Revealing the Hidden Patterns of News Photos: Analysis of Millions of News Photos Using GDELT and Deep Learning-based Vision APIs
This study provides insights into media bias and representation for researchers and journalists, though it is incremental as it applies existing deep learning tools to a new dataset.
The authors analyzed over two million news photos from January 2016 to identify common objects, sentiments, alignment with text tone, gender treatment, and portrayal of political candidates, revealing patterns in media representation.
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.