Predicting the Politics of an Image
Using Webly Supervised Data
Abstract
The news media shape public opinion, and often, the visual bias they contain is evident for human
observers. This bias can be inferred from how different media sources portray different subjects or
topics. In this paper, we model visual political bias in contemporary media sources at scale, using
webly supervised data. We collect a dataset of over one million unique images and associated news
articles from left- and right-leaning news sources, and develop a method to predict the image’s
political leaning. This problem is particularly challenging because of the enormous intra-class visual
and semantic diversity of our data. We propose a two-stage method to tackle this problem. In the first
stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings
computed from articles paired with the images, enable the model to predict bias. In the second stage, we
remove the requirement of the text domain and train a visual classifier from the features of the former
model. We show this two-stage approach facilitates learning and outperforms several strong baselines. We
also present extensive qualitative results demonstrating the nuances of the data.