COVID-HateBERT: a Pre-trained Language Model for COVID-19 related Hate Speech Detection
COVID-HateBERT: a Pre-trained Language Model for COVID-19 related Hate Speech Detection
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COVID-HateBERT：用于 COVID-19 相关仇恨言论检测的预训练语言模型
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Feng Luo
中科院分区：
文献类型：
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作者：
Mingqi Li;Song Liao;Ebuka Okpala;Max Tong;Matthew Costello;Long Cheng;Hongxin Hu;Feng Luo
随着新冠肺炎大流行期间社交媒体上仇恨言论的急剧增长，迫切需要有效检测各种仇恨言论。现有的方法只有在训练数据和测试数据来自相同的数据分布时才能获得高性能。在传统仇恨数据集上训练的模型不能很好地适应新冠肺炎相关数据集。同时，为有监督的学习手动标注仇恨语音数据集是很耗时的。在这里，我们提出了一个预先训练的语言模型COVID-HateBERT来检测英语推文中的仇恨言论来解决这个问题。我们收集了2亿条基于与新冠肺炎相关的可恶关键词和标签的英文推文。然后，我们使用分类器提取1.27M条潜在的仇恨推文来重新训练BERT-BASE。我们在四个基准数据集上评估了我们的COVID-HateBERT。与BERT和BERTweet分类器相比，COVID-HateBERT在传统仇恨语音检测上的宏观平均F1得分提高了14.8%-23.8%，在新冠肺炎相关仇恨语音检测上的宏观平均F1得分提高了2.6%-6.73%，这表明COVID-HateBERT在不同的数据集上具有很好的泛化能力。
With the dramatic growth of hate speech on social media during the COVID-19 pandemic, there is an urgent need to detect various hate speech effectively. Existing methods only achieve high performance when the training and testing data come from the same data distribution. The models trained on the traditional hateful dataset cannot fit well on COVID-19 related dataset. Meanwhile, manually annotating the hate speech dataset for supervised learning is time-consuming. Here, we propose COVID-HateBERT, a pre-trained language model to detect hate speech on English Tweets to address this problem. We collect 200M English tweets based on COVID-19 related hateful keywords and hashtags. Then, we use a classifier to extract the 1.27M potential hateful tweets to re-train BERT-base. We evaluate our COVID-HateBERT on four benchmark datasets. The COVID-HateBERT achieves a 14.8%-23.8% higher macro average F1 score on traditional hate speech detection comparing to baseline methods and a 2.6%-6.73% higher macro average F1 score on COVID-19 related hate speech detection comparing to classifiers using BERT and BERTweet, which shows that COVID-HateBERT can generalize well on different datasets.