An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

TL;DR

This paper presents a computer vision approach to identify high-quality, beautiful photos in social media platforms like Flickr, where popularity does not necessarily reflect intrinsic aesthetic value, thus revealing hidden gems.

Contribution

It introduces a novel method that leverages aesthetics scoring to surface beautiful images from low-popularity content, bridging the gap between popularity and quality.

Findings

The method retrieves photos with median beauty scores comparable to popular images.

On average, the beauty scores of surfaced images are only 1.5% lower than the most popular ones.

The approach effectively uncovers high-quality images that are otherwise overlooked due to low visibility.

Abstract

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%.