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