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| Chapter title |
The Princeton Web Transparency and Accountability Project
|
|---|---|
| Chapter number | 3 |
| 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_3 |
| Book ISBNs |
978-3-31-954023-8, 978-3-31-954024-5
|
| Authors |
Arvind Narayanan, Dillon Reisman |
| Editors |
Tania Cerquitelli, Daniele Quercia, Frank Pasquale |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 4% |
| Unknown | 22 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 35% |
| Student > Ph. D. Student | 3 | 13% |
| Student > Postgraduate | 2 | 9% |
| Professor | 2 | 9% |
| Researcher | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 4 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 48% |
| Social Sciences | 3 | 13% |
| Business, Management and Accounting | 2 | 9% |
| Economics, Econometrics and Finance | 1 | 4% |
| Engineering | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 5 | 22% |