine that diversity (specifically photo based entropy in age and race) are associated with income inequality and the combined model is significantly (though weakly) predictive of inequality. Neighborhoods that have high scores in racial diversity seem to have a correlation with lower inequality, while neighborhoods that have high scores in age diversity seem to have a correlation with higher inequality. We discuss the possible implications of this work on research in sociology and associated areas and suggest further work based on these findings.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7604
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (vi, 34 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Multiculturalism
Subject (authority = ETD-LCSH)
Topic
Social media
Note (type = statement of responsibility)
by Saket Hegde
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
Rutgers University. Graduate School - New Brunswick
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Type
License
Name
Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.