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Multimodal Fake News Detection with Textual, Visual and Semantic Information

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Multimodal Fake News Detection with Textual, Visual and Semantic Information

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dc.contributor.author Giachanou, Anastasia es_ES
dc.contributor.author Zhang, Guobiao es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2021-12-27T08:37:27Z
dc.date.available 2021-12-27T08:37:27Z
dc.date.issued 2020-09-11 es_ES
dc.identifier.isbn 978-3-030-58323-1 es_ES
dc.identifier.uri http://hdl.handle.net/10251/178911
dc.description.abstract [EN] Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to di erentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news. es_ES
dc.description.sponsorship Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31) es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Text, Speech, and Dialogue. 23rd International Conference, TSD 2020 es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;12284 es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Multimodal fake news detection es_ES
dc.subject Visual features es_ES
dc.subject Textual features es_ES
dc.subject Image-text similarity es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Multimodal Fake News Detection with Textual, Visual and Semantic Information es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-58323-1_3 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/SNSF//P2TIP2 181441/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Giachanou, A.; Zhang, G.; Rosso, P. (2020). Multimodal Fake News Detection with Textual, Visual and Semantic Information. Springer. 30-38. https://doi.org/10.1007/978-3-030-58323-1_3 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 23rd International Conference on Text, Speech and Dialogue (TSD 2020) es_ES
dc.relation.conferencedate Septiembre 08-11,2020 es_ES
dc.relation.conferenceplace Online es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-58323-1_3 es_ES
dc.description.upvformatpinicio 30 es_ES
dc.description.upvformatpfin 38 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\434386 es_ES
dc.contributor.funder China Scholarship Council es_ES
dc.contributor.funder Swiss National Science Foundation es_ES
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