  Considering the large amount of available content, social media platforms
increasingly employ machine learning (ML) systems to curate news. This paper
examines how well different explanations help expert users understand why
certain news stories are recommended to them. The expert users were
journalists, who are trained to judge the relevance of news. Surprisingly, none
of the explanations are perceived as helpful. Our investigation provides a
first indication of a gap between what is available to explain ML-based
curation systems and what users need to understand such systems. We call this
the Explanatory Gap in Machine Learning-based Curation Systems.
