Fact or Factitious? Contextualized Opinion Spam Detection
Stefan Kennedy, Niall Walsh, Kirils Sloka, Andrew McCarren, Jennifer Foster
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.- Anthology ID:
- P19-2048
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Fernando Alva-Manchego, Eunsol Choi, Daniel Khashabi
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 344–350
- Language:
- URL:
- https://aclanthology.org/P19-2048
- DOI:
- 10.18653/v1/P19-2048
- Cite (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.
- Cite (Informal):
- Fact or Factitious? Contextualized Opinion Spam Detection (Kennedy et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/P19-2048.pdf
- Code
- CPSSD/LUCAS