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Character N-Grams for Detecting Deceptive Controversial Opinions

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Character N-Grams for Detecting Deceptive Controversial Opinions

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Sánchez-Junquera, JJ.; Luis Villaseñor Pineda; Montes Gomez, M.; Rosso, P. (2018). Character N-Grams for Detecting Deceptive Controversial Opinions. Lecture Notes in Computer Science. 11018:135-140. https://doi.org/10.1007/978-3-319-98932-7_13

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/147680

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Metadatos del ítem

Título: Character N-Grams for Detecting Deceptive Controversial Opinions
Autor: Sánchez-Junquera, Juan Javier Luis Villaseñor Pineda Montes Gomez, Manuel Rosso, Paolo
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] Controversial topics are present in the everyday life, and opinions about them can be either truthful or deceptive. Deceptive opinions are emitted to mislead other people in order to gain some advantage. In the most ...[+]
Palabras clave: Deception detection , Controversial opinions , Char n-grams
Derechos de uso: Reserva de todos los derechos
Fuente:
Lecture Notes in Computer Science. (issn: 0302-9743 )
DOI: 10.1007/978-3-319-98932-7_13
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/978-3-319-98932-7_13
Título del congreso: 9th Conference and Labs of the Evaluation Forum (CLEF 2018)
Lugar del congreso: Avignon, France
Fecha congreso: Septiembre 10-14,2018
Código del Proyecto:
info:eu-repo/grantAgreement/CONACyT//613411/
info:eu-repo/grantAgreement/CONACyT//CB-2015-01-257383/
info:eu-repo/grantAgreement/CONACyT//FC-2016%2F2410/
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/
Agradecimientos:
We would like to thank CONACyT for partially supporting this work under grants 613411, CB-2015-01-257383, and FC-2016/2410. The work of the last author was partially funded by the Spanish MINECO under the research project ...[+]
Tipo: Artículo Comunicación en congreso Capítulo de libro

References

Aritsugi, M., et al.: Combining word and character n-grams for detecting deceptive opinions, vol. 1, pp. 828–833. IEEE (2017)

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Cagnina, L.C., Rosso, P.: Detecting deceptive opinions: intra and cross-domain classification using an efficient representation. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 25(Suppl. 2), 151–174 (2017) [+]
Aritsugi, M., et al.: Combining word and character n-grams for detecting deceptive opinions, vol. 1, pp. 828–833. IEEE (2017)

Buller, D.B., Burgoon, J.K.: Interpersonal deception theory. Commun. Theory 6(3), 203–242 (1996)

Cagnina, L.C., Rosso, P.: Detecting deceptive opinions: intra and cross-domain classification using an efficient representation. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 25(Suppl. 2), 151–174 (2017)

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Hernández-Castañeda, Á., Calvo, H., Gelbukh, A., Flores, J.J.G.: Cross-domain deception detection using support vector networks. Soft Comput. 21(3), 1–11 (2016)

Mihalcea, R., Strapparava, C.: The lie detector: explorations in the automatic recognition of deceptive language. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 309–312. Association for Computational Linguistics (2009)

Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 309–319. Association for Computational Linguistics (2011)

Pérez-Rosas, V., Mihalcea, R.: Cross-cultural deception detection. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 440–445 (2014)

Sapkota, U., Solorio, T., Montes-y-Gómez, M., Bethard, S.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–102 (2015)

Vrij, A.: Detecting Lies and Deceit: Pitfalls and Opportunities. Wiley, Hoboken (2008)

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