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.- Anthology ID:
- W19-3516
- Volume:
- Proceedings of the Third Workshop on Abusive Language Online
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ALW
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 146–156
- Language:
- URL:
- https://aclanthology.org/W19-3516
- DOI:
- 10.18653/v1/W19-3516
- Cite (ACL):
- Johannes Skjeggestad Meyer and Björn Gambäck. 2019. A Platform Agnostic Dual-Strand Hate Speech Detector. In Proceedings of the Third Workshop on Abusive Language Online, pages 146–156, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- A Platform Agnostic Dual-Strand Hate Speech Detector (Meyer & Gambäck, ALW 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/W19-3516.pdf