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Recent discussions on alternative facts, fake news, and post truth politicshave motivated research on creating technologies that allow people not only toaccess information, but also to assess the credibility of the informationpresented to them by information retrieval systems. Whereas technology is inplace for filtering information according to relevance and/or credibility, nosingle measure currently exists for evaluating the accuracy or precision (andmore generally effectiveness) of both the relevance and the credibility ofretrieved results. One obvious way of doing so is to measure relevance andcredibility effectiveness separately, and then consolidate the two measuresinto one. There at least two problems with such an approach: (I) it is notcertain that the same criteria are applied to the evaluation of both relevanceand credibility (and applying different criteria introduces bias to theevaluation); (II) many more and richer measures exist for assessing relevanceeffectiveness than for assessing credibility effectiveness (hence riskingfurther bias).  Motivated by the above, we present two novel types of evaluation measuresthat are designed to measure the effectiveness of both relevance andcredibility in ranked lists of retrieval results. Experimental evaluation on asmall human-annotated dataset (that we make freely available to the researchcommunity) shows that our measures are expressive and intuitive in theirinterpretation.

Evaluation Measures for Relevance and Credibility in Ranked Lists