This repository contains: A deep learning model which distinguishes between Hostililty against East Asia, Criticism of East Asia, Discussion of East Asian prejudice and Neutral content. The F1 score is 0.83. A detailed annotation codebook used for marking up the tweets. A labelled dataset with 20,000 entries. A dataset with all 40,000 annotations, which can be used to investigate annotation processes for abusive content moderation. A list of thematic hashtag replacements. Three sets of annotations for the 1,000 most used hashtags in the original database of COVID-19 related tweets. Hashtags were annotated for COVID-19 relevance, East Asian relevance and stance. The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. This data repository is for a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic. This work is a collaboration between The Alan Turing Institute and the Oxford Internet Institute. It was funded by the Criminal JusticeTheme of the Alan Turing Institute under Wave 1 of The UKRI Strategic Priorities Fund, EPSRC Grant EP/T001569/1