Abstract
Detecting hate speech in non-direct forms, such as irony, sarcasm, and innuendos, remains a persistent challenge for social networks. Although sarcasm and hate speech are regarded as distinct expressions, our work explores whether integrating sarcasm as a pre-training step improves implicit hate speech detection and, by extension, explicit hate speech detection. Incorporating samples from ETHOS, Sarcasm on Reddit, and Implicit Hate Corpus, we devised two training strategies to compare the effectiveness of sarcasm pre-training on a CNN+LSTM and BERT+BiLSTM model. The first strategy is a single-step training approach, where a model trained only on sarcasm is then tested on hate speech. The second strategy uses sequential transfer learning to fine-tune models for sarcasm, implicit hate, and explicit hate. Our results show that sarcasm pre-training improved the BERT+BiLSTM's recall by 9.7%, AUC by 7.8%, and F1-score by 6% on ETHOS. On the Implicit Hate Corpus, precision increased by 7.8% when tested only on implicit samples. By incorporating sarcasm into the training process, we show that models can more effectively detect both implicit and explicit hate.
Abstract (translated)
在社交媒体平台上，识别非直接形式的仇恨言论（如讽刺、讽刺性言论和暗讽）仍然是一个持续的挑战。尽管讽刺和仇恨言论被视为不同的表达方式，但我们的研究探讨了将讽刺作为预训练步骤是否能改善隐含仇恨言论的检测，并进而提升明确仇恨言论的检测效果。我们利用ETHOS数据集、Reddit上的讽刺语料库以及隐含仇恨语料库设计了两种训练策略，以比较在CNN+LSTM和BERT+BiLSTM模型中使用讽刺预训练的有效性。 第一种策略采用单步训练方法，在此方法下仅用讽刺样本训练的模型随后会被用于检测仇恨言论。第二种策略则利用顺序迁移学习技术，先对模型进行讽刺语料库的微调，再依次针对隐含仇恨和明确仇恨进行进一步调整。我们的实验结果表明，通过将讽刺纳入预训练过程，BERT+BiLSTM模型在ETHOS数据集上的召回率提高了9.7%，AUC（曲线下面积）提升了7.8%，F1分数增加了6%。 而在处理隐含仇恨语料库的测试样本时，仅使用隐含样本进行检测的精度则提升了7.8%。这表明将讽刺纳入训练过程可以有效增强模型在识别隐含和明确仇恨言论方面的能力。
URL
https://arxiv.org/abs/2508.16555
