@InProceedings{barbieri-EtAl:2017:WNUT,
  author    = {Barbieri, Francesco  and  Espinosa Anke, Luis  and  Ballesteros, Miguel  and  Soler, Juan  and  Saggion, Horacio},
  title     = {Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {11--20},
  abstract  = {Videogame streaming platforms have become a paramount example of noisy
	user-generated text. These are websites where gaming is broadcasted, and allows
	interaction with viewers via integrated chatrooms. Probably the best known
	platform of this kind is Twitch, which has more than 100 million monthly
	viewers. Despite these numbers, and unlike other platforms featuring short
	messages (e.g. Twitter), Twitch has not received much attention from the
	Natural Language Processing community. In this paper we aim at bridging this
	gap by proposing two important tasks specific to the Twitch platform, namely
	(1) Emote prediction; and (2) Trolling detection. In our experiments, we
	evaluate three models: a BOW baseline, a logistic supervised classifiers based
	on word embeddings, and a bidirectional long short-term memory recurrent neural
	network (LSTM). Our results show that the LSTM model outperforms the other two
	models, where explicit features with proven effectiveness for similar tasks
	were encoded.},
  url       = {http://www.aclweb.org/anthology/W17-4402}
}