  Recent policy initiatives have acknowledged the importance of disaggregating
data pertaining to diverse Asian ethnic communities to gain a more
comprehensive understanding of their current status and to improve their
overall well-being. However, research on anti-Asian racism has thus far fallen
short of properly incorporating data disaggregation practices. Our study
addresses this gap by collecting 12-month-long data from X (formerly known as
Twitter) that contain diverse sub-ethnic group representations within Asian
communities. In this dataset, we break down anti-Asian toxic messages based on
both temporal and ethnic factors and conduct a series of comparative analyses
of toxic messages, targeting different ethnic groups. Using temporal
persistence analysis, $n$-gram-based correspondence analysis, and topic
modeling, this study provides compelling evidence that anti-Asian messages
comprise various distinctive narratives. Certain messages targeting sub-ethnic
Asian groups entail different topics that distinguish them from those targeting
Asians in a generic manner or those aimed at major ethnic groups, such as
Chinese and Indian. By introducing several techniques that facilitate
comparisons of online anti-Asian hate towards diverse ethnic communities, this
study highlights the importance of taking a nuanced and disaggregated approach
for understanding racial hatred to formulate effective mitigation strategies.
