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dc.contributor.author | Anastasia Giachanou | es_ES |
dc.contributor.author | Rosso, Paolo | es_ES |
dc.contributor.author | Crestani, Fabio | es_ES |
dc.date.accessioned | 2022-11-14T19:02:00Z | |
dc.date.available | 2022-11-14T19:02:00Z | |
dc.date.issued | 2021-09 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/189719 | |
dc.description.abstract | [EN] Fake news is considered one of the main threats of our society. The aim of fake news is usually to confuse readers and trigger intense emotions to them in an attempt to be spread through social networks. Even though recent studies have explored the effectiveness of different linguistic patterns for fake news detection, the role of emotional signals has not yet been explored. In this paper, we focus on extracting emotional signals from claims and evaluating their effectiveness on credibility assessment. First, we explore different methodologies for extracting the emotional signals that can be triggered to the users when they read a claim. Then, we present emoCred, a model that is based on a long-short term memory model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. In addition, we perform an analysis to understand which emotional signals and which terms are the most useful for the different credibility classes. We conduct extensive experiments and a thorough analysis on real-world datasets. Our results indicate the importance of incorporating emotional signals in the credibility assessment problem. | es_ES |
dc.description.sponsorship | Generalitat Valenciana, Grant/Award Number: DeepPattern (PROMETEO/2019/121); Ministerio de Ciencia e Innovacion, Grant/Award Number: PGC2018-096212-B-C31; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung, Grant/Award Number: P2TIP2_181441 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | John Wiley & Sons, Inc. | es_ES |
dc.relation.ispartof | Journal of the Association for Information Science and Technology (Online) | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | The Impact of Emotional Signals on Credibility Assessment | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1002/asi.24480 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096212-B-C31/ES/DESINFORMACION Y AGRESIVIDAD EN SOCIAL MEDIA: AGREGANDO INFORMACION Y ANALIZANDO EL LENGUAJE/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//PROMETEO%2F2019%2F121//DEEP LEARNING FOR ADAPTATIVE AND MULTIMODAL INTERACTION IN PATTERN RECOGNITION/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/SNSF//P2TIP2 181441/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.description.bibliographicCitation | Anastasia Giachanou; Rosso, P.; Crestani, F. (2021). The Impact of Emotional Signals on Credibility Assessment. Journal of the Association for Information Science and Technology (Online). 72(9):1117-1132. https://doi.org/10.1002/asi.24480 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1002/asi.24480 | es_ES |
dc.description.upvformatpinicio | 1117 | es_ES |
dc.description.upvformatpfin | 1132 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 72 | es_ES |
dc.description.issue | 9 | es_ES |
dc.identifier.eissn | 2330-1643 | es_ES |
dc.identifier.pmid | 34589557 | es_ES |
dc.identifier.pmcid | PMC8453501 | es_ES |
dc.relation.pasarela | S\460636 | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Swiss National Science Foundation | es_ES |