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