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- Publisher
- Addleton Academic Publishers
- Copyright
- © 2009 Addleton Academic Publishers
- ISSN
- 1948-9137
- eISSN
- 2162-2752
- Publisher site
- See Article on Publisher Site
Abstract
We inspect the relevant literature on the decision-making logic of big data algorithmic analytics, providing both quantitative evidence on trends and numerous indepth empirical examples. Building our argument by drawing on data collected from Pew Research Center, we performed analyses and made estimates regarding % of users who say they frequently/sometimes see on social media posts that are overly dramatic or exaggerated/ people making accusations or starting arguments without having all the facts/posts that teach you something useful you had not known before/posts that appear to be about one thing but turn out to be about something else and % of users who say, after being directed to view their Facebook “ad preferences” page, that they did not know Facebook maintained this list of their interests and traits/they are not comfortable with Facebook compiling this information/the listings do not very or at all accurately represent them. Data collected from 4,200 respondents are tested against the research model by using structural equation modeling. Keywords: decision-making logic; big data algorithmic analytics; machine learning
Journal
Contemporary Readings in Law and Social Justice – Addleton Academic Publishers
Published: Jan 1, 2019