@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/Add-Cong-Liu-Florida-Atlantic-University-author-id/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."
}
Markdown (Informal)
[A Platform Agnostic Dual-Strand Hate Speech Detector](https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/W19-3516/) (Meyer & Gambäck, ALW 2019)
ACL