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dc.contributor.author
Przybyla, Piotr
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dc.contributor.author
Soto, Axel Juan
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dc.date.available
2021-08-04T12:28:15Z
dc.date.issued
2021-09-12
dc.identifier.citation
Przybyla, Piotr; Soto, Axel Juan; When classification accuracy is not enough: Explaining news credibility assessment; Pergamon-Elsevier Science Ltd; Information Processing & Management; 58; 5; 12-9-2021; 1-20; 102653
dc.identifier.issn
0306-4573
dc.identifier.uri
http://hdl.handle.net/11336/137736
dc.description.abstract
Dubious credibility of online news has become a major problem with negative consequences for both readers and the whole society. Despite several efforts in the development of automatic methods for measuring credibility in news stories, there has been little previous work focusing on providing explanations that go beyond a black-box decision or score. In this work, we use two machine learning approaches for computing a credibility score for any given news story: one is a linear method trained on stylometric features and the other one is a recurrent neural network. Our goal is to study whether we can explain the rationale behind these automatic methods and improve a reader's confidence in their credibility assessment. Therefore, we first adapted the classifiers to the constraints of a browser extension so that the text can be analysed while browsing online news. We also propose a set of interactive visualisations to explain to the user the rationale behind the automatic credibility assessment. We evaluated our adapted methods by means of standard machine learning performance metrics and through two user studies. The adapted neural classifier showed better performance on the test data than the stylometric classifier, despite the latter appearing to be easier to interpret by the participants. Also, users were significantly more accurate in their assessment after they interacted with the tool as well as more confident with their decisions.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
CREDIBILITY
dc.subject
FAKE NEWS
dc.subject
NATURAL LANGUAGE PROCESSING
dc.subject
TEXT CLASSIFICATION
dc.subject
VISUAL ANALYTICS
dc.subject.classification
Ciencias de la Computación
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
When classification accuracy is not enough: Explaining news credibility assessment
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-07-27T14:58:59Z
dc.journal.volume
58
dc.journal.number
5
dc.journal.pagination
1-20; 102653
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Przybyla, Piotr. Polish Academy of Sciences; Argentina
dc.description.fil
Fil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
dc.journal.title
Information Processing & Management
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0306457321001412
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.ipm.2021.102653
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