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.

本研究针对社交媒体上用户产生内容迅速增长的情况，结合多种数据集实现二分类任务来保障商业品牌的安全，即识别广告不应出现的背景，同时论证了构建针对品牌安全的数据集的必要性，并就文本分类中的加权抽样策略进行了经验性分析。

超越出于毒性 —— 仅有的毒性检测数据集并不足以保障品牌安全

Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety

Toxic comment classification has become an active research field with many
recently proposed approaches. However, while these approaches address some of
the task's challenges others still remain unsolved and directions for further
research are needed. To this end, we compare different deep learning and
shallow approaches on a new, large comment dataset and propose an ensemble that
outperforms all individual models. Further, we validate our findings on a
second dataset. The results of the ensemble enable us to perform an extensive
error analysis, which reveals open challenges for state-of-the-art methods and
directions towards pending future research. These challenges include missing
paradigmatic context and inconsistent dataset labels.

该研究提出了一种新的集成方法来解决有毒评论分类问题，并比较了不同的深度学习和浅层方法。该方法优于所有个体模型，同时对其结果进行了验证和分析，发现了当前方法的瓶颈和未来研究方向，如语境不全和标签不一致的问题。