An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
This addresses the issue for photo-sharing communities by helping discover valuable content from less popular photographers, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of overlooked high-quality photos on Flickr by using a computer vision method to surface beautiful pictures from low-popularity items, achieving median perceived beauty scores equal to the most popular photos and an average only 1.5% lower.
The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.