Hate Speech and harassment are widespread in online communication, due to users' freedom and anonymity and the lack of regulation provided by social media platforms. Hate speech is topically focused (misogyny, sexism, racism, xenophobia, homophobia, etc.), and each specific manifestation of hate speech targets different vulnerable groups based on characteristics such as gender (misogyny, sexism), ethnicity, race, religion (xenophobia, racism, Islamophobia), sexual orientation (homophobia), and so on. Most automatic hate speech detection approaches cast the problem into a binary classification task without addressing either the topical focus or the target-oriented nature of hate speech. In this paper, we propose to tackle, for the first time, hate speech detection from a multi-target perspective. We leverage manually annotated datasets, to investigate the problem of transferring knowledge from different datasets with different topical focuses and targets. Our contribution is threefold: (1) we explore the ability of hate speech detection models to capture common properties from topic-generic datasets and transfer this knowledge to recognize specific manifestations of hate speech; (2) we experiment with the development of models to detect both topics (racism, xenophobia, sexism, misogyny) and hate speech targets, going beyond standard binary classification, to investigate how to detect hate speech at a finer level of granularity and how to transfer knowledge across different topics and targets; and (3) we study the impact of affective knowledge encoded in sentic computing resources (SenticNet, EmoSenticNet) and in semantically structured hate lexicons (HurtLex) in determining specific manifestations of hate speech. We experimented with different neural models including multitask approaches. Our study shows that: (1) training a model on a combination of several (training sets from several) topic-specific datasets is more effective than training a model on a topic-generic dataset; (2) the multi-task approach outperforms a single-task model when detecting both the hatefulness of a tweet and its topical focus in the context of a multi-label classification approach; and (3) the models incorporating EmoSenticNet emotions, the first level emotions of SenticNet, a blend of SenticNet and EmoSenticNet emotions or affective features based on Hurtlex, obtained the best results. Our results demonstrate that multi-target hate speech detection from existing datasets is feasible, which is a first step towards hate speech detection for a specific topic/target when dedicated annotated data are missing. Moreover, we prove that domain-independent affective knowledge, injected into our models, helps finer-grained hate speech detection.

由于用户的自由和匿名性以及社交媒体平台缺乏监管，仇恨言论和骚扰在在线交流中普遍存在。仇恨言论具有主题性（厌女症、性别歧视、种族主义、仇外心理、恐同心理等），仇恨言论的每种具体表现形式根据性别（厌女症、性别歧视）、民族、种族、宗教（仇外心理）等特征针对不同的弱势群体。 、种族主义、伊斯兰恐惧症）、性取向（恐同症）等等。大多数自动仇恨言论检测方法将问题转化为二元分类任务，而没有解决仇恨言论的主题焦点或面向目标的性质。在本文中，我们建议首次从多目标角度解决仇恨言论检测问题。我们利用手动注释的数据集来研究从具有不同主题焦点和目标的不同数据集转移知识的问题。我们的贡献有三个：（1）我们探索仇恨言论检测模型从主题通用数据集中捕获共同属性并将这些知识转移到识别仇恨言论的特定表现的能力； (2) 我们尝试开发模型来检测主题（种族主义、仇外心理、性别歧视、厌女症）和仇恨言论目标，超越标准二元分类，研究如何在更精细的粒度水平上检测仇恨言论以及如何跨不同主题和目标转移知识； （3）我们研究情感计算资源（SenticNet、EmoSenticNet）和语义结构仇恨词典（HurtLex）中编码的情感知识在确定仇恨言论的具体表现方面的影响。 我们尝试了不同的神经模型，包括多任务方法。我们的研究表明：（1）在多个（来自多个）特定主题数据集的组合上训练模型比在主题通用数据集上训练模型更有效； （2）在多标签分类方法的背景下检测推文的可恶性及其主题焦点时，多任务方法优于单任务模型； （3）结合EmoSenticNet情感的模型，SenticNet的第一级情感，SenticNet和EmoSenticNet情感的混合或基于Hurtlex的情感特征，获得了最好的结果。我们的结果表明，从现有数据集中进行多目标仇恨言论检测是可行的，这是在缺少专用注释数据时针对特定主题/目标进行仇恨言论检测的第一步。此外，我们证明，将独立于领域的情感知识注入我们的模型中，有助于更细粒度的仇恨语音检测。