  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.
