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Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?

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Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?

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Fornaciari, T.; Cagnina, L.; Rosso, P.; Poesio, M. (2020). Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?. Language Resources and Evaluation. 54(4):1019-1058. https://doi.org/10.1007/s10579-020-09486-5

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Título: Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
Autor: Fornaciari, Tommaso Cagnina, Leticia Rosso, Paolo Poesio, Massimo
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is difficult, because normally it is not possible to know whether reviews are genuine. ...[+]
Palabras clave: Deception detection , Crowdsourcing , Ground truth , Probabilistic labeling
Derechos de uso: Reserva de todos los derechos
Fuente:
Language Resources and Evaluation. (issn: 1574-020X )
DOI: 10.1007/s10579-020-09486-5
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s10579-020-09486-5
Código del Proyecto:
info:eu-repo/grantAgreement/UKRI//ES%2FM010236%2F1/GB/Human Rights and Information Technology in the Era of Big Data/
info:eu-repo/grantAgreement/MINECO//TIN2015-71147-C2-1-P/ES/COMPRENSION DEL LENGUAJE EN LOS MEDIOS DE COMUNICACION SOCIAL - REPRESENTANDO CONTEXTOS DE FORMA CONTINUA/
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/
Agradecimientos:
Leticia Cagnina thanks CONICET for the continued financial support. This work was funded by MINECO/FEDER (Grant No. SomEMBED TIN2015-71147-C2-1-P). The work of Paolo Rosso was partially funded by the MISMIS-FAKEnHATE Spanish ...[+]
Tipo: Artículo

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