Ranking News-Quality Multimedia

TL;DR

This paper presents a framework that ranks and filters social-media images to help news editors find high-quality, relevant photos quickly, combining aesthetic, social, and deep-learning features.

Contribution

It introduces a novel ranking and filtering framework specifically designed for news-media quality standards, integrating multiple features and a spam detection module.

Findings

Achieved a retrieval MAP of 64.5%

Reached a classification precision of 70%

Effective at identifying high-quality news photos

Abstract

News editors need to find the photos that best illustrate a news piece and fulfill news-media quality standards, while being pressed to also find the most recent photos of live events. Recently, it became common to use social-media content in the context of news media for its unique value in terms of immediacy and quality. Consequently, the amount of images to be considered and filtered through is now too much to be handled by a person. To aid the news editor in this process, we propose a framework designed to deliver high-quality, news-press type photos to the user. The framework, composed of two parts, is based on a ranking algorithm tuned to rank professional media highly and a visual SPAM detection module designed to filter-out low-quality media. The core ranking algorithm is leveraged by aesthetic, social and deep-learning semantic features. Evaluation showed that the proposed framework is effective at finding high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and a classification precision of 70%.