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.
Not that bias-free news is possible, but deep learning appears to be useful in foregrounding bias against particular candidates:
We then conducted a case study of assessing the portrayal of Democratic and Republican party presidential candidates in news photos. We found that all the candidates but Sanders had a similar proportion of being labeled as an athlete, which is typically associates with a victory pose or a sharp focus on a face with blurred background. Pro-Clinton media recognized by their endorsements show the same tendency; their Sanders photos are not labeled as an athlete at all. Furthermore, we found that Clinton expresses joy more than Sanders does in the six popular news media. Similarly. pro-Clinton media shows a higher proportion of Clinton expressing joy than Sanders.
If the requirement is an “appearance” of lack of bias, the same techniques enable the monitoring/shaping of your content to prevent your bias from being discovered by others.
Data scientists who can successfully wield this framework will be in high demand for political campaigns.