@inproceedings{kumar-etal-2019-online,
    title = "Online abuse detection: the value of preprocessing and neural attention models",
    author = "Kumar, Dhruv  and
      Cohen, Robin  and
      Golab, Lukasz",
    editor = "Balahur, Alexandra  and
      Klinger, Roman  and
      Hoste, Veronique  and
      Strapparava, Carlo  and
      De Clercq, Orphee",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-1303/",
    doi = "10.18653/v1/W19-1303",
    pages = "16--24",
    abstract = "We propose an attention-based neural network approach to detect abusive speech in online social networks. Our approach enables more effective modeling of context and the semantic relationships between words. We also empirically evaluate the value of text pre-processing techniques in addressing the challenge of out-of-vocabulary words in toxic content. Finally, we conduct extensive experiments on the Wikipedia Talk page datasets, showing improved predictive power over the previous state-of-the-art."
}