Today, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information increase exposure to harmful content such as cyber-bullying and cyber-hatred. Models based on machine learning and natural language offer a way to make online platforms safer by identifying hate speech in web text autonomously. However, the main difficulty is annotating a sufficiently large number of examples to train these models. This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech. We build an interpretable two-dimensional visualization tool of the constructed hate speech representation -- dubbed the Map of Hate -- in which multiple datasets can be projected and comparatively analyzed. The hateful content is annotated differently across the two datasets (racist and sexist in one dataset, hateful and offensive in another). However, the common representation successfully projects the harmless class of both datasets into the same space and can be used to uncover labeling errors (false positives). We also show that the joint representation boosts prediction performances when only a limited amount of supervision is available. These methods and insights hold the potential for safer social media and reduce the need to expose human moderators and annotators to distressing online messaging.
翻译:今天,互联网是我们日常生活不可分割的一部分,使得人们能够比以往更加紧密地连接。然而,这种更大的连通性和获取信息的机会增加了对有害内容,例如网络欺凌和网络仇恨的暴露。基于机器学习和自然语言的模型提供了一种方法,通过在网络文本中自主识别仇恨言论,使在线平台更加安全。然而,主要困难在于指出足够多的例子来培训这些模型。本文使用一种转移学习技术,联合利用两个独立的数据集,并构建一个单一的仇恨言论代表。我们建立了一个可解释的双维可视化工具,用于构建仇恨言论代表(称为《仇恨地图》),其中可以预测和比较分析多个数据集。仇恨内容在两个数据集(一个数据集中的种族主义和性别歧视,另一个数据集中的仇恨和攻击性)中具有不同的注释性。然而,共同代表成功地将两个数据集的无害类别投放到同一空间,并可用于发现标签错误(假正)。我们还表明,当只有有限的社会感知力才能降低媒体的预测业绩,而只有更安全的感官才需要这些方法。