  In this work, we analyze more than two million news photos published in
January 2016. We demonstrate i) which objects appear the most in news photos;
ii) what the sentiments of news photos are; iii) whether the sentiment of news
photos is aligned with the tone of the text; iv) how gender is treated; and v)
how differently political candidates are portrayed. To our best knowledge, this
is the first large-scale study of news photo contents using deep learning-based
vision APIs.
