@inproceedings{huang-etal-2018-cyberbullying,
    title = "Cyberbullying Intervention Based on Convolutional Neural Networks",
    author = "Huang, Qianjia  and
      Inkpen, Diana  and
      Zhang, Jianhong  and
      Van Bruwaene, David",
    editor = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Zampieri, Marcos  and
      Malmasi, Shervin",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-4405/",
    pages = "42--51",
    abstract = "This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contributions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools. Second, we present a mechanism that provides individuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text, but also provides a system-level flagging/intervention in a situation related to cyberbullying."
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%0 Conference Proceedings
%T Cyberbullying Intervention Based on Convolutional Neural Networks
%A Huang, Qianjia
%A Inkpen, Diana
%A Zhang, Jianhong
%A Van Bruwaene, David
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%S Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F huang-etal-2018-cyberbullying
%X This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contributions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools. Second, we present a mechanism that provides individuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text, but also provides a system-level flagging/intervention in a situation related to cyberbullying.
%U https://aclanthology.org/W18-4405/
%P 42-51
Markdown (Informal)
[Cyberbullying Intervention Based on Convolutional Neural Networks](https://aclanthology.org/W18-4405/) (Huang et al., TRAC 2018)
ACL