论文标题
拖网拖网：数据集
Trawling for Trolling: A Dataset
论文作者
论文摘要
自动准确检测和过滤进攻内容的能力对于确保丰富而多样化的数字话语很重要。拖钓是一种在社交媒体中普遍存在的伤害性或令人反感的内容，但在数据集中代表不足，无法进行进攻性内容检测。在这项工作中，我们提出了一个数据集，该数据集将拖钓作为进攻内容的子类别进行建模。该数据集是通过从著名数据集中收集样本并根据不同类别的进攻内容的精确定义来创建数据集创建的。该数据集有12,490个样本，分为5个类；正常，亵渎，拖曳，贬义和仇恨言论。它涵盖了Twitter，Reddit和Wikipedia Talk页面的内容。在我们的数据集中训练的模型表现出明显的性能，没有任何重要的超参数调整，并且可以有效地学习有意义的语言信息。我们发现这些模型对数据消融很敏感，这表明数据集在很大程度上没有虚假的统计伪像，这些统计伪像，否则可能会分散和混淆分类模型。
The ability to accurately detect and filter offensive content automatically is important to ensure a rich and diverse digital discourse. Trolling is a type of hurtful or offensive content that is prevalent in social media, but is underrepresented in datasets for offensive content detection. In this work, we present a dataset that models trolling as a subcategory of offensive content. The dataset was created by collecting samples from well-known datasets and reannotating them along precise definitions of different categories of offensive content. The dataset has 12,490 samples, split across 5 classes; Normal, Profanity, Trolling, Derogatory and Hate Speech. It encompasses content from Twitter, Reddit and Wikipedia Talk Pages. Models trained on our dataset show appreciable performance without any significant hyperparameter tuning and can potentially learn meaningful linguistic information effectively. We find that these models are sensitive to data ablation which suggests that the dataset is largely devoid of spurious statistical artefacts that could otherwise distract and confuse classification models.
