  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.
