The Anti-AAPI Hate Speech Project aims to detect, monitor, and study anti-AAPI hate speech and incident on social media platform (i.e., Twitter) using advanced computational methods and online/field experiments. Since the outbreak of the novel coronavirus, we have witnessed the surge of anti-Asian attacks. Based on a report from stopaapihate.org, there were over 4000 self-reported hate incidents targeting AAPI communities. But we lack critical data on the scale and scope as well as the consequences of these incidents. This project aims to address these issues. This project is funded by a seed grant from the Institute of Advanced Computational Social Science (IACS) at Stony Brook University. IACS is an interdisciplinary institute advancing the intellectual foundations of computation and data, with high-impact applications in engineering and the physical, environmental, life sciences and the humanities. The AAPI project is led by a research team from IACS Computational Social Science Lab (Zlab) and Dr. Yifan Sun’s lab at Stony Brook University with collaborators from multiple universities. In this project, we introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic. Relying on Twitter’s academic API, we retrieve hateful and counter-hate tweets from the Twitter Historical Database. To build contextual understanding and collect related racial cues, we also collect instances of heated arguments, often political, but not necessarily hateful, discussing Chinese issues. We then use the state-of-the-art hate speech classifiers to discern whether these tweets express hatred. These datasets can be used to study hate speech, general anti-Asian or Chinese sentiment, and hate linguistics by social scientists as well as to evaluate and build hate speech or sentiment analysis classifiers by computational scholars.