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Anti-Asian hate/toxic/racial speech has recently increased, especially inresponse to the outbreak of COVID-19. However, heavy focus on the COVID-19context and subsequent Sinophobia may have over-represented Chinese-attackingphenomena rather than offering insights applicable to pan-Asian communities. Tofill this gap, this paper underscores a cross-ethnic contextual understandingby taking a multi-ethnic approach to identifying and describing anti-Asianracism expressed in Twitter. The study examines (1) cross-ethnicity difference(or similarity) of anti-Asian expressions; (2) temporal dynamics ofcross-ethnicity difference/similarity; (3) topical contexts underlyinganti-Asian tweets; and (4) comparison between Sinophobic tweets and pan-Asianor non-Chinese Asian targeting tweets. The study uses a 12 month-longlarge-scale tweets that contain ethnicity-indicative search keywords, fromwhich anti-Asian tweets are identified using deep-learning models. Multiplecorrespondence analysis and topic modeling are employed to address researchquestions. Results show anti-Asian speeches are fragmented into severaldistinctive groups, and such groupings change dynamically in response topolitical, social, and cultural reality surrounding ethnic relations. Findingssuggest that China is not a representative entity for pan-Asian ethnicity: Mostof the times during the observed period, anti-China tweets are positioneddistantly from generically mentioned anti-Asian tweets in the $n$-gram-basedmultidimensional space. Moreover, generic anti-Asian tweets show greatertopical similarities to the combined set of tweets that attack other Asianethnic groups than to the anti-China tweets. Pan-Asian tweets andChina-specific tweets become semantically similar in the aftermath of thepandemic outbreak (from Feb. 2020 to Apr. 2020) yet only temporarily.

Sinophobia, misogyny, facism, and many more: A multi-ethnic approach to identifying anti-Asian racism in social media