Towards the understanding of gaming audiences by modeling Twitch emotes

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  • dc.contributor.author Barbieri, Francescoca
  • dc.contributor.author Espinosa-Anke, Luisca
  • dc.contributor.author Ballesteros, Miguelca
  • dc.contributor.author Soler Company, Juanca
  • dc.contributor.author Saggion, Horacioca
  • dc.date.accessioned 2017-11-21T10:22:31Z
  • dc.date.available 2017-11-21T10:22:31Z
  • dc.date.issued 2017
  • dc.description Comunicació presentada al Third Workshop on Noisy User-generated Text (W-NUT 2017), celebrat el dia 7 de setembre de 2017 a Copenhaguen, Dinamarca.
  • dc.description.abstract Videogame streaming platforms have become a paramount example of noisy usergenerated 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.en
  • dc.description.sponsorship Francesco, Luis and Horacio acknowledge support from the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE) and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
  • dc.format.mimetype application/pdfca
  • dc.identifier.citation Barbieri F, Espinosa-Anke L, Ballesteros M, Soler-Company J, Saggion H. Towards the understanding of gaming audiences by modeling Twitch emotes. In: Third Workshop on Noisy User-generated Text (W-NUT 2017); 2017 Sep 7; Copenhagen, Denmark. Stroudsburg (PA): ACL; 2017. p. 11-20.
  • dc.identifier.uri http://hdl.handle.net/10230/33289
  • dc.language.iso eng
  • dc.publisher ACL (Association for Computational Linguistics)ca
  • dc.relation.ispartof Third Workshop on Noisy User-generated Text (W-NUT 2017); 2017 Sep 7; Copenhagen, Denmark. Stroudsburg (PA): ACL; 2017. p. 11-20.
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-5-R
  • dc.rights © 2017 The Association for Computational Linguistics This material is licensed on a Creative Commons Attribution 4.0 License
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.other Tractament del llenguatge natural (Informàtica)
  • dc.title Towards the understanding of gaming audiences by modeling Twitch emotesca
  • dc.type info:eu-repo/semantics/conferenceObject
  • dc.type.version info:eu-repo/semantics/publishedVersion

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