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Transparent Data Mining for Big and Small Data

Overview of attention for book
Attention for Chapter 2: Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens
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2 X users

Citations

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Chapter title
Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens
Chapter number 2
Book title
Transparent Data Mining for Big and Small Data
Published by
Springer International Publishing, January 2017
DOI 10.1007/978-3-319-54024-5_2
Book ISBNs
978-3-31-954023-8, 978-3-31-954024-5
Authors

Nicholas Diakopoulos

Editors

Tania Cerquitelli, Daniele Quercia, Frank Pasquale

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 20%
Professor > Associate Professor 2 13%
Other 1 7%
Lecturer 1 7%
Student > Bachelor 1 7%
Other 3 20%
Unknown 4 27%
Readers by discipline Count As %
Computer Science 3 20%
Social Sciences 3 20%
Unspecified 1 7%
Economics, Econometrics and Finance 1 7%
Arts and Humanities 1 7%
Other 0 0%
Unknown 6 40%