@InProceedings{pavlopoulos-malakasiotis-androutsopoulos:2017:EMNLP2017,
  author    = {Pavlopoulos, John  and  Malakasiotis, Prodromos  and  Androutsopoulos, Ion},
  title     = {Deeper Attention to Abusive User Content Moderation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1125--1135},
  abstract  = {Experimenting with a new dataset of 1.6M
	user comments from a news portal and an
	existing dataset of 115K Wikipedia talk
	page comments, we show that an RNN operating
	on word embeddings outpeforms
	the previous state of the art in moderation,
	which used logistic regression or an MLP
	classifier with character or word n-grams.
	We also compare against a CNN operating
	on word embeddings, and a word-list
	baseline. A novel, deep, classificationspecific
	attention mechanism improves the
	performance of the RNN further, and can
	also highlight suspicious words for free,
	without including highlighted words in the
	training data. We consider both fully automatic
	and semi-automatic moderation.},
  url       = {https://www.aclweb.org/anthology/D17-1117}
}