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://www.aclweb.org/anthology/W17-4209
DOI:
10.18653/v1/W17-4209
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4209.pdf