Abstract
Gender plays an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates’ Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unfollower images. Through a case study of the ‘woman card’ controversy, we demonstrate how gender is informing the 2016 presidential election. Our framework of analysis can be readily generalized to other case studies and elections.
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Notes
- 1.
By ‘unfollower’, we mean people who previously followed a candidate on Twitter and later unfollowed him/her.
- 2.
For a detailed analysis of follower growth patterns, see [21].
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- 4.
The full list of label names together with the validation data set and the trained model, is available at the first author’s website.
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Wang, Y., Feng, Y., Luo, J. (2017). Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_4
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DOI: https://doi.org/10.1007/978-3-319-60240-0_4
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