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license: mit

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