@inproceedings{meyer-gamback-2019-platform,
    title = "A Platform Agnostic Dual-Strand Hate Speech Detector",
    author = {Meyer, Johannes Skjeggestad  and
      Gamb{\"a}ck, Bj{\"o}rn},
    editor = "Roberts, Sarah T.  and
      Tetreault, Joel  and
      Prabhakaran, Vinodkumar  and
      Waseem, Zeerak",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-3516/",
    doi = "10.18653/v1/W19-3516",
    pages = "146--156",
    abstract = "Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform. For instance, the information stored about the text{'}s author may differ between services, and so using such data would reduce a system{'}s general applicability. The paper thus focuses on using exclusively text-based input in the detection, in an optimised architecture combining Convolutional Neural Networks and Long Short-Term Memory-networks. The hate speech detector merges two strands with character n-grams and word embeddings to produce the final classification, and is shown to outperform comparable previous approaches."
}