Here we provide our dataset for multi-label hate speech and abusive language detection in the Indonesian Twitter. The main dataset can be seen at re_dataset with labels information as follows:
- HS : hate speech label;
- Abusive : abusive language label;
- HS_Individual : hate speech targeted to an individual;
- HS_Group : hate speech targeted to a group;
- HS_Religion : hate speech related to religion/creed;
- HS_Race : hate speech related to race/ethnicity;
- HS_Physical : hate speech related to physical/disability;
- HS_Gender : hate speech related to gender/sexual orientation;
- HS_Gender : hate related to other invective/slander;
- HS_Weak : weak hate speech;
- HS_Moderate : moderate hate speech;
- HS_Strong : strong hate speech.
For each label, 1 means yes (tweets including that label), 0 mean no (tweets are not included in that label).
Due to Twitter's Terms of Service (now X), we do not provide the hydrated tweets (unfortunately, we forget to store the tweet ID when we collect the data). All usernames and URLs in this dataset are changed into USER and URL.
For text normalization in our experiment, we built typo and slang words dictionaries named new_kamusalay.csv, that contain two columns (first columns are the typo and slang words, and the second one is the formal words). Here the examples of mapping:
- beud --> banget
- jgn --> jangan
- loe --> kamu
Furthermore, we also built abusive lexicon list named abusive.csv that can be used for feature extraction.
If you want to know how this dataset was build (include the explanation of crawling and annotation technique) and how we did our experiment in multi-label hate speech and abusive language detection in Indonesian language using this dataset, you can read our paper in here: https://www.aclweb.org/anthology/W19-3506.pdf.
This dataset and the other resource can be used for free, but if you want to publish paper/publication using this dataset, please cite this publication:
Muhammad Okky Ibrohim and Indra Budi. 2019. Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter. In ALW3: 3rd Workshop on Abusive Language Online, 46-57. (Every paper template may have different citation writting. For LaTex user, you can see citation.bib).
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
