Accurate detection and classification of online hate is a difficult task. Implicit hate is particularly challenging as such content tends to have unusual syntax, polysemic words, and fewer markers of prejudice (e.g., slurs). This problem is heightened with multimodal content, such as memes (combinations of text and images), as they are often harder to decipher than unimodal content (e.g., text alone). This paper evaluates the role of semantic and multimodal context for detecting implicit and explicit hate. We show that both text- and visual- enrichment improves model performance, with the multimodal model (0.771) outperforming other models' F1 scores (0.544, 0.737, and 0.754). While the unimodal-text context-aware (transformer) model was the most accurate on the subtask of implicit hate detection, the multimodal model outperformed it overall because of a lower propensity towards false positives. We find that all models perform better on content with full annotator agreement and that multimodal models are best at classifying the content where annotators disagree. To conduct these investigations, we undertook high-quality annotation of a sample of 5,000 multimodal entries. Tweets were annotated for primary category, modality, and strategy. We make this corpus, along with the codebook, code, and final model, freely available.

中文翻译：

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破译隐性仇恨：评估多模态仇恨的自动检测算法

准确检测和分类网络仇恨是一项艰巨的任务。含蓄的仇恨尤其具有挑战性，因为此类内容往往具有不寻常的句法、多义词和较少的偏见标记（例如，诽谤）。这个问题在多模态内容中更加突出，例如模因（文本和图像的组合），因为它们通常比单模态内容（例如，单独的文本）更难破译。本文评估了语义和多模态上下文在检测隐性和显性仇恨方面的作用。我们表明文本和视觉丰富都提高了模型性能，多模态模型 (0.771) 优于其他模型的 F1 分数 (0.544、0.737 和 0.754)。虽然单峰文本上下文感知（转换器）模型在隐式仇恨检测的子任务上最准确，由于误报的倾向较低，多模态模型的整体表现优于它。我们发现所有模型在具有完全注释者同意的内容上表现更好，并且多模态模型最擅长对注释者不同意的内容进行分类。为了进行这些调查，我们对 5,000 个多模式条目的样本进行了高质量的注释。推文被注释为主要类别、方式和策略。我们免费提供该语料库以及密码本、代码和最终模型。000 多模式条目。推文被注释为主要类别、方式和策略。我们免费提供该语料库以及密码本、代码和最终模型。000 多模式条目。推文被注释为主要类别、方式和策略。我们免费提供该语料库以及密码本、代码和最终模型。