  The increasingly rapid spread of information about COVID-19 on the web calls
for automatic measures of quality assurance. In that context, we check the
credibility of news content using selected linguistic features. We present two
empirical studies to evaluate the usability of graphical interfaces that offer
such credibility assessment. In a moderated qualitative interview with six
participants, we identify rating scale, sub-criteria and algorithm authorship
as important predictors of the usability. A subsequent quantitative online
survey with 50 participants reveals a conflict between transparency and
conciseness in the interface design, as well as a perceived hierarchy of
metadata: the authorship of a news text is more important than the authorship
of the credibility algorithm used to assess the content quality. Finally, we
make suggestions for future research, such as proactively documenting
credibility-related metadata for Natural Language Processing and Language
Technology services and establishing an explicit hierarchical taxonomy of
usability predictors for automatic credibility assessment.
