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--- |
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language: |
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- ru |
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tags: |
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- toxic comments classification |
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license: cc |
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task_categories: |
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- text-classification |
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size_categories: |
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- 10K<n<100K |
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--- |
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## General concept of the model |
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Sensitive topics are such topics that have a high chance of initiating a toxic conversation: homophobia, politics, racism, etc. This dataset uses 18 topics. |
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More details can be found [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. |
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This paper presents the first version of this dataset. Here you can see the last version of the dataset which is significantly larger and also properly filtered. |
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## Licensing Information |
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. |
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] |
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png |
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## Citation |
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If you find this repository helpful, feel free to cite our publication: |
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``` |
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@inproceedings{babakov-etal-2021-detecting, |
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title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation", |
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author = "Babakov, Nikolay and |
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Logacheva, Varvara and |
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Kozlova, Olga and |
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Semenov, Nikita and |
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Panchenko, Alexander", |
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booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", |
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month = apr, |
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year = "2021", |
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address = "Kiyv, Ukraine", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4", |
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pages = "26--36", |
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abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.", |
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} |
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``` |
