Automatically Selecting Striking Images for Social Cards

TL;DR

This paper presents an algorithm to automatically select striking images for social media cards, improving visual appeal when web resources lack metadata, with high prediction accuracy demonstrated on news and scholarly articles.

Contribution

It introduces a novel method for automatically choosing striking images for social cards, addressing metadata deficiencies in web resources and archived content.

Findings

Over 40% of archived news articles lack striking images.

22% of scholarly articles lack striking images.

Achieved Precision@1 of 0.83 for news articles and 0.78 for scholarly articles.

Abstract

To allow previewing a web page, social media platforms have developed social cards: visualizations consisting of vital information about the underlying resource. At a minimum, social cards often include features such as the web resource's title, text summary, striking image, and domain name. News and scholarly articles on the web are frequently subject to social card creation when being shared on social media. However, we noticed that not all web resources offer sufficient metadata elements to enable appealing social cards. For example, the COVID-19 emergency has made it clear that scholarly articles, in particular, are at an aesthetic disadvantage in social media platforms when compared to their often more flashy disinformation rivals. Also, social cards are often not generated correctly for archived web resources, including pages that lack or predate standards for specifying striking images. With these observations, we are motivated to quantify the levels of inclusion of required metadata in web resources, its evolution over time for archived resources, and create and evaluate an algorithm to automatically select a striking image for social cards. We find that more than 40% of archived news articles sampled from the NEWSROOM dataset and 22% of scholarly articles sampled from the PubMed Central dataset fail to supply striking images. We demonstrate that we can automatically predict the striking image with a Precision@1 of 0.83 for news articles from NEWSROOM and 0.78 for scholarly articles from the open access journal PLOS ONE.