Images as Data for Social Science Research
An Introduction to Convolutional Neural Nets for Image Classification
Published on 13. August 2020
Book
Paperback/Softback
86 pages
978-1-108-81685-4 (ISBN)
Description
Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises; 32 Halftones, black and white
Dimensions
Height: 225 mm
Width: 151 mm
Thickness: 6 mm
Weight
142 gr
ISBN-13
978-1-108-81685-4 (9781108816854)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Nora Webb Williams | Andreu Casas | John D. Wilkerson
Images as Data for Social Science Research
An Introduction to Convolutional Neural Nets for Image Classification
E-Book
08/2020
Cambridge University Press
€20.99
Available for download
Persons
Content
1. Introduction; 2. Prerequisites for computer vision methods and tutorials; 3. Introduction to CNNs for social scientists; 4. Overview of fine-tuning a CNN classifier for images; 5. Political science working example: images related to a Black Lives Matter protest; 6. The promise and limits of autotaggers; 7. Application: fine-tuning an open source CNN; 8. Legal and ethical concerns in using images as data; 9. Conclusion; 10. References.