The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia, leading to widespread discrimination against individuals of Chinese descent. Large language models (LLMs) are pre-trained deep learning models used for natural language processing (NLP) tasks. The ability of LLMs to understand and generate human-like text makes them particularly useful for analysing social media data to detect and evaluate sentiments. We present a sentiment analysis framework utilising LLMs for longitudinal sentiment analysis of the Sinophobic sentiments expressed in X (Twitter) during the COVID-19 pandemic. The results show a significant correlation between the spikes in Sinophobic tweets, Sinophobic sentiments and surges in COVID-19 cases, revealing that the evolution of the pandemic influenced public sentiment and the prevalence of Sinophobic discourse. Furthermore, the sentiment analysis revealed a predominant presence of negative sentiments, such as annoyance and denial, which underscores the impact of political narratives and misinformation shaping public opinion. The lack of empathetic sentiment which was present in previous studies related to COVID-19 highlights the way the political narratives in media viewed the pandemic and how it blamed the Chinese community. Our study highlights the importance of transparent communication in mitigating xenophobic sentiments during global crises.

本研究解决了COVID-19疫情期间因反华情绪加剧而导致的歧视问题，特别是针对华人的不公现象。通过利用大型语言模型（LLMs），我们提出了一种情感分析框架，对社交媒体上的反华情绪进行了纵向分析，发现反华推文的激增与COVID-19病例的增加存在显著相关性。这一发现强调了疫情如何影响公众情感，并指出政治叙述和错误信息对社会舆论的影响。

使用大语言模型对COVID-19期间的反华情绪进行纵向情感分析