  Recent discussions on alternative facts, fake news, and post truth politics
have motivated research on creating technologies that allow people not only to
access information, but also to assess the credibility of the information
presented to them by information retrieval systems. Whereas technology is in
place for filtering information according to relevance and/or credibility, no
single measure currently exists for evaluating the accuracy or precision (and
more generally effectiveness) of both the relevance and the credibility of
retrieved results. One obvious way of doing so is to measure relevance and
credibility effectiveness separately, and then consolidate the two measures
into one. There at least two problems with such an approach: (I) it is not
certain that the same criteria are applied to the evaluation of both relevance
and credibility (and applying different criteria introduces bias to the
evaluation); (II) many more and richer measures exist for assessing relevance
effectiveness than for assessing credibility effectiveness (hence risking
further bias).
  Motivated by the above, we present two novel types of evaluation measures
that are designed to measure the effectiveness of both relevance and
credibility in ranked lists of retrieval results. Experimental evaluation on a
small human-annotated dataset (that we make freely available to the research
community) shows that our measures are expressive and intuitive in their
interpretation.
