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Description: COVID-19 has engendered a global surge of Sinophobia. Previous research using social media datasets shows that the pandemic triggered waves of negative attitudes toward Chinese communities. However, limited research has examined how Chinese language users responded to COVID-19 on western social media. We address this gap by compiling a unique database (CNTweets) with over 25 million Chinese tweets mentioning any Chinese characters related to China, the Chinese Communist Party (CCP), Chinese, and Asians from December 2019 to April 2021. Our analysis of Twitter users' self-reported geographic information shows that most Chinese language users on Twitter originated from Mainland China, Hong Kong, Taiwan, and the United States. We then adopt the Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) and structural topic modeling to further analyze the sentiments, content, and topics of Chinese tweets during the COVID-19 pandemic. Our results suggest that 61.8\% of tweets in our database were contributed by only 1\% of Twitter users and 62.2\% of tweets were negative toward China. Despite the prevalence of anti-China sentiments, the target entity analysis shows that these negative sentiments were more likely to target the Chinese government and CCP instead of the Chinese people. Our findings also show that the most popular topics and themes were politics (e.g., Hong Kong protests and Taiwan issues), COVID-19, and the United States (e.g., the US-China relations and domestic issues). Anti-China users focused relatively more on political issues such as democracy and freedom, while pro-China users mentioned cultural and economic topics more. Our social network analysis reveals that these pro-China and anti-China Twitter users lacked in-depth engagement and were highly segregated from each other. We conclude by discussing our contributions to China and social media studies and possible policy implications.