@inproceedings{kshirsagar-etal-2018-predictive,
title = "Predictive Embeddings for Hate Speech Detection on {T}witter",
author = "Kshirsagar, Rohan and
Cukuvac, Tyrus and
McKeown, Kathy and
McGregor, Susan",
editor = "Fi{\v{s}}er, Darja and
Huang, Ruihong and
Prabhakaran, Vinodkumar and
Voigt, Rob and
Waseem, Zeerak and
Wernimont, Jacqueline",
booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5104/",
doi = "10.18653/v1/W18-5104",
pages = "26--32",
abstract = "We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods."
}
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<abstract>We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.</abstract>
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%0 Conference Proceedings
%T Predictive Embeddings for Hate Speech Detection on Twitter
%A Kshirsagar, Rohan
%A Cukuvac, Tyrus
%A McKeown, Kathy
%A McGregor, Susan
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kshirsagar-etal-2018-predictive
%X We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.
%R 10.18653/v1/W18-5104
%U https://aclanthology.org/W18-5104/
%U https://doi.org/10.18653/v1/W18-5104
%P 26-32
Markdown (Informal)
[Predictive Embeddings for Hate Speech Detection on Twitter](https://aclanthology.org/W18-5104/) (Kshirsagar et al., ALW 2018)
ACL