@InProceedings{pavlopoulos-EtAl:2017:NLPmJ,
  author    = {Pavlopoulos, John  and  Malakasiotis, Prodromos  and  Bakagianni, Juli  and  Androutsopoulos, Ion},
  title     = {Improved Abusive Comment Moderation with User Embeddings},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {51--55},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-4209}
}