Arabic_hatespeech

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Arabic_hatespeech

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Religious Hate Speech Detection for Arabic Tweets

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#  Religious Hate Speech Detection for Arabic Tweets

This repo contains both training (```train.csv```) and testing (```test.csv```) datasets that are used in our paper. The training dataset contains 5,569 examples, while the testing dataset contains 567 examples. As per [Twitter's Developer Policy](https://developer.twitter.com/en/developer-terms/policy), we are only allowed to publicaly share tweet ids along with its annotaion (1 = hate , 0 = not hate). Full tweets (except for those that have been deleted or made private) can be obtained by hydrating tweet ids using tools such as [twarc]( https://github.com/edsu/twarc).

We also provide three Arabic hate term lexicons. Each term is assigned a positive or a negative real-valued score.  positive scores represent positive association with hate class, while negative scores reflect negative assocation with hate class. 
1. ```AraHate-PMI.csv```: Hate scores were assigned based on the Pointwise Mutual Information (PMI) metric.
1. ```AraHate-CHI.csv```: Hate scores were calculated using the chi-square statistic. 
1. ```AraHate-BNS.csv```: Hate scores were computed using the Bi-Normal Separation (BNS) method.

Finally, we provide a list of 356 Arabic stop words (```stop_words.csv```) accounting for both Dialectal Arabic and Modern Standard Arabic.

Please cite our paper if you find any of our data helpful for your research:

```
@inproceedings{Albadi2018are,
  title={Are They Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere},
  author={Albadi, Nuha and Kurdi, Maram and Mishra, Shivakant},
  booktitle={Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
  pages={69--76},
  year={2018},
  organization={ACM}
}
```

Arabic_hatespeech

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