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This paper describes, develops, and validates SciLens, a method to evaluatethe quality of scientific news articles. The starting point for our work arestructured methodologies that define a series of quality aspects for manuallyevaluating news. Based on these aspects, we describe a series of indicators ofnews quality. According to our experiments, these indicators help non-expertsevaluate more accurately the quality of a scientific news article, compared tonon-experts that do not have access to these indicators. Furthermore, SciLenscan also be used to produce a completely automated quality score for anarticle, which agrees more with expert evaluators than manual evaluations doneby non-experts. One of the main elements of SciLens is the focus on bothcontent and context of articles, where context is provided by (1) explicit andimplicit references on the article to scientific literature, and (2) reactionsin social media referencing the article. We show that both contextual elementscan be valuable sources of information for determining article quality. Thevalidation of SciLens, done through a combination of expert and non-expertannotation, demonstrates its effectiveness for both semi-automatic andautomatic quality evaluation of scientific news.

SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators