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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 | 2022-01-19T09:17:23Z | |
dc.date.available | 2022-01-19T09:17:23Z | |
dc.date.issued | 2020-10-09 | es_ES |
dc.identifier.isbn | 978-1-7281-8206-3 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/179921 | |
dc.description | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | es_ES |
dc.description.abstract | [EN] Recent years have seen a large increase in the amount of false information that is posted online. Fake news are created and propagated in order to deceive users and manipulate opinions and subsequently have a negative impact on the society. The automatic detection of fake news is very challenging since some of those news are created in sophisticated ways containing text or images that have been deliberately modified. Combining information from different modalities can be very useful for determining which of the online articles are fake. In this paper, we propose a multimodal multi-image system that combines information from different modalities in order to detect fake news posted online. In particular, our system combines textual, visual and semantic information. For the textual representation we use the Bidirectional Encoder Representations from Transformers (BERT) to better capture the underlying semantic and contextual meaning of the text. For the visual representation we extract image tags from multiple images that the articles contain using the VGG-16 model. The semantic representation refers to the text-image similarity calculated using the cosine similarity between the title and image tags embeddings. Our experimental results on a real world dataset show that combining features from the different modalities is effective for fake news detection. In particular, our multimodal multi-image system significantly outperforms the BERT baseline by 4.19% and SpotFake by 5.39%. | 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 MISMISFAKEnHATE 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 | IEEE Computer Society | es_ES |
dc.relation.ispartof | Proceedings. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Multimodal fake news detection | es_ES |
dc.subject | Multi-image system | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Multimodal Multi-image Fake News Detection | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.identifier.doi | 10.1109/DSAA49011.2020.00091 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/SNSF//P2TIP2 181441/ | 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.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 Multi-image Fake News Detection. IEEE Computer Society. 647-654. https://doi.org/10.1109/DSAA49011.2020.00091 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020) | es_ES |
dc.relation.conferencedate | Octubre 06-09,2020 | es_ES |
dc.relation.conferenceplace | Online | es_ES |
dc.relation.publisherversion | https://doi.org/10.1109/DSAA49011.2020.00091 | es_ES |
dc.description.upvformatpinicio | 647 | es_ES |
dc.description.upvformatpfin | 654 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\437335 | es_ES |
dc.contributor.funder | China Scholarship Council | es_ES |
dc.contributor.funder | Swiss National Science Foundation | es_ES |