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Fact or factitious? Contextualized opinion spam detection

Kennedy, Steffan, Walsh, Niall, Sloka, Kirils, McCarren, Andrew orcid logoORCID: 0000-0002-7297-0984 and Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 (2019) Fact or factitious? Contextualized opinion spam detection. In: 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 28 Jul - 2 Aug 2019, Florence, Italy.

Abstract
In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews. We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potential of contextualised embeddings for fake review detection, and lay the groundwork for future research in this area.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Research Institutes and Centres > ADAPT
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://dx.doi.org/10.18653/v1/P19-2048
Copyright Information:© 2019 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fun
ID Code:26566
Deposited On:06 Jan 2022 17:07 by Andrew Mccarren . Last Modified 06 Jan 2022 17:07
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