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Description: How do the Centers for Disease Control (CDC) and the World Health Organization (WHO) utilize twitter to discuss and communicate important information regarding COVID-19 over time? This proposed presentation will discuss findings comparing linguistic and rhetorical analysis of CDC and WHO tweets, from January 2019 to the present day on COVID-19. Each dataset includes tweets from the WHO and CDC twitter accounts including: CDCgov, CDCDiabetes, CDC_Cancer, and CDCtravel and is composed of 1 million words. The full dataset continues to be updated regularly over time, using python. This paper utilizes methods from corpus linguistics and digital humanities in order to understand the differences and similarities between governmental messaging regarding in COVID-19. Previous work has underscored the relevance of corpus and DH methods with media representations and cultural understandings, including work on health-related press reporting and twitter data (Mautner 2008; Jaspal and Nerlich 2017; Mowery et al. 2017). Since this work is ongoing, the presentation will discuss current findings as well as plans for further analysis. Current methods of analysis include collocational and keyword analysis (Kretzschmar et al. 2004; Scott and Tribble 2006), comparison of top lexical bundles or n-grams (Hunston 2010; Scott 2010; Römer 2010), and topic modeling (Underwood 2019), using R packages polmineR() in conjunction with CQP (Blätte and Leonhardt 2019; Evert and Hardie 2011). Each of these methods highlight a different level of linguistic and rhetorical communication and will provide a unique perspective for understanding this current health crisis and public health messaging.