@inproceedings{kennedy-etal-2019-fact,
title = "Fact or Factitious? Contextualized Opinion Spam Detection",
author = "Kennedy, Stefan and
Walsh, Niall and
Sloka, Kirils and
McCarren, Andrew and
Foster, Jennifer",
editor = "Alva-Manchego, Fernando and
Choi, Eunsol and
Khashabi, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-2048",
doi = "10.18653/v1/P19-2048",
pages = "344--350",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Fact or Factitious? Contextualized Opinion Spam Detection
%A Kennedy, Stefan
%A Walsh, Niall
%A Sloka, Kirils
%A McCarren, Andrew
%A Foster, Jennifer
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kennedy-etal-2019-fact
%X 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.
%R 10.18653/v1/P19-2048
%U https://aclanthology.org/P19-2048
%U https://doi.org/10.18653/v1/P19-2048
%P 344-350
Markdown (Informal)
[Fact or Factitious? Contextualized Opinion Spam Detection](https://aclanthology.org/P19-2048) (Kennedy et al., ACL 2019)
ACL
- Stefan Kennedy, Niall Walsh, Kirils Sloka, Andrew McCarren, and Jennifer Foster. 2019. Fact or Factitious? Contextualized Opinion Spam Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 344–350, Florence, Italy. Association for Computational Linguistics.