Abstract
In this research, we studied the relationship between data augmentation and model accuracy for the task of fake review detection. We used data generation methods to augment two different fake review datasets and compared the performance of models trained with the original data and with the augmented data. Our results show that the accuracy of our fake review detection model can be improved by 0.31 percentage points on DeRev Test and by 7.65 percentage points on Amazon Test by using the augmented datasets.- Anthology ID:
- 2023.ranlp-1.73
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 673–680
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.73
- DOI:
- Cite (ACL):
- Ming Liu and Massimo Poesio. 2023. Data Augmentation for Fake Reviews Detection. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 673–680, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Data Augmentation for Fake Reviews Detection (Liu & Poesio, RANLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.ranlp-1.73.pdf