Improved Abusive Comment Moderation with User Embeddings
John Pavlopoulos, Prodromos Malakasiotis, Juli Bakagianni, Ion Androutsopoulos
Abstract
Experimenting with a dataset of approximately 1.6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases. We observe improvements in all cases, with user embeddings leading to the biggest performance gains.- Anthology ID:
- W17-4209
- Volume:
- Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 51–55
- Language:
- URL:
- https://aclanthology.org/W17-4209
- DOI:
- 10.18653/v1/W17-4209
- Cite (ACL):
- John Pavlopoulos, Prodromos Malakasiotis, Juli Bakagianni, and Ion Androutsopoulos. 2017. Improved Abusive Comment Moderation with User Embeddings. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 51–55, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Improved Abusive Comment Moderation with User Embeddings (Pavlopoulos et al., 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W17-4209.pdf