TL;DR
This paper introduces new evaluation measures for assessing both relevance and credibility in ranked retrieval results, addressing biases and limitations of existing separate measures.
Contribution
The paper proposes two novel evaluation measures that jointly assess relevance and credibility effectiveness in ranked lists, with experimental validation on a human-annotated dataset.
Findings
Measures are expressive and intuitive
Experimental evaluation confirms effectiveness
Dataset is publicly available
Abstract
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…
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