\aImages as Data for Social Science Research / \cNora Webb Williams, Andreu Casas, John D. Wilkerson
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\a1st ed.
260
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\aCambridge : \bCambridge University Press, \c2020
300
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\a80 pages : \billustrations ; \c23 cm.
490
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\aCambridge elements.
490
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\aElements in quantitative and computational methods for the social sciences.
504
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\aIncludes bibliographical references.
505
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\a1. 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. Data availability statement
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\aImages 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.
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