|
--- |
|
license: mit |
|
language: |
|
- ar |
|
- fr |
|
- en |
|
--- |
|
# 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 |
