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---
license: mit
---
# Disclaimer
*This is a hate speech dataset (in Arabic, French, and English).*
*Offensive content that does not reflect the opinions of the authors.*
# Dataset of our EMNLP 2019 Paper (Multilingual and Multi-Aspect Hate Speech Analysis)
For more details about our dataset, please check our paper:
@inproceedings{ousidhoum-etal-multilingual-hate-speech-2019,
title = "Multilingual and Multi-Aspect Hate Speech Analysis",
author = "Ousidhoum, Nedjma
and Lin, Zizheng
and Zhang, Hongming
and Song, Yangqiu
and Yeung, Dit-Yan",
booktitle = "Proceedings of EMNLP",
year = "2019",
publisher = "Association for Computational Linguistics",
}
(You can preview our paper on https://arxiv.org/pdf/1908.11049.pdf)
## Clarification
The multi-labelled tasks are *the hostility type of the tweet* and the *annotator's sentiment*. (We kept labels on which at least two annotators agreed.)
## Taxonomy
In further experiments that involved binary classification tasks of the hostility/hate/abuse type, we considered single-labelled *normal* instances to be *non-hate/non-toxic* and all the other instances to be *toxic*.
## Dataset
Our dataset is composed of three csv files sorted by language. They contain the tweets and the annotations described in our paper:
the hostility type *(column: tweet sentiment)*
hostility directness *(column: directness)*
target attribute *(column: target)*
target group *(column: group)*
annotator's sentiment *(column: annotator sentiment)*.
## Experiments
To replicate our experiments, please see https://github.com/HKUST-KnowComp/MLMA_hate_speech/blob/master/README.md
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