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With the COVID-19 pandemic continuing, hatred against Asians is intensifyingin countries outside Asia, especially among the Chinese. There is an urgentneed to detect and prevent hate speech towards Asians effectively. In thiswork, we first create COVID-HATE-2022, an annotated dataset including 2,025annotated tweets fetched in early February 2022, which are labeled based onspecific criteria, and we present the comprehensive collection of scenarios ofhate and non-hate tweets in the dataset. Second, we fine-tune the BERT modelbased on the relevant datasets and demonstrate several strategies related tothe "cleaning" of the tweets. Third, we investigate the performance of advancedfine-tuning strategies with various model-centric and data-centric approaches,and we show that both strategies generally improve the performance, whiledata-centric ones outperform the others, and it demonstrates the feasibilityand effectiveness of the data-centric approaches in the associated tasks.

Speech Detection Task Against Asian Hate: BERT the Central, While Data-Centric Studies the Crucial