Detecting Cultural Differences in News Video Thumbnails via Computational Aesthetics
This work addresses the problem of identifying cultural biases in media for researchers and analysts, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of detecting cultural differences in news video thumbnails by analyzing aesthetic features, finding that U.S. thumbnails are less colorful, more saturated, darker, and more detailed, while Chinese ones are less formal and more candid, based on a dataset of 2,400 thumbnails from U.S. and Chinese YouTube channels.
We propose a two-step approach for detecting differences in the style of images across sources of differing cultural affinity, where images are first clustered into finer visual themes based on content before their aesthetic features are compared. We test this approach on 2,400 YouTube video thumbnails taken equally from two U.S. and two Chinese YouTube channels, and relating equally to COVID-19 and the Ukraine conflict. Our results suggest that while Chinese thumbnails are less formal and more candid, U.S. channels tend to use more deliberate, proper photographs as thumbnails. In particular, U.S. thumbnails are less colorful, more saturated, darker, more finely detailed, less symmetric, sparser, less varied, and more up close and personal than Chinese thumbnails. We suggest that most of these differences reflect cultural preferences, and that our methods and observations can serve as a baseline against which suspected visual propaganda can be computed and compared.