Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913518675034112 |
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| author | Korotkova, Elizaveta Chung, Isaac |
| author_facet | Korotkova, Elizaveta Chung, Isaac |
| contents | The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these tasks to brand safety. Brand safety aims to protect commercial branding by identifying contexts where advertisements should not appear and covers not only toxicity, but also other potentially harmful content. As these datasets contain different label sets, we approach the overall problem as a binary classification task. We demonstrate the need for building brand safety specific datasets via the application of common toxicity detection datasets to a subset of brand safety and empirically analyze the effects of weighted sampling strategies in text classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_15110 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety Korotkova, Elizaveta Chung, Isaac Computation and Language Artificial Intelligence The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these tasks to brand safety. Brand safety aims to protect commercial branding by identifying contexts where advertisements should not appear and covers not only toxicity, but also other potentially harmful content. As these datasets contain different label sets, we approach the overall problem as a binary classification task. We demonstrate the need for building brand safety specific datasets via the application of common toxicity detection datasets to a subset of brand safety and empirically analyze the effects of weighted sampling strategies in text classification. |
| title | Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2303.15110 |