The outbreak of COVID-19 has raised concerns about the spread of Sinophobia and other forms of East Asian prejudice across the world, with reports of online and offline abuse directed against East Asian people in the first few months of the pandemic, including physical attacks. The United Nations High Commissioner for Human Rights has drawn attention to increased prejudice against people of East Asian background and has called on UN member states to fight such discrimination. Thus far, most of the academic response to COVID-19 has focused on understanding its health- and economic- impacts and how these can be mitigated. There is a pressing need to also research and understand other forms of harm and danger which are spreading during the pandemic.
Social media is one of the most important battlegrounds in the fight against social hazards during COVID-19. As life moves increasingly online, it is crucial that social media platforms and other online spaces remain safe, accessible and free from abuse– and that people’s fears and distress during this time are not exploited and social tensions stirred up. Computational tools, utilizing recent advances in machine learning and natural language processing, offer powerful ways of creating scalable and robust models for detecting and measuring prejudice. These, in turn, can assist with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice.