SciLens: evaluating the quality of scientific news articles using social media and scientific literature indicators

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  • dc.contributor.author Smeros, Panayiotis
  • dc.contributor.author Castillo, Carlos
  • dc.contributor.author Aberer, Karl
  • dc.date.accessioned 2019-06-12T09:28:40Z
  • dc.date.available 2019-06-12T09:28:40Z
  • dc.date.issued 2019
  • dc.description.abstract This paper describes, develops, and validates SciLens, a method to evaluate the quality of scientific news articles. The starting point for our work are structured methodologies that define a series of quality aspects for manually evaluating news. Based on these aspects,we describe a series of indicators of news quality. According to our experiments, these indicators help non-experts evaluate more accurately the quality of a scientific news article, compared to nonexperts that do not have access to these indicators. Furthermore, SciLens can also be used to produce a completely automated quality score for an article, which agrees more with expert evaluators than manual evaluations done by non-experts. One of the main elements of SciLens is the focus on both content and context of articles, where context is provided by (1) explicit and implicit references on the article to scientific literature, and (2) reactions in social media referencing the article. We show that both contextual elements can be valuable sources of information for determining article quality. The validation of SciLens, done through a combination of expert and non-expert annotation, demonstrates its effectiveness for both semi-automatic and automatic quality evaluation of scientific news.
  • dc.description.sponsorship This work is partially supported by the Open Science Fund of EPFL (http://www.epfl.ch/ research/initiatives/open-science-fund) and the La Caixa project (LCF/PR/PR16/11110009).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Smeros P, Castillo C, Aberer K. SciLens: evaluating the quality of scientific news articles using social media and scientific literature indicators. In: Liu L, White R, editors. The World Wide Web Conference 2019; 2019 May 13-7; San Francisco, USA. New York: Association for Computer Machinery; 2019. p. 1747-58. DOI: 10.1145/3308558.3313657
  • dc.identifier.doi http://dx.doi.org/10.1145/3308558.3313657
  • dc.identifier.isbn 978-1-4503-6674-8
  • dc.identifier.uri http://hdl.handle.net/10230/41742
  • dc.language.iso eng
  • dc.publisher ACM Association for Computer Machinery
  • dc.relation.ispartof Liu L, White R, editors. The World Wide Web Conference 2019; 2019 May 13-7; San Francisco, USA. New York: Association for Computer Machinery; 2019. p. 1747-58.
  • dc.rights © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License. This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri https://creativecommons.org/licenses/by/4.0/
  • dc.title SciLens: evaluating the quality of scientific news articles using social media and scientific literature indicators
  • dc.type info:eu-repo/semantics/conferenceObject
  • dc.type.version info:eu-repo/semantics/publishedVersion

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